bug: remove A100 as recommended specs

* bug: ahref update

* fix: linter

* fix: typo fix

.cloud-build/execute_changed_notebooks_helper.py

@@ -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/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: '3.12'      
     - 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==7.2.0
+black==26.1.0
+pyupgrade==3.21.2
+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==5.29.6
+opencv-python-headless==4.11.0.86
+docutils==0.16
+urllib3==2.6.3
+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==5.29.6
+pyasn1==0.6.2
+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==2.6.3
+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 +1,14 @@
 """Common util functions for notebook."""
 
 import base64
+from collections.abc import Sequence
 import datetime
 import io
 import json
 import os
 import subprocess
 import time
-from typing import Any, Dict, Sequence
+from typing import Any
 
 from google import auth
 from google.cloud import storage
@@ -283,7 +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:
@@ -509,6 +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")
@@ -518,12 +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:
@@ -533,6 +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.
@@ -548,7 +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",
   }
@@ -563,6 +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
@@ -591,8 +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."
@@ -605,13 +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,
   )

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

@@ -102,10 +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,
     ])

notebooks/community/CODEOWNERS

@@ -26,6 +26,9 @@
 /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/alphagenome/cloudai_alphagenome_finetune.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/agent_engine/adk_inline_source_deployment.ipynb

@@ -0,0 +1,552 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f705f4be70e9"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2025 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9a05a17e4e2f"
+      },
+      "source": [
+        "# Deploy ADK Agents to Vertex AI Agent Engine Using Inline Source\n",
+        "\n",
+        "<table><tbody><tr>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fagent_engine%2Fadk_inline_source_deployment.ipynb\">\n",
+        "      <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/agent_engine/adk_inline_source_deployment.ipynb\">\n",
+        "      <img alt=\"GitHub logo\" src=\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\" width=\"32px\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</tr></tbody></table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e6e9014cd53a"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates how to deploy [Google Agent Development Kit (ADK)](https://google.github.io/adk-docs/) agents to [Vertex AI Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using **inline source deployment**.\n",
+        "\n",
+        "I built this pattern while developing a multi-agent system with ADK. The approach is also featured in Google's [Agent Starter Pack community showcase](https://github.com/GoogleCloudPlatform/agent-starter-pack#community-showcase).\n",
+        "\n",
+        "Inline source deployment sends your agent's Python source code directly to Agent Engine, avoiding serialization issues that can occur with the default pickle-based deployment.\n",
+        "\n",
+        "### Why Inline Source Deployment?\n",
+        "\n",
+        "| Approach | How it works | Best for |\n",
+        "|----------|--------------|----------|\n",
+        "| Default (pickle) | Serializes agent object | Simple agents, quick prototypes |\n",
+        "| **Inline source** | Sends Python source directly | Complex agents, custom dependencies |\n",
+        "\n",
+        "Inline source deployment is useful when:\n",
+        "- Your agent uses custom classes that don't serialize well\n",
+        "- You need precise control over the deployment environment\n",
+        "- You want to avoid pickle compatibility issues across Python versions\n",
+        "\n",
+        "### Objectives\n",
+        "\n",
+        "- Create an ADK agent with tools\n",
+        "- Deploy to Agent Engine using inline source\n",
+        "- Test the deployed agent\n",
+        "- Clean up resources\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI Agent Engine\n",
+        "* Vertex AI (Gemini API)\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4d998a5140b2"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c832028166e3"
+      },
+      "source": [
+        "### Install required packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6b2e64d59bf4"
+      },
+      "outputs": [],
+      "source": [
+        "# Define requirements once for reuse\n",
+        "REQUIREMENTS = [\n",
+        "    \"google-cloud-aiplatform[adk,agent_engines]>=1.88.0\",\n",
+        "    \"google-adk>=1.18.0\",\n",
+        "]\n",
+        "\n",
+        "!pip install --upgrade --quiet {\" \".join(REQUIREMENTS)}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2f2e1483e7a8"
+      },
+      "source": [
+        "### Restart runtime\n",
+        "\n",
+        "After installing packages, restart the runtime to ensure the new packages are loaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d76628df8180"
+      },
+      "outputs": [],
+      "source": [
+        "import IPython\n",
+        "\n",
+        "app = IPython.Application.instance()\n",
+        "app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "586675a64f57"
+      },
+      "source": [
+        "### Authenticate your notebook environment (Colab only)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2082bbcb771c"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "    from google.colab import auth\n",
+        "\n",
+        "    auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "296a2345bc1a"
+      },
+      "source": [
+        "### Set Google Cloud project information"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "abafb08d10b1"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type: \"string\", placeholder: \"[your-project-id]\", isTemplate: true}\n",
+        "if not PROJECT_ID or PROJECT_ID == \"[your-project-id]\":\n",
+        "    PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n",
+        "\n",
+        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
+        "\n",
+        "STAGING_BUCKET = f\"gs://{PROJECT_ID}-agent-engine-staging\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c7863dcf6a38"
+      },
+      "source": [
+        "### Create staging bucket\n",
+        "\n",
+        "Agent Engine requires a staging bucket to store deployment artifacts."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "17ece3f9fb6e"
+      },
+      "outputs": [],
+      "source": [
+        "!gsutil mb -l {LOCATION} -p {PROJECT_ID} {STAGING_BUCKET} 2>/dev/null || echo \"Bucket already exists\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1029f595561d"
+      },
+      "source": [
+        "### Initialize Vertex AI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cac3c1b09203"
+      },
+      "outputs": [],
+      "source": [
+        "import vertexai\n",
+        "\n",
+        "vertexai.init(\n",
+        "    project=PROJECT_ID,\n",
+        "    location=LOCATION,\n",
+        "    staging_bucket=STAGING_BUCKET,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "46fb887b5ea3"
+      },
+      "source": [
+        "## Create an ADK Agent\n",
+        "\n",
+        "First, let's create a simple ADK agent with a tool. This agent can answer questions and get the current time."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9d0dd0808c69"
+      },
+      "outputs": [],
+      "source": [
+        "import datetime\n",
+        "from zoneinfo import ZoneInfo, ZoneInfoNotFoundError\n",
+        "\n",
+        "from google.adk.agents import LlmAgent\n",
+        "\n",
+        "\n",
+        "def get_current_time(timezone: str = \"UTC\") -> str:\n",
+        "    \"\"\"Get the current time in the specified timezone.\n",
+        "\n",
+        "    Args:\n",
+        "        timezone: IANA timezone name (e.g., 'America/New_York', 'UTC')\n",
+        "\n",
+        "    Returns:\n",
+        "        Current time as a formatted string\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        tz = ZoneInfo(timezone)\n",
+        "        now = datetime.datetime.now(tz)\n",
+        "        return (\n",
+        "            f\"The current time in {timezone} is {now.strftime('%Y-%m-%d %H:%M:%S %Z')}\"\n",
+        "        )\n",
+        "    except ZoneInfoNotFoundError:\n",
+        "        return f\"Error: Timezone '{timezone}' not found. Please use a valid IANA timezone name (e.g., 'America/New_York', 'UTC').\"\n",
+        "    except Exception as e:\n",
+        "        return f\"An unexpected error occurred while getting time for timezone {timezone}: {e}\"\n",
+        "\n",
+        "\n",
+        "# Create the agent\n",
+        "agent = LlmAgent(\n",
+        "    name=\"time_agent\",\n",
+        "    model=\"gemini-1.5-flash-001\",\n",
+        "    instruction=\"You are a helpful assistant. Use the get_current_time tool when asked about the time.\",\n",
+        "    tools=[get_current_time],\n",
+        ")\n",
+        "\n",
+        "print(f\"Created agent: {agent.name}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ad0b9cc230ca"
+      },
+      "source": [
+        "### Test the agent locally\n",
+        "\n",
+        "Before deploying, let's test the agent locally to make sure it works."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1d3d71bde31e"
+      },
+      "outputs": [],
+      "source": [
+        "from google.adk import Runner\n",
+        "from google.adk.sessions import InMemorySessionService\n",
+        "from google.genai import types\n",
+        "\n",
+        "# Create a local runner\n",
+        "runner = Runner(\n",
+        "    agent=agent,\n",
+        "    app_name=\"test_app\",\n",
+        "    session_service=InMemorySessionService(),\n",
+        ")\n",
+        "\n",
+        "# Create a session and test\n",
+        "session = runner.session_service.create_session(\n",
+        "    app_name=\"test_app\",\n",
+        "    user_id=\"test_user\",\n",
+        ")\n",
+        "\n",
+        "# Send a test message\n",
+        "response = runner.run(\n",
+        "    user_id=\"test_user\",\n",
+        "    session_id=session.id,\n",
+        "    new_message=types.Content(\n",
+        "        role=\"user\",\n",
+        "        parts=[types.Part(text=\"What time is it in New York?\")],\n",
+        "    ),\n",
+        ")\n",
+        "\n",
+        "# Print the response\n",
+        "for event in response:\n",
+        "    if hasattr(event, \"content\") and event.content:\n",
+        "        for part in event.content.parts:\n",
+        "            if hasattr(part, \"text\") and part.text:\n",
+        "                print(part.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "06bdc3c10ea6"
+      },
+      "source": [
+        "## Deploy Using Inline Source\n",
+        "\n",
+        "Now let's deploy the agent to Agent Engine using inline source deployment.\n",
+        "\n",
+        "The key difference from default deployment is using `AgentEngineApp.from_agent()` which packages the source code directly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d641a5e29291"
+      },
+      "outputs": [],
+      "source": [
+        "from vertexai import agent_engines\n",
+        "from vertexai.agent_engines import AgentEngineApp\n",
+        "\n",
+        "# Create the app from the agent (inline source)\n",
+        "app = AgentEngineApp.from_agent(\n",
+        "    agent=agent,\n",
+        "    enable_tracing=True,\n",
+        ")\n",
+        "\n",
+        "print(\"App created successfully\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cfd8fddbf181"
+      },
+      "source": [
+        "### Deploy to Agent Engine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ebd6642375a8"
+      },
+      "outputs": [],
+      "source": [
+        "# Deploy the app\n",
+        "# This may take a few minutes\n",
+        "remote_app = agent_engines.create(\n",
+        "    app,\n",
+        "    requirements=REQUIREMENTS,\n",
+        "    display_name=\"time-agent-inline\",\n",
+        ")\n",
+        "\n",
+        "print(f\"Deployed agent: {remote_app.resource_name}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "43b739d2f980"
+      },
+      "source": [
+        "## Test the Deployed Agent\n",
+        "\n",
+        "Now let's test the deployed agent by sending it a query."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8ec0598a1138"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a session with the remote agent\n",
+        "remote_session = remote_app.create_session(user_id=\"test_user\")\n",
+        "\n",
+        "print(f\"Created session: {remote_session['id']}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f85603f0e4fd"
+      },
+      "outputs": [],
+      "source": [
+        "# Query the agent\n",
+        "response = remote_app.stream_query(\n",
+        "    user_id=\"test_user\",\n",
+        "    session_id=remote_session[\"id\"],\n",
+        "    message=\"What time is it in Tokyo?\",\n",
+        ")\n",
+        "\n",
+        "# Print the response\n",
+        "for chunk in response:\n",
+        "    if hasattr(chunk, \"content\") and chunk.content:\n",
+        "        for part in chunk.content.parts:\n",
+        "            if hasattr(part, \"text\") and part.text:\n",
+        "                print(part.text, end=\"\")\n",
+        "\n",
+        "print()  # Newline at end"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e70d97acf3a3"
+      },
+      "source": [
+        "## Cleaning up\n",
+        "\n",
+        "Delete the deployed agent to avoid ongoing charges."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6d2d34a6ba7c"
+      },
+      "outputs": [],
+      "source": [
+        "# Delete the deployed agent\n",
+        "delete_agent = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "if delete_agent:\n",
+        "    remote_app.delete()\n",
+        "    print(\"Agent deleted successfully\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "35870ecf063a"
+      },
+      "outputs": [],
+      "source": [
+        "# Delete the staging bucket\n",
+        "delete_bucket = False  # @param {type:\"boolean\"}\n",
+        "\n",
+        "if delete_bucket:\n",
+        "    !gsutil rm -r {STAGING_BUCKET}\n",
+        "    print(\"Bucket deleted successfully\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "11f2cc08759a"
+      },
+      "source": [
+        "## Summary\n",
+        "\n",
+        "In this notebook, you learned how to:\n",
+        "\n",
+        "1. Create an ADK agent with tools\n",
+        "2. Test the agent locally\n",
+        "3. Deploy to Agent Engine using inline source with `AgentEngineApp.from_agent()`\n",
+        "4. Query the deployed agent\n",
+        "\n",
+        "### Next steps\n",
+        "\n",
+        "- Learn more about [ADK](https://google.github.io/adk-docs/)\n",
+        "- Explore [Agent Engine documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview)\n",
+        "- Try adding more complex tools and multi-agent orchestration"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "adk_inline_source_deployment.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/alphagenome/README.md

@@ -0,0 +1,97 @@
+![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 inference**](#quick-start-inference) | 
+[**Quick start finetune**](#quick-start-finetune)
+
+## 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 inference
+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/).
+
+## Quick start finetune
+The quickest way to get started with the AlphaGenome fineutning in Google Cloud Platform is to run [our finetuning notebook](cloudai_alphagenome_finetune.ipynb) in [Google Cloud Platform Enterprise Colab](https://docs.cloud.google.com/colab/docs/introduction).

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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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"
       ]
     }
   ],

notebooks/community/gapic/automl/showcase_automl_tabular_forecasting_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_forecasting_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_forecasting_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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",
@@ -779,14 +777,11 @@
       },
       "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 = \"deaths\"  # @param {type:\"string\"}\n",
+        "! gcloud storage cat $IMPORT_FILE | head\n",        "\n",
+        "heading = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "label_column = \"deaths\"  # @param {type:\"string\"}\n",
         "time_column = \"date\"  # @param {type:\"string\"}\n",
         "time_series_identifier_column = \"county\"  # @param {type:\"string\"}\n",
         "print(\"Label Column Name\", label_column)\n",
@@ -1358,8 +1353,7 @@
         "    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",
@@ -1627,8 +1621,7 @@
       "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",
@@ -1645,10 +1638,8 @@
         "            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)"
       ]
     },
@@ -1742,8 +1733,7 @@
         "    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_regression_online_bq.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_regression_online_bq.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_regression_online_bq.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -397,7 +397,6 @@
         "\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"
       ]
     },
@@ -1614,7 +1613,7 @@
         "    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_text_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_text_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_text_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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",
@@ -881,12 +879,10 @@
         "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",
@@ -1314,8 +1310,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n2\n",
-        "if len(test_items[0]) == 3:\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n2\n",        "if len(test_items[0]) == 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",
         "else:\n",
@@ -1371,8 +1366,7 @@
         "    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",
@@ -1644,8 +1638,7 @@
       "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",
@@ -1662,10 +1655,8 @@
         "            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)"
       ]
     },
@@ -1759,8 +1750,7 @@
         "    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_text_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_text_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_text_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -716,12 +716,10 @@
         "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",
@@ -1327,7 +1325,7 @@
       },
       "outputs": [],
       "source": [
-        "test_item = ! gsutil cat $IMPORT_FILE | head -n1\n",
+        "test_item = ! gcloud storage cat $IMPORT_FILE | head -n1\n",
         "if len(test_item[0]) == 3:\n",
         "    _, test_item, test_label = str(test_item[0]).split(\",\")\n",
         "else:\n",
@@ -1533,8 +1531,7 @@
         "    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_text_entity_extraction_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_text_entity_extraction_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_text_entity_extraction_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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"
       ]
     },
     {
@@ -888,11 +886,11 @@
         "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"
       ]
     },
     {
@@ -1377,7 +1375,7 @@
         "    f.write(json.dumps(data) + \"\\n\")\n",
         "\n",
         "print(gcs_input_uri)\n",
-        "! gsutil cat $gcs_input_uri"
+        "! gcloud storage cat $gcs_input_uri"
       ]
     },
     {
@@ -1652,8 +1650,8 @@
       "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",
@@ -1671,9 +1669,9 @@
         "            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)"
       ]
@@ -1768,7 +1766,7 @@
         "    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_text_entity_extraction_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_text_entity_extraction_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_text_entity_extraction_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -723,12 +723,10 @@
         "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",
@@ -1544,8 +1542,7 @@
         "    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_text_multi-label_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_text_multi-label_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_text_multi-label_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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",
@@ -881,12 +879,10 @@
         "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",
@@ -1314,8 +1310,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n2\n",
-        "\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n2\n",        "\n",
         "cols_1 = str(test_items[0]).split(\",\")\n",
         "cols_2 = str(test_items[1]).split(\",\")\n",
         "test_item_1 = cols_1[0]\n",
@@ -1372,8 +1367,7 @@
         "    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",
@@ -1645,8 +1639,7 @@
       "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",
@@ -1663,10 +1656,8 @@
         "            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)"
       ]
     },
@@ -1760,8 +1751,7 @@
         "    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_text_multi-label_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_text_multi-label_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_text_multi-label_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -716,12 +716,10 @@
         "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",
@@ -1327,8 +1325,7 @@
       },
       "outputs": [],
       "source": [
-        "test_item = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "\n",
+        "test_item = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "\n",
         "cols = str(test_item[0]).split(\",\")\n",
         "test_item = cols[0]\n",
         "test_label = cols[1:]\n",
@@ -1533,8 +1530,7 @@
         "    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_text_sentiment_analysis_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_text_sentiment_analysis_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_text_sentiment_analysis_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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",
@@ -883,12 +881,10 @@
         "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",
@@ -1316,8 +1312,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n2\n",
-        "\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n2\n",        "\n",
         "if len(test_items[0]) == 4:\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",
@@ -1375,8 +1370,7 @@
         "    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",
@@ -1643,8 +1637,7 @@
       "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",
@@ -1661,10 +1654,8 @@
         "            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)"
       ]
     },
@@ -1758,8 +1749,7 @@
         "    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_text_sentiment_analysis_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_text_sentiment_analysis_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_text_sentiment_analysis_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -718,12 +718,10 @@
         "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",
@@ -1329,8 +1327,7 @@
       },
       "outputs": [],
       "source": [
-        "test_item = ! gsutil cat $IMPORT_FILE | head -n1\n",
-        "if len(test_item[0]) == 3:\n",
+        "test_item = ! gcloud storage cat $IMPORT_FILE | head -n1\n",        "if len(test_item[0]) == 3:\n",
         "    _, test_item, test_label, max = str(test_item[0]).split(\",\")\n",
         "else:\n",
         "    test_item, test_label, max = str(test_item[0]).split(\",\")\n",
@@ -1534,8 +1531,7 @@
         "    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_video_action_recognition_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_video_action_recognition_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_video_action_recognition_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -420,8 +420,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location=$REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -440,8 +439,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -888,12 +886,10 @@
         "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",
@@ -1310,8 +1306,7 @@
         "import json\n",
         "\n",
         "import_file = IMPORT_FILES[0]\n",
-        "test_items = ! gsutil cat $import_file | head -n2\n",
-        "\n",
+        "test_items = ! gcloud storage cat $import_file | head -n2\n",        "\n",
         "cols = str(test_items[0]).split(',')\n",
         "test_item_1 = str(cols[0])\n",
         "test_label_1 = str(cols[-1])\n",
@@ -1360,8 +1355,7 @@
         "    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",
@@ -1637,8 +1631,7 @@
       "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",
@@ -1655,10 +1648,8 @@
         "            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)"
       ]
     },
@@ -1752,8 +1743,7 @@
         "    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_video_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_video_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_video_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -420,8 +420,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -440,8 +439,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -885,12 +883,10 @@
         "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",
@@ -1301,8 +1297,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n2\n",
-        "\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n2\n",        "\n",
         "if len(test_items[0]) == 5:\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",
@@ -1351,8 +1346,7 @@
         "    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",
@@ -1630,8 +1624,7 @@
       "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",
@@ -1648,10 +1641,8 @@
         "            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)"
       ]
     },
@@ -1745,8 +1736,7 @@
         "    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_video_object_tracking_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_video_object_tracking_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_video_object_tracking_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
@@ -420,8 +420,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
-      ]
+        "! gcloud storage buckets create --location $REGION $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -440,8 +439,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
-      ]
+        "! gcloud storage ls --all-versions --long $BUCKET_NAME"      ]
     },
     {
       "cell_type": "markdown",
@@ -890,12 +888,10 @@
         "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",
@@ -1309,8 +1305,7 @@
       },
       "outputs": [],
       "source": [
-        "test_items = ! gsutil cat $IMPORT_FILE | head -n2\n",
-        "\n",
+        "test_items = ! gcloud storage cat $IMPORT_FILE | head -n2\n",        "\n",
         "cols_1 = test_items[0].split(',')\n",
         "cols_2 = test_items[1].split(',')\n",
         "if len(cols_1) > 12:\n",
@@ -1365,8 +1360,7 @@
         "    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",
@@ -1638,8 +1632,7 @@
       "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",
@@ -1656,10 +1649,8 @@
         "            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)"
       ]
     },
@@ -1753,8 +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/custom/showcase_custom_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/custom/showcase_custom_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/custom/showcase_custom_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_image_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/custom/showcase_custom_image_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/custom/showcase_custom_image_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_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/custom/showcase_custom_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/custom/showcase_custom_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_image_classification_online_ab_testing.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/custom/showcase_custom_image_classification_online_ab_testing.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/custom/showcase_custom_image_classification_online_ab_testing.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_image_classification_online_container.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/custom/showcase_custom_image_classification_online_container.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/custom/showcase_custom_image_classification_online_container.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_image_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/custom/showcase_custom_image_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/custom/showcase_custom_image_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://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.ipynb

@@ -35,18 +35,18 @@
         "\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.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/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.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/notebooks/community/gapic/custom/showcase_custom_image_classification_online_explain_example_based_api.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/gapic/custom/showcase_custom_image_classification_online_exported_ds.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/custom/showcase_custom_image_classification_online_exported_ds.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/custom/showcase_custom_image_classification_online_exported_ds.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",

notebooks/community/gapic/custom/showcase_custom_image_classification_online_pipeline.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/custom/showcase_custom_image_classification_online_pipeline.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/custom/showcase_custom_image_classification_online_pipeline.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",