feat: WeatherNext2 Initial Updates

* feat:MongoDB Voyage 3.5-Lite

* chore:apply linter

* Update voyage-3.5-lite.ipynb

Fixing MODEL_NAME

.cloud-build/execute_changed_notebooks_helper.py

@@ -238,7 +238,7 @@ def _get_notebook_python_version(notebook_path: str) -> str:
 
             # Look for the python version specification pattern
             re_match = re.search(
-                "python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
+                r"python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
             )
             if re_match:
                 # get the version number
@@ -365,7 +365,7 @@ def process_and_execute_notebook(
             # Use gcloud to get tail
             try:
                 result.error_message = subprocess.check_output(
-                    ["gsutil", "cat", "-r", "-1000", log_file_uri], encoding="UTF-8"
+                    ["gcloud", "storage", "cat", "--range", "-1000", log_file_uri], encoding="UTF-8"
                 )
             except Exception as error:
                 result.error_message = str(error)

.cloud-build/execute_notebook_helper.py

@@ -56,8 +56,8 @@ def execute_notebook(
     print("\n=== DOWNLOAD EXECUTED NOTEBOOK ===\n")
     print(f"Please debug the executed notebook by downloading the executed notebook:")
 
-    print("Option 1. Using gsutil. Run the following command in your terminal.")
-    print(f'\tgsutil cp "{output_file_or_uri}" .')
+    print("Option 1. Using gcloud storage. Run the following command in your terminal.")
+    print(f'\tgcloud storage cp "{output_file_or_uri}" .')
 
     print("Option 2. Using this link.")
     print(f"\thttps://storage.googleapis.com/{output_file_or_uri[5:]}")

.cloud-build/test_notebook_vm.txt

@@ -1,5 +1,3 @@
-notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb
-notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb
 notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb
 notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb
 .cloud-build/tests/python_version_test.ipynb

.cloud-build/utils/NotebookProcessors.py

@@ -108,7 +108,7 @@ class VertexAIInstallProprocessor(Preprocessor):
         if "google-cloud-aiplatform" not in content:
             return content
         return (
-            f"gsutil cp {self.vertex_ai_wheel} google-cloud-aiplatform.whl\n" + 
+            f"gcloud storage cp {self.vertex_ai_wheel} google-cloud-aiplatform.whl\n" + 
             content.replace("google-cloud-aiplatform\n", "google-cloud-aiplatform.whl\n")
             .replace("google-cloud-aiplatform ", "google-cloud-aiplatform.whl ")
         )

.cloud-build/utils/util.py

@@ -15,7 +15,7 @@ def download_file(bucket_name: str, blob_name: str, destination_file: str) -> st
     remote_file_path = "".join(["gs://", "/".join([bucket_name, blob_name])])
 
     subprocess.check_output(
-        ["gsutil", "cp", remote_file_path, destination_file], encoding="UTF-8"
+        ["gcloud", "storage", "cp", remote_file_path, destination_file], encoding="UTF-8"
     )
 
     return destination_file
@@ -27,7 +27,7 @@ def upload_file(
 ) -> str:
     """Copies a local file to a GCS path"""
     subprocess.check_output(
-        ["gsutil", "cp", local_file_path, remote_file_path], encoding="UTF-8"
+        ["gcloud", "storage", "cp", local_file_path, remote_file_path], encoding="UTF-8"
     )
 
     return remote_file_path

.github/workflows/ci.yaml

@@ -7,11 +7,11 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
-        python-version: '3.x'      
+        python-version: '3.14'      
     - name: Fetch pull request branch
-      uses: actions/checkout@v4
+      uses: actions/checkout@v6
       with:
         fetch-depth: 0      
     - name: Fetch base main branch

.github/workflows/linter/Dockerfile

@@ -4,7 +4,7 @@
 # 2. To lint specific notebooks:
 # docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest notebooks/1.ipynb notebooks/2.ipynb
 
-FROM python:3.13
+FROM python:3.14
 
 WORKDIR setup
 

.github/workflows/linter/requirements.txt

@@ -2,9 +2,9 @@ git+https://github.com/tensorflow/docs
 ipython
 jupyter
 nbconvert
-black==25.1.0
-pyupgrade==3.19.1
-isort==6.0.1
-flake8==7.1.1
+black==25.12.0
+pyupgrade==3.21.0
+isort==7.0.0
+flake8==7.3.0
 nbqa==1.9.1
 

community-content/CODEOWNERS

@@ -29,4 +29,5 @@
 /vertex_model_garden/model_oss/vllm @kathyyu-google
 /vertex_model_garden/benchmarking_reports @lavraicse
 /vertex_model_garden/model_oss/autogluon @lavraicse
+/vertex_distributed_training/a3mega/llama-3-8b-nemo-pretraining @mstyer-google @erwinh85 @mchrestkha
 

community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml

@@ -148,7 +148,7 @@ implementation:
 
       # Downloading the model archive from GCS
       # TODO: Fix gsutil bugs (requires project ID, has auth issues) and use gsutil instead.
-      # gsutil cp "$model_archive_uri" "$model_archive_local_path"
+      # gcloud storage cp "$model_archive_uri" "$model_archive_local_path"
       pip install google-cloud-storage
       python -c '
       import sys

community-content/pipeline_components/google-cloud/storage/download/component.yaml

@@ -24,12 +24,12 @@ implementation:
 
             # Checking whether the URI points to a single blob, a directory or a URI pattern
             # URI points to a blob when that URI does not end with slash and listing that URI only yields the same URI
-            if [[ "$uri" != */ ]] && (gsutil ls "$uri" | grep --fixed-strings --line-regexp "$uri"); then
+            if [[ "$uri" != */ ]] && (gcloud storage ls "$uri" | grep --fixed-strings --line-regexp "$uri"); then
                 mkdir -p "$(dirname "$output_path")"
-                gsutil -m cp -r "$uri" "$output_path"
+                gcloud storage cp --recursive "$uri" "$output_path"
             else
                 mkdir -p "$output_path" # When source path is a directory, gsutil requires the destination to also be a directory
-                gsutil -m rsync -r "$uri" "$output_path" # gsutil cp has different path handling than Linux cp. It always puts the source directory (name) inside the destination directory. gsutil rsync does not have that problem.
+                gcloud storage rsync --recursive "$uri" "$output_path" # gsutil cp has different path handling than Linux cp. It always puts the source directory (name) inside the destination directory. gsutil rsync does not have that problem.
             fi
         - inputValue: GCS path
         - outputPath: Data

community-content/pytorch_image_classification_distributed_data_parallel_training_with_vertex_sdk/trainer/requirements.txt

@@ -1,3 +1,3 @@
-torch==2.2.0
+torch==2.8.0
 torchvision==0.9.1
 tensorboard==2.5.0

community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/trainer/requirements.txt

@@ -1,3 +1,3 @@
-torch==2.2.0
+torch==2.7.0
 torchvision==0.9.1
 tensorboard==2.5.0

community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/vertex_prediction_with_custom_torchserve_container.ipynb

@@ -110,7 +110,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $gcs_output_uri_prefix"
+        "! gcloud storage ls $gcs_output_uri_prefix"
       ]
     },
     {
@@ -192,7 +192,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil cp -r $gcs_output_uri_prefix/model ./model_server/"
+        "! gcloud storage cp --recursive $gcs_output_uri_prefix/model ./model_server/"
       ]
     },
     {
@@ -556,7 +556,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil rm -rf $gcs_output_uri_prefix"
+        "! gcloud storage rm --recursive --continue-on-error $gcs_output_uri_prefix"
       ]
     },
     {

community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/vertex_training_with_custom_container.ipynb

@@ -412,7 +412,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $gcs_output_uri_prefix"
+        "! gcloud storage ls $gcs_output_uri_prefix"
       ]
     }
   ],

community-content/pytorch_text_classification_using_vertex_sdk_and_gcloud/custom_container/scripts/train-cloud.sh

@@ -77,4 +77,4 @@ echo "After the job is completed successfully, model files will be saved at $JOB
 
 # # Verify the model was exported
 # echo "Verify the model was exported:"
-# gsutil ls ${JOB_DIR}/
+# gcloud storage ls ${JOB_DIR}/

community-content/pytorch_text_classification_using_vertex_sdk_and_gcloud/predictor/Dockerfile.serve

@@ -34,4 +34,4 @@ RUN echo "service_envelope=json\n"     "inference_address=http://0.0.0.0:${AIP_H
 USER model-server
 
 # run Torchserve HTTP serve to respond to prediction requests
-CMD ["echo", "AIP_STORAGE_URI=${AIP_STORAGE_URI}", ";",     "gsutil", "cp", "-r", "${AIP_STORAGE_URI}/${MODEL_NAME}.mar", "/home/model-server/model-store/", ";",     "ls", "-ltr", "/home/model-server/model-store/", ";",     "torchserve", "--start", "--ts-config=/home/model-server/config.properties",     "--models", "${MODEL_NAME}=${MODEL_NAME}.mar",     "--model-store", "/home/model-server/model-store"]
+CMD ["echo", "AIP_STORAGE_URI=${AIP_STORAGE_URI}", ";",     "gcloud", "storage", "cp", "--recursive", "${AIP_STORAGE_URI}/${MODEL_NAME}.mar", "/home/model-server/model-store/", ";",     "ls", "-ltr", "/home/model-server/model-store/", ";",     "torchserve", "--start", "--ts-config=/home/model-server/config.properties",     "--models", "${MODEL_NAME}=${MODEL_NAME}.mar",     "--model-store", "/home/model-server/model-store"]

community-content/pytorch_text_classification_using_vertex_sdk_and_gcloud/python_package/scripts/train-cloud.sh

@@ -67,4 +67,4 @@ echo "After the job is completed successfully, model files will be saved at $JOB
 
 # # Verify the model was exported
 # echo "Verify the model was exported:"
-# gsutil ls ${JOB_DIR}/
+# gcloud storage ls ${JOB_DIR}/

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb

@@ -478,8 +478,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -498,8 +497,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -582,8 +580,7 @@
       "outputs": [],
       "source": [
         "# Download the sample data into your RAW_DATA_PATH\n",
-        "! gsutil cp \"gs://cloud-samples-data/vertex-ai/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/u.data\" $RAW_DATA_PATH"
-      ]
+        "! gcloud storage cp \"gs://cloud-samples-data/vertex-ai/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/u.data\" $RAW_DATA_PATH"      ]
     },
     {
       "cell_type": "code",
@@ -1621,9 +1618,7 @@
         "! gcloud scheduler jobs delete $SIMULATOR_SCHEDULER_JOB --quiet\n",
         "\n",
         "# Delete Cloud Storage objects that were created.\n",
-        "! gsutil -m rm -r $PIPELINE_ROOT\n",
-        "! gsutil -m rm -r $TRAINING_ARTIFACTS_DIR"
-      ]
+        "! gcloud storage rm --recursive $PIPELINE_ROOT\n",        "! gcloud storage rm --recursive $TRAINING_ARTIFACTS_DIR"      ]
     }
   ],
   "metadata": {

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/step_by_step_sdk_tf_agents_bandits_movie_recommendation/step_by_step_sdk_tf_agents_bandits_movie_recommendation.ipynb

@@ -398,6 +398,7 @@
         "if not IS_GOOGLE_CLOUD_NOTEBOOK:\n",
         "    if \"google.colab\" in sys.modules:\n",
         "        from google.colab import auth as google_auth\n",
+        "\n",
         "        google_auth.authenticate_user()\n",
         "\n",
         "    # If you are running this notebook locally, replace the string below with the\n",
@@ -472,7 +473,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"
       ]
     },
     {
@@ -492,7 +493,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"
       ]
     },
     {
@@ -565,7 +566,7 @@
       "outputs": [],
       "source": [
         "# Copy the sample data into your DATA_PATH\n",
-        "! gsutil cp \"gs://cloud-samples-data/vertex-ai/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/u.data\"  $DATA_PATH"
+        "! gcloud storage cp \"gs://cloud-samples-data/vertex-ai/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/u.data\"  $DATA_PATH"
       ]
     },
     {
@@ -579,11 +580,15 @@
         "# Set hyperparameters.\n",
         "BATCH_SIZE = 8  # @param {type:\"integer\"} Training and prediction batch size.\n",
         "TRAINING_LOOPS = 5  # @param {type:\"integer\"} Number of training iterations.\n",
-        "STEPS_PER_LOOP = 2  # @param {type:\"integer\"} Number of driver steps per training iteration.\n",
+        "STEPS_PER_LOOP = (\n",
+        "    2  # @param {type:\"integer\"} Number of driver steps per training iteration.\n",
+        ")\n",
         "\n",
         "# Set MovieLens simulation environment parameters.\n",
         "RANK_K = 20  # @param {type:\"integer\"} Rank for matrix factorization in the MovieLens environment; also the observation dimension.\n",
-        "NUM_ACTIONS = 20  # @param {type:\"integer\"} Number of actions (movie items) to choose from.\n",
+        "NUM_ACTIONS = (\n",
+        "    20  # @param {type:\"integer\"} Number of actions (movie items) to choose from.\n",
+        ")\n",
         "PER_ARM = False  # Use the non-per-arm version of the MovieLens environment.\n",
         "\n",
         "# Set agent parameters.\n",
@@ -621,7 +626,8 @@
       "source": [
         "# Define RL environment.\n",
         "env = movielens_py_environment.MovieLensPyEnvironment(\n",
-        "    DATA_PATH, RANK_K, BATCH_SIZE, num_movies=NUM_ACTIONS, csv_delimiter=\"\\t\")\n",
+        "    DATA_PATH, RANK_K, BATCH_SIZE, num_movies=NUM_ACTIONS, csv_delimiter=\"\\t\"\n",
+        ")\n",
         "environment = tf_py_environment.TFPyEnvironment(env)\n",
         "\n",
         "# Define RL agent/algorithm.\n",
@@ -631,7 +637,8 @@
         "    tikhonov_weight=TIKHONOV_WEIGHT,\n",
         "    alpha=AGENT_ALPHA,\n",
         "    dtype=tf.float32,\n",
-        "    accepts_per_arm_features=PER_ARM)\n",
+        "    accepts_per_arm_features=PER_ARM,\n",
+        ")\n",
         "print(\"TimeStep Spec (for each batch):\\n\", agent.time_step_spec, \"\\n\")\n",
         "print(\"Action Spec (for each batch):\\n\", agent.action_spec, \"\\n\")\n",
         "print(\"Reward Spec (for each batch):\\n\", environment.reward_spec(), \"\\n\")\n",
@@ -639,7 +646,8 @@
         "# Define RL metric.\n",
         "optimal_reward_fn = functools.partial(\n",
         "    environment_utilities.compute_optimal_reward_with_movielens_environment,\n",
-        "    environment=environment)\n",
+        "    environment=environment,\n",
+        ")\n",
         "regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn)\n",
         "metrics = [regret_metric]"
       ]
@@ -704,35 +712,38 @@
         "    if training_data_spec_transformation_fn is None:\n",
         "        data_spec = agent.policy.trajectory_spec\n",
         "    else:\n",
-        "        data_spec = training_data_spec_transformation_fn(\n",
-        "            agent.policy.trajectory_spec)\n",
-        "    replay_buffer = trainer.get_replay_buffer(data_spec, environment.batch_size,\n",
-        "                                              steps_per_loop)\n",
+        "        data_spec = training_data_spec_transformation_fn(agent.policy.trajectory_spec)\n",
+        "    replay_buffer = trainer.get_replay_buffer(\n",
+        "        data_spec, environment.batch_size, steps_per_loop\n",
+        "    )\n",
         "\n",
         "    # `step_metric` records the number of individual rounds of bandit interaction;\n",
         "    # that is, (number of trajectories) * batch_size.\n",
         "    step_metric = tf_metrics.EnvironmentSteps()\n",
         "    metrics = [\n",
         "        tf_metrics.NumberOfEpisodes(),\n",
-        "        tf_metrics.AverageEpisodeLengthMetric(batch_size=environment.batch_size)\n",
+        "        tf_metrics.AverageEpisodeLengthMetric(batch_size=environment.batch_size),\n",
         "    ]\n",
         "    if additional_metrics:\n",
         "        metrics += additional_metrics\n",
         "\n",
         "    if isinstance(environment.reward_spec(), dict):\n",
-        "        metrics += [tf_metrics.AverageReturnMultiMetric(\n",
-        "            reward_spec=environment.reward_spec(),\n",
-        "            batch_size=environment.batch_size)]\n",
-        "    else:\n",
         "        metrics += [\n",
-        "            tf_metrics.AverageReturnMetric(batch_size=environment.batch_size)]\n",
+        "            tf_metrics.AverageReturnMultiMetric(\n",
+        "                reward_spec=environment.reward_spec(), batch_size=environment.batch_size\n",
+        "            )\n",
+        "        ]\n",
+        "    else:\n",
+        "        metrics += [tf_metrics.AverageReturnMetric(batch_size=environment.batch_size)]\n",
         "\n",
         "    # Store intermediate metric results, indexed by metric names.\n",
         "    metric_results = defaultdict(list)\n",
         "\n",
         "    if training_data_spec_transformation_fn is not None:\n",
-        "        def add_batch_fn(data): return replay_buffer.add_batch(training_data_spec_transformation_fn(data)) \n",
-        "        \n",
+        "\n",
+        "        def add_batch_fn(data):\n",
+        "            return replay_buffer.add_batch(training_data_spec_transformation_fn(data))\n",
+        "\n",
         "    else:\n",
         "        add_batch_fn = replay_buffer.add_batch\n",
         "\n",
@@ -742,10 +753,12 @@
         "        env=environment,\n",
         "        policy=agent.collect_policy,\n",
         "        num_steps=steps_per_loop * environment.batch_size,\n",
-        "        observers=observers)\n",
+        "        observers=observers,\n",
+        "    )\n",
         "\n",
         "    training_loop = trainer.get_training_loop_fn(\n",
-        "        driver, replay_buffer, agent, steps_per_loop)\n",
+        "        driver, replay_buffer, agent, steps_per_loop\n",
+        "    )\n",
         "    saver = policy_saver.PolicySaver(agent.policy)\n",
         "\n",
         "    for _ in range(training_loops):\n",
@@ -783,7 +796,8 @@
         "    environment=environment,\n",
         "    training_loops=TRAINING_LOOPS,\n",
         "    steps_per_loop=STEPS_PER_LOOP,\n",
-        "    additional_metrics=metrics)\n",
+        "    additional_metrics=metrics,\n",
+        ")\n",
         "\n",
         "tf.profiler.experimental.stop()"
       ]
@@ -1092,11 +1106,15 @@
       },
       "outputs": [],
       "source": [
-        "RUN_HYPERPARAMETER_TUNING = True  # Execute hyperparameter tuning instead of regular training.\n",
+        "RUN_HYPERPARAMETER_TUNING = (\n",
+        "    True  # Execute hyperparameter tuning instead of regular training.\n",
+        ")\n",
         "TRAIN_WITH_BEST_HYPERPARAMETERS = False  # Do not train.\n",
         "\n",
         "HPTUNING_RESULT_DIR = \"hptuning/\"  # @param {type: \"string\"} Directory to store the best hyperparameter(s) in `BUCKET_NAME` and locally (temporarily).\n",
-        "HPTUNING_RESULT_PATH = os.path.join(HPTUNING_RESULT_DIR, \"result.json\")  # @param {type: \"string\"} Path to the file containing the best hyperparameter(s)."
+        "HPTUNING_RESULT_PATH = os.path.join(\n",
+        "    HPTUNING_RESULT_DIR, \"result.json\"\n",
+        ")  # @param {type: \"string\"} Path to the file containing the best hyperparameter(s)."
       ]
     },
     {
@@ -1124,7 +1142,7 @@
         "    image_uri: str,\n",
         "    args: List[str],\n",
         "    location: str = \"us-central1\",\n",
-        "    api_endpoint: str = \"us-central1-aiplatform.googleapis.com\"\n",
+        "    api_endpoint: str = \"us-central1-aiplatform.googleapis.com\",\n",
         ") -> None:\n",
         "    \"\"\"Creates a hyperparameter tuning job using a custom container.\n",
         "\n",
@@ -1197,8 +1215,8 @@
         "\n",
         "    # Create job\n",
         "    response = client.create_hyperparameter_tuning_job(\n",
-        "        parent=parent,\n",
-        "        hyperparameter_tuning_job=hyperparameter_tuning_job)\n",
+        "        parent=parent, hyperparameter_tuning_job=hyperparameter_tuning_job\n",
+        "    )\n",
         "    job_id = response.name.split(\"/\")[-1]\n",
         "    print(\"Job ID:\", job_id)\n",
         "    print(\"Job config:\", response)\n",
@@ -1242,7 +1260,8 @@
         "    image_uri=f\"gcr.io/{PROJECT_ID}/{HPTUNING_TRAINING_CONTAINER}:latest\",\n",
         "    args=args,\n",
         "    location=REGION,\n",
-        "    api_endpoint=f\"{REGION}-aiplatform.googleapis.com\")"
+        "    api_endpoint=f\"{REGION}-aiplatform.googleapis.com\",\n",
+        ")"
       ]
     },
     {
@@ -1292,7 +1311,8 @@
         "    name = client.hyperparameter_tuning_job_path(\n",
         "        project=project,\n",
         "        location=location,\n",
-        "        hyperparameter_tuning_job=hyperparameter_tuning_job_id)\n",
+        "        hyperparameter_tuning_job=hyperparameter_tuning_job_id,\n",
+        "    )\n",
         "    response = client.get_hyperparameter_tuning_job(name=name)\n",
         "    return response"
       ]
@@ -1313,7 +1333,8 @@
         "        location=REGION,\n",
         "        api_endpoint=f\"{REGION}-aiplatform.googleapis.com\")\n",
         "    if response.state.name == 'JOB_STATE_SUCCEEDED':\n",
-        "        print(\"Job succeeded.\\nJob Time:\", response.update_time - response.create_time)\n",
+        "        print(\"Job succeeded.\n",
+        "Job Time:\", response.update_time - response.create_time)\n",
         "        trials = response.trials\n",
         "        print(\"Trials:\", trials)\n",
         "        break\n",
@@ -1348,8 +1369,8 @@
         "if trials:\n",
         "    # Dict mapping from metric names to the best metric values seen so far\n",
         "    best_objective_values = dict.fromkeys(\n",
-        "        [metric.metric_id for metric in trials[0].final_measurement.metrics],\n",
-        "        -np.inf)\n",
+        "        [metric.metric_id for metric in trials[0].final_measurement.metrics], -np.inf\n",
+        "    )\n",
         "    # Dict mapping from metric names to a list of the best combination(s) of\n",
         "    # hyperparameter(s). Each combination is a dict mapping from hyperparameter\n",
         "    # names to their values.\n",
@@ -1358,12 +1379,13 @@
         "        # `final_measurement` and `parameters` are `RepeatedComposite` objects.\n",
         "        # Reference the structure above to extract the value of your interest.\n",
         "        for metric in trial.final_measurement.metrics:\n",
-        "            params = {\n",
-        "                param.parameter_id: param.value for param in trial.parameters}\n",
+        "            params = {param.parameter_id: param.value for param in trial.parameters}\n",
         "            if metric.value > best_objective_values[metric.metric_id]:\n",
         "                best_params[metric.metric_id] = [params]\n",
         "            elif metric.value == best_objective_values[metric.metric_id]:\n",
-        "                best_params[param.parameter_id].append(params)  # Handle cases where multiple hyperparameter values lead to the same performance.\n",
+        "                best_params[param.parameter_id].append(\n",
+        "                    params\n",
+        "                )  # Handle cases where multiple hyperparameter values lead to the same performance.\n",
         "    print(\"Best hyperparameter value(s):\")\n",
         "    for metric, params in best_params.items():\n",
         "        print(f\"Metric={metric}: {sorted(params)}\")\n",
@@ -1443,7 +1465,9 @@
       },
       "outputs": [],
       "source": [
-        "PREDICTION_CONTAINER = \"prediction-custom-container\"  # @param {type:\"string\"} Name of the container image."
+        "PREDICTION_CONTAINER = (\n",
+        "    \"prediction-custom-container\"  # @param {type:\"string\"} Name of the container image.\n",
+        ")"
       ]
     },
     {
@@ -1475,7 +1499,7 @@
         "  machineType: 'E2_HIGHCPU_8'\"\"\".format(\n",
         "    PROJECT_ID=PROJECT_ID,\n",
         "    PREDICTION_CONTAINER=PREDICTION_CONTAINER,\n",
-        "    ARTIFACTS_DIR=ARTIFACTS_DIR\n",
+        "    ARTIFACTS_DIR=ARTIFACTS_DIR,\n",
         ")\n",
         "\n",
         "with open(\"cloudbuild.yaml\", \"w\") as fp:\n",
@@ -1592,8 +1616,12 @@
       },
       "outputs": [],
       "source": [
-        "RUN_HYPERPARAMETER_TUNING = False  # Execute regular training instead of hyperparameter tuning.\n",
-        "TRAIN_WITH_BEST_HYPERPARAMETERS = True  # @param {type:\"bool\"} Whether to use learned hyperparameters in training."
+        "RUN_HYPERPARAMETER_TUNING = (\n",
+        "    False  # Execute regular training instead of hyperparameter tuning.\n",
+        ")\n",
+        "TRAIN_WITH_BEST_HYPERPARAMETERS = (\n",
+        "    True  # @param {type:\"bool\"} Whether to use learned hyperparameters in training.\n",
+        ")"
       ]
     },
     {
@@ -1633,10 +1661,12 @@
         "job = aiplatform.CustomContainerTrainingJob(\n",
         "    display_name=\"train-movielens\",\n",
         "    container_uri=f\"gcr.io/{PROJECT_ID}/{HPTUNING_TRAINING_CONTAINER}:latest\",\n",
-        "    command=[\"python3\", \"-m\", \"src.training.task\"] + args,  # Pass in training arguments, including hyperparameters.\n",
+        "    command=[\"python3\", \"-m\", \"src.training.task\"]\n",
+        "    + args,  # Pass in training arguments, including hyperparameters.\n",
         "    model_serving_container_image_uri=f\"gcr.io/{PROJECT_ID}/{PREDICTION_CONTAINER}:latest\",\n",
         "    model_serving_container_predict_route=\"/predict\",\n",
-        "    model_serving_container_health_route=\"/health\")\n",
+        "    model_serving_container_health_route=\"/health\",\n",
+        ")\n",
         "\n",
         "print(\"Training Spec:\", job._managed_model)\n",
         "\n",
@@ -1645,7 +1675,8 @@
         "    replica_count=1,\n",
         "    machine_type=\"n1-standard-4\",\n",
         "    accelerator_type=\"ACCELERATOR_TYPE_UNSPECIFIED\",\n",
-        "    accelerator_count=0)"
+        "    accelerator_count=0,\n",
+        ")"
       ]
     },
     {
@@ -1784,7 +1815,7 @@
         "! gcloud ai models delete $model.name --quiet\n",
         "\n",
         "# Delete Cloud Storage objects that were created\n",
-        "! gsutil -m rm -r $ARTIFACTS_DIR"
+        "! gcloud storage rm --recursive $ARTIFACTS_DIR"
       ]
     }
   ],

community-content/tf_keras_image_classification_distributed_multi_worker_with_vertex_sdk/multi_worker_vertex_training_on_cpu_with_custom_container.ipynb

@@ -324,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $gcs_output_uri_prefix"
+        "! gcloud storage ls $gcs_output_uri_prefix"
       ]
     },
     {
@@ -344,7 +344,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil rm -rf $gcs_output_uri_prefix"
+        "! gcloud storage rm --recursive --continue-on-error $gcs_output_uri_prefix"
       ]
     }
   ],

community-content/tf_keras_image_classification_distributed_multi_worker_with_vertex_sdk/multi_worker_vertex_training_on_gpu_with_custom_container.ipynb

@@ -328,7 +328,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $gcs_output_uri_prefix"
+        "! gcloud storage ls $gcs_output_uri_prefix"
       ]
     },
     {
@@ -348,7 +348,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil rm -rf $gcs_output_uri_prefix"
+        "! gcloud storage rm --recursive --continue-on-error $gcs_output_uri_prefix"
       ]
     }
   ],

community-content/tf_keras_text_classification_distributed_single_worker_gpus_with_gcloud_local_run_and_vertex_sdk/vertex_training_with_local_mode_container.ipynb

@@ -341,7 +341,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $gcs_output_uri_prefix"
+        "! gcloud storage ls $gcs_output_uri_prefix"
       ]
     },
     {
@@ -361,7 +361,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil rm -rf $gcs_output_uri_prefix"
+        "! gcloud storage rm --recursive --continue-on-error $gcs_output_uri_prefix"
       ]
     }
   ],

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/README.md

@@ -0,0 +1,126 @@
+# Vertex AI Training: Llama 3.1 8B pre-training using Nvidia A3 Mega VMs (H100)
+This document provides a step-by-step guide for pre-training a Llama 3.1 8B model on the `en-wiki` dataset using multiple [Vertex AI Custom Training](https://cloud.google.com/vertex-ai/docs/training/overview) `a3-megagpu-8g` nodes.
+
+We will use a custom container based on NVIDIA's [NeMo Framework](https://docs.nvidia.com/nemo-framework/user-guide/24.07/overview.html) to demonstrate a scalable, multi-node training workflow. All required artifacts and commands are included.
+
+## 1. Prerequisites
+
+### 1.1. Google Cloud Project setup
+- **Enable APIs:** Ensure the Vertex AI API is [enabled for your project](http://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).
+- **H100 Mega Quota:** A3 Mega VMs are powered by H100 GPUs. Request quota for `custom_model_training_nvidia_h100_mega_gpus` in one of the [supported regions](https://cloud.google.com/vertex-ai/docs/general/locations#accelerator_support). If using Spot VMs, request `custom_model_training_preemptible_nvidia_h100_mega_gpus` quota instead.
+- **Reservations (Optional but recommended):** For guaranteed capacity, [create a reservation](https://cloud.google.com/compute/docs/instances/reservations-shared) and ensure the reservation is shared with the Vertex AI service account. This guide requires a minimum of **16 H100 GPUs** (2 full A3 Mega nodes).
+
+### 1.2. GCS bucket
+Create a [Cloud Storage bucket](https://cloud.google.com/storage/docs/creating-buckets) in the same region where you have quota. If you're using Hierarchical Namespace for your bucket, you may need to update permissions of the Vertex AI Custom Code Service Agent .
+
+This bucket is used for:
+- Staging the training application.
+- Storing model checkpoints and logs.
+- Storing data if you use your own data.
+
+
+## 2. Setup & configuration
+
+### 2.1. Clone the repo
+First clone the repo into your development environment.
+
+```bash
+git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
+```
+
+Navigate to the root folder for this sample.
+
+### 2.2. Environment Setup
+First, configure your local environment. These variables are used in subsequent commands.
+
+```bash
+# Required: Update with your values
+export PROJECT_ID="<your-project-id>"
+export REPOSITORY="<your-artifact-registry-repo-name>" # e.g., "my-containers"
+export BUCKET="<your-gcs-bucket-name>"
+
+# Optional: Change if needed
+export REGION="us-central1"
+
+# --- Do not change the lines below ---
+export ARTIFACT_REGISTRY="${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}"
+export REPO_ROOT=$(git rev-parse --show-toplevel)
+```
+
+## 3. Build and push a docker container image to Artifact Registry
+Normally, you can use any custom training container on Vertex AI Training. In this example you build a NeMo Docker image that is based on the [Nvidia’s NeMo 24.09](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags) image. Use Cloud Build to build and push the container image.
+
+This document picked NeMo as the demonstrating container since it’s a widely adopted GPU LLM training framework providing high performance and versatile training functionalities.
+
+In addition to the base image, some customizations are included to form the final prebuilt image:
+- Some dependencies are installed to integrate with Vertex AI Training.
+- An entrypoint script that sets up required environments and calls the training job.
+- Some patches are applied to the NeMo code to let it  load the dataset from a GCS bucket.
+
+Run this command to build the container and push the container into the Google Artifact Registry.
+
+```bash
+cd "${REPO_ROOT}/community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining"
+export IMAGE_NAME="vertex-nemo-llama"
+gcloud builds submit . \
+    --project="${PROJECT_ID}" \
+    --region="${REGION}" \
+    --config=docker/cloudbuild.yml \
+    --substitutions="_ARTIFACT_REGISTRY=${ARTIFACT_REGISTRY},_IMAGE_NAME=${IMAGE_NAME}" \
+    --timeout="2h" \
+    --machine-type="e2-highcpu-32"
+```
+
+## 4. Launch the Training Job
+
+
+###  4.1. Job Configuration File
+Once the container is built, update the job_config.json to set up the training job.
+File: job_config.json
+```json
+{
+  "project_id": "<project-id>",
+  "region": "<region>",
+  "zone": "<zone if using reservation>",
+  "bucket": "<bucket>",
+  "dataset_bucket": "github-repo/data/third-party/enwiki-latest-pages-articles",
+  "image_uri": "<docker image uri from artifact registry>",
+  "strategy": "spot",
+  "nodes": "2",
+  "machine_type": "a3-megagpu-8g",
+  "gpu_type": "NVIDIA_H100_MEGA_80GB",
+  "gpus_per_node": "8",
+  "recipe_name": "llama3_1_8b_pretrain_a3mega",
+  "job_prefix": "vertex-spot-",
+  "reservation_name": ""
+}
+```
+
+### 4.2 Launch the Training Job
+
+First, create a Python virtual environment using your tool of choice, then install
+the requirements specified in `requirements.txt`. Using `pip`, the command would be:
+```bash
+pip install -r requirements.txt
+```
+
+Now launch the Vertex AI training job using the provided Python script.
+
+```bash
+python3 scripts/launch.py --config_file=job_config.json
+```
+
+This script reads job_config.json, defines the cluster specification (2 nodes, 8 GPUs each), and submits the custom training job to Vertex AI.
+
+## 5. Monitor and Clean Up
+
+### 5.1. Monitoring
+Vertex AI Console: Track the job's status in the Google Cloud Console under Vertex AI > Training > Custom Jobs.
+Logs: View detailed logs in Cloud Logging by filtering for your job name.
+Checkpoints: Model checkpoints are saved to your GCS bucket at the path specified in your training script's configuration.
+
+### 5.2. Cleaning Up
+To avoid ongoing charges, delete the resources you created:
+- The Artifact Registry image.
+- The contents of the GCS bucket (checkpoints, logs).
+- The Vertex AI Custom Job will eventually complete or fail, incurring no further cost.

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/configs/llama3_1_8b_pretrain_a3mega.yaml

@@ -0,0 +1,265 @@
+# Reference:
+# https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/24.07/launcher_scripts/conf/training/llama/llama3_1_8b.yaml
+name: llama3_1_8b_pretrain_a3mega
+restore_from_path: null  # used when starting from a .nemo file
+
+trainer:
+  devices: 8
+  num_nodes: 1
+  accelerator: gpu
+  precision: bf16
+  logger: false  # logger provided by exp_manager
+  enable_checkpointing: false
+  use_distributed_sampler: false
+  max_epochs: -1  # PTL default. In practice, max_steps will be reached first.
+  max_steps: 30  # consumed_samples = global_step * micro_batch_size * data_parallel_size * accumulate_grad_batches
+  log_every_n_steps: 1
+  val_check_interval: null
+  limit_val_batches: 1
+  limit_test_batches: 1
+  accumulate_grad_batches: 1  # do not modify, grad acc is automatic for training megatron models
+  gradient_clip_val: 1.0
+  benchmark: false
+  enable_model_summary: false  # default PTL callback for this does not support model parallelism, instead we log manually
+
+exp_manager:
+  explicit_log_dir: null
+  exp_dir: /data
+  name: ${name}
+  create_dllogger_logger: true
+  dllogger_logger_kwargs:
+    verbose: true
+    stdout: true
+    json_file: "/data/dllogger.json"
+  create_wandb_logger: false
+  wandb_logger_kwargs:
+    project: null
+    name: null
+  resume_if_exists: true
+  resume_ignore_no_checkpoint: true
+  create_checkpoint_callback: false
+  checkpoint_callback_params:
+    monitor: val_loss
+    save_top_k: 3
+    mode: min
+    always_save_nemo: false  # saves nemo file during validation, not implemented for model parallel
+    save_nemo_on_train_end: false  # not recommended when training large models on clusters with short time limits
+    filename: 'megatron_gpt--{val_loss:.2f}-{step}-{consumed_samples}'
+    model_parallel_size: ${multiply:${model.tensor_model_parallel_size}, ${model.pipeline_model_parallel_size}}
+  seconds_to_sleep: 5  # Allows node_rank!=0 to sleep and let node0 to init, like preparing data
+
+model:
+  mcore_gpt: true
+  # specify micro_batch_size, global_batch_size, and model parallelism
+  # gradient accumulation will be done automatically based on data_parallel_size
+  micro_batch_size: 1  # limited by GPU memory
+  global_batch_size: 1024  # will use more micro batches to reach global batch size
+  tensor_model_parallel_size: 1  # intra-layer model parallelism
+  pipeline_model_parallel_size: 2  # inter-layer model parallelism
+  context_parallel_size: 1
+  virtual_pipeline_model_parallel_size: null  # interleaved pipeline
+  ## Sequence Parallelism
+  # Makes tensor parallelism more memory efficient for LLMs (20B+) by parallelizing layer norms and dropout sequentially
+  # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
+  sequence_parallel: false
+
+  fsdp: false
+  fsdp_cpu_offload: true
+  fsdp_sharding_strategy: "full"  # Method to shard model states. Available options are 'full', 'hybrid', and 'grad'.
+  fsdp_grad_reduce_dtype: "16"  # Gradient reduction data type.
+  fsdp_sharded_checkpoint: false  # Store and load FSDP shared checkpoint.
+  fsdp_use_orig_params: false  # Set to True to use FSDP for specific peft scheme.
+
+  # Distributed checkpoint setup
+  dist_ckpt_format: "torch_dist"  # Set to 'torch_dist' to use PyTorch distributed checkpoint format.
+  dist_ckpt_load_on_device: true  # whether to load checkpoint weights directly on GPU or to CPU
+  dist_ckpt_parallel_save: true  # if true, each worker will write its own part of the dist checkpoint
+  dist_ckpt_parallel_save_within_dp: false  # if true, save will be parallelized only within a DP group (whole world otherwise), which might slightly reduce the save overhead
+  dist_ckpt_parallel_load: false  # if true, each worker will load part of the dist checkpoint and exchange with NCCL. Might use some extra GPU memory
+  dist_ckpt_torch_dist_multiproc: 2  # number of extra processes per rank used during ckpt save with PyTorch distributed format
+  dist_ckpt_assume_constant_structure: false  # set to True only if the state dict structure doesn't change within a single job. Allows caching some computation across checkpoint saves.
+  dist_ckpt_parallel_dist_opt: true  # parallel save/load of a DistributedOptimizer. 'True' allows performant save and reshardable checkpoints. Set to 'False' only in order to minimize the number of checkpoint files.
+  dist_ckpt_load_strictness: null  # defines checkpoint keys mismatch behavior (only during dist-ckpt load). Choices: assume_ok_unexpected (default - try loading without any check), log_all (log mismatches), raise_all (raise mismatches)
+
+  # model architecture
+  encoder_seq_length: 8192
+  max_position_embeddings: ${.encoder_seq_length}
+  num_layers: 32  # 8b: 32 | 70b: 80 | 405b: 126
+  hidden_size: 4096  # 8b: 4096 | 70b: 8192 | 405b: 16384
+  ffn_hidden_size: 14336  # 8b: 14336 | 70b: 28672 | 405b: 53248
+  num_attention_heads: 32  # 8b: 32 | 70b: 64 | 405b: 128
+  num_query_groups: 8  # Number of query groups for group query attention. If None, normal attention is used. 8b: 8 | 70b: 8 | 405b: 16
+  init_method_std: 0.01  # Standard deviation of the zero mean normal distribution used for weight initialization.  8b: 0.01 | 70b: 0.008944 | 405b: 0.02
+  use_scaled_init_method: true  # use scaled residuals initialization
+  hidden_dropout: 0.0  # Dropout probability for hidden state transformer.
+  attention_dropout: 0.0  # Dropout probability for attention
+  ffn_dropout: 0.0  # Dropout probability in the feed-forward layer.
+  kv_channels: null  # Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if null
+  apply_query_key_layer_scaling: true  # scale Q * K^T by 1 / layer-number.
+  normalization: 'rmsnorm'  # Normalization layer to use. Options are 'layernorm', 'rmsnorm'
+  layernorm_epsilon: 1e-5
+  do_layer_norm_weight_decay: false  # True means weight decay on all params
+  make_vocab_size_divisible_by: 128  # Pad the vocab size to be divisible by this value for computation efficiency.
+  pre_process: true  # add embedding
+  post_process: true  # add pooler
+  persist_layer_norm: true  # Use of persistent fused layer norm kernel.
+  bias: false  # Whether to use bias terms in all weight matrices.
+  activation: 'fast-swiglu'  # Options ['gelu', 'geglu', 'swiglu', 'reglu', 'squared-relu', 'fast-geglu', 'fast-swiglu', 'fast-reglu']
+  headscale: false  # Whether to learn extra parameters that scale the output of the each self-attention head.
+  transformer_block_type: 'pre_ln'  # Options ['pre_ln', 'post_ln', 'normformer']
+  openai_gelu: false  # Use OpenAI's GELU instead of the default GeLU
+  normalize_attention_scores: true  # Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). This arg is provided as a configuration option mostly for compatibility with models that have been weight-converted from HF. You almost always want to se this to True.
+  position_embedding_type: 'rope'  # Position embedding type. Options ['learned_absolute', 'rope']
+  rotary_percentage: 1.0  # If using position_embedding_type=rope, then the per head dim is multiplied by this.
+  attention_type: 'multihead'  # Attention type. Options ['multihead']
+  share_embeddings_and_output_weights: false  # Share embedding and output layer weights.
+  scale_positional_embedding: true  # This is false for llama3 models. Only used for >= llama3.1.
+
+  # Use GPT2BPETokenizer for test, because the testing dataset is tokenized by this tokenizer.
+  # https://docs.nvidia.com/nemo-framework/user-guide/24.07/playbooks/singlenodepretrain.html#data-download-and-pre-processing
+  tokenizer:
+    library: megatron
+    type: GPT2BPETokenizer
+    model: null  # /path/to/tokenizer.model
+    vocab_file: null
+    merge_file: null
+    delimiter: null  # only used for tabular tokenizer
+    sentencepiece_legacy: false  # Legacy=True allows you to add special tokens to sentencepiece tokenizers.
+
+  # Mixed precision
+  native_amp_init_scale: 4294967296  # 2 ** 32
+  native_amp_growth_interval: 1000
+  hysteresis: 2  # Gradient scale hysteresis
+  fp32_residual_connection: false  # Move residual connections to fp32
+  fp16_lm_cross_entropy: false  # Move the cross entropy unreduced loss calculation for lm head to fp16
+
+  # Megatron O2-style half-precision
+  megatron_amp_O2: true  # Enable O2-level automatic mixed precision using main parameters
+  grad_allreduce_chunk_size_mb: 125
+
+  # Fusion
+  grad_div_ar_fusion: true  # Fuse grad division into torch.distributed.all_reduce. Only used with O2 and no pipeline parallelism..
+  gradient_accumulation_fusion: true  # Fuse weight gradient accumulation to GEMMs. Only used with pipeline parallelism and O2.
+  bias_activation_fusion: true  # Use a kernel that fuses the bias addition from weight matrices with the subsequent activation function.
+  bias_dropout_add_fusion: true  # Use a kernel that fuses the bias addition, dropout and residual connection addition.
+  masked_softmax_fusion: true  # Use a kernel that fuses the attention softmax with it's mask.
+  apply_rope_fusion: true  # Use a kernel to add rotary positional embeddings. Only used if position_embedding_type=rope
+  cross_entropy_loss_fusion: true
+
+  # Miscellaneous
+  seed: 1234
+  resume_from_checkpoint: null  # manually set the checkpoint file to load from
+  use_cpu_initialization: false  # Init weights on the CPU (slow for large models)
+  onnx_safe: false  # Use work-arounds for known problems with Torch ONNX exporter.
+  apex_transformer_log_level: 30  # Python logging level displays logs with severity greater than or equal to this
+  gradient_as_bucket_view: true  # PyTorch DDP argument. Allocate gradients in a contiguous bucket to save memory (less fragmentation and buffer memory)
+  sync_batch_comm: false  # Enable stream synchronization after each p2p communication between pipeline stages
+
+  ## Activation Checkpointing
+  # NeMo Megatron supports 'selective' activation checkpointing where only the memory intensive part of attention is checkpointed.
+  # These memory intensive activations are also less compute intensive which makes activation checkpointing more efficient for LLMs (20B+).
+  # See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
+  # 'full' will checkpoint the entire transformer layer.
+  activations_checkpoint_granularity: null  # 'selective' or 'full'
+  activations_checkpoint_method: null  # 'uniform', 'block'
+  # 'uniform' divides the total number of transformer layers and checkpoints the input activation
+  # of each chunk at the specified granularity. When used with 'selective', 'uniform' checkpoints all attention blocks in the model.
+  # 'block' checkpoints the specified number of layers per pipeline stage at the specified granularity
+  activations_checkpoint_num_layers: null
+  # when using 'uniform' this creates groups of transformer layers to checkpoint. Usually set to 1. Increase to save more memory.
+  # when using 'block' this this will checkpoint the first activations_checkpoint_num_layers per pipeline stage.
+  num_micro_batches_with_partial_activation_checkpoints: null
+  # This feature is valid only when used with pipeline-model-parallelism.
+  # When an integer value is provided, it sets the number of micro-batches where only a partial number of Transformer layers get checkpointed
+  # and recomputed within a window of micro-batches. The rest of micro-batches in the window checkpoint all Transformer layers. The size of window is
+  # set by the maximum outstanding micro-batch backpropagations, which varies at different pipeline stages. The number of partial layers to checkpoint
+  # per micro-batch is set by 'activations_checkpoint_num_layers' with 'activations_checkpoint_method' of 'block'.
+  # This feature enables using activation checkpoint at a fraction of micro-batches up to the point of full GPU memory usage.
+  activations_checkpoint_layers_per_pipeline: null
+  # This feature is valid only when used with pipeline-model-parallelism.
+  # When an integer value (rounded down when float is given) is provided, it sets the number of Transformer layers to skip checkpointing at later
+  # pipeline stages. For example, 'activations_checkpoint_layers_per_pipeline' of 3 makes pipeline stage 1 to checkpoint 3 layers less than
+  # stage 0 and stage 2 to checkpoint 6 layers less stage 0, and so on. This is possible because later pipeline stage
+  # uses less GPU memory with fewer outstanding micro-batch backpropagations. Used with 'num_micro_batches_with_partial_activation_checkpoints',
+  # this feature removes most of activation checkpoints at the last pipeline stage, which is the critical execution path.
+
+  ## Transformer Engine
+  transformer_engine: true
+  fp8: false  # enables fp8 in TransformerLayer forward
+  fp8_e4m3: false  # sets fp8_format = recipe.Format.E4M3
+  fp8_hybrid: false  # sets fp8_format = recipe.Format.HYBRID
+  fp8_margin: 0  # scaling margin
+  fp8_interval: 1  # scaling update interval
+  fp8_amax_history_len: 1024  # Number of steps for which amax history is recorded per tensor
+  fp8_amax_compute_algo: 'max'  # 'most_recent' or 'max'. Algorithm for computing amax from history
+  ub_tp_comm_overlap: false  # do not turn on because of b/397797926
+  use_flash_attention: true
+  gc_interval: 100
+
+  ## Offloading Activations/Weights to CPU
+  cpu_offloading: false
+  cpu_offloading_num_layers: ${sum:${.num_layers},-1}  # This value should be between [1,num_layers-1] as we don't want to offload the final layer's activations and expose any offloading duration for the final layer
+  cpu_offloading_activations: true
+  cpu_offloading_weights: true
+
+  data:
+    # Path to data must be specified by the user.
+    # Supports List, String and Dictionary
+    # List : can override from the CLI: "model.data.data_prefix=[.5,/raid/data/pile/my-gpt3_00_text_document,.5,/raid/data/pile/my-gpt3_01_text_document]",
+    # Or see example below:
+    # data_prefix:
+    # - .5
+    # - /raid/data/pile/my-gpt3_00_text_document
+    # - .5
+    # - /raid/data/pile/my-gpt3_01_text_document
+    # Dictionary: can override from CLI "model.data.data_prefix"={"train":[1.0, /path/to/data], "validation":/path/to/data, "test":/path/to/test}
+    # Or see example below:
+    # "model.data.data_prefix: {train:[1.0,/path/to/data], validation:[/path/to/data], test:[/path/to/test]}"
+    data_prefix: [1.0, /data/hfbpe_gpt_training_data_text_document]
+    index_mapping_dir: null  # path to save index mapping .npy files, by default will save in the same location as data_prefix
+    data_impl: mmap
+    splits_string: 900,50,50
+    seq_length: ${model.encoder_seq_length}
+    skip_warmup: true
+    num_workers: 2
+    dataloader_type: single  # cyclic
+    reset_position_ids: false  # Reset position ids after end-of-document token
+    reset_attention_mask: false  # Reset attention mask after end-of-document token
+    eod_mask_loss: false  # Mask loss for the end of document tokens
+    validation_drop_last: true  # Set to false if the last partial validation samples is to be consumed
+    no_seqlen_plus_one_input_tokens: false  # Set to True to disable fetching (sequence length + 1) input tokens, instead get (sequence length) input tokens and mask the last token
+    pad_samples_to_global_batch_size: false  # Set to True if you want to pad the last partial batch with -1's to equal global batch size
+    shuffle_documents: true  # Set to False to disable documents shuffling. Sample index will still be shuffled
+
+  # Nsys profiling options
+  nsys_profile:
+    enabled: false
+    start_step: 0  # Global batch to start profiling
+    end_step: 1  # Global batch to end profiling
+    ranks: [0]  # Global rank IDs to profile
+    gen_shape: false  # Generate model and kernel details including input shapes
+
+  memory_profile:
+    enabled: false
+    start_step: 0
+    end_step: 1
+    ranks: [0]
+    output_path: /data  # Must be a dir
+
+  optim:
+    name: distributed_fused_adam  # E.g., fused_adam or set _target_: torch.optim.AdamW field
+    lr: 2e-5
+    weight_decay: 0.01
+    betas:
+    - 0.9
+    - 0.98
+    bucket_cap_mb: 125
+    overlap_grad_sync: true
+    overlap_param_sync: true
+    contiguous_grad_buffer: true
+    contiguous_param_buffer: true
+    sched:
+      name: CosineAnnealing
+      warmup_steps: 400
+      constant_steps: 0
+      min_lr: 2e-6

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/cloudbuild.yml

@@ -0,0 +1,26 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+steps:
+- name: 'gcr.io/cloud-builders/docker'
+  args:
+  - 'build'
+  - '--tag=${_ARTIFACT_REGISTRY}/${_IMAGE_NAME}'
+  - '--file=docker/vertex-dist-recipes.Dockerfile'
+  - '.'
+  automapSubstitutions: true
+  env:
+  - 'DOCKER_BUILDKIT=1'
+images:
+- '${_ARTIFACT_REGISTRY}/${_IMAGE_NAME}'

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/gpu_stats.patch

@@ -0,0 +1,41 @@
+diff --git a/nemo/collections/nlp/parts/megatron_trainer_builder.py b/nemo/collections/nlp/parts/megatron_trainer_builder.py
+index b2c85cde4..a3a9670c3 100644
+--- a/nemo/collections/nlp/parts/megatron_trainer_builder.py
++++ b/nemo/collections/nlp/parts/megatron_trainer_builder.py
+@@ -19,6 +19,7 @@ from lightning_fabric.utilities.exceptions import MisconfigurationException
+ from omegaconf import DictConfig
+ from pytorch_lightning import Trainer
+ from pytorch_lightning.callbacks import ModelSummary
++from pytorch_lightning.callbacks import Callback
+ from pytorch_lightning.plugins.environments import TorchElasticEnvironment
+
+ from nemo.collections.common.metrics.perf_metrics import FLOPsMeasurementCallback
+@@ -38,6 +39,23 @@ from nemo.utils.callbacks.dist_ckpt_io import (
+     AsyncFinalizerCallback,
+     DistributedCheckpointIO,
+ )
++from vmg.util.device_stats import gpu_stats_str
++
++class GpuStatsMon(Callback):
++    def on_train_start(self, trainer, pl_module) -> None:
++        rank=pl_module.global_rank
++        print(f'train_start: {rank=} {gpu_stats_str()}', flush=True)
++
++    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx) -> None:
++        rank=pl_module.global_rank
++        print(f'batch_start: {rank=} {gpu_stats_str()}', flush=True)
++
++    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx) -> None:
++        rank=pl_module.global_rank
++        print(f'batch_end: {rank=} {gpu_stats_str()}', flush=True)
+
+
+ class MegatronTrainerBuilder:
+@@ -178,6 +196,7 @@ class MegatronTrainerBuilder:
+         if self.cfg.get('exp_manager', {}).get('log_tflops_per_sec_per_gpu', True):
+             callbacks.append(FLOPsMeasurementCallback(self.cfg))
+
++        callbacks.append(GpuStatsMon())
+         return callbacks
+
+     def create_trainer(self, callbacks=None) -> Trainer:

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/local_rank.patch

@@ -0,0 +1,41 @@
+diff -ruN old-datasets/blended_megatron_dataset_builder.py datasets/blended_megatron_dataset_builder.py
+--- old-datasets/blended_megatron_dataset_builder.py	2025-05-02 04:08:45.369199665 +0000
++++ datasets/blended_megatron_dataset_builder.py	2025-05-02 04:10:47.369119891 +0000
+@@ -2,6 +2,7 @@
+
+ import logging
+ import math
++import os
+ from concurrent.futures import ThreadPoolExecutor
+ from typing import Any, Callable, Iterable, List, Optional, Type, Union
+
+@@ -353,7 +354,7 @@
+         num_dataset_builder_threads = self.config.num_dataset_builder_threads
+
+         if torch.distributed.is_initialized():
+-            rank = torch.distributed.get_rank()
++            rank = int(os.getenv("LOCAL_RANK", "0"))
+             # First, build on rank 0
+             if rank == 0:
+                 num_workers = num_dataset_builder_threads
+@@ -475,7 +476,7 @@
+             Optional[Union[DistributedDataset, Iterable]]: The DistributedDataset instantion, the Iterable instantiation, or None
+         """
+         if torch.distributed.is_initialized():
+-            rank = torch.distributed.get_rank()
++            rank = int(os.getenv("LOCAL_RANK", "0"))
+
+             dataset = None
+
+diff -ruN old-datasets/gpt_dataset.py datasets/gpt_dataset.py
+--- old-datasets/gpt_dataset.py	2025-05-02 04:08:45.369199665 +0000
++++ datasets/gpt_dataset.py	2025-05-02 04:09:30.309170278 +0000
+@@ -351,7 +351,7 @@
+
+         if not path_to_cache or (
+             not cache_hit
+-            and (not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0)
++            and (not torch.distributed.is_initialized() or int(os.getenv("LOCAL_RANK", "0")) == 0)
+         ):
+
+             log_single_rank(

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/nemo2hf.patch

@@ -0,0 +1,13 @@
+diff --git a/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py b/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
+index 8da15148d..005cae6c9 100644
+--- a/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
++++ b/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py
+@@ -104,6 +104,8 @@ def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) ->
+     dummy_trainer = Trainer(devices=1, accelerator='cpu', strategy=NLPDDPStrategy())
+     model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True)
+     model_config.tensor_model_parallel_size = 1
++    model_config.virtual_pipeline_model_parallel_size = None
++    model_config.sequence_parallel = False
+     model_config.pipeline_model_parallel_size = 1
+     if cpu_only:
+         map_location = torch.device('cpu')

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/sigabort.patch

@@ -0,0 +1,24 @@
+diff --git a/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py b/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
+index bfe8ea359..dfeaf93b5 100644
+--- a/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
++++ b/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py
+@@ -13,6 +13,8 @@
+ # limitations under the License.
+
+ import torch.multiprocessing as mp
++import torch.distributed as dist
++
+ from omegaconf.omegaconf import OmegaConf
+
+ from nemo.collections.nlp.models.language_modeling.megatron_gpt_sft_model import MegatronGPTSFTModel
+@@ -76,6 +78,10 @@ def main(cfg) -> None:
+
+     trainer.fit(model)
+
++    if dist.is_available() and dist.is_initialized():
++        dist.barrier()
++        dist.destroy_process_group()
++
+
+ if __name__ == '__main__':
+     main()

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/patches/24.09/throughput_calc.patch

@@ -0,0 +1,13 @@
+diff --git a/src/utils/training_metrics/process_training_results.py b/src/utils/training_metrics/process_training_results.py
+index 3e82a66..e61e1d8 100644
+--- a/src/utils/training_metrics/process_training_results.py
++++ b/src/utils/training_metrics/process_training_results.py
+@@ -134,7 +134,7 @@ def get_average_step_time(file: str, start_step: int, end_step: int) -> float:
+     for line in datajson:
+         if line.get("step") != "PARAMETER":
+             step = line.get("step")
+-            if step >= start_step and step <= end_step:
++            if step >= start_step and step <= end_step and "train_step_timing in s" in line["data"]:
+                 time_step_accumulator += line["data"].get("train_step_timing in s")
+                 num_steps += 1
+     if num_steps == 0:

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/requirements.txt

@@ -0,0 +1,10 @@
+dllogger@git+https://github.com/NVIDIA/dllogger@v1.0.0
+
+# Fixing these libraries versions to avoid conflicting or broken packages.
+immutabledict==4.2.1
+protobuf==4.25.8
+opencv-python-headless==4.11.0.86
+docutils==0.16
+urllib3==2.6.0
+google-cloud-storage==3.0.0
+retrying

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/uninstall.txt

@@ -0,0 +1,18 @@
+# cuml-cu12==24.8.0 was installed in nemo:24.09
+# Removing cuml=24.4.0 to avoid conflicting packages.
+cudf==24.4.0
+cugraph==24.4.0
+cugraph-service-server==24.4.0
+cuml==24.4.0
+dask-cudf==24.4.0
+raft-dask==24.4.0
+cugraph-dgl==24.4.0
+cugraph-pyg==24.4.0
+# The following packages are removed temporarily to avoid conflicting packages
+# and can be brought back if needed.
+tensorrt-llm==0.12.0
+img2dataset==1.45.0
+Sphinx==8.1.3
+sphinxcontrib-bibtex==2.6.3
+torchx==0.7.0
+nemo-run

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/docker/vertex-dist-recipes.Dockerfile

@@ -0,0 +1,66 @@
+# Dockerfile wrapping NeMo.
+#
+# To workaround base nemo docker image using too many layers, we use Multi-stage
+# build to first collect the additional files we'll need.
+FROM alpine:latest AS prep_files
+WORKDIR /workspace
+RUN mkdir -p configs vdt vdt/util
+COPY scripts/*.py vdt/
+COPY scripts/util/*.py vdt/util/
+COPY configs/* configs/
+COPY docker/patches/24.09/* vdt/patches/
+RUN chmod a+rwX -R vdt
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+# Available tags
+# https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags
+# It installs NeMo source code in /opt/NeMo folder, with tag=r2.0.0
+FROM nvcr.io/nvidia/nemo:24.09
+
+RUN apt-get update && apt-get install -y sudo zsh tmux && \
+    rm -rf /var/lib/apt/lists*
+
+RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | \
+    tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
+    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \
+    apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
+    apt-get update -y && apt-get install google-cloud-sdk -y && \
+    rm -rf /var/lib/apt/lists*
+
+# Install libraries with pip
+ENV PIP_ROOT_USER_ACTION=ignore
+
+# We expect this will be run in the root directory of the vertex-dist-recipes repo
+ARG HOST_SRC_DIR="."
+
+# The pre-installed NeMo introduces a lot of deps conflicts.
+# We uninstall the confilicting libs and reinstall some of them as needed.
+COPY ${HOST_SRC_DIR}/docker/uninstall.txt /tmp/uninstall.txt
+RUN cat /tmp/uninstall.txt | grep -v '#' | xargs pip uninstall -y
+COPY ${HOST_SRC_DIR}/docker/requirements.txt /tmp/requirements.txt
+RUN pip install -r /tmp/requirements.txt
+
+# Make sure there's no inconsistent pip libraries.
+RUN pip check
+
+WORKDIR /workspace
+
+# Copy configs
+COPY ${HOST_SRC_DIR}/configs/* /opt/NeMo/examples/nlp/language_modeling/conf/
+
+# Copy all additional files we need from `prep_files` image.
+COPY --from=prep_files /workspace/ .
+
+# Install for `src/utils/training_metrics/process_training_results.py` to report
+# throughput and MFU numbers.
+RUN git clone https://github.com/AI-Hypercomputer/gpu-recipes.git
+
+# This hack is needed for multi-node training while not using a sharing file system.
+RUN patch --verbose -l -d /opt/megatron-lm/megatron/core/datasets -p1 -i /workspace/vdt/patches/local_rank.patch; \
+    git -C /workspace/gpu-recipes apply /workspace/vdt/patches/throughput_calc.patch; \
+    git -C /opt/NeMo apply /workspace/vdt/patches/nemo2hf.patch; \
+    git -C /opt/NeMo apply /workspace/vdt/patches/sigabort.patch;
+# git -C /opt/NeMo apply /workspace/vdt/patches/gpu_stats.patch;
+
+# Do not put an entrypoint here. Specify the entrypoint in the docker run script.

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/job_config.json

@@ -0,0 +1,16 @@
+{
+  "project_id": "<your_project_id>",
+  "region": "us-central1",
+  "zone": "us-central1-c",
+  "bucket": "<your_bucket",
+  "dataset_bucket": "github-repo/data/third-party/enwiki-latest-pages-articles",
+  "image_uri": "<your_image_uri>",
+  "strategy": "spot",
+  "nodes": "2",
+  "machine_type": "a3-megagpu-8g",
+  "gpu_type": "NVIDIA_H100_MEGA_80GB",
+  "gpus_per_node": "8",
+  "recipe_name": "llama3_1_8b_pretrain_a3mega",
+  "job_prefix": "vertex-ai",
+  "reservation_name": ""
+}

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/requirements.txt

@@ -0,0 +1,49 @@
+absl-py==2.2.2
+annotated-types==0.7.0
+anyio==4.9.0
+black==25.1.0
+cachetools==5.5.2
+certifi==2025.4.26
+charset-normalizer==3.4.2
+click==8.1.8
+docstring_parser==0.16
+google-api-core==2.24.2
+google-auth==2.40.1
+google-cloud-aiplatform==1.92.0
+google-cloud-bigquery==3.31.0
+google-cloud-core==2.4.3
+google-cloud-resource-manager==1.14.2
+google-cloud-storage==2.19.0
+google-crc32c==1.7.1
+google-genai==1.14.0
+google-resumable-media==2.7.2
+googleapis-common-protos==1.70.0
+grpc-google-iam-v1==0.14.2
+grpcio==1.71.0
+grpcio-status==1.71.0
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+idna==3.10
+mypy_extensions==1.1.0
+numpy==2.2.5
+packaging==25.0
+pathspec==0.12.1
+platformdirs==4.3.8
+proto-plus==1.26.1
+protobuf==5.29.4
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pydantic==2.11.4
+pydantic_core==2.33.2
+python-dateutil==2.9.0.post0
+pytz==2025.2
+requests==2.32.4
+rsa==4.9.1
+shapely==2.1.0
+six==1.17.0
+sniffio==1.3.1
+typing-inspection==0.4.0
+typing_extensions==4.13.2
+urllib3==2.6.0
+websockets==15.0.1

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/launch.py

@@ -0,0 +1,173 @@
+"""Launch script for Vertex distributed training"""
+
+# Copy the sample_job_config.json file to job_config.json
+# to define the job parameters.
+#
+# Run like this:
+#
+#   python3 vertex_dist_train/launch.py --config_file=job_config.json
+#
+
+import datetime
+import json
+import os
+import pprint
+from collections.abc import Sequence
+from typing import Any, List
+
+from absl import app, flags
+from google.cloud import aiplatform
+from google.cloud.aiplatform_v1.types.custom_job import Scheduling
+from pytz import timezone
+
+FLAGS = flags.FLAGS
+flags.DEFINE_string("config_file", None, "Path to JSON config file")
+flags.DEFINE_boolean(
+    "debug", False, "Debug mode: just print the command, don't run it."
+)
+
+
+def launch_job(
+    job_name: str,
+    project: str,
+    region: str,
+    gcs_bucket: str,
+    image_uri: str,
+    entrypoint_cmd: List[str],
+    trainer_args: List[Any],
+    num_nodes: int,
+    machine_type: str,
+    num_gpus_per_node: int,
+    gpu_type: str,
+    strategy: str,
+    reservation_name: str = "",
+):
+    assert strategy in ("dws", "spot", "reservation")
+    aiplatform.init(
+        project=project, location=region, staging_bucket=gcs_bucket
+    )
+
+    train_job = aiplatform.CustomContainerTrainingJob(
+        display_name=job_name,
+        container_uri=image_uri,
+        command=entrypoint_cmd,
+    )
+
+    job_args = dict(
+        args=trainer_args,
+        enable_web_access=True,
+        replica_count=num_nodes,
+        machine_type=machine_type,
+        accelerator_type=gpu_type,
+        accelerator_count=num_gpus_per_node,
+        boot_disk_size_gb=1000,
+        restart_job_on_worker_restart=True,
+        #restart_job_on_worker_restart=False,
+    )
+
+    if strategy == "spot":
+        job_args.update({"scheduling_strategy": Scheduling.Strategy.SPOT.name})
+    elif strategy == "dws":
+        job_args.update(
+            {"scheduling_strategy": Scheduling.Strategy.FLEX_START.name}
+        )
+    elif strategy == "reservation":
+        assert reservation_name != "", (
+            "If using a reservation, provide the reservation_name in the "
+            "format `projects/{project_id_or_number}/zones/{zone}/"
+            "reservations/{reservation_name}`"
+        )
+        job_args.update(
+            {
+                "reservation_affinity_type": "SPECIFIC_RESERVATION",
+                "reservation_affinity_key": "compute.googleapis.com/reservation-name",
+                "reservation_affinity_values": [reservation_name],
+            }
+        )
+
+    pprint.pprint(job_args)
+    if not FLAGS.debug:
+        train_job.submit(**job_args)
+
+
+def main(argv: Sequence[str]) -> None:
+    config_file_path = FLAGS.config_file
+    print(f"Reading job config from {config_file_path}")
+    with open(config_file_path, encoding="utf-8") as config_file:
+        config = json.load(config_file)
+
+    project_id = config["project_id"]
+    region = config["region"]
+    zone = config["zone"]
+    bucket = config["bucket"]
+    dataset_bucket = config["dataset_bucket"]
+    n_nodes = int(config["nodes"])
+    machine_type = config["machine_type"]
+    num_gpus_per_node = int(config["gpus_per_node"])
+    gpu_type = config["gpu_type"]
+    reservation_name = config.get("reservation_name")
+    reservation_full_name = (
+        f"projects/{project_id}/zones/{zone}/reservations/{reservation_name}"
+        if "reservation_name" in config
+        else ""
+    )
+
+    strategy = config["strategy"]
+    recipe_name = config["recipe_name"]
+    job_prefix = config["job_prefix"]
+    image_uri = config["image_uri"]
+
+    # Job name
+    timestamp = (
+        datetime.datetime.now()
+        .astimezone(timezone("US/Pacific"))
+        .strftime("%Y%m%d_%H%M%S")
+    )
+    job_name = f"{recipe_name}-{timestamp}"
+    if job_prefix:
+        job_name = f"{job_prefix}-{job_name}"
+
+    base_output_dir = os.path.join("/gcs", bucket, job_name)
+
+    # Training command and args
+    entrypoint_cmd = ["python3", "vdt/run.py"]
+
+    dataset_bucket = f"gs://{config['dataset_bucket']}"
+
+    trainer_args = [
+        f"--train_data_gcs={dataset_bucket}",
+        "/opt/NeMo/examples/nlp/language_modeling/megatron_gpt_pretraining.py",
+        "--config-path=conf/",
+        f"--config-name={recipe_name}.yaml",
+        f"exp_manager.explicit_log_dir={base_output_dir}",
+        f"exp_manager.dllogger_logger_kwargs.json_file={base_output_dir}/dllogger.json",
+        "+exp_manager.create_tensorboard_logger=true",
+        "exp_manager.create_checkpoint_callback=false",
+        f"trainer.num_nodes={n_nodes}",
+        f"trainer.devices={num_gpus_per_node}",
+        "trainer.max_steps=10",
+        "trainer.log_every_n_steps=1",
+        "model.tokenizer.vocab_file=/data/gpt2-vocab.json",
+        "model.tokenizer.merge_file=/data/gpt2-merges.txt",
+        "model.data.data_prefix=[1.0,/data/hfbpe_gpt_training_data_text_document]",
+    ]
+
+    launch_job(
+        job_name=job_name,
+        project=project_id,
+        region=region,
+        gcs_bucket=bucket,
+        image_uri=image_uri,
+        entrypoint_cmd=entrypoint_cmd,
+        trainer_args=trainer_args,
+        num_nodes=n_nodes,
+        machine_type=machine_type,
+        num_gpus_per_node=num_gpus_per_node,
+        gpu_type=gpu_type,
+        strategy=strategy,
+        reservation_name=reservation_full_name,
+    )
+
+
+if __name__ == "__main__":
+    app.run(main)

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/run.py

@@ -0,0 +1,85 @@
+"""Entrypoint for Vertex Distributed Training container."""
+
+import argparse
+import os
+import sys
+from collections.abc import Sequence
+from subprocess import STDOUT, check_output, run
+
+from absl import app, flags, logging
+from util import cluster_spec
+
+from retrying import retry
+
+# PyTorch barrier call which synchronizes all of the nodes before launching the training process.
+# This makes sure that processes will block until all processes are ready.
+# Improves the reliability of spot VM usage for multi-node training jobs
+
+@retry(stop_max_attempt_number=100, wait_exponential_multiplier=1000)
+def barrier_with_retry() -> None:
+    import torch
+    logging.info("Starting barrier on RANK {}".format(os.environ["RANK"]))
+    torch.distributed.init_process_group()
+    torch.distributed.barrier()
+    torch.distributed.destroy_process_group()
+    logging.info("Finished barrier on RANK {}".format(os.environ["RANK"]))
+
+def main(unused_argv: Sequence[str]) -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--train_data_gcs",
+        type=str,
+        help="Download training data from gcs path",
+    )
+    args, unknown = parser.parse_known_args()
+
+    for key, val in os.environ.items():
+        logging.info("ENV %s=%s", key, val)
+
+    if args.train_data_gcs:
+        local_dir = "/data"
+        if not os.path.exists(local_dir):
+            os.mkdir(local_dir)
+        logging.info("downloading %s to %s...", args.train_data_gcs, local_dir)
+        check_output(
+            [
+                "gcloud",
+                "storage",
+                "cp",
+                "-r",
+                f"{args.train_data_gcs}/*",
+                local_dir,
+            ],
+            stderr=STDOUT,
+        )
+        logging.info("%s downloaded.", args.train_data_gcs)
+
+    primary_node_addr, primary_node_port, node_rank, num_nodes = (
+        cluster_spec.get_cluster_spec()
+    )
+
+    cmd = [
+        "torchrun",
+        "--nproc-per-node=8",
+        f"--nnodes={num_nodes}",
+        f"--node_rank={node_rank}",
+    ]
+    if num_nodes > 1:
+        cmd += [
+            "--max-restarts=3",
+            "--rdzv-backend=static",
+            f'--rdzv_id={os.getenv("CLOUD_ML_JOB_ID", primary_node_port)}',
+            f"--rdzv-endpoint={primary_node_addr}:{primary_node_port}",
+        ]
+    cmd += unknown
+
+    logging.info("launching with cmd: \n%s", " \\\n".join(cmd))
+    barrier_with_retry()
+    run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
+
+
+if __name__ == "__main__":
+    logging.get_absl_handler().python_handler.stream = sys.stdout
+    app.run(
+        main, flags_parser=lambda _args: flags.FLAGS(_args, known_only=True)
+    )

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/util/cluster_spec.py

@@ -0,0 +1,81 @@
+"""Get cluster info from environment variables."""
+
+import dataclasses
+import json
+import os
+
+from absl import logging
+
+
+@dataclasses.dataclass
+class ClusterInfo:
+    """Contains information about the cluster.
+
+    Attributes:
+      primary_node_addr: The address of the primary node.
+      primary_node_port: The port of the primary node.
+      node_rank: The rank of the node.
+      num_nodes: The number of nodes in the cluster.
+    """
+
+    primary_node_addr: str | None = None
+    primary_node_port: str | None = None
+    node_rank: int = 0
+    num_nodes: int = 1
+
+    # Allows unpacking operation like
+    # primary_node_addr, primary_node_port, _, _ = ClusterInfo()
+    # See https://stackoverflow.com/a/70753113
+    def __iter__(self):
+        return iter(dataclasses.astuple(self))
+
+
+def get_cluster_spec() -> ClusterInfo:
+    """Parses CLUSTER_SPEC environment variable and returns the cluster info.
+
+    Returns:
+      A ClusterInfo object.
+    """
+    cluster_spec = os.getenv("CLUSTER_SPEC", None)
+
+    # If CLUSTER_SPEC is not set, use individual vars to construct cluster info.
+    if not cluster_spec:
+        cluster_info = ClusterInfo(
+            primary_node_addr=os.getenv("MASTER_ADDR", None),
+            primary_node_port=os.getenv("MASTER_PORT", None),
+            node_rank=int(os.getenv("RANK", "0")),
+            num_nodes=int(os.getenv("NNODES", "1")),
+        )
+        return cluster_info
+
+    cluster_data = json.loads(cluster_spec)
+    # Get primary node info
+    primary_node = cluster_data["cluster"]["workerpool0"][0]
+    logging.info("primary node: %s", primary_node)
+    primary_node_addr, primary_node_port = primary_node.split(":")
+    logging.info("primary node address: %s", primary_node_addr)
+    logging.info("primary node port: %s", primary_node_port)
+
+    # Determine node rank of this machine
+    workerpool = cluster_data["task"]["type"]
+    if workerpool == "workerpool0":
+        node_rank = 0
+    elif workerpool == "workerpool1":
+        # Add 1 for the primary node, since `index` is the index of workerpool1.
+        node_rank = cluster_data["task"]["index"] + 1
+    else:
+        raise ValueError(
+            "Only workerpool0 and workerpool1 are supported. Unknown workerpool:"
+            f" {workerpool}"
+        )
+    logging.info("node rank: %s", node_rank)
+
+    # Calculate total nodes.
+    num_nodes = 1  # For the primary node.
+    if "workerpool1" in cluster_data["cluster"]:
+        num_nodes += len(cluster_data["cluster"]["workerpool1"])
+    logging.info("num nodes: %s", num_nodes)
+
+    return ClusterInfo(
+        primary_node_addr, primary_node_port, node_rank, num_nodes
+    )

community-content/vertex-distributed-training/a3mega/llama-3-8b-nemo-pretraining/scripts/util/cluster_spec_test.py

@@ -0,0 +1,59 @@
+"""Add tests for cluster_spec.py."""
+
+import os
+
+from . import cluster_spec
+
+
+# TODO(styer): Use pytest instead
+class ClusterSpecTest(googletest.TestCase):
+
+    def setUp(self):
+        super().setUp()
+        self.curr_env_var = os.environ.copy()
+
+    def tearDown(self):
+        super().tearDown()
+        os.environ = self.curr_env_var
+
+    def test_get_cluster_spec_from_env_vars(self):
+        os.environ["CLUSTER_SPEC"] = ""
+        os.environ["MASTER_ADDR"] = "127.0.0.1"
+        os.environ["MASTER_PORT"] = "8080"
+        os.environ["RANK"] = "0"
+        os.environ["NNODES"] = "2"
+        cluster_info = cluster_spec.get_cluster_spec()
+        self.assertEqual(cluster_info.primary_node_addr, "127.0.0.1")
+        self.assertEqual(cluster_info.primary_node_port, "8080")
+        self.assertEqual(cluster_info.node_rank, 0)
+        self.assertEqual(cluster_info.num_nodes, 2)
+
+    def test_get_cluster_spec_from_cluster_spec(self):
+        os.environ[
+            "CLUSTER_SPEC"
+        ] = """
+    {
+      "cluster": {
+        "workerpool0": [
+          "127.0.0.1:8080"
+        ],
+        "workerpool1": [
+          "127.0.0.2:8080",
+          "127.0.0.3:8080"
+        ]
+      },
+      "task": {
+        "type": "workerpool1",
+        "index": 0
+      }
+    }
+    """
+        cluster_info = cluster_spec.get_cluster_spec()
+        self.assertEqual(cluster_info.primary_node_addr, "127.0.0.1")
+        self.assertEqual(cluster_info.primary_node_port, "8080")
+        self.assertEqual(cluster_info.node_rank, 1)
+        self.assertEqual(cluster_info.num_nodes, 3)
+
+
+if __name__ == "__main__":
+    googletest.main()

community-content/vertex_model_garden/model_oss/cloudnerf/local_colmap_and_resize.sh

@@ -66,7 +66,7 @@ mkdir -p "$local_folder"
 mkdir -p "$output_folder"
 
 # Download the content from the GCS URI
-gsutil -m cp -r "$gcs_dataset_path"/* "$local_folder/"
+gcloud storage cp --recursive "$gcs_dataset_path"/* "$local_folder/"
 
 # Process files in the local folder
 for file in "$local_folder"/*; do
@@ -122,23 +122,23 @@ cp -r "$output_folder" "$images_folder"/images_2
 pushd "$images_folder"/images_2
 ls | xargs -P 8 -I {} mogrify -resize 50% {}
 popd
-gsutil -m cp -r "$images_folder"/images_2/*  "$gcs_experiment_path"/data/images_2
+gcloud storage cp --recursive "$images_folder"/images_2/*  "$gcs_experiment_path"/data/images_2
 
 cp -r "$output_folder" "$images_folder"/images_4
 pushd "$images_folder"/images_4
 ls | xargs -P 8 -I {} mogrify -resize 25% {}
 popd
-gsutil -m cp -r "$images_folder"/images_4/*  "$gcs_experiment_path"/data/images_4
+gcloud storage cp --recursive "$images_folder"/images_4/*  "$gcs_experiment_path"/data/images_4
 
 cp -r "$output_folder" "$images_folder"/images_8
 pushd "$images_folder"/images_8
 ls | xargs -P 8 -I {} mogrify -resize 12.5% {}
 popd
-gsutil -m cp "$images_folder"/images_8/*  "$gcs_experiment_path"/data/images_8
+gcloud storage cp "$images_folder"/images_8/*  "$gcs_experiment_path"/data/images_8
 
 # Copy images and sparse reconstruction files to gcs experiment folder.
-gsutil -m cp "$images_folder"/images/*  "$gcs_experiment_path"/data/images
-gsutil -m cp -r "$local_folder"/sparse  "$gcs_experiment_path"/data
-gsutil -m cp "$local_folder"/database.db  "$gcs_experiment_path"/data
+gcloud storage cp "$images_folder"/images/*  "$gcs_experiment_path"/data/images
+gcloud storage cp --recursive "$local_folder"/sparse  "$gcs_experiment_path"/data
+gcloud storage cp "$local_folder"/database.db  "$gcs_experiment_path"/data
 
 echo "Processing complete."

community-content/vertex_model_garden/model_oss/cloudnerf/render.sh

@@ -99,14 +99,14 @@ create_dir_if_not_exists "$CHECKPOINTS_PATH"
 touch "$local_experiment_path/$exp_folder_name/log_render.txt"
 
 # Copy experiment from GCS bucket to local
-gsutil -m cp -r "${args[-gcs_experiment_path]}/data" "$local_experiment_path/$exp_folder_name" || exit 1
-gsutil -m cp -r "${args[-gcs_experiment_path]}/checkpoints/${training_job_name}/*" "$CHECKPOINTS_PATH" || exit 1
+gcloud storage cp --recursive "${args[-gcs_experiment_path]}/data" "$local_experiment_path/$exp_folder_name" || exit 1
+gcloud storage cp --recursive "${args[-gcs_experiment_path]}/checkpoints/${training_job_name}/*" "$CHECKPOINTS_PATH" || exit 1
 
 # Check and copy keyframes file.
 if [[ -n ${args[-gcs_keyframes_file]} ]]; then
   keyframes_file_basename=$(basename "${args[-gcs_keyframes_file]}")
   local_keyframes_file="$local_dataset_path/$keyframes_file_basename"
-  gsutil cp "${args[-gcs_keyframes_file]}" "$local_keyframes_file" || exit 1
+  gcloud storage cp "${args[-gcs_keyframes_file]}" "$local_keyframes_file" || exit 1
   echo "Local keyframe file: $local_keyframes_file"
   launch_rendering "$local_keyframes_file"
 else
@@ -114,4 +114,4 @@ else
 fi
 
 # Copy rendered data back to GCS.
-gsutil -m cp -r "$OUTPUT_RENDER_PATH" "${args[-gcs_experiment_path]}/render/${rendering_job_name}"
+gcloud storage cp --recursive "$OUTPUT_RENDER_PATH" "${args[-gcs_experiment_path]}/render/${rendering_job_name}"

community-content/vertex_model_garden/model_oss/cloudnerf/train.sh

@@ -74,7 +74,7 @@ create_dir_if_not_exists "$local_experiment_path"
 create_dir_if_not_exists "$local_experiment_path/$scene_folder_name"
 
 # Copy experiment from GCS bucket to local.
-gsutil -m cp -r "${gcs_experiment_path}/data" "$local_experiment_path/$scene_folder_name" || exit 1
+gcloud storage cp --recursive "${gcs_experiment_path}/data" "$local_experiment_path/$scene_folder_name" || exit 1
 
 echo "GCS Experiment: $gcs_experiment_path"
 echo "Gin Config File: $gin_config_file"
@@ -89,6 +89,6 @@ accelerate launch train.py --gin_configs="$gin_config_file" \
   --gin_bindings="Config.factor = ${factor}" \
   --gin_bindings="Config.max_steps = ${max_training_steps}"
 
-gsutil -m rm -r "${gcs_experiment_path}/checkpoints/${training_job_name}"
-gsutil -m cp -r "$local_experiment_path/$scene_folder_name/config.gin" "${gcs_experiment_path}/${training_job_name}_config.gin"
-gsutil -m cp -r "$local_experiment_path/$scene_folder_name/checkpoints/*/*" "${gcs_experiment_path}/checkpoints/${training_job_name}"
+gcloud storage rm --recursive "${gcs_experiment_path}/checkpoints/${training_job_name}"
+gcloud storage cp --recursive "$local_experiment_path/$scene_folder_name/config.gin" "${gcs_experiment_path}/${training_job_name}_config.gin"
+gcloud storage cp --recursive "$local_experiment_path/$scene_folder_name/checkpoints/*/*" "${gcs_experiment_path}/checkpoints/${training_job_name}"

community-content/vertex_model_garden/model_oss/notebook_util/common_util.py

@@ -1,13 +1,16 @@
 """Common util functions for notebook."""
 
 import base64
+from collections.abc import Sequence
 import datetime
 import io
 import json
 import os
 import subprocess
-from typing import Any, Dict, Sequence
+import time
+from typing import Any
 
+from google import auth
 from google.cloud import storage
 import matplotlib.pyplot as plt
 import numpy as np
@@ -281,7 +284,7 @@ def decode_image(
   return image
 
 
-def get_label_map(label_map_yaml_filepath: str) -> Dict[int, str]:
+def get_label_map(label_map_yaml_filepath: str) -> dict[int, str]:
   """Returns class id to label mapping given a filepath to the label map.
 
   Args:
@@ -331,6 +334,7 @@ def vqa_predict(
     image: Any,
     language_code: str = "en",
     new_width: int = 1000,
+    use_dedicated_endpoint: bool = False,
 ) -> Sequence[str]:
   """Predicts the answer to a question about an image using an Endpoint."""
   # Resize and convert image to base64 string.
@@ -354,7 +358,9 @@ def vqa_predict(
         "image": resized_image_base64,
     })
 
-  response = endpoint.predict(instances=instances)
+  response = endpoint.predict(
+      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
+  )
   return [pred.get("response") for pred in response.predictions]
 
 
@@ -364,6 +370,7 @@ def caption_predict(
     image: Any,
     caption_prompt: bool = False,
     new_width: int = 1000,
+    use_dedicated_endpoint: bool = False,
 ) -> str:
   """Predicts a caption for a given image using an Endpoint."""
   # Resize and convert image to base64 string.
@@ -378,7 +385,9 @@ def caption_predict(
     instance["prompt"] = caption_prompt_format.format(language_code)
 
   instances = [instance]
-  response = endpoint.predict(instances=instances)
+  response = endpoint.predict(
+      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
+  )
   return response.predictions[0].get("response")
 
 
@@ -387,6 +396,7 @@ def ocr_predict(
     ocr_prompt: str,
     image: Any,
     new_width: int = 1000,
+    use_dedicated_endpoint: bool = False,
 ) -> str:
   """Extracts text from a given image using an Endpoint."""
   # Resize and convert image to base64 string.
@@ -398,7 +408,9 @@ def ocr_predict(
     instance["prompt"] = ocr_prompt
   instances = [instance]
 
-  response = endpoint.predict(instances=instances)
+  response = endpoint.predict(
+      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
+  )
   return response.predictions[0].get("response")
 
 
@@ -407,6 +419,7 @@ def detect_predict(
     detect_prompt: str,
     image: Any,
     new_width: int = 1000,
+    use_dedicated_endpoint: bool = False,
 ) -> str:
   """Predicts the answer to a question about an image using an Endpoint."""
   # Resize and convert image to base64 string.
@@ -418,7 +431,9 @@ def detect_predict(
     instance["prompt"] = detect_prompt
   instances = [instance]
 
-  response = endpoint.predict(instances=instances)
+  response = endpoint.predict(
+      instances=instances, use_dedicated_endpoint=use_dedicated_endpoint
+  )
   return response.predictions[0].get("response")
 
 
@@ -495,6 +510,17 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
   ):
     return -1
   all_regions_data = quota_data[0]["consumerQuotaLimits"][0]["quotaBuckets"]
+
+  # If the quota data does not have dimensions, it is global quota. However,
+  # global quota may be overridden by regional quota. So we need to check the
+  # global quota first.
+  global_quota = -1
+  if (
+      all_regions_data
+      and "dimensions" not in all_regions_data[0]
+      and "effectiveLimit" in all_regions_data[0]
+  ):
+    global_quota = int(all_regions_data[0]["effectiveLimit"])
   for region_data in all_regions_data:
     if (
         region_data.get("dimensions")
@@ -504,12 +530,13 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
         return int(region_data["effectiveLimit"])
       else:
         return 0
-  return -1
+  return global_quota
 
 
 def get_resource_id(
     accelerator_type: str,
     is_for_training: bool,
+    is_spot: bool = False,
     is_restricted_image: bool = False,
     is_dynamic_workload_scheduler: bool = False,
 ) -> str:
@@ -519,6 +546,7 @@ def get_resource_id(
     accelerator_type: The accelerator type.
     is_for_training: Whether the resource is used for training. Set false for
       serving use case.
+    is_spot: Whether the resource is used with Spot.
     is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
     is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
       Workload Scheduler.
@@ -534,7 +562,9 @@ def get_resource_id(
       "NVIDIA_A100_80GB": "nvidia_a100_80gb_gpus",
       "NVIDIA_H100_80GB": "nvidia_h100_gpus",
       "NVIDIA_H100_MEGA_80GB": "nvidia_h100_mega_gpus",
+      "NVIDIA_H200_141GB": "nvidia_h200_gpus",
       "NVIDIA_TESLA_T4": "nvidia_t4_gpus",
+      "TPU_V6e": "tpu_v6e",
       "TPU_V5e": "tpu_v5e",
       "TPU_V3": "tpu_v3",
   }
@@ -549,6 +579,10 @@ def get_resource_id(
   restricted_image_training_accelerator_map = {
       "NVIDIA_A100_80GB": "restricted_image_training_nvidia_a100_80gb_gpus",
   }
+  spot_serving_accelerator_map = {
+      key: f"custom_model_serving_preemptible_{accelerator_suffix_map[key]}"
+      for key in accelerator_suffix_map
+  }
   serving_accelerator_map = {
       key: f"custom_model_serving_{accelerator_suffix_map[key]}"
       for key in accelerator_suffix_map
@@ -577,8 +611,11 @@ def get_resource_id(
   else:
     if is_dynamic_workload_scheduler:
       raise ValueError("Dynamic Workload Scheduler does not work for serving.")
-    if accelerator_type in serving_accelerator_map:
-      return serving_accelerator_map[accelerator_type]
+    accelerator_map = (
+        spot_serving_accelerator_map if is_spot else serving_accelerator_map
+    )
+    if accelerator_type in accelerator_map:
+      return accelerator_map[accelerator_type]
     else:
       raise ValueError(
           f"Could not find accelerator type: {accelerator_type} for serving."
@@ -591,13 +628,28 @@ def check_quota(
     accelerator_type: str,
     accelerator_count: int,
     is_for_training: bool,
+    is_spot: bool = False,
     is_restricted_image: bool = False,
     is_dynamic_workload_scheduler: bool = False,
-):
-  """Checks if the project and the region has the required quota."""
+) -> None:
+  """Checks if the project and the region has the required quota.
+
+  Args:
+    project_id: The project id.
+    region: The region.
+    accelerator_type: The accelerator type.
+    accelerator_count: The number of accelerators to check quota for.
+    is_for_training: Whether the resource is used for training. Set false for
+      serving use case.
+    is_spot: Whether the resource is used with Spot.
+    is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
+    is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
+      Workload Scheduler.
+  """
   resource_id = get_resource_id(
       accelerator_type,
       is_for_training=is_for_training,
+      is_spot=is_spot,
       is_restricted_image=is_restricted_image,
       is_dynamic_workload_scheduler=is_dynamic_workload_scheduler,
   )
@@ -634,3 +686,62 @@ def get_deploy_source() -> str:
       # Legacy workbench, legacy colab, or other custom environments.
       return "notebook_environment_unspecified"
 
+
+def _is_operation_done(op_name: str, region: str) -> bool:
+  """Checks if the operation is done.
+
+  Args:
+    op_name: The name of the operation to poll.
+    region: The region of the operation.
+
+  Returns:
+    True if the operation is done, False otherwise.
+
+  Raises:
+    ValueError: If the operation failed.
+  """
+  creds, _ = auth.default()
+  auth_req = auth.transport.requests.Request()
+  creds.refresh(auth_req)
+  headers = {
+      "Authorization": f"Bearer {creds.token}",
+  }
+  url = f"https://{region}-aiplatform.googleapis.com/ui/{op_name}"
+  response = requests.get(url, headers=headers)
+  operation_data = response.json()
+  if "error" in operation_data:
+    raise ValueError(f"Operation failed: {operation_data['error']}")
+  return operation_data.get("done", False)
+
+
+def poll_and_wait(
+    op_name: str, region: str, total_wait: int, interval: int = 60
+) -> None:
+  """Polls the operation and waits for it to complete.
+
+  Args:
+    op_name: The name of the operation to poll.
+    region: The region of the operation.
+    total_wait: The total wait time in seconds.
+    interval: The interval between each poll in seconds.
+
+  Raises:
+    TimeoutError: If the operation times out.
+  """
+  start_time = time.time()
+  while True:
+    if _is_operation_done(op_name, region):
+      break
+    time_elapsed = time.time() - start_time
+    if time_elapsed > total_wait:
+      raise TimeoutError(
+          f"Operation timed out after {int(time_elapsed)} seconds."
+      )
+    print(
+        "\rStill waiting for operation... Elapsed time in seconds:"
+        f" {int(time_elapsed):<6}",
+        end="",
+        flush=True,
+    )
+    time.sleep(interval)
+

community-content/vertex_model_garden/model_oss/notebook_util/dataset_validation_util.py

@@ -7,7 +7,7 @@ import json
 import multiprocessing
 import os
 import subprocess
-from typing import Any, Callable, Dict, Union
+from typing import Any, Callable, Dict, Tuple, Union
 from absl import logging
 import accelerate
 import datasets
@@ -70,7 +70,9 @@ def force_gcs_fuse_path(gcs_uri: str) -> str:
 
 
 def download_gcs_uri_to_local(
-    gcs_uri: str, destination_dir: str = LOCAL_BASE_MODEL_DIR
+    gcs_uri: str,
+    destination_dir: str = LOCAL_BASE_MODEL_DIR,
+    check_path_exists: bool = True,
 ) -> str:
   """Downloads GCS URI to local.
 
@@ -81,6 +83,7 @@ def download_gcs_uri_to_local(
   Args:
     gcs_uri: GCS URI to download.
     destination_dir: Local directory directory.
+    check_path_exists: Whether to check if the path exists.
 
   Returns:
     Local path to target folder/file.
@@ -89,7 +92,7 @@ def download_gcs_uri_to_local(
       destination_dir,
       os.path.basename(os.path.normpath(gcs_uri)),
   )
-  if os.path.exists(target):
+  if check_path_exists and os.path.exists(target):
     logging.info("File %s already exists.", target)
     return target
   if accelerate.PartialState().is_local_main_process:
@@ -99,10 +102,10 @@ def download_gcs_uri_to_local(
     if not os.path.exists(destination_dir):
       os.mkdir(destination_dir)
     subprocess.check_output([
-        "gsutil",
-        "-m",
+        "gcloud",
+        "storage",
         "cp",
-        "-r",
+        "--recursive",
         gcs_uri,
         destination_dir,
     ])
@@ -415,13 +418,42 @@ def get_filtered_dataset(
   return filtered_dataset
 
 
+def format_dataset(
+    dataset: datasets.Dataset,
+    input_column: str,
+    template: str = None,
+    tokenizer: transformers.PreTrainedTokenizer | None = None,
+) -> datasets.Dataset:
+  """Takes a raw dataset and formats it using a template and tokenizer.
+
+  Args:
+    dataset: The raw (unprocessed) dataset to format.
+    input_column: The input column in the dataset to be used or updaded by the
+      template. If it does not exist, the template's `prompt_no_input` will be
+      used, and the input_column will be created.
+    template: Name of the JSON template file under `templates/` or GCS path to
+      the template file.
+    tokenizer: The tokenizer to use for chat_template templates.
+
+  Returns:
+    A dataset compatible with the template.
+  """
+  return dataset.map(
+      _format_template_fn(
+          template,
+          input_column=input_column,
+          tokenizer=tokenizer,
+      )
+  )
+
+
 def load_dataset_with_template(
     dataset_name: str,
     split: str,
     input_column: str,
     template: str = None,
     tokenizer: transformers.PreTrainedTokenizer | None = None,
-) -> Any:
+) -> Tuple[Any, Any]:
   """Loads dataset with templates.
 
   Args:
@@ -435,19 +467,15 @@ def load_dataset_with_template(
     tokenizer: The tokenizer to use for chat_template templates.
 
   Returns:
-    A dataset compatible with the template.
+    The raw dataset and the dataset compatible with the template.
   """
-  dataset = _get_dataset(dataset_name, split=split)
+  raw = _get_dataset(dataset_name, split=split)
   if template:
-    dataset = dataset.map(
-        _format_template_fn(
-            template,
-            input_column=input_column,
-            tokenizer=tokenizer,
-        )
-    )
+    templated = format_dataset(raw, input_column, template, tokenizer)
+  else:
+    templated = None
 
-  return dataset
+  return raw, templated
 
 
 def validate_dataset_with_template(
@@ -521,12 +549,11 @@ def validate_dataset_with_template(
         f" https://github.com/GoogleCloudPlatform/{_VERTEX_AI_SAMPLES_GITHUB_REPO_NAME}/tree/main/{_VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR}."
     )
 
-  dataset = _get_dataset(dataset_name, split, num_proc).map(
-      _format_template_fn(
-          template_path,
-          input_column=input_column,
-          tokenizer=tokenizer,
-      )
+  dataset = format_dataset(
+      _get_dataset(dataset_name, split, num_proc),
+      input_column,
+      template_path,
+      tokenizer,
   )
 
   if tokenizer is not None:

community-content/vertex_model_garden/model_oss/peft/train/test_utils/command_builder.py

@@ -32,6 +32,7 @@ class DockerCommandBuilder(CommandBuilder):
     super().__init__()
     self._docker_uri = [docker_uri]
     self.privilege_mode = []
+    self.entrypoint = []
 
     self._defaults = [
         'docker',
@@ -62,6 +63,9 @@ class DockerCommandBuilder(CommandBuilder):
   def add_privilege_mode(self):
     self.privilege_mode = ['--privileged']
 
+  def add_entrypoint(self, entrypoint: list[str]):
+    self.entrypoint = entrypoint
+
   def build_cmd(self) -> str:
     return (
         self._defaults
@@ -69,6 +73,7 @@ class DockerCommandBuilder(CommandBuilder):
         + self._mount_maps
         + self.privilege_mode
         + self._docker_uri
+        + self.entrypoint
     )
 
 
@@ -85,3 +90,6 @@ class PythonCommandBuilder(CommandBuilder):
   def build_cmd(self) -> str:
     os.environ.update(self._env_vars)
     return self._defaults
+
+  def add_entrypoint(self, entrypoint: list[str]):
+    self._defaults = entrypoint

community-content/vertex_model_garden/model_oss/peft/train/test_utils/test_util.py

@@ -3,6 +3,7 @@
 import copy
 import dataclasses
 import datetime
+import inspect
 import os
 import signal
 import subprocess
@@ -11,7 +12,7 @@ from absl import flags
 from absl import logging
 from absl.testing import parameterized
 import command_builder
-import frozendict
+import immutabledict
 import torch
 
 _DOCKER_URI = flags.DEFINE_string('docker_uri', None, 'docker image uri')
@@ -33,19 +34,19 @@ _LOCAL_OUTPUT_DIR = flags.DEFINE_string(
 
 _GCS_INPUT_DIR = flags.DEFINE_string(
     'gcs_input_dir',
-    'gs://peft-docker-test',
+    'gs://vmg-tuning-docker-test',
     'GCS directory that stores model checkpoint, dataset and etc.',
 )
 
 _GCS_OUTPUT_DIR = flags.DEFINE_string(
     'gcs_output_dir',
-    'gs://peft-docker-test/output',
+    'gs://vmg-tuning-docker-test/output',
     'GCS directory that stores test output.',
 )
 
 _GCS_TESTDATA_DIR = 'peft-train-image-test'
 
-_THROUGHPUT_TEST_EXCEPTIONS = frozendict.frozendict({
+_THROUGHPUT_TEST_EXCEPTIONS = immutabledict.immutabledict({
     ('bm_deepspeed_zero3_8gpu_gemma-2-9b-it_4bit.txt', '12.0'): float('inf'),
     ('bm_fsdp_8gpu_llama3.1-70b-hf_4bit.txt', '20.0'): float('inf'),
     ('bm_deepspeed_zero2_8gpu_gemma-2-2b-it_bfloat16.txt', '12.0'): 20.0,
@@ -101,26 +102,25 @@ class TestBase(parameterized.TestCase):
     return self.command_builder.build_cmd() + self.task_cmd_builder.build_cmd()
 
   def run_cmd(self) -> int:
-    logging.info('running command: \n%s', ' \\\n'.join(self.cmd()))
-    if _DRY_RUN.value:
-      return 0
-
-    p = subprocess.Popen(self.cmd(), stdout=sys.stdout, stderr=sys.stderr)
-    try:
-      unused_output, unused_error = p.communicate()
-      return p.returncode
-    except KeyboardInterrupt:
-      p.send_signal(signal.SIGINT)
-      return 0
+    return run_cmd(self.cmd(), output_file=None)
 
   def gcs_output_dir(self):
     return _GCS_OUTPUT_DIR.value
 
+  def local_input_dir(self):
+    """Returns local input dir in host/docker."""
+    return _LOCAL_INPUT_DIR.value
+
   def local_output_dir(self):
+    """Returns local output dir in host/docker."""
     return _LOCAL_OUTPUT_DIR.value
 
-  def local_input_dir(self):
-    return _LOCAL_INPUT_DIR.value
+  def get_testcase_name(self):
+    """Returns the function name at the calling site."""
+    # https://docs.python.org/3/library/inspect.html#inspect.FrameInfo
+    cur_frame = inspect.currentframe()
+    # https://stackoverflow.com/a/17366561
+    return cur_frame.f_back.f_code.co_name
 
 
 def get_timestamp():
@@ -157,13 +157,44 @@ def get_test_data_path(name: str, download: bool = True) -> str:
 
   local_data = os.path.join(_LOCAL_INPUT_DIR.value, name)
   if not os.path.exists(local_data):
-    download_from_gcs(
-        os.path.join(_GCS_INPUT_DIR.value, name), _LOCAL_INPUT_DIR.value
-    )
+    # If `name` is a file in sub-folders, then create the sub-folders under
+    # `_LOCAL_INPUT_DIR`.
+    local_data_dir = os.path.dirname(local_data)
+    if not os.path.exists(local_data_dir):
+      os.makedirs(local_data_dir)
+
+    download_from_gcs(os.path.join(_GCS_INPUT_DIR.value, name), local_data_dir)
 
   return local_data
 
 
+def run_cmd(cmd: list[str], output_file: str = None) -> int:
+  """Runs the command and returns the return code.
+
+  Args:
+    cmd: The command to run.
+    output_file: The file to write the output to.
+
+  Returns:
+    The return code of the command.
+  """
+  logging.info('running command: \n%s', ' \\\n'.join(cmd))
+  if _DRY_RUN.value:
+    return 0
+  stdout = sys.stdout if output_file is None else open(output_file, 'w')
+  p = subprocess.Popen(cmd, stdout=stdout, stderr=sys.stderr)
+  try:
+    unused_output, unused_error = p.communicate()
+    return_code = p.returncode
+  except KeyboardInterrupt:
+    p.send_signal(signal.SIGINT)
+    return_code = 0
+  finally:
+    if output_file is not None:
+      stdout.close()
+  return return_code
+
+
 def get_pretrained_model_name_or_path(model_id: str) -> str:
   # If `model_id` contains `/`, it is assumed to be HF model or model from GCS.
   if '/' in model_id:

community-content/vertex_model_garden/model_oss/peft/train/util/cluster_spec.py

@@ -0,0 +1,79 @@
+"""Get cluster info from environment variables."""
+
+import dataclasses
+import json
+import os
+
+from absl import logging
+
+
+@dataclasses.dataclass
+class ClusterInfo:
+  """Contains information about the cluster.
+
+  Attributes:
+    primary_node_addr: The address of the primary node.
+    primary_node_port: The port of the primary node.
+    node_rank: The rank of the node.
+    num_nodes: The number of nodes in the cluster.
+  """
+
+  primary_node_addr: str | None = None
+  primary_node_port: str | None = None
+  node_rank: int = 0
+  num_nodes: int = 1
+
+  # Allows unpacking operation like
+  # primary_node_addr, primary_node_port, _, _ = ClusterInfo()
+  # See https://stackoverflow.com/a/70753113
+  def __iter__(self):
+    return iter(dataclasses.astuple(self))
+
+
+def get_cluster_spec() -> ClusterInfo:
+  """Parses CLUSTER_SPEC environment variable and returns the cluster info.
+
+  Returns:
+    A ClusterInfo object.
+  """
+  cluster_spec = os.getenv('CLUSTER_SPEC', None)
+
+  # If CLUSTER_SPEC is not set, use individual vars to construct cluster info.
+  if not cluster_spec:
+    cluster_info = ClusterInfo(
+        primary_node_addr=os.getenv('MASTER_ADDR', None),
+        primary_node_port=os.getenv('MASTER_PORT', None),
+        node_rank=int(os.getenv('RANK', '0')),
+        num_nodes=int(os.getenv('NNODES', '1')),
+    )
+    return cluster_info
+
+  cluster_data = json.loads(cluster_spec)
+  # Get primary node info
+  primary_node = cluster_data['cluster']['workerpool0'][0]
+  logging.info('primary node: %s', primary_node)
+  primary_node_addr, primary_node_port = primary_node.split(':')
+  logging.info('primary node address: %s', primary_node_addr)
+  logging.info('primary node port: %s', primary_node_port)
+
+  # Determine node rank of this machine
+  workerpool = cluster_data['task']['type']
+  if workerpool == 'workerpool0':
+    node_rank = 0
+  elif workerpool == 'workerpool1':
+    # Add 1 for the primary node, since `index` is the index of workerpool1.
+    node_rank = cluster_data['task']['index'] + 1
+  else:
+    raise ValueError(
+        'Only workerpool0 and workerpool1 are supported. Unknown workerpool:'
+        f' {workerpool}'
+    )
+  logging.info('node rank: %s', node_rank)
+
+  # Calculate total nodes.
+  num_nodes = 1  # For the primary node.
+  if 'workerpool1' in cluster_data['cluster']:
+    num_nodes += len(cluster_data['cluster']['workerpool1'])
+  logging.info('num nodes: %s', num_nodes)
+
+  return ClusterInfo(primary_node_addr, primary_node_port, node_rank, num_nodes)

community-content/vertex_model_garden/model_oss/peft/train/util/common_util.py

@@ -0,0 +1,24 @@
+"""Utility functions."""
+
+import logging
+import subprocess
+import sys
+import time
+
+
+def run_cmd(cmd: list[str]) -> float:
+  """Runs the command and logs the output.
+
+  Args:
+    cmd: The command to run.
+
+  Returns:
+    The time it took to run the command.
+  """
+  cmd_str = ' \\\n'.join(cmd)
+  logging.info('launching cmd: \n%s', cmd_str)
+  start_time = time.time()
+  subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
+  elapsed_time = round(time.time() - start_time, 2)
+  logging.info('Command %s finished in %0.2f seconds.', cmd_str, elapsed_time)
+  return elapsed_time

community-content/vertex_model_garden/model_oss/peft/train/util/dataset_stats.py

@@ -0,0 +1,197 @@
+"""Calculate dataset statistics like token, example and character counts."""
+
+from collections.abc import Mapping, Sequence
+import dataclasses
+import json
+from typing import Any
+import datasets
+import numpy as np
+import transformers
+from util import dataset_validation_util
+
+_MAX_NUM_DATASET_SAMPLES = 6
+
+
+@dataclasses.dataclass
+class SupervisedTuningDatasetBucket:
+  """Represents a histogram bucket for tuning dataset distribution stats."""
+
+  count: float = 0
+  left: float = 0
+  right: float = 0
+
+
+@dataclasses.dataclass
+class SupervisedTuningDatasetDistribution:
+  """Represents a histogram with summary statistics for tuning dataset distribution stats."""
+
+  sum: int = 0
+  billable_sum: int = 0
+  min: float = 0
+  max: float = 0
+  mean: float = 0
+  median: float = 0
+  p5: float = 0
+  p95: float = 0
+  buckets: list[SupervisedTuningDatasetBucket] = dataclasses.field(
+      default_factory=list
+  )
+
+
+# Represents detailed tuning dataset statistics.
+@dataclasses.dataclass
+class SupervisedTuningDataStats:
+  """Represents detailed tuning dataset stats."""
+
+  tuning_dataset_example_count: int = 0
+  total_tuning_character_count: int = 0
+  total_billable_token_count: int = 0
+  tuning_step_count: int = 0
+  # Represents a histogram and some summary statistics of the number of input
+  # tokens across examples.
+  user_input_token_distribution: SupervisedTuningDatasetDistribution | None = (
+      None
+  )
+  # Represents a histogram and some summary statistics for the number of output
+  # tokens across examples.
+  user_output_token_distribution: SupervisedTuningDatasetDistribution | None = (
+      None
+  )
+  # Represents the number of "messages" (a single-turn conversation will have a
+  # single message) across examples.
+  user_message_per_example_distribution: (
+      SupervisedTuningDatasetDistribution | None
+  ) = None
+  user_dataset_examples: list[str] = dataclasses.field(default_factory=list)
+
+
+def get_dataset_stats(
+    *,
+    raw: Any,
+    templated: Any,
+    template: str,
+    tokenizer: transformers.PreTrainedTokenizer,
+    column: str,
+    effective_batch_size: int,
+) -> Mapping[str, Any]:
+  """Calculates dataset statistics for managed fine-tuning, e.g., total number of tokens."""
+  tokenized_dataset = templated.map(lambda x: tokenizer(x[column]))
+  inputs = tokenized_dataset["input_ids"]
+  tuning_dataset_example_count = int(len(inputs))
+  total_billable_token_count = int(np.sum([len(ex) for ex in inputs]))
+  total_tuning_character_count = int(
+      np.sum([len(ex[column]) for ex in templated])
+  )
+  tuning_step_count = (
+      tuning_dataset_example_count + effective_batch_size - 1
+  ) // effective_batch_size
+
+  # Assume that data is represented as ChatCompletions or Vertex Text-Bison
+  # formats to extract per-example input/output tokens.
+  user_inputs = []
+  user_outputs = []
+  user_input_messages_counts = []
+
+  for ex in raw:
+    if "messages" in ex:
+      messages = ex["messages"]
+      if messages:
+        # For ChatCompletions assume the last turn (i.e. the instruction
+        # response) is the expected output.
+        user_inputs.append({**ex, "messages": messages[:-1]})
+        user_outputs.append({**ex, "messages": messages[-1:]})
+        # Exclude everything but the last message for the number of input
+        # messages.
+        user_input_messages_counts.append(len(messages[:-1]))
+    elif "input_text" in ex:
+      # For Vertex Text-Bison, the `output_text` field is the expected output.
+      user_inputs.append({**ex, "output_text": ""})
+      user_outputs.append(
+          {**ex, "input_text": ex["output_text"], "output_text": ""}
+      )
+      # Vertex Text-Bison goes from input -> output; i.e. there is only a single
+      # input "message".
+      user_input_messages_counts.append(1)
+
+  def calc_histogram(
+      counts: Sequence[int],
+  ) -> SupervisedTuningDatasetDistribution:
+    mean = np.mean(counts)
+    median = np.median(counts).item()
+    max_count = np.max(counts).item()
+    min_count = np.min(counts).item()
+    count_sum = np.sum(counts).item()
+    p5 = np.percentile(counts, 0.05).item()
+    p95 = np.percentile(counts, 0.95).item()
+    hist, bin_edges = np.histogram(counts, bins=10)
+
+    return SupervisedTuningDatasetDistribution(
+        sum=count_sum,
+        billable_sum=count_sum,
+        min=min_count,
+        max=max_count,
+        mean=mean,
+        median=median,
+        p5=p5,
+        p95=p95,
+        buckets=[
+            SupervisedTuningDatasetBucket(
+                count=hist[i].item(),
+                left=bin_edges[i].item(),
+                right=bin_edges[i + 1].item(),
+            )
+            for i in range(len(hist))
+        ],
+    )
+
+  # Tokenize input and output messages separately to generate separate summary
+  # statistics about them.
+  user_input_token_distribution = None
+  if user_inputs:
+    user_input_dataset = dataset_validation_util.format_dataset(
+        datasets.Dataset.from_list(user_inputs), column, template, tokenizer
+    )
+    user_input_tokenized_dataset = user_input_dataset.map(
+        lambda x: tokenizer(x[column])
+    )
+    user_input_tokens = user_input_tokenized_dataset["input_ids"]
+    user_input_token_counts = np.array([len(ex) for ex in user_input_tokens])
+    user_input_token_distribution = calc_histogram(user_input_token_counts)
+
+  user_output_token_distribution = None
+  if user_outputs:
+    user_output_dataset = dataset_validation_util.format_dataset(
+        datasets.Dataset.from_list(user_outputs), column, template, tokenizer
+    )
+    user_output_tokenized_dataset = user_output_dataset.map(
+        lambda x: tokenizer(x[column])
+    )
+    user_output_tokens = user_output_tokenized_dataset["input_ids"]
+    user_output_token_counts = np.array([len(ex) for ex in user_output_tokens])
+    user_output_token_distribution = calc_histogram(user_output_token_counts)
+
+  user_messages_per_example_distribution = None
+  if user_input_messages_counts:
+    user_input_messages_counts = np.array(user_input_messages_counts)
+    user_messages_per_example_distribution = calc_histogram(
+        user_input_messages_counts
+    )
+
+  user_dataset_examples = [
+      json.dumps(ex)
+      for ex in raw.shuffle().select(
+          range(min(len(raw), _MAX_NUM_DATASET_SAMPLES))
+      )
+  ]
+
+  dataset_stats = SupervisedTuningDataStats(
+      tuning_dataset_example_count=tuning_dataset_example_count,
+      total_tuning_character_count=total_tuning_character_count,
+      total_billable_token_count=total_billable_token_count,
+      tuning_step_count=tuning_step_count,
+      user_input_token_distribution=user_input_token_distribution,
+      user_output_token_distribution=user_output_token_distribution,
+      user_message_per_example_distribution=user_messages_per_example_distribution,
+      user_dataset_examples=user_dataset_examples,
+  )
+  return dataclasses.asdict(dataset_stats)

community-content/vertex_model_garden/model_oss/peft/train/util/device_stats.py

@@ -0,0 +1,140 @@
+"""Util functions for reporting device (GPU, CPU) stats."""
+
+import dataclasses
+
+import psutil
+import pynvml
+import torch
+
+
+@dataclasses.dataclass
+class GpuStats:
+  """Holds information about GPU usage stats.
+
+  For memory related, see
+  https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management
+  """
+
+  # device id
+  device_id: int
+  # memory reserved.
+  reserved: float
+  # memory occupied.
+  occupied: float
+  # memory reserved, but not used.
+  unused: float
+  # nvidia-smi usually reports more memory usages than pytorch (for driver,
+  # kernel and etc). `smi_diff` tracks this difference.
+  smi_diff: float
+  # Gpu utilization.
+  util: float
+
+  # Allows unpacking operation like
+  # device_id, reserved, occupied, unused, smi_diff, util = GpuStats(...)
+  # See https://stackoverflow.com/a/70753113
+  def __iter__(self):
+    return iter(dataclasses.astuple(self))
+
+
+def gpu_stats() -> GpuStats:
+  """Reports GPU memory usage and utilization."""
+  # See https://pytorch.org/docs/stable/notes/cuda.html#memory-management
+  bytes_per_gb = 1024.0**3
+  device = torch.cuda.current_device()
+  occupied = torch.cuda.memory_allocated(device) / bytes_per_gb
+  reserved = torch.cuda.memory_reserved(device) / bytes_per_gb
+  unused = reserved - occupied
+
+  def smi_mem(device):
+    try:
+      pynvml.nvmlInit()
+      handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+      info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+      return info.used / bytes_per_gb
+    except pynvml.NVMLError:
+      return 0.0
+
+  mem_used_smi = smi_mem(device)
+  smi_diff = mem_used_smi - reserved
+
+  util = torch.cuda.utilization(device)
+  return GpuStats(device, reserved, occupied, unused, smi_diff, util)
+
+
+def gpu_stats_str(stats: GpuStats | None = None) -> str:
+  if stats is None:
+    stats = gpu_stats()
+  device, reserved, occupied, unused, smi_diff, util = stats
+  return (
+      f"GPU ({device=}) memory: {reserved:.2f}({occupied=:.2f}, {unused=:.2f}),"
+      f" {smi_diff=:.2f} GB. Utilization: {util:.2f}%"
+  )
+
+
+@dataclasses.dataclass
+class CpuStats:
+  """Holds information about CPU usage stats."""
+
+  # Total CPU virtual memory i.e. virtual memory allocated + unallocated.
+  total_virtual_mem: float
+  # CPU virtual memory available for use.
+  unallocated_virtual_mem: float
+  # CPU virtual memory already used.
+  allocated_virtual_mem: float
+  # Total CPU swap memory i.e. swap memory allocated + unallocated.
+  total_swap_mem: float
+  # CPU swap memory available for use.
+  unallocated_swap_mem: float
+  # CPU swap memory already used.
+  allocated_swap_mem: float
+  # CPU utilization percentage.
+  utilization: float
+
+
+def cpu_stats() -> CpuStats:
+  """Reports CPU memory usage and utilization."""
+
+  # https://psutil.readthedocs.io/en/latest/#memory
+  gb = 1024.0**3
+  vmem = psutil.virtual_memory()
+  vmem_total = vmem.total / gb
+  vmem_available = vmem.available / gb
+  vmem_used = vmem_total - vmem_available
+  smem = psutil.swap_memory()
+  swap_total = smem.total / gb
+  swap_free = smem.free / gb
+  swap_used = smem.used / gb
+  # https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
+  cpu_util = psutil.cpu_percent(interval=1e-6)
+  return CpuStats(
+      total_virtual_mem=vmem_total,
+      unallocated_virtual_mem=vmem_available,
+      allocated_virtual_mem=vmem_used,
+      total_swap_mem=swap_total,
+      unallocated_swap_mem=swap_free,
+      allocated_swap_mem=swap_used,
+      utilization=cpu_util,
+  )
+
+
+def cpu_stats_str(stats: CpuStats | None = None) -> str:
+  """Returns a string representation of the CPU stats."""
+
+  if stats is None:
+    stats = cpu_stats()
+  total, occupied, unused = (
+      stats.total_virtual_mem,
+      stats.allocated_virtual_mem,
+      stats.unallocated_virtual_mem,
+  )
+  virtual_mem = (
+      f"CPU virtual memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
+  )
+  total, occupied, unused = (
+      stats.total_swap_mem,
+      stats.allocated_swap_mem,
+      stats.unallocated_swap_mem,
+  )
+  swap_mem = f"CPU swap memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
+  percent = stats.utilization
+  return f"{virtual_mem} {swap_mem} CPU Utilization: {percent:.2f}%"

community-content/vertex_model_garden/model_oss/peft/train/vmg/callbacks.py

@@ -11,7 +11,7 @@ from transformers.trainer_callback import TrainerCallback
 from transformers.trainer_callback import TrainerControl
 from transformers.trainer_callback import TrainerState
 
-from vertex_vision_model_garden_peft.train.vmg import utils
+from util import device_stats
 
 
 class TrainerStatsCallback(TrainerCallback):
@@ -75,13 +75,15 @@ class TrainerStatsCallback(TrainerCallback):
             state.global_step - 1
         )
 
-      gpu_stats = utils.gpu_stats()
-      self._peak_mem = max(gpu_stats.total_mem, self._peak_mem)
+      gpu_stats = device_stats.gpu_stats()
+      self._peak_mem = max(
+          gpu_stats.reserved + gpu_stats.smi_diff, self._peak_mem
+      )
       logging.info(
           'on_step_end: Throughput: %.2f token/s. %s, %s',
           throughput,
-          utils.gpu_stats_str(gpu_stats),
-          utils.cpu_stats_str(),
+          device_stats.gpu_stats_str(gpu_stats),
+          device_stats.cpu_stats_str(),
       )
 
   def on_train_begin(
@@ -95,8 +97,8 @@ class TrainerStatsCallback(TrainerCallback):
       self._start_time = time.time()
       logging.info(
           'on_train_begin: %s, %s',
-          utils.gpu_stats_str(),
-          utils.cpu_stats_str(),
+          device_stats.gpu_stats_str(),
+          device_stats.cpu_stats_str(),
       )
 
   def on_train_end(

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/qwen2_fsdp_8gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/requirements.txt

@@ -16,6 +16,7 @@ diffusers==0.25.1
 evaluate==0.4.3
 fsspec==2024.3.1
 gcsfs==2024.3.1
+immutabledict==4.2.1
 ninja==1.11.1 # Needed to avoid `ninja 1.11.1.1 is not supported on this platform` error
 nltk==3.9.1
 optimum==1.17.1

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/train.Dockerfile

@@ -68,10 +68,12 @@ RUN mkdir -p ./vertex_vision_model_garden_peft/
 COPY model_oss/peft/train/vmg/configs/* ./vertex_vision_model_garden_peft/
 COPY model_oss/peft/train/vmg/*.py ./vertex_vision_model_garden_peft/train/vmg/
 COPY model_oss/peft/train/vmg/templates /diffusers/examples/util/templates
-COPY model_oss/util /diffusers/examples/util
+COPY model_oss/peft/train/util/*.py /diffusers/examples/util/
+COPY model_oss/util/* /diffusers/examples/util/
 COPY model_oss/notebook_util/dataset_validation_util.py /diffusers/examples/util
 COPY model_oss/peft/train/vmg/tests/*.py ./vertex_vision_model_garden_peft/tests/
 COPY model_oss/peft/train/test_utils/test_util.py ./vertex_vision_model_garden_peft/tests/
+COPY model_oss/peft/train/test_utils/command_builder.py ./vertex_vision_model_garden_peft/tests/
 
 RUN chmod a+rwX -R /diffusers/examples/
 ENV PYTHONPATH /diffusers/examples/

community-content/vertex_model_garden/model_oss/peft/train/vmg/eval_lib.py

@@ -37,7 +37,6 @@ class EvalConfig:
     steps: The number of steps to run evaluation.
     tasks: The list of tasks to run evaluation on.
     per_device_batch_size: The per device batch size for evaluation.
-    num_fewshot: The number of few-shot examples to use for evaluation.
     limit: The maximum number of examples to evaluate.
     metric_name: The name of the metric to compute.
     tokenize_dataset: Whether to tokenize the dataset.
@@ -50,7 +49,6 @@ class EvalConfig:
 
   steps: int
   per_device_batch_size: int
-  num_fewshot: int | None
   limit: float | None
   metric_name: Sequence[str]
   tokenize_dataset: bool
@@ -99,7 +97,7 @@ def create_trainer(
   kwargs["tokenizer"] = tokenizer
 
   try:
-    eval_dataset = dataset_validation_util.load_dataset_with_template(
+    _, eval_dataset = dataset_validation_util.load_dataset_with_template(
         dataset_name=eval_config.dataset_path,
         split=eval_config.split,
         input_column=eval_config.column,

community-content/vertex_model_garden/model_oss/peft/train/vmg/instruct_lora.py

@@ -1,7 +1,6 @@
 """Instruct/Chat with LoRA models."""
 
 from collections.abc import Callable, Mapping, Sequence
-import dataclasses
 import datetime
 import json
 import os
@@ -23,6 +22,8 @@ import trl
 import wandb
 
 from util import dataset_validation_util
+from util import dataset_stats
+from util import device_stats
 from vertex_vision_model_garden_peft.train.vmg import callbacks
 from vertex_vision_model_garden_peft.train.vmg import eval_lib
 from vertex_vision_model_garden_peft.train.vmg import utils
@@ -231,11 +232,6 @@ _PER_DEVICE_EVAL_BATCH_SIZE = flags.DEFINE_integer(
     'The per device batch size for model evaluation.',
 )
 
-_EVAL_NUM_FEWSHOT = flags.DEFINE_integer(
-    'eval_num_fewshot',
-    None,
-    'Run N-shot language model evaluation. Not implemented in `builtin_eval`.',
-)
 
 _EVAL_LIMIT = flags.DEFINE_float(
     'eval_limit',
@@ -255,8 +251,7 @@ _EVAL_METRIC_NAME = flags.DEFINE_list(
 _EVAL_DATASET = flags.DEFINE_string(
     'eval_dataset',
     None,
-    'Overrides the default evaluation dataset path. In `builtin_eval` mode,'
-    ' this can be any Hugging Face dataset name or path.',
+    'The Hugging Face dataset name or path to use for evaluation.',
 )
 
 # We set the default eval split as `test`, based on observation from
@@ -264,13 +259,13 @@ _EVAL_DATASET = flags.DEFINE_string(
 _EVAL_SPLIT = flags.DEFINE_string(
     'eval_split',
     'test',
-    'Eval split name in the eval dataset for `builtin_eval`.',
+    'Eval split name in the eval dataset.',
 )
 
 _EVAL_TEMPLATE = flags.DEFINE_string(
     'eval_template',
     None,
-    'Template for formatting language model evaluation data for `builtin_eval`.'
+    'Template for formatting language model evaluation data.'
     ' Must be a filename under `templates` folder, without `.json` extension,'
     ' e.g. `alpaca`, or a Cloud Storage URI to a JSON file.',
 )
@@ -278,7 +273,7 @@ _EVAL_TEMPLATE = flags.DEFINE_string(
 _EVAL_COLUMN = flags.DEFINE_string(
     'eval_column',
     None,
-    'Eval column name in the eval dataset for `builtin_eval`.',
+    'Eval column name in the eval dataset.',
 )
 
 _METRIC_FOR_BEST_MODEL = flags.DEFINE_string(
@@ -576,8 +571,8 @@ def finetune_instruct(
   """Finetunes instruct."""
   logging.info(
       'on entering instruct_lora, %s,\n%s',
-      utils.gpu_stats_str(),
-      utils.cpu_stats_str(),
+      device_stats.gpu_stats_str(),
+      device_stats.cpu_stats_str(),
   )
   gradient_checkpointing_kwargs = {}
   # DDP provides limited support with the reentrant variant of gradient
@@ -594,7 +589,7 @@ def finetune_instruct(
       access_token=access_token,
   )
 
-  train_dataset_with_template = (
+  train_dataset, train_dataset_with_template = (
       dataset_validation_util.load_dataset_with_template(
           train_dataset,
           split=train_split,
@@ -621,18 +616,20 @@ def finetune_instruct(
           'getting tuning data stats with effective batch size %s',
           effective_batch_size,
       )
-      train_dataset_stats = utils.get_dataset_stats(
-          train_dataset_with_template,
-          tokenizer,
-          train_column,
-          effective_batch_size,
+      train_dataset_stats = dataset_stats.get_dataset_stats(
+          raw=train_dataset,
+          templated=train_dataset_with_template,
+          template=train_template,
+          tokenizer=tokenizer,
+          column=train_column,
+          effective_batch_size=effective_batch_size,
       )
       logging.info('stats: %s', train_dataset_stats)
       tuning_data_stats_file = dataset_validation_util.force_gcs_fuse_path(
           tuning_data_stats_file
       )
       with open(tuning_data_stats_file, 'w') as out_f:
-        json.dump(dataclasses.asdict(train_dataset_stats), out_f)
+        json.dump(train_dataset_stats, out_f)
 
   model = utils.load_model(
       pretrained_model_name_or_path=pretrained_model_name_or_path,
@@ -663,7 +660,9 @@ def finetune_instruct(
     # `get_peft_model`, which may revert other changes we did before. That's why
     # we are calling `get_peft_model` explicitly here.
     model = get_peft_model(model, peft_config)
-
+    adapter_for_eval_dir = os.path.join(output_dir, 'adapter_for_eval')
+    logging.info('saving adapter for evaluation to %s...', adapter_for_eval_dir)
+    peft_config.save_pretrained(adapter_for_eval_dir)
     # This is to work-around mix-precision training. This issue is not fixed as
     # of transformers==4.41.2.
     # See b/332760883#comment30 for more details.
@@ -840,7 +839,6 @@ def main(unused_argv: Sequence[str]) -> None:
   if _EVAL_DATASET.value:
     eval_config = eval_lib.EvalConfig(
         per_device_batch_size=_PER_DEVICE_EVAL_BATCH_SIZE.value,
-        num_fewshot=_EVAL_NUM_FEWSHOT.value,
         limit=_EVAL_LIMIT.value,
         metric_name=_EVAL_METRIC_NAME.value,
         steps=_EVAL_STEPS.value,

community-content/vertex_model_garden/model_oss/peft/train/vmg/merge_causal_language_model_lora.py

@@ -31,7 +31,7 @@ _MERGE_BASE_AND_LORA_OUTPUT_DIR = flags.DEFINE_string(
 
 _MERGE_MODEL_PRECISION_MODE = flags.DEFINE_enum(
     'merge_model_precision_mode',
-    constants.PRECISION_MODE_16,
+    constants.PRECISION_MODE_16B,
     [
         constants.PRECISION_MODE_4,
         constants.PRECISION_MODE_8,
@@ -86,10 +86,19 @@ def main(unused_argv: Sequence[str]) -> None:
         )
     )
 
+  finetuned_lora_model_dir = fileutils.force_gcs_path(
+      _FINETUNED_LORA_MODEL_DIR.value
+  )
+  if dataset_validation_util.is_gcs_path(finetuned_lora_model_dir):
+    finetuned_lora_model_dir = (
+        dataset_validation_util.download_gcs_uri_to_local(
+            finetuned_lora_model_dir
+        )
+    )
   utils.merge_causal_language_model_with_lora(
       pretrained_model_name_or_path=pretrained_model_name_or_path,
       precision_mode=_MERGE_MODEL_PRECISION_MODE.value,
-      finetuned_lora_model_dir=_FINETUNED_LORA_MODEL_DIR.value,
+      finetuned_lora_model_dir=finetuned_lora_model_dir,
       merged_model_output_dir=_MERGE_BASE_AND_LORA_OUTPUT_DIR.value,
       access_token=_HUGGINGFACE_ACCESS_TOKEN.value,
   )

community-content/vertex_model_garden/model_oss/peft/train/vmg/sequence_classification_lora.py

@@ -1,232 +0,0 @@
-"""Sequence classification with LoRA models."""
-
-from typing import Sequence
-
-from absl import app
-from absl import flags
-from datasets import load_dataset
-import evaluate
-from peft import get_peft_model
-from peft import LoraConfig
-import torch
-from torch.optim import AdamW
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-from transformers import AutoModelForSequenceClassification
-from transformers import AutoTokenizer
-from transformers import get_linear_schedule_with_warmup
-
-from util import dataset_validation_util
-
-
-_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
-    "pretrained_model_name_or_path",
-    None,
-    "The pretrained model name or path. Supported models can be causal language"
-    " modeling models from https://github.com/huggingface/peft/tree/main. Note,"
-    " there might be different paddings for different models. This tool assumes"
-    " the pretrained_model_name_or_path contains model name, and then choose"
-    " proper padding methods. e.g. it must contain `llama` for `Llama2"
-    " models`.",
-)
-
-_OUTPUT_DIR = flags.DEFINE_string(
-    "output_dir",
-    None,
-    "The output directory.",
-)
-
-_DATASET_NAME = flags.DEFINE_string(
-    "dataset_name",
-    None,
-    "The dataset name in huggingface.",
-)
-
-_LORA_RANK = flags.DEFINE_integer(
-    "lora_rank",
-    16,
-    "The rank of the update matrices, expressed in int. Lower rank results in"
-    " smaller update matrices with fewer trainable parameters, referring to"
-    " https://huggingface.co/docs/peft/conceptual_guides/lora.",
-)
-
-_LORA_ALPHA = flags.DEFINE_integer(
-    "lora_alpha",
-    32,
-    "LoRA scaling factor, referring to"
-    " https://huggingface.co/docs/peft/conceptual_guides/lora.",
-)
-
-_LORA_DROPOUT = flags.DEFINE_float(
-    "lora_dropout",
-    0.05,
-    "dropout probability of the LoRA layers, referring to"
-    " https://huggingface.co/docs/peft/task_guides/token-classification-lora.",
-)
-
-_NUM_TRAIN_EPOCHS = flags.DEFINE_integer(
-    "num_train_epochs",
-    None,
-    "The number of training epochs.",
-)
-
-_BATCH_SIZE = flags.DEFINE_integer(
-    "batch_size",
-    32,
-    "The batch size.",
-)
-
-_LEARNING_RATE = flags.DEFINE_float(
-    "learning_rate",
-    2e-4,
-    "The learning rate after the potential warmup period.",
-)
-
-
-def finetune_sequence_classification(
-    pretrained_model_name_or_path: str,
-    dataset_name: str,
-    output_dir: str,
-    lora_rank: int = 8,
-    lora_alpha: int = 16,
-    lora_dropout: float = 0.1,
-    num_train_epochs: int = 20,
-    batch_size: int = 32,
-    learning_rate: float = 3e-4,
-) -> None:
-  """Finetunes sequence classification."""
-  task = "mrpc"
-  device = "cuda"
-
-  peft_config = LoraConfig(
-      task_type="SEQ_CLS",
-      inference_mode=False,
-      r=lora_rank,
-      lora_alpha=lora_alpha,
-      lora_dropout=lora_dropout,
-  )
-  if any(k in pretrained_model_name_or_path for k in ("gpt", "opt", "bloom")):
-    padding_side = "left"
-  else:
-    padding_side = "right"
-
-  tokenizer = AutoTokenizer.from_pretrained(
-      pretrained_model_name_or_path, padding_side=padding_side
-  )
-  if getattr(tokenizer, "pad_token_id") is None:
-    tokenizer.pad_token_id = tokenizer.eos_token_id
-
-  datasets = load_dataset(dataset_name, task)
-  metric = evaluate.load(dataset_name, task)
-
-  def tokenize_function(examples):
-    # max_length=None => use the model max length (it's actually the default)
-    outputs = tokenizer(
-        examples["sentence1"],
-        examples["sentence2"],
-        truncation=True,
-        max_length=None,
-    )
-    return outputs
-
-  tokenized_datasets = datasets.map(
-      tokenize_function,
-      batched=True,
-      remove_columns=["idx", "sentence1", "sentence2"],
-  )
-
-  # We also rename the 'label' column to 'labels' which is the expected name for
-  # labels by the models of the transformers library.
-  tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
-
-  def collate_fn(examples):
-    return tokenizer.pad(examples, padding="longest", return_tensors="pt")
-
-  # Instantiate dataloaders.
-  train_dataloader = DataLoader(
-      tokenized_datasets["train"],
-      shuffle=True,
-      collate_fn=collate_fn,
-      batch_size=batch_size,
-  )
-  eval_dataloader = DataLoader(
-      tokenized_datasets["validation"],
-      shuffle=False,
-      collate_fn=collate_fn,
-      batch_size=batch_size,
-  )
-
-  model = AutoModelForSequenceClassification.from_pretrained(
-      pretrained_model_name_or_path, return_dict=True
-  )
-  model = get_peft_model(model, peft_config)
-  model.print_trainable_parameters()
-
-  optimizer = AdamW(params=model.parameters(), lr=learning_rate)
-
-  # Instantiate scheduler
-  lr_scheduler = get_linear_schedule_with_warmup(
-      optimizer=optimizer,
-      num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),
-      num_training_steps=(len(train_dataloader) * num_train_epochs),
-  )
-
-  model.to(device)
-  for epoch in range(num_train_epochs):
-    model.train()
-    for _, batch in enumerate(tqdm(train_dataloader)):
-      batch.to(device)
-      outputs = model(**batch)
-      loss = outputs.loss
-      loss.backward()
-      optimizer.step()
-      lr_scheduler.step()
-      optimizer.zero_grad()
-
-    model.eval()
-    for _, batch in enumerate(tqdm(eval_dataloader)):
-      batch.to(device)
-      with torch.no_grad():
-        outputs = model(**batch)
-      predictions = outputs.logits.argmax(dim=-1)
-      references = batch["labels"]
-      metric.add_batch(
-          predictions=predictions,
-          references=references,
-      )
-
-    eval_metric = metric.compute()
-    print(f"epoch {epoch}:", eval_metric)
-
-  model.save_pretrained(output_dir)
-
-
-def main(unused_argv: Sequence[str]) -> None:
-  if dataset_validation_util.is_gcs_path(_PRETRAINED_MODEL_NAME_OR_PATH.value):
-    pretrained_model_name_or_path = (
-        dataset_validation_util.download_gcs_uri_to_local(
-            _PRETRAINED_MODEL_NAME_OR_PATH.value
-        )
-    )
-  else:
-    pretrained_model_name_or_path = _PRETRAINED_MODEL_NAME_OR_PATH.value
-  pretrained_model_path = dataset_validation_util.force_gcs_fuse_path(
-      pretrained_model_name_or_path
-  )
-  output_dir = dataset_validation_util.force_gcs_fuse_path(_OUTPUT_DIR.value)
-
-  finetune_sequence_classification(
-      pretrained_model_name_or_path=pretrained_model_path,
-      dataset_name=_DATASET_NAME.value,
-      output_dir=output_dir,
-      lora_rank=_LORA_RANK.value,
-      lora_alpha=_LORA_ALPHA.value,
-      lora_dropout=_LORA_DROPOUT.value,
-      num_train_epochs=int(_NUM_TRAIN_EPOCHS.value),
-      batch_size=_BATCH_SIZE.value,
-      learning_rate=_LEARNING_RATE.value,
-  )
-
-
-if __name__ == "__main__":
-  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/qwen2_5.json

@@ -0,0 +1,7 @@
+{
+  "description": "Chat template used by Qwen 2.5.",
+  "source": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/tokenizer_config.json#L198",
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "instruction_separator": "<|im_start|>user\n",
+  "response_separator": "<|im_start|>assistant\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_instruct_lora_throughput.py

@@ -64,6 +64,7 @@ class TrainerThroughputTest(test_util.TestBase):
           'Mistral-7B-v0.1',
           'Mixtral-8x7B-v0.1',
           'gemma-2-9b-it',
+          'Qwen2.5-32B-Instruct',
       ],
       precision=['4bit', '8bit', 'bfloat16'],
       max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
@@ -89,6 +90,7 @@ class TrainerThroughputTest(test_util.TestBase):
           'Mistral-7B-v0.1',
           'Mixtral-8x7B-v0.1',
           'gemma-2-9b-it',
+          'Qwen2.5-32B-Instruct',
       ],
       precision=['4bit', '8bit', 'bfloat16'],
       max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
@@ -119,7 +121,11 @@ class TrainerThroughputTest(test_util.TestBase):
     self.assertEqual(self.run_cmd_and_handle_failure(), 0)
 
   @parameterized.product(
-      model_name=['llama3.1-8b-hf', 'llama3.1-70b-hf'],
+      model_name=[
+          'llama3.1-8b-hf',
+          'llama3.1-70b-hf',
+          'Qwen2.5-32B-Instruct',
+      ],
       precision=['4bit', '8bit', 'bfloat16'],
       max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
       num_gpus=[8],
@@ -136,9 +142,16 @@ class TrainerThroughputTest(test_util.TestBase):
         self.test_suite_output_dir,
         f'bm_fsdp_{num_gpus}gpu_{model_name}_{precision}.txt',
     )
-    self.task_cmd_builder.config_file = (
-        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
-    )
+    if 'llama' in model_name.lower():
+      self.task_cmd_builder.config_file = (
+          'vertex_vision_model_garden_peft/llama2_fsdp_8gpu.yaml'
+      )
+    elif 'qwen' in model_name.lower():
+      self.task_cmd_builder.config_file = (
+          'vertex_vision_model_garden_peft/qwen2_fsdp_8gpu.yaml'
+      )
+    else:
+      self.fail(f'Unsupported model: {model_name}')
 
     self.command_builder.add_env_var(
         'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_instruct_lora_trained_model_quality.py

@@ -140,6 +140,70 @@ class TrainedModelQualityTest(test_util.TestBase):
 
     self.assertEqual(self.run_cmd(), 0)
 
+  @parameterized.named_parameters(
+      ('Qwen2.5-32B-Instruct', 'Qwen2.5-32B-Instruct'),
+  )
+  def test_qwen_model_deepspeed(self, model_name):
+    self.setup_output_dir(f'test_deepspeed_{model_name}')
+    self.task_cmd_builder.pretrained_model_name_or_path = model_name
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/deepspeed_zero2_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-train.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'messages'
+    self.task_cmd_builder.train_template = 'qwen2_5'
+    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-eval.jsonl'
+    )
+    self.task_cmd_builder.eval_split = 'train'
+    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
+    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+    # Note(lavrai): The following parameters are needed for the opposite-word
+    # dataset to converge properly.
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.num_train_epochs = 10.0
+    self.task_cmd_builder.logging_steps = 1
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  @parameterized.named_parameters(
+      ('Qwen2.5-32B-Instruct', 'Qwen2.5-32B-Instruct'),
+  )
+  def test_qwen_model_fsdp(self, model_name):
+    self.setup_output_dir(f'test_fsdp_{model_name}')
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/qwen2_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-train.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'messages'
+    self.task_cmd_builder.train_template = 'qwen2_5'
+    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-eval.jsonl'
+    )
+    self.task_cmd_builder.eval_split = 'train'
+    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
+    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+    # Note(lavrai): The following parameters are needed for the opposite-word
+    # dataset to converge properly.
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.num_train_epochs = 10.0
+    self.task_cmd_builder.logging_steps = 1
+
+    self.assertEqual(self.run_cmd(), 0)
+
 
 if __name__ == '__main__':
   absltest.main()

community-content/vertex_model_garden/model_oss/peft/train/vmg/train_entrypoint.py

@@ -9,19 +9,17 @@ environment. Otherwise, `python3` is used.
 
 import argparse
 from collections.abc import MutableSequence, Sequence
-import json
 import multiprocessing
-import os
 import subprocess
 import sys
 from absl import app
 from absl import flags
 from absl import logging
 from util import dataset_validation_util
-from vertex_vision_model_garden_peft.train.vmg import gcs_syncer
+from util import cluster_spec
 from vertex_vision_model_garden_peft.train.vmg import utils
 from util import constants
-from util import fileutils
+from util import gcs_syncer
 from util import hypertune_utils
 
 
@@ -41,9 +39,12 @@ _TASK_TO_SCRIPT = {
     constants.INSTRUCT_LORA: (
         'vertex_vision_model_garden_peft/train/vmg/instruct_lora.py'
     ),
-    constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA: 'vertex_vision_model_garden_peft/train/vmg/merge_causal_language_model_lora.py',
-    constants.SEQUENCE_CLASSIFICATION_LORA: 'vertex_vision_model_garden_peft/train/vmg/sequence_classification_lora.py',
-    constants.VALIDATE_DATASET_WITH_TEMPLATE: 'vertex_vision_model_garden_peft/train/vmg/validate_dataset_with_template.py',
+    constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA: (
+        'vertex_vision_model_garden_peft/train/vmg/merge_causal_language_model_lora.py'
+    ),
+    constants.VALIDATE_DATASET_WITH_TEMPLATE: (
+        'vertex_vision_model_garden_peft/train/vmg/validate_dataset_with_template.py'
+    ),
     constants.RUN_TESTS: 'vertex_vision_model_garden_peft/tests/run_tests.py',
 }
 
@@ -71,53 +72,17 @@ def launch_script_cmd(
 
 def _get_accelerate_args() -> argparse.Namespace:
   """Returns the accelerate args."""
-  # For the format of the cluster spec, see
-  # https://cloud.google.com/vertex-ai/docs/training/distributed-training#cluster-spec-format # pylint: disable=line-too-long
-  cluster_spec = os.getenv('CLUSTER_SPEC', default=None)
-  if not cluster_spec:
-    return argparse.Namespace()
-  logging.info('CLUSTER_SPEC: %s', cluster_spec)
-
-  cluster_data = json.loads(cluster_spec)
-  if (
-      'workerpool1' not in cluster_data['cluster']
-      or not cluster_data['cluster']['workerpool1']
-  ):
-    return argparse.Namespace()
-
-  # Get primary node info
-  primary_node = cluster_data['cluster']['workerpool0'][0]
-  logging.info('primary node: %s', primary_node)
-  primary_node_addr, primary_node_port = primary_node.split(':')
-  logging.info('primary node address: %s', primary_node_addr)
-  logging.info('primary node port: %s', primary_node_port)
-
-  # Determine node rank of this machine
-  workerpool = cluster_data['task']['type']
-  if workerpool == 'workerpool0':
-    node_rank = 0
-  elif workerpool == 'workerpool1':
-    # Add 1 for the primary node, since `index` is the index of workerpool1.
-    node_rank = cluster_data['task']['index'] + 1
-  else:
-    raise ValueError(
-        'Only workerpool0 and workerpool1 are supported. Unknown workerpool:'
-        f' {workerpool}'
-    )
-  logging.info('node rank: %s', node_rank)
-
-  # Calculate total nodes
-  num_worker_nodes = len(cluster_data['cluster']['workerpool1'])
-  num_nodes = num_worker_nodes + 1  # Add 1 for the primary node
-  logging.info('num nodes: %s', num_nodes)
-
+  primary_node_addr, primary_node_port, node_rank, num_nodes = (
+      cluster_spec.get_cluster_spec()
+  )
   accelerate_args = argparse.Namespace()
-  accelerate_args.machine_rank = node_rank
-  accelerate_args.num_machines = num_nodes
-  accelerate_args.main_process_ip = primary_node_addr
-  accelerate_args.main_process_port = primary_node_port
-  accelerate_args.max_restarts = 0
-  accelerate_args.monitor_interval = 120
+  if num_nodes > 1:
+    accelerate_args.machine_rank = node_rank
+    accelerate_args.num_machines = num_nodes
+    accelerate_args.main_process_ip = primary_node_addr
+    accelerate_args.main_process_port = primary_node_port
+    accelerate_args.max_restarts = 0
+    accelerate_args.monitor_interval = 120
 
   return accelerate_args
 
@@ -131,45 +96,6 @@ def _append_args_to_command_in_place(
       command.append(f'--{key}={value}')
 
 
-def _is_gcs_or_gcsfuse_path(path: str) -> bool:
-  """Returns if the path is a GCS or gcsfuse path.
-
-  Args:
-    path: The path to check.
-
-  Returns:
-    True if the path is a GCS or gcsfuse path.
-  """
-  return path.startswith(
-      (constants.GCS_URI_PREFIX, constants.GCSFUSE_URI_PREFIX)
-  )
-
-
-def _manage_training_path(path: str, node_rank: int) -> tuple[str, str]:
-  """Returns local dir and GCS location for the given path if the given path is a GCS or gcsfuse path.
-
-  It will also create a local directory if it does not exist. Othereise, it
-  returns the same path.
-
-  Args:
-    path: The local or GCS path to manage.
-    node_rank: The node rank to be appended to the GCS path.
-
-  Returns:
-    The local and GCS paths.
-  """
-  local_dir = path
-  gcs_dir = path
-  if _is_gcs_or_gcsfuse_path(path):
-    local_dir = os.path.join(
-        constants.LOCAL_OUTPUT_DIR,
-        dataset_validation_util.force_gcs_fuse_path(path)[1:],
-    )
-    gcs_dir = fileutils.force_gcs_path(path)
-    os.makedirs(local_dir, exist_ok=True)
-  return local_dir, os.path.join(gcs_dir, f'node-{node_rank}')
-
-
 def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
     task_type: str, config_file: str, unknown: Sequence[str]
 ) -> Sequence[Sequence[str]]:
@@ -203,11 +129,11 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
       dataset_validation_util.force_gcs_fuse_path(training_args.output_dir)
   )
 
-  local_output_dir, gcs_output_dir = _manage_training_path(
+  local_output_dir, gcs_output_dir = gcs_syncer.manage_sync_path(
       training_args.output_dir, node_rank
   )
   training_args.output_dir = local_output_dir
-  if _is_gcs_or_gcsfuse_path(gcs_output_dir):
+  if gcs_syncer.is_gcs_or_gcsfuse_path(gcs_output_dir):
     dirs_to_sync.append((local_output_dir, gcs_output_dir))
 
   # Merge only flags.
@@ -217,11 +143,11 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
   merge_args, unknown = merge_parser.parse_known_args(unknown)
 
   if merge_args.merge_base_and_lora_output_dir:
-    merge_local_dir, merge_gcs_dir = _manage_training_path(
-        merge_args.merge_base_and_lora_output_dir, node_rank
+    merge_local_dir, merge_gcs_dir = gcs_syncer.manage_sync_path(
+        merge_args.merge_base_and_lora_output_dir, None
     )
     merge_args.merge_base_and_lora_output_dir = merge_local_dir
-    if _is_gcs_or_gcsfuse_path(merge_gcs_dir):
+    if gcs_syncer.is_gcs_or_gcsfuse_path(merge_gcs_dir):
       dirs_to_sync.append((merge_local_dir, merge_gcs_dir))
 
   # Common flags shared by merging and training.
@@ -239,8 +165,10 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
   # Only the main node runs merging.
   if merge_args.merge_base_and_lora_output_dir and node_rank == 0:
     lora_dir = utils.get_final_checkpoint_path(training_args.output_dir)
-    lora_local_dir, lora_gcs_dir = _manage_training_path(lora_dir, node_rank)
-    if _is_gcs_or_gcsfuse_path(lora_gcs_dir):
+    lora_local_dir, lora_gcs_dir = gcs_syncer.manage_sync_path(
+        lora_dir, node_rank
+    )
+    if gcs_syncer.is_gcs_or_gcsfuse_path(lora_gcs_dir):
       dirs_to_sync.append((lora_local_dir, lora_gcs_dir))
 
     merge_cmd = [
@@ -263,46 +191,37 @@ def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
   return commands, dirs_to_sync
 
 
-def _setup_gcs_rsync(
-    dirs_to_sync: Sequence[tuple[str, str]],
-    mp_queue: multiprocessing.Queue,
-    gcs_rsync_interval_secs: int,
-) -> multiprocessing.Process:
-  """Sets up the GCS rsync process.
+def _get_merge_cmd_and_dirs_to_sync(
+    task_type: str, config_file: str, unknown: Sequence[str]
+) -> Sequence[Sequence[str]]:
+  """Returns the merge command and dirs to sync.
 
   Args:
-    dirs_to_sync: The absolute directory paths which will be synced to GCS.
-    mp_queue: The multiprocessing queue to check if the training is finished.
-    gcs_rsync_interval_secs: Integer, interval in seconds to run gcs rsync.
+    task_type: The task type.
+    config_file: The accelerate config file path.
+    unknown: The unknown args which are not recognised by the parser.
 
   Returns:
-    The GCS rsync process.
+    The bash commands to execute and the directories to sync.
   """
-  rsync_process = multiprocessing.Process(
-      target=gcs_syncer.start_gcs_rsync,
-      args=(dirs_to_sync, mp_queue, gcs_rsync_interval_secs),
-  )
-  rsync_process.start()
-  return rsync_process
+  # Merge only flags.
+  merge_parser = argparse.ArgumentParser()
+  merge_parser.add_argument('--merge_base_and_lora_output_dir')
+  merge_args, unknown = merge_parser.parse_known_args(unknown)
 
-
-def _cleanup_gcs_rsync(
-    rsync_process: multiprocessing.Process, mp_queue: multiprocessing.Queue
-) -> None:
-  """Cleans up the GCS rsync process.
-
-  Args:
-    rsync_process: The GCS rsync process.
-    mp_queue: The multiprocessing queue.
-  """
-  mp_queue.put('training finished')
-  rsync_process.join()
-  if rsync_process.exitcode == 0:
-    logging.info('Artifacts have been uploaded to GCS.')
-  else:
-    logging.error(
-        'GCS rsync process failed with exit code %d.', rsync_process.exitcode
+  dirs_to_sync = []
+  if merge_args.merge_base_and_lora_output_dir:
+    merge_local_dir, merge_gcs_dir = gcs_syncer.manage_sync_path(
+        merge_args.merge_base_and_lora_output_dir, None
     )
+    merge_args.merge_base_and_lora_output_dir = merge_local_dir
+    if gcs_syncer.is_gcs_or_gcsfuse_path(merge_gcs_dir):
+      dirs_to_sync.append((merge_local_dir, merge_gcs_dir))
+
+  cmd = launch_script_cmd(_TASK_TO_SCRIPT[task_type], config_file)
+  _append_args_to_command_in_place(merge_args, cmd)
+  cmd.extend(unknown)
+  return [cmd], dirs_to_sync
 
 
 def main(unused_argv: Sequence[str]) -> None:
@@ -335,6 +254,10 @@ def main(unused_argv: Sequence[str]) -> None:
     commands, dirs_to_sync = _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
         task_type=task, config_file=args.config_file, unknown=unknown
     )
+  elif task in [constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA]:
+    commands, dirs_to_sync = _get_merge_cmd_and_dirs_to_sync(
+        task_type=task, config_file=args.config_file, unknown=unknown
+    )
   else:
     assert task in _TASK_TO_SCRIPT
     cmd = launch_script_cmd(_TASK_TO_SCRIPT[task], args.config_file)
@@ -344,7 +267,7 @@ def main(unused_argv: Sequence[str]) -> None:
   rsync_process = None
   mp_queue = multiprocessing.Queue(maxsize=1)
   if dirs_to_sync:
-    rsync_process = _setup_gcs_rsync(
+    rsync_process = gcs_syncer.setup_gcs_rsync(
         dirs_to_sync, mp_queue, args.gcs_rsync_interval_secs
     )
 
@@ -361,7 +284,7 @@ def main(unused_argv: Sequence[str]) -> None:
         rsync_process.terminate()
       raise e
   if rsync_process is not None:
-    _cleanup_gcs_rsync(rsync_process, mp_queue)
+    gcs_syncer.cleanup_gcs_rsync(rsync_process, mp_queue)
 
 
 if __name__ == '__main__':

community-content/vertex_model_garden/model_oss/peft/train/vmg/utils.py

@@ -1,7 +1,6 @@
 """Common libraries for PEFT."""
 
 from collections.abc import Mapping, Sequence
-import dataclasses
 import datetime
 import gc
 import os
@@ -11,12 +10,9 @@ from absl import logging
 import accelerate
 from accelerate import DistributedType
 from accelerate import PartialState
-import numpy as np
 import peft
 from peft import PeftModel
 from peft import prepare_model_for_kbit_training
-import psutil
-import pynvml
 import torch
 import transformers
 from transformers import AutoModelForCausalLM
@@ -28,7 +24,6 @@ import trl
 from util import dataset_validation_util
 from util import constants
 
-
 _LLAMA_3_1_405B_MODEL_ID = "Meta-Llama-3.1-405B"
 _LOCAL_MERGED_MODEL_DIR = "/tmp/merged_model"
 _GEMMA2_MODEL = "gemma-2"
@@ -126,7 +121,7 @@ def load_model(
       "device_map": device_map,
       "torch_dtype": torch_dtype,
       "quantization_config": quantization_config,
-      "trust_remote_code": True,
+      "trust_remote_code": False,
       "token": access_token,
       "attn_implementation": attn_implementation,
   }
@@ -310,171 +305,12 @@ def convert_model_to_fp8(
   PartialState().wait_for_everyone()
 
 
-@dataclasses.dataclass
-class TuningDataStats:
-  tuning_dataset_example_count: int
-  total_billable_token_count: int
-  tuning_step_count: int
-
-
-def get_dataset_stats(
-    dataset: Any,
-    tokenizer: transformers.PreTrainedTokenizer,
-    column: str,
-    effective_batch_size: int,
-) -> TuningDataStats:
-  """Calculates dataset statistics, e.g., total number of tokens."""
-  tokenized_dataset = dataset.map(lambda x: tokenizer(x[column]))
-  inputs = tokenized_dataset["input_ids"]
-  tuning_dataset_example_count = int(len(inputs))
-  total_billable_token_count = int(np.sum([len(ex) for ex in inputs]))
-  tuning_step_count = (
-      tuning_dataset_example_count + effective_batch_size - 1
-  ) // effective_batch_size
-  return TuningDataStats(
-      tuning_dataset_example_count,
-      total_billable_token_count,
-      tuning_step_count,
-  )
-
-
 def force_gc():
   """Collects garbage immediately to release unused CPU/GPU resources."""
   gc.collect()
   torch.cuda.empty_cache()
 
 
-@dataclasses.dataclass
-class GpuStats:
-  """Holds information about GPU usage stats.
-
-  For memory related, see
-  https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management
-  """
-
-  # total memory
-  total_mem: float
-  # memory occupied.
-  occupied: float
-  # memory reserved, but not used.
-  unused: float
-  # nvidia-smi usually reports more memory usages than pytorch (for driver,
-  # kernel and etc). `smi_diff` tracks this difference.
-  smi_diff: float
-  # Gpu utilization.
-  util: float
-
-  # Allows unpacking operation like
-  # total_mem, occupied, unused, smi_diff, util = GpuStats(...)
-  # See https://stackoverflow.com/a/70753113
-  def __iter__(self):
-    return iter(dataclasses.astuple(self))
-
-
-def gpu_stats() -> GpuStats:
-  """Reports GPU memory usage and utilization."""
-  # See https://pytorch.org/docs/stable/notes/cuda.html#memory-management
-  bytes_per_gb = 1024.0**3
-  device = torch.cuda.current_device()
-  occupied = torch.cuda.memory_allocated(device) / bytes_per_gb
-  reserved = torch.cuda.memory_reserved(device) / bytes_per_gb
-  unused = reserved - occupied
-
-  def smi_mem(device):
-    try:
-      pynvml.nvmlInit()
-      handle = pynvml.nvmlDeviceGetHandleByIndex(device)
-      info = pynvml.nvmlDeviceGetMemoryInfo(handle)
-      return info.used / bytes_per_gb
-    except pynvml.NVMLError:
-      return 0.0
-
-  mem_used_smi = smi_mem(device)
-  smi_diff = mem_used_smi - reserved
-
-  util = torch.cuda.utilization(device)
-  return GpuStats(mem_used_smi, occupied, unused, smi_diff, util)
-
-
-def gpu_stats_str(stats: GpuStats | None = None) -> str:
-  if stats is None:
-    stats = gpu_stats()
-  total, occupied, unused, smi_diff, util = stats
-  return (
-      f"GPU memory: {total:.2f}({occupied=:.2f}, {unused=:.2f},"
-      f" {smi_diff=:.2f}) GB. Utilization: {util:.2f}%"
-  )
-
-
-@dataclasses.dataclass
-class CpuStats:
-  """Holds information about CPU usage stats."""
-
-  # Total CPU virtual memory i.e. virtual memory allocated + unallocated.
-  total_virtual_mem: float
-  # CPU virtual memory available for use.
-  unallocated_virtual_mem: float
-  # CPU virtual memory already used.
-  allocated_virtual_mem: float
-  # Total CPU swap memory i.e. swap memory allocated + unallocated.
-  total_swap_mem: float
-  # CPU swap memory available for use.
-  unallocated_swap_mem: float
-  # CPU swap memory already used.
-  allocated_swap_mem: float
-  # CPU utilization percentage.
-  utilization: float
-
-
-def cpu_stats() -> CpuStats:
-  """Reports CPU memory usage and utilization."""
-
-  # https://psutil.readthedocs.io/en/latest/#memory
-  gb = 1024.0**3
-  vmem = psutil.virtual_memory()
-  vmem_total = vmem.total / gb
-  vmem_available = vmem.available / gb
-  vmem_used = vmem_total - vmem_available
-  smem = psutil.swap_memory()
-  swap_total = smem.total / gb
-  swap_free = smem.free / gb
-  swap_used = smem.used / gb
-  # https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
-  cpu_util = psutil.cpu_percent(interval=1e-6)
-  return CpuStats(
-      total_virtual_mem=vmem_total,
-      unallocated_virtual_mem=vmem_available,
-      allocated_virtual_mem=vmem_used,
-      total_swap_mem=swap_total,
-      unallocated_swap_mem=swap_free,
-      allocated_swap_mem=swap_used,
-      utilization=cpu_util,
-  )
-
-
-def cpu_stats_str(stats: CpuStats | None = None) -> str:
-  """Returns a string representation of the CPU stats."""
-
-  if stats is None:
-    stats = cpu_stats()
-  total, occupied, unused = (
-      stats.total_virtual_mem,
-      stats.allocated_virtual_mem,
-      stats.unallocated_virtual_mem,
-  )
-  virtual_mem = (
-      f"CPU virtual memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
-  )
-  total, occupied, unused = (
-      stats.total_swap_mem,
-      stats.allocated_swap_mem,
-      stats.unallocated_swap_mem,
-  )
-  swap_mem = f"CPU swap memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
-  percent = stats.utilization
-  return f"{virtual_mem} {swap_mem} CPU Utilization: {percent:.2f}%"
-
-
 def init_partial_state(
     timeout: datetime.timedelta = datetime.timedelta(seconds=600),
 ) -> None:

community-content/vertex_model_garden/model_oss/util/fileutils.py

@@ -1,9 +1,12 @@
 """Fileutil lib to copy files between gcs and local."""
 
+import filecmp
 import fnmatch
 import os
 import pathlib
 import shutil
+import subprocess
+import time
 from typing import List, Optional, Tuple
 import uuid
 
@@ -57,6 +60,96 @@ def force_gcs_path(uri: str) -> str:
     return uri
 
 
+def is_file_available(
+    file_path: str, retry_interval_secs: int = 60, timeout_secs: int = 3600
+) -> bool:
+  """Checks and waits for a file to be available in GCS.
+
+  Args:
+    file_path: The file path to check.
+    retry_interval_secs: The interval in seconds to check the file.
+    timeout_secs: The timeout in seconds to wait for the file.
+
+  Returns:
+    True if the file is available, False otherwise.
+  """
+  start_time = time.time()
+  while True:
+    try:
+      file_check_cmd = ['gcloud', 'storage', 'ls', file_path]
+      result = subprocess.run(
+          file_check_cmd, capture_output=True, text=True, check=True
+      )
+      if file_path in result.stdout:
+        logging.info('File %s exists.', file_path)
+        return True
+    except subprocess.CalledProcessError as e:
+      elapsed_time = time.time() - start_time
+      if elapsed_time > timeout_secs:
+        logging.info(
+            "Timeout: File '%s' not found after %d seconds. Error: %s",
+            file_path,
+            elapsed_time,
+            e,
+        )
+        return False
+
+      logging.info(
+          "File '%s' not found yet. Checking again in %d seconds. Error: %s",
+          file_path,
+          retry_interval_secs,
+          e,
+      )
+      time.sleep(retry_interval_secs)
+
+
+def compare_dirs(
+    local_dir: str,
+    gcsfuse_dir: str,
+    retry_interval_secs: int = 30,
+    timeout_secs: int = 3600,
+) -> bool:
+  """Compares two directories and returns True if they are the same.
+
+  Args:
+    local_dir: The local directory.
+    gcsfuse_dir: The gcsfuse directory.
+    retry_interval_secs: The interval in seconds to check the directories.
+    timeout_secs: The timeout in seconds to wait for the directories.
+
+  Returns:
+    True if the directories are the same, False otherwise.
+  """
+  start_time = time.time()
+  while True:
+    if os.path.exists(local_dir) and os.path.exists(gcsfuse_dir):
+      comparison = filecmp.dircmp(local_dir, gcsfuse_dir)
+      if (
+          not comparison.left_only
+          and not comparison.right_only
+          and not comparison.diff_files
+      ):
+        return True
+    elapsed_time = time.time() - start_time
+    if elapsed_time > timeout_secs:
+      logging.info(
+          "Timeout: Directories '%s' and '%s' do not match after %d seconds.",
+          local_dir,
+          gcsfuse_dir,
+          elapsed_time,
+      )
+      return False
+
+    logging.info(
+        "Directories '%s' and '%s' do not match yet. Checking again in %d"
+        ' seconds.',
+        local_dir,
+        gcsfuse_dir,
+        retry_interval_secs,
+    )
+    time.sleep(retry_interval_secs)
+
+
 def download_gcs_file_to_memory(gcs_uri: str) -> bytes:
   """Downloads a gcs file to in memory.
 
@@ -352,3 +445,15 @@ def get_output_video_file(video_output_file_path: str) -> str:
       file_extension, '_overlay' + file_extension
   )
   return out_local_video_file_name
+
+
+def delete_local_file(local_file_path: str) -> None:
+  """Deletes a local file."""
+  if os.path.exists(local_file_path):
+    os.remove(local_file_path)
+
+
+def delete_local_dir(local_dir: str) -> None:
+  """Deletes a local directory recursively."""
+  if os.path.exists(local_dir):
+    shutil.rmtree(local_dir)

community-content/vertex_model_garden/model_oss/util/gcs_download_launcher.sh

@@ -0,0 +1,119 @@
+#!/bin/bash
+#
+# This launcher downloads model files from GCS to local model directory before
+# launching the actual command.
+#
+# If GCS URI is passed as an environment variable, set GCS_URI_ENV_KEY to the
+# environment variable name.
+# If GCS URI is passed as an argument, set GCS_URI_ARG_KEY to the argument name.
+# The argument must be in the format of '--$GCS_URI_ARG_KEY=gs://*'. Do not
+# separate argument name and value with spaces.
+# This script will also try reading from AIP_STORAGE_URI or AIP_STORAGE_DIR.
+# Note that AIP_STORAGE_DIR is expected to be a local path, so it bypasses the
+# download process.
+#
+# Input priority: AIP_STORAGE_DIR > AIP_STORAGE_URI > GCS_URI_ENV_KEY > GCS_URI_ARG_KEY.
+# Will output the local model directory to GCS_URI_ENV_KEY and GCS_URI_ARG_KEY
+# if they are set. Both will be updated if both set.
+#
+# Requires google-cloud-sdk as a dependency (for gcloud storage CLI).
+
+set -e
+
+readonly LOCAL_MODEL_DIR=${LOCAL_MODEL_DIR:-"/tmp/model_dir"}
+readonly LOCAL_ARGS_FILE=${LOCAL_ARGS_FILE:-"/tmp/args.txt"}
+
+update_model_id() {
+  if [[ ! -z "$GCS_URI_ENV_KEY" ]]; then
+    echo "Updating env var $GCS_URI_ENV_KEY to $AIP_STORAGE_DIR."
+    export "$GCS_URI_ENV_KEY"="$AIP_STORAGE_DIR"
+  fi
+
+  if [[ ! -z "$GCS_URI_ARG_KEY" ]]; then
+    echo "Updating args $GCS_URI_ARG_KEY to $AIP_STORAGE_DIR."
+    updated=0
+    for (( i=1; i <= $#; i++)); do
+      arg="${!i}"
+      if [[ "$arg" == "--$GCS_URI_ARG_KEY="* ]]; then
+        echo "Found $arg, updating to $AIP_STORAGE_DIR."
+        set -- "${@:1:(($i-1))}" "--$GCS_URI_ARG_KEY=$AIP_STORAGE_DIR" "${@:$(($i+1))}";
+        updated=1
+        break
+      fi
+    done
+    if [[ $updated -eq 0 ]]; then
+      echo "Appending args $GCS_URI_ARG_KEY to $AIP_STORAGE_DIR."
+      set -- "$@" "--$GCS_URI_ARG_KEY=$AIP_STORAGE_DIR";
+    fi
+  fi
+  echo "$*" > "$LOCAL_ARGS_FILE"
+}
+
+maybe_download_model() {
+  if [[ -z "$GCS_URI_ENV_KEY" ]] && [[ -z "$GCS_URI_ARG_KEY" ]]; then
+    echo "Internal error: Required GCS_URI_ENV_KEY or GCS_URI_ARG_KEY."
+    exit 1
+  fi
+
+  echo "$*" > "$LOCAL_ARGS_FILE"
+  gcs_uri=""
+  if [[ ! -z "$AIP_STORAGE_DIR" ]]; then
+    # AIP_STORAGE_DIR is expected to be a local path.
+    echo "AIP_STORAGE_DIR set, proceeding to run the launcher."
+    update_model_id "$@"
+    return
+  elif [[ $AIP_STORAGE_URI == gs://* ]]; then
+    # Check AIP_STORAGE_URI environment variable.
+    echo "AIP_STORAGE_URI set and starts with 'gs://', proceeding to download from GCS."
+    gcs_uri="$AIP_STORAGE_URI"
+  elif [[ ! -z "$GCS_URI_ENV_KEY" ]] && [[ ${!GCS_URI_ENV_KEY} == gs://* ]]; then
+    # Check custom environment variable.
+    echo "Custom environment variable ${GCS_URI_ENV_KEY} set and starts with 'gs://', proceeding to download from GCS."
+    gcs_uri="${!GCS_URI_ENV_KEY}"
+  elif [[ ! -z "$GCS_URI_ARG_KEY" ]]; then
+    # Check custom args.
+    for arg in "$@"; do
+      if [[ "$arg" == "--$GCS_URI_ARG_KEY=gs://"* ]]; then
+        gcs_uri="${arg#*=}"
+        echo "Custom args ${GCS_URI_ARG_KEY} set and starts with 'gs://', proceeding to download from GCS."
+        break
+      elif [[ "$arg" == "--$GCS_URI_ARG_KEY" ]]; then
+        echo "Found $GCS_URI_ARG_KEY, but it's not in the format of '--$GCS_URI_ARG_KEY=gs://*'."
+        echo "Ensure the value of $GCS_URI_ARG_KEY is within the same arg, separated by '='."
+        exit 1
+      fi
+    done
+  fi
+
+  if [[ -z "$gcs_uri" ]]; then
+    echo "No GCS URI found, proceeding to run the launcher."
+    return
+  fi
+
+  # Remove trailing '/' if any.
+  gcs_uri="${gcs_uri%%/}"
+  export AIP_STORAGE_DIR="$LOCAL_MODEL_DIR/${gcs_uri##gs://}"
+
+  # Create the target directory.
+  mkdir -p "$AIP_STORAGE_DIR"
+  echo "Downloading model from ${gcs_uri} to ${AIP_STORAGE_DIR}."
+
+  # Use gcloud storage CLI to copy the content from GCS to the target directory.
+  if gcloud storage cp -r "$gcs_uri/*" "$AIP_STORAGE_DIR"; then
+    echo "Model downloaded successfully to ${AIP_STORAGE_DIR}."
+    update_model_id "$@"
+  else
+    echo "Failed to download model from GCS."
+    exit 1
+  fi
+}
+
+run_local_command() {
+  command=$(cat "$LOCAL_ARGS_FILE")
+  rm -f "$LOCAL_ARGS_FILE"
+  echo "Launch command: $command"
+  eval "$command"
+}
+
+maybe_download_model "$@"
+run_local_command

community-content/vertex_model_garden/model_oss/util/gcs_syncer.py

@@ -1,17 +1,107 @@
 """Sync local directory to GCS directory using rsync."""
 
-from collections.abc import Sequence
 import multiprocessing
 import os
 import subprocess
 import time
+from typing import Optional, Sequence, Tuple
 
 from absl import logging
 
+from util import constants
+from util import fileutils
+
 _GCS_COMMAND_RETRIES = 3
 _RSYNC_RETRY_INTERVAL_SECS = 30
 
 
+def is_gcs_or_gcsfuse_path(path: str) -> bool:
+  """Returns if the path is a GCS or gcsfuse path.
+
+  Args:
+    path: The path to check.
+
+  Returns:
+    True if the path is a GCS or gcsfuse path.
+  """
+  return path.startswith(
+      (constants.GCS_URI_PREFIX, constants.GCSFUSE_URI_PREFIX)
+  )
+
+
+def manage_sync_path(
+    path: str, node_rank: Optional[int] = None
+) -> Tuple[str, str]:
+  """Returns local dir and GCS location for the given path if the given path is a GCS or gcsfuse path.
+
+  It will also create a local directory if it does not exist. Otherwise, it
+  returns the same path.
+
+  Args:
+    path: The local or GCS path to manage.
+    node_rank: The node rank to be appended to the GCS path.
+
+  Returns:
+    The local and GCS paths.
+  """
+  local_dir = path
+  gcs_dir = path
+  if is_gcs_or_gcsfuse_path(path):
+    local_dir = os.path.join(
+        constants.LOCAL_OUTPUT_DIR,
+        fileutils.force_gcs_fuse_path(path)[1:],
+    )
+    gcs_dir = fileutils.force_gcs_path(path)
+  if not os.path.exists(local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+
+  if node_rank is None:
+    return local_dir, gcs_dir
+  return local_dir, os.path.join(gcs_dir, f"node-{node_rank}")
+
+
+def setup_gcs_rsync(
+    dirs_to_sync: Sequence[Tuple[str, str]],
+    mp_queue: multiprocessing.Queue,
+    gcs_rsync_interval_secs: int,
+) -> multiprocessing.Process:
+  """Sets up the GCS rsync process.
+
+  Args:
+    dirs_to_sync: The absolute directory paths which will be synced to GCS.
+    mp_queue: The multiprocessing queue to check if the training is finished.
+    gcs_rsync_interval_secs: Integer, interval in seconds to run gcs rsync.
+
+  Returns:
+    The GCS rsync process.
+  """
+  rsync_process = multiprocessing.Process(
+      target=start_gcs_rsync,
+      args=(dirs_to_sync, mp_queue, gcs_rsync_interval_secs),
+  )
+  rsync_process.start()
+  return rsync_process
+
+
+def cleanup_gcs_rsync(
+    rsync_process: multiprocessing.Process, mp_queue: multiprocessing.Queue
+) -> None:
+  """Cleans up the GCS rsync process.
+
+  Args:
+    rsync_process: The GCS rsync process.
+    mp_queue: The multiprocessing queue.
+  """
+  mp_queue.put("finish rsync process")
+  rsync_process.join()
+  if rsync_process.exitcode == 0:
+    logging.info("Artifacts have been uploaded to GCS.")
+  else:
+    logging.error(
+        "GCS rsync process failed with exit code %d.", rsync_process.exitcode
+    )
+
+
 def _rsync_local_to_gcs(local_dir: str, gcs_dir: str) -> None:
   """Syncs the local directory to GCS.
 
@@ -57,7 +147,7 @@ def _rsync_local_to_gcs(local_dir: str, gcs_dir: str) -> None:
 
 
 def start_gcs_rsync(
-    dirs_to_sync: Sequence[tuple[str, str]],
+    dirs_to_sync: Sequence[Tuple[str, str]],
     mp_queue: multiprocessing.Queue,
     gcs_rsync_interval_secs: int,
 ) -> None:

community-content/vertex_model_garden/model_oss/util/pytorch_startup_prober.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+# !/bin/bash
+# The Startup prober built to check whether models listed in local disk are
+# loaded in memory and are ready to serve traffic. The script returns 0 if
+# succeed. Any other returned value are consider as an error. More detail could be
+# found from [shell script Exit codes](http://shellscript.sh/exitcodes.html).
+#
+# TorchServe: The Management API listens on port 8081 and is only accessible
+# from localhost by default.
+
+if [[ -z "${MNG_PORT}" ]]; then
+  MNG_PORT=7081  # We default the management_port to 7081.
+else
+  MNG_PORT="${MNG_PORT}"
+fi
+
+check_model_availability(){
+  local MODEL_NAME=$1
+  # Returns whether "READY" is found in the model status.
+  # Reference: https://pytorch.org/serve/management_api.html#describe-model.
+  curl -s "http://localhost:${MNG_PORT}/models/${MODEL_NAME}" | grep "READY" -q
+}
+
+main(){
+  check_model_availability "$MODEL"  # Assume Dockerfile sets MODEL environment parameter.
+  local available=$?
+  if [[ $available -gt 0 ]]
+  then
+    echo "Warning: Model(${MODEL}) is not yet available."
+    return 1
+  fi
+  return 0
+}
+main

notebooks/community/CODEOWNERS

@@ -26,6 +26,8 @@
 /vertex_endpoints/find_ideal_machine_type/find_ideal_machine_type/find_ideal_machine_type.ipynb @entrpn
 /vertex_endpoints/nvidia-triton/nvidia-triton-custom-container-prediction.ipynb @RajeshThallam
 /vertex_endpoints/optimized_tensorflow_runtime @vlasenkoalexey
+/notebooks/community/alphagenome/cloudai_alphagenome_vai_quickstart.ipynb @dpanigra
+/notebooks/community/weathernext/weathernext_2_early_access_program.ipynb @dpanigra
 /notebooks/community/ml_ops/stage2/get_started_with_visionapi_and_automl.ipynb @mansari
 /notebooks/community/neo4j/graph_paysim.ipynb @benofben @laeg
 /notebooks/community/ml_ops/stage1/get_started_with_visionapi_and_vertex_datasets.ipynb @mansari

notebooks/community/alphagenome/README.md

@@ -0,0 +1,93 @@
+![AlphaGenome header image](https://raw.githubusercontent.com/google-deepmind/alphagenome/refs/heads/main/docs/source/_static/header.png)
+
+# AlphaGenome
+[**Overview**](#overview) | [**Use Cases**](#use-cases) | [**Documentation**](#documentation) | [**Pricing**](#pricing) | [**Quick start**](#quick-start)
+
+## Overview
+**Disclaimer:** *Experimental*.
+
+*The AlphaGenome Private Preview is a "Pre-GA Offering" subject to the "Pre-GA
+Offerings Terms" in the General Service Terms section of the Google Cloud
+[Service Specific Terms](https://cloud.google.com/terms/service-terms). It is
+also a “Generative AI Preview Product” as defined in and subject to the
+[Additional Terms for Generative AI Preview Products](https://cloud.google.com/trustedtester/aitos?e=48754805&hl=en).
+Pre-GA products are available "as is" and might have limited support. For more
+information, see the [launch stage](https://cloud.google.com/products?e=48754805#product-launch-stages)
+descriptions.*
+
+Access to the AlphaGenome model capabilities requires application and approval.
+Users must be added to an allowlist to use the service.
+If you are interested in applying to the program, **Request Access** above.
+
+ 
+
+AlphaGenome is Google DeepMind’s unifying model for deciphering the regulatory
+code within DNA sequences.
+
+AlphaGenome offers multimodal predictions, encompassing diverse functional
+outputs such as gene expression, splicing patterns, chromatin features, and
+contact maps (see diagram below). The model analyzes DNA sequences of up to 1
+million base pairs in length and can deliver predictions at single base-pair
+resolution for most outputs.
+
+Training data was sourced from large public consortia including
+[ENCODE](http://encodeproject.org/), [GTEx](https://www.gtexportal.org/),
+[4D Nucleome](https://4dnucleome.org/) and
+[FANTOM5](https://fantom.gsc.riken.jp/5/), which experimentally measured these
+properties covering important modalities of gene regulation across hundreds of
+human and mouse cell types and tissues.
+
+![Diagram showing an overview of the AlphaGenome model architecture and its inputs/outputs](https://www.alphagenomedocs.com/_images/model_overview.png)
+
+## Use Cases
+*   **Sequence-to-function predictions:** Predict multiple functional tracks (such as gene expression, splicing) from DNA sequences across a wide variety of tissues and cell types.
+
+*   **Variant effect scoring:** Assess the impact of genetic variants by comparing predictions for the reference and alternative alleles and summarising the differences between them.
+
+*   **Identify functional regions:** Use in silico mutagenesis (ISM) to identify functionally important regions in the DNA sequence.
+
+*   **Human and mouse capability:** Generate predictions for both human and mouse genomes.
+
+## Documentation
+This API provides access to AlphaGenome, Google DeepMind's unifying model for
+deciphering the regulatory code within DNA sequences. AlphaGenome offers
+multimodal predictions, encompassing diverse functional outputs including gene
+expression, splicing patterns, chromatin features, and contact maps (see diagram
+below). The model analyzes up to 1 million base pairs of DNA sequence and can
+deliver predictions at single base-pair resolution for most modalities.
+AlphaGenome achieves state-of-the-art performance across a range of genomic
+prediction benchmarks, including diverse variant effect prediction tasks.
+
+The Google Cloud API for AlphaGenome provides a way for Google Cloud customers
+to explore the AlphaGenome API for commercial use cases. This API is in private
+preview (Request Access above). Once allowlisted, customers can access the API
+directly or use the [colab](cloudai_alphagenome_vai_quickstart.ipynb).
+
+### Acknowledgements
+
+*Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., ... Kohli, P. (2025). AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model. bioRxiv.* [https://doi.org/10.1101/2025.06.25.661532](https://doi.org/10.1101/2025.06.25.661532)
+
+### Contact
+If you have any questions on using these models on Google Cloud please contact:
+[alphagenome-cloud-external@google.com](mailto:alphagenome-cloud-external@google.com) or join the community [Discourse](https://www.alphagenomecommunity.com/) for more generic questions on AlphaGenome.
+
+### Links
+
+*   Read our [paper](https://doi.org/10.1101/2025.06.25.661532)
+*   Read our [blog post](https://deepmind.google/discover/blog/alphagenome-ai-for-better-understanding-the-genome)
+*   Join the [community](https://www.alphagenomecommunity.com/)
+*   Check out the [AlphaGenome 101 Video](https://youtu.be/Xbvloe13nak)
+
+## Pricing
+Access to AlphaGenome on Vertex AI is currently restricted.
+To utilize these models via this service:
+
+*   You must **Request Access** using your Google contact.
+*   Your application will be reviewed, and if approved, you will be **added to
+    an allowlist**.
+*   Only allowlisted users can access the API
+*   **Pricing information** will be shared directly with users upon approval
+    and placement on the allowlist.
+
+## Quick start
+The quickest way to get started with the AlphaGenome in Google Cloud Platform is to run [our example notebook](cloudai_alphagenome_vai_quickstart.ipynb) in [Google Colab](https://colab.research.google.com/).

notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb

@@ -37,18 +37,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/bigquery_ml/Anomaly_detection_in_Cloud_Audit_logs_with_BQML.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",

notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb

@@ -47,18 +47,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/bigquery_ml/bq_ml_with_vision_translation_nlp.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/bigquery_ml/bqml-online-prediction.ipynb

@@ -35,18 +35,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",
@@ -359,7 +359,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gcloud storage buckets create --location={REGION} --project={PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -1098,7 +1098,7 @@
         "    ! bq rm -r -f $PROJECT_ID:$BQ_DATASET_NAME\n",
         "# delete the Cloud Storage bucket\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "    ! gcloud storage rm --recursive $BUCKET_URI"
       ]
     }
   ],

notebooks/community/cohere/cohere_embedding_with_matching_engine.ipynb

@@ -38,7 +38,7 @@
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/cohere/cohere_embedding_with_matching_engine.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -449,7 +449,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gcloud storage buckets create --location=$REGION $BUCKET_NAME"
       ]
     },
     {
@@ -470,7 +470,7 @@
       "outputs": [],
       "source": [
         "# this will not return anything if the bucket is empty\n",
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"
       ]
     },
     {
@@ -588,7 +588,7 @@
         "# NOTE: Everything in this GCS DIR will be DELETED before uploading the data.\n",
         "# A CommandException is expected if no data is present\n",
         "\n",
-        "! gsutil rm -rf {BUCKET_NAME}/*"
+        "! gcloud storage rm --recursive --continue-on-error {BUCKET_NAME}/*"
       ]
     },
     {
@@ -599,7 +599,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil cp cohere_embeddings.json {BUCKET_NAME}/cohere_embeddings.json"
+        "! gcloud storage cp cohere_embeddings.json {BUCKET_NAME}/cohere_embeddings.json"
       ]
     },
     {
@@ -610,7 +610,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls {BUCKET_NAME}"
+        "! gcloud storage ls {BUCKET_NAME}"
       ]
     },
     {

notebooks/community/experiments/vertex_ai_model_experimentation.ipynb

@@ -36,18 +36,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/vertex_ai_experiments_classification.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/vertex_ai_experiments_classification.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/vertex_ai_experiments_classification.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",
@@ -537,7 +537,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gcloud storage buckets create --location=$REGION --project=$PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -557,7 +557,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_URI"
+        "! gcloud storage ls --all-versions --long $BUCKET_URI"
       ]
     },
     {
@@ -627,9 +627,9 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_URI\n",
+        "! gcloud storage buckets add-iam-policy-binding $BUCKET_URI --member=serviceAccount:{SERVICE_ACCOUNT} --role=roles/storage.objectCreator\n",
         "\n",
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_URI"
+        "! gcloud storage buckets add-iam-policy-binding $BUCKET_URI --member=serviceAccount:{SERVICE_ACCOUNT} --role=roles/storage.objectViewer"
       ]
     },
     {
@@ -1736,7 +1736,7 @@
         "delete_bucket = False\n",
         "\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -rf {BUCKET_URI}"
+        "    ! gcloud storage rm --recursive --continue-on-error {BUCKET_URI}"
       ]
     }
   ],

notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb

@@ -33,18 +33,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/explainable_ai/SDK_Custom_Container_XAI.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",
@@ -405,7 +405,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gcloud storage buckets create --location=$REGION --project=$PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -425,7 +425,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_URI"
+        "! gcloud storage ls --all-versions --long $BUCKET_URI"
       ]
     },
     {
@@ -698,7 +698,7 @@
       },
       "outputs": [],
       "source": [
-        "!gsutil cp app/model.joblib {BUCKET_URI}/{MODEL_ARTIFACT_DIR}/"
+        "!gcloud storage cp app/model.joblib {BUCKET_URI}/{MODEL_ARTIFACT_DIR}/"
       ]
     },
     {
@@ -1516,7 +1516,7 @@
         "\n",
         "delete_bucket = False\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -rf {BUCKET_URI}"
+        "    ! gcloud storage rm --recursive --continue-on-error {BUCKET_URI}"
       ]
     }
   ],

notebooks/community/exploratory_data_analysis/eda_with_r_and_bigquery.ipynb

@@ -637,20 +637,20 @@
    },
    "outputs": [],
    "source": [
-    "# Upload CSV data to Cloud Storage by passing gsutil commands to system\n",
+    "# Upload CSV data to Cloud Storage by passing gcloud storage commands to system\n",
     "gcs_url <- paste0(\"gs://\", BUCKET_NAME, \"/\")\n",
     "\n",
-    "command <- paste(\"gsutil mb\", gcs_url)\n",
+    "command <- paste(\"gcloud storage buckets create\", gcs_url)\n",
     "\n",
     "system(command)\n",
     "\n",
     "gcs_data_dir <- paste0(\"gs://\", BUCKET_NAME, \"/data\")\n",
     "\n",
-    "command <- paste(\"gsutil cp data/*_data.csv\", gcs_data_dir)\n",
+    "command <- paste(\"gcloud storage cp data/*_data.csv\", gcs_data_dir)\n",
     "\n",
     "system(command)\n",
     "\n",
-    "command <- paste(\"gsutil ls -l\", gcs_data_dir)\n",
+    "command <- paste(\"gcloud storage ls --long\", gcs_data_dir)\n",
     "\n",
     "system(command, intern = TRUE)"
    ]
@@ -679,4 +679,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

notebooks/community/feature_store/gapic-feature-store.ipynb

@@ -33,12 +33,12 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/feature_store/gapic-feature-store.ipynb\"\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/feature_store/gapic-feature-store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/feature_store/get_started_vertex_feature_store.ipynb

@@ -34,20 +34,20 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_vertex_feature_store_serving.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "    \n",
         "  <td>\n",
         "        <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_vertex_feature_store_serving.ipynb\">\n",
-        "        <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "        <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "        </a>\n",
         "  </td>\n",
         "    \n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_vertex_feature_store_serving.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>\n",
@@ -471,8 +471,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_URI"
-      ]
+        "! gcloud storage buckets create --location=$REGION $BUCKET_URI"      ]
     },
     {
       "cell_type": "markdown",
@@ -491,8 +490,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_URI"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_URI"      ]
     },
     {
       "cell_type": "markdown",
@@ -511,8 +509,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil uniformbucketlevelaccess set on {BUCKET_URI}"
-      ]
+        "! gcloud storage buckets update --uniform-bucket-level-access {BUCKET_URI}"      ]
     },
     {
       "cell_type": "markdown",
@@ -1910,9 +1907,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil -m rm -r $BUCKET_URI\n",
-        "! gsutil rb $BUCKET_URI"
-      ]
+        "! gcloud storage rm --recursive $BUCKET_URI\n",        "! gcloud storage buckets delete $BUCKET_URI"      ]
     }
   ],
   "metadata": {

notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb

@@ -33,18 +33,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/vertex-ai-samples/blob/main/notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/mobile_gaming/mobile_gaming_feature_store.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\">\n",
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>                                                                                               \n",

notebooks/community/feature_store/sdk-feature-store.ipynb

@@ -32,12 +32,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\"\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/automl/showcase_automl_image_classification_batch.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -421,7 +421,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gcloud storage buckets create --location=$REGION $BUCKET_NAME"
       ]
     },
     {
@@ -441,7 +441,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"
       ]
     },
     {
@@ -478,9 +478,7 @@
         "import time\n",
         "\n",
         "from google.cloud.aiplatform import gapic as aip\n",
-        "from google.protobuf import json_format\n",
-        "from google.protobuf.json_format import MessageToJson, ParseDict\n",
-        "from google.protobuf.struct_pb2 import Struct, Value"
+        "from google.protobuf import json_format"
       ]
     },
     {
@@ -886,11 +884,11 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",
         "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
+        "! gcloud storage cat $FILE | head"
       ]
     },
     {
@@ -1329,7 +1327,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = !gsutil cat $IMPORT_FILE | head -n2\n",
+        "test_items = !gcloud storage cat $IMPORT_FILE | head -n2\n",
         "if len(str(test_items[0]).split(\",\")) == 3:\n",
         "    _, test_item_1, test_label_1 = str(test_items[0]).split(\",\")\n",
         "    _, test_item_2, test_label_2 = str(test_items[1]).split(\",\")\n",
@@ -1363,8 +1361,8 @@
         "file_1 = test_item_1.split(\"/\")[-1]\n",
         "file_2 = test_item_2.split(\"/\")[-1]\n",
         "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
+        "! gcloud storage cp $test_item_1 $BUCKET_NAME/$file_1\n",
+        "! gcloud storage cp $test_item_2 $BUCKET_NAME/$file_2\n",
         "\n",
         "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
         "test_item_2 = BUCKET_NAME + \"/\" + file_2"
@@ -1408,7 +1406,7 @@
         "    f.write(json.dumps(data) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
+        "! gcloud storage cat $gcs_input_uri"
       ]
     },
     {
@@ -1692,8 +1690,8 @@
       "outputs": [],
       "source": [
         "def get_latest_predictions(gcs_out_dir):\n",
-        "    \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
-        "    folders = !gsutil ls $gcs_out_dir\n",
+        "    \"\"\"Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
+        "    folders = !gcloud storage ls $gcs_out_dir\n",
         "    latest = \"\"\n",
         "    for folder in folders:\n",
         "        subfolder = folder.split(\"/\")[-2]\n",
@@ -1711,9 +1709,9 @@
         "            raise Exception(\"Batch Job Failed\")\n",
         "    else:\n",
         "        folder = get_latest_predictions(predictions)\n",
-        "        ! gsutil ls $folder/prediction*.jsonl\n",
+        "        ! gcloud storage ls $folder/prediction*.jsonl\n",
         "\n",
-        "        ! gsutil cat $folder/prediction*.jsonl\n",
+        "        ! gcloud storage cat $folder/prediction*.jsonl\n",
         "        break\n",
         "    time.sleep(60)"
       ]
@@ -1808,7 +1806,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"
       ]
     }
   ],

notebooks/community/gapic/automl/showcase_automl_image_classification_export_edge.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_export_edge.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_export_edge.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -416,7 +416,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gcloud storage buckets create --location=$REGION $BUCKET_NAME"
       ]
     },
     {
@@ -436,7 +436,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"
       ]
     },
     {
@@ -474,7 +474,6 @@
         "\n",
         "from google.cloud.aiplatform import gapic as aip\n",
         "from google.protobuf import json_format\n",
-        "from google.protobuf.json_format import MessageToJson, ParseDict\n",
         "from google.protobuf.struct_pb2 import Struct, Value"
       ]
     },
@@ -784,11 +783,11 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",
         "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
+        "! gcloud storage cat $FILE | head"
       ]
     },
     {
@@ -1215,7 +1214,6 @@
         "    }\n",
         "    response = clients[\"model\"].export_model(name=name, output_config=output_config)\n",
         "    print(\"Long running operation:\", response.operation.name)\n",
-        "    result = response.result(timeout=1800)\n",
         "    metadata = response.operation.metadata\n",
         "    artifact_uri = str(metadata.value).split(\"\\\\\")[-1][4:-1]\n",
         "    print(\"Artifact Uri\", artifact_uri)\n",
@@ -1244,9 +1242,9 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $model_package\n",
+        "! gcloud storage ls $model_package\n",
         "# Download the model artifacts\n",
-        "! gsutil cp -r $model_package tflite\n",
+        "! gcloud storage cp --recursive $model_package tflite\n",
         "\n",
         "tflite_path = \"tflite/model.tflite\""
       ]
@@ -1305,7 +1303,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n1\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n1\n",
         "test_item = test_items[0].split(\",\")[0]\n",
         "\n",
         "with tf.io.gfile.GFile(test_item, \"rb\") as f:\n",
@@ -1449,7 +1447,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"
       ]
     }
   ],

notebooks/community/gapic/automl/showcase_automl_image_classification_online.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -721,12 +721,10 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
-      ]
+        "! gcloud storage cat $FILE | head"      ]
     },
     {
       "cell_type": "markdown",
@@ -1342,8 +1340,7 @@
       },
       "outputs": [],
       "source": [
-        "test_item = !gsutil cat $IMPORT_FILE | head -n1\n",
-        "if len(str(test_item[0]).split(\",\")) == 3:\n",
+        "test_item = !gcloud storage cat $IMPORT_FILE | head -n1\n",        "if len(str(test_item[0]).split(\",\")) == 3:\n",
         "    _, test_item, test_label = str(test_item[0]).split(\",\")\n",
         "else:\n",
         "    test_item, test_label = str(test_item[0]).split(\",\")\n",
@@ -1568,8 +1565,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_image_classification_online_proxy.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online_proxy.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_classification_online_proxy.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -721,12 +721,10 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
-      ]
+        "! gcloud storage cat $FILE | head"      ]
     },
     {
       "cell_type": "markdown",
@@ -1342,8 +1340,7 @@
       },
       "outputs": [],
       "source": [
-        "test_item = !gsutil cat $IMPORT_FILE | head -n1\n",
-        "if len(str(test_item[0]).split(\",\")) == 3:\n",
+        "test_item = !gcloud storage cat $IMPORT_FILE | head -n1\n",        "if len(str(test_item[0]).split(\",\")) == 3:\n",
         "    _, test_item, test_label = str(test_item[0]).split(\",\")\n",
         "else:\n",
         "    test_item, test_label = str(test_item[0]).split(\",\")\n",
@@ -1747,8 +1744,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_image_object_detection_batch.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -421,7 +421,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gcloud storage buckets create --location=$REGION $BUCKET_NAME"
       ]
     },
     {
@@ -441,7 +441,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"
       ]
     },
     {
@@ -478,9 +478,7 @@
         "import time\n",
         "\n",
         "from google.cloud.aiplatform import gapic as aip\n",
-        "from google.protobuf import json_format\n",
-        "from google.protobuf.json_format import MessageToJson, ParseDict\n",
-        "from google.protobuf.struct_pb2 import Struct, Value"
+        "from google.protobuf import json_format"
       ]
     },
     {
@@ -887,11 +885,11 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",
         "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
+        "! gcloud storage cat $FILE | head"
       ]
     },
     {
@@ -1333,7 +1331,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = !gsutil cat $IMPORT_FILE | head -n2\n",
+        "test_items = !gcloud storage cat $IMPORT_FILE | head -n2\n",
         "cols_1 = str(test_items[0]).split(\",\")\n",
         "cols_2 = str(test_items[1]).split(\",\")\n",
         "if len(cols_1) == 11:\n",
@@ -1373,8 +1371,8 @@
         "file_1 = test_item_1.split(\"/\")[-1]\n",
         "file_2 = test_item_2.split(\"/\")[-1]\n",
         "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
+        "! gcloud storage cp $test_item_1 $BUCKET_NAME/$file_1\n",
+        "! gcloud storage cp $test_item_2 $BUCKET_NAME/$file_2\n",
         "\n",
         "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
         "test_item_2 = BUCKET_NAME + \"/\" + file_2"
@@ -1418,7 +1416,7 @@
         "    f.write(json.dumps(data) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
+        "! gcloud storage cat $gcs_input_uri"
       ]
     },
     {
@@ -1704,8 +1702,8 @@
       "outputs": [],
       "source": [
         "def get_latest_predictions(gcs_out_dir):\n",
-        "    \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
-        "    folders = !gsutil ls $gcs_out_dir\n",
+        "    \"\"\"Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
+        "    folders = !gcloud storage ls $gcs_out_dir\n",
         "    latest = \"\"\n",
         "    for folder in folders:\n",
         "        subfolder = folder.split(\"/\")[-2]\n",
@@ -1723,9 +1721,9 @@
         "            raise Exception(\"Batch Job Failed\")\n",
         "    else:\n",
         "        folder = get_latest_predictions(predictions)\n",
-        "        ! gsutil ls $folder/prediction*.jsonl\n",
+        "        ! gcloud storage ls $folder/prediction*.jsonl\n",
         "\n",
-        "        ! gsutil cat $folder/prediction*.jsonl\n",
+        "        ! gcloud storage cat $folder/prediction*.jsonl\n",
         "        break\n",
         "    time.sleep(60)"
       ]
@@ -1820,7 +1818,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"
       ]
     }
   ],

notebooks/community/gapic/automl/showcase_automl_image_object_detection_export_edge.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_export_edge.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_export_edge.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -416,7 +416,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"
       ]
     },
     {
@@ -436,7 +436,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"
       ]
     },
     {
@@ -473,9 +473,7 @@
         "import time\n",
         "\n",
         "from google.cloud.aiplatform import gapic as aip\n",
-        "from google.protobuf import json_format\n",
-        "from google.protobuf.json_format import MessageToJson, ParseDict\n",
-        "from google.protobuf.struct_pb2 import Struct, Value"
+        "from google.protobuf import json_format\n"
       ]
     },
     {
@@ -785,11 +783,11 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",
         "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
+        "! gcloud storage cat $FILE | head"
       ]
     },
     {
@@ -1218,7 +1216,6 @@
         "    }\n",
         "    response = clients[\"model\"].export_model(name=name, output_config=output_config)\n",
         "    print(\"Long running operation:\", response.operation.name)\n",
-        "    result = response.result(timeout=1800)\n",
         "    metadata = response.operation.metadata\n",
         "    artifact_uri = str(metadata.value).split(\"\\\\\")[-1][4:-1]\n",
         "    print(\"Artifact Uri\", artifact_uri)\n",
@@ -1247,9 +1244,9 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls $model_package\n",
+        "! gcloud storage ls $model_package\n",
         "# Download the model artifacts\n",
-        "! gsutil cp -r $model_package tflite\n",
+        "! gcloud storage cp --recursive $model_package tflite\n",
         "\n",
         "tflite_path = \"tflite/model.tflite\""
       ]
@@ -1308,7 +1305,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n1\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n1\n",
         "test_item = test_items[0].split(\",\")[0]\n",
         "\n",
         "with tf.io.gfile.GFile(test_item, \"rb\") as f:\n",
@@ -1452,7 +1449,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"
       ]
     }
   ],

notebooks/community/gapic/automl/showcase_automl_image_object_detection_online.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_object_detection_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -396,9 +396,7 @@
         "import time\n",
         "\n",
         "from google.cloud.aiplatform import gapic as aip\n",
-        "from google.protobuf import json_format\n",
-        "from google.protobuf.json_format import MessageToJson, ParseDict\n",
-        "from google.protobuf.struct_pb2 import Struct, Value"
+        "from google.protobuf import json_format"
       ]
     },
     {
@@ -722,11 +720,11 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",
         "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
+        "! gcloud storage cat $FILE | head"
       ]
     },
     {
@@ -1345,7 +1343,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = !gsutil cat $IMPORT_FILE | head -n1\n",
+        "test_items = !gcloud storage cat $IMPORT_FILE | head -n1\n",
         "cols = str(test_items[0]).split(\",\")\n",
         "if len(cols) == 11:\n",
         "    test_item = str(cols[1])\n",
@@ -1574,7 +1572,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"
       ]
     }
   ],

notebooks/community/gapic/automl/showcase_automl_image_segmentation_batch.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -421,8 +421,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -441,8 +440,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -891,12 +889,10 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
-      ]
+        "! gcloud storage cat $FILE | head"      ]
     },
     {
       "cell_type": "markdown",
@@ -1325,8 +1321,7 @@
       "source": [
         "import json\n",
         "\n",
-        "test_items = !gsutil cat $IMPORT_FILE | head -n2\n",
-        "test_data_1 = test_items[0].replace(\"'\", '\"')\n",
+        "test_items = !gcloud storage cat $IMPORT_FILE | head -n2\n",        "test_data_1 = test_items[0].replace(\"'\", '\"')\n",
         "test_data_1 = json.loads(test_data_1)\n",
         "test_data_2 = test_items[0].replace(\"'\", '\"')\n",
         "test_data_2 = json.loads(test_data_2)\n",
@@ -1367,9 +1362,8 @@
         "file_1 = test_item_1.split(\"/\")[-1]\n",
         "file_2 = test_item_2.split(\"/\")[-1]\n",
         "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
-        "\n",
+        "! gcloud storage cp $test_item_1 $BUCKET_NAME/$file_1\n",
+        "! gcloud storage cp $test_item_2 $BUCKET_NAME/$file_2\n",        "\n",
         "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
         "test_item_2 = BUCKET_NAME + \"/\" + file_2"
       ]
@@ -1412,8 +1406,7 @@
         "    f.write(json.dumps(data) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
-      ]
+        "! gcloud storage cat $gcs_input_uri"      ]
     },
     {
       "cell_type": "markdown",
@@ -1681,8 +1674,7 @@
       "source": [
         "def get_latest_predictions(gcs_out_dir):\n",
         "    \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
-        "    folders = !gsutil ls $gcs_out_dir\n",
-        "    latest = \"\"\n",
+        "    folders = !gcloud storage ls $gcs_out_dir\n",        "    latest = \"\"\n",
         "    for folder in folders:\n",
         "        subfolder = folder.split(\"/\")[-2]\n",
         "        if subfolder.startswith(\"prediction-\"):\n",
@@ -1699,10 +1691,8 @@
         "            raise Exception(\"Batch Job Failed\")\n",
         "    else:\n",
         "        folder = get_latest_predictions(predictions)\n",
-        "        ! gsutil ls $folder/prediction*.jsonl\n",
-        "\n",
-        "        ! gsutil cat $folder/prediction*.jsonl\n",
-        "        break\n",
+        "        ! gcloud storage ls $folder/prediction*.jsonl\n",        "\n",
+        "        ! gcloud storage cat $folder/prediction*.jsonl\n",        "        break\n",
         "    time.sleep(60)"
       ]
     },
@@ -1796,8 +1786,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_image_segmentation_online.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_image_segmentation_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -726,12 +726,10 @@
         "else:\n",
         "    FILE = IMPORT_FILE\n",
         "\n",
-        "count = ! gsutil cat $FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $FILE | head"
-      ]
+        "! gcloud storage cat $FILE | head"      ]
     },
     {
       "cell_type": "markdown",
@@ -1338,8 +1336,7 @@
       "source": [
         "import json\n",
         "\n",
-        "test_items = !gsutil cat $IMPORT_FILE | head -n1\n",
-        "test_data = test_items[0].replace(\"'\", '\"')\n",
+        "test_items = !gcloud storage cat $IMPORT_FILE | head -n1\n",        "test_data = test_items[0].replace(\"'\", '\"')\n",
         "test_data = json.loads(test_data)\n",
         "try:\n",
         "    test_item = test_data[\"image_gcs_uri\"]\n",
@@ -1555,8 +1552,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_batch.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -421,8 +421,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -441,8 +440,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -809,14 +807,11 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
-        "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
         "    raise Exception(\"label column missing\")"
@@ -1377,8 +1372,7 @@
         "    f.write(str(INSTANCE_2) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
-      ]
+        "! gcloud storage cat $gcs_input_uri"      ]
     },
     {
       "cell_type": "markdown",
@@ -1646,8 +1640,7 @@
       "source": [
         "def get_latest_predictions(gcs_out_dir):\n",
         "    \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
-        "    folders = !gsutil ls $gcs_out_dir\n",
-        "    latest = \"\"\n",
+        "    folders = !gcloud storage ls $gcs_out_dir\n",        "    latest = \"\"\n",
         "    for folder in folders:\n",
         "        subfolder = folder.split(\"/\")[-2]\n",
         "        if subfolder.startswith(\"prediction-\"):\n",
@@ -1664,10 +1657,8 @@
         "            raise Exception(\"Batch Job Failed\")\n",
         "    else:\n",
         "        folder = get_latest_predictions(predictions)\n",
-        "        ! gsutil ls $folder/prediction*.csv\n",
-        "\n",
-        "        ! gsutil cat $folder/prediction*.csv\n",
-        "        break\n",
+        "        ! gcloud storage ls $folder/prediction*.csv\n",        "\n",
+        "        ! gcloud storage cat $folder/prediction*.csv\n",        "        break\n",
         "    time.sleep(60)"
       ]
     },
@@ -1761,8 +1752,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_online.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -735,14 +735,11 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
-        "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
         "    raise Exception(\"label column missing\")"
@@ -1668,8 +1665,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -421,8 +421,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -441,8 +440,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -810,14 +808,11 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
-        "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
         "    raise Exception(\"label column missing\")"
@@ -1363,8 +1358,7 @@
         "    f.write(str(INSTANCE_2) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
-      ]
+        "! gcloud storage cat $gcs_input_uri"      ]
     },
     {
       "cell_type": "markdown",
@@ -1632,8 +1626,7 @@
       "source": [
         "def get_latest_predictions(gcs_out_dir):\n",
         "    \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
-        "    folders = !gsutil ls $gcs_out_dir\n",
-        "    latest = \"\"\n",
+        "    folders = !gcloud storage ls $gcs_out_dir\n",        "    latest = \"\"\n",
         "    for folder in folders:\n",
         "        subfolder = folder.split(\"/\")[-2]\n",
         "        if subfolder.startswith(\"prediction-\"):\n",
@@ -1650,10 +1643,8 @@
         "            raise Exception(\"Batch Job Failed\")\n",
         "    else:\n",
         "        folder = get_latest_predictions(predictions)\n",
-        "        ! gsutil ls $folder/prediction*.csv\n",
-        "\n",
-        "        ! gsutil cat $folder/prediction*.csv\n",
-        "        break\n",
+        "        ! gcloud storage ls $folder/prediction*.csv\n",        "\n",
+        "        ! gcloud storage cat $folder/prediction*.csv\n",        "        break\n",
         "    time.sleep(60)"
       ]
     },
@@ -1747,8 +1738,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch_explain.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch_explain.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_batch_explain.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -421,8 +421,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -441,8 +440,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -810,14 +808,11 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
-        "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
         "    raise Exception(\"label column missing\")"
@@ -1363,8 +1358,7 @@
         "    f.write(str(INSTANCE_2) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
-      ]
+        "! gcloud storage cat $gcs_input_uri"      ]
     },
     {
       "cell_type": "markdown",
@@ -1631,8 +1625,7 @@
       "source": [
         "def get_latest_predictions(gcs_out_dir):\n",
         "    \"\"\" Get the latest prediction subfolder using the timestamp in the subfolder name\"\"\"\n",
-        "    folders = !gsutil ls $gcs_out_dir\n",
-        "    latest = \"\"\n",
+        "    folders = !gcloud storage ls $gcs_out_dir\n",        "    latest = \"\"\n",
         "    for folder in folders:\n",
         "        subfolder = folder.split(\"/\")[-2]\n",
         "        if subfolder.startswith(\"prediction-\"):\n",
@@ -1649,10 +1642,8 @@
         "            raise Exception(\"Batch Job Failed\")\n",
         "    else:\n",
         "        folder = get_latest_predictions(predictions)\n",
-        "        ! gsutil ls $folder/explanation*.csv\n",
-        "\n",
-        "        ! gsutil cat $folder/explanation*.csv\n",
-        "        break\n",
+        "        ! gcloud storage ls $folder/explanation*.csv\n",        "\n",
+        "        ! gcloud storage cat $folder/explanation*.csv\n",        "        break\n",
         "    time.sleep(60)"
       ]
     },
@@ -1746,8 +1737,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_classification_export_cloud.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_export_cloud.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_export_cloud.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -416,8 +416,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -436,8 +435,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -749,14 +747,11 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
-        "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
         "    raise Exception(\"label column missing\")"
@@ -1283,15 +1278,12 @@
       "source": [
         "print(\"Model Package:\", model_package)\n",
         "print(\"Contents:\")\n",
-        "! gsutil ls $model_package\n",
-        "\n",
+        "! gcloud storage ls $model_package\n",        "\n",
         "print(\"\\nTF Saved Model\")\n",
         "path = model_package + \"/predict\"\n",
-        "files = ! gsutil ls $path\n",
-        "saved_dir = files[1]\n",
+        "files = ! gcloud storage ls $path\n",        "saved_dir = files[1]\n",
         "print(saved_dir)\n",
-        "! gsutil ls $saved_dir"
-      ]
+        "! gcloud storage ls $saved_dir"      ]
     },
     {
       "cell_type": "markdown",
@@ -1312,8 +1304,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil cp -r $model_package ."
-      ]
+        "! gcloud storage cp --recursive $model_package ."      ]
     },
     {
       "cell_type": "markdown",
@@ -1599,8 +1590,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_classification_online.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -735,14 +735,11 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
-        "print(\"Number of Examples\", int(count[0]))\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",        "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
-        "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
         "    raise Exception(\"label column missing\")"
@@ -1645,8 +1642,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
-      ]
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"      ]
     }
   ],
   "metadata": {

notebooks/community/gapic/automl/showcase_automl_tabular_classification_online_explain.ipynb

@@ -34,12 +34,12 @@
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online_explain.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/gapic/automl/showcase_automl_tabular_classification_online_explain.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://www.svgrepo.com/download/217753/github.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -397,7 +397,6 @@
         "\n",
         "import google.cloud.aiplatform_v1beta1 as aip\n",
         "from google.protobuf import json_format\n",
-        "from google.protobuf.json_format import MessageToJson, ParseDict\n",
         "from google.protobuf.struct_pb2 import Struct, Value"
       ]
     },
@@ -735,13 +734,13 @@
       },
       "outputs": [],
       "source": [
-        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
+        "count = ! gcloud storage cat $IMPORT_FILE | wc -l\n",
         "print(\"Number of Examples\", int(count[0]))\n",
         "\n",
         "print(\"First 10 rows\")\n",
-        "! gsutil cat $IMPORT_FILE | head\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",
         "\n",
-        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",
         "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
         "print(\"Label Column Name\", label_column)\n",
         "if label_column is None:\n",
@@ -1820,7 +1819,7 @@
         "    print(e)\n",
         "\n",
         "if delete_bucket and \"BUCKET_NAME\" in globals():\n",
-        "    ! gsutil rm -r $BUCKET_NAME"
+        "    ! gcloud storage rm --recursive $BUCKET_NAME"
       ]
     }
   ],