port: kfp2

notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb

@@ -157,14 +157,14 @@
       "outputs": [],
       "source": [
         "# Install dependencies\n",
-        "! pip3 install --upgrade --quiet    google-cloud-aiplatform==1.10.0 \\\n",
-        "                                    google_cloud_pipeline_components==1.0.1 \\\n",
-        "                                    google-api-core==2.8.2 \\\n",
-        "                                    google-auth==1.35.0\n",
+        "! pip3 install --upgrade --quiet    google-cloud-aiplatform \\\n",
+        "                                    google_cloud_pipeline_components \\\n",
+        "                                    google-api-core \\\n",
+        "                                    google-auth\n",
         "\n",
         "! pip3 install --upgrade --quiet    tensorflow==2.8.0 \\\n",
         "                                    tensorflow-hub==0.12.0 \\\n",
-        "                                    kfp==1.8.9"
+        "                                    kfp"
       ]
     },
     {
@@ -390,7 +390,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
       ]
     },
     {
@@ -566,9 +566,8 @@
         "\n",
         "import tensorflow_hub as hub\n",
         "from google.cloud import aiplatform as vertex_ai\n",
-        "from kfp import dsl\n",
-        "from kfp.v2 import compiler\n",
-        "from kfp.v2.dsl import component\n",
+        "from kfp import compiler, dsl\n",
+        "from kfp.dsl import component\n",
         "\n",
         "os.environ[\"TFHUB_MODEL_LOAD_FORMAT\"] = \"UNCOMPRESSED\""
       ]
@@ -1421,7 +1420,7 @@
       "source": [
         "## Compile and Run the pipeline\n",
         "\n",
-        "Pass the necessary constants and parameters to the pipeline and compile it to a json file."
+        "Pass the necessary constants and parameters to the pipeline and compile it to a yaml file."
       ]
     },
     {
@@ -1433,7 +1432,7 @@
       "outputs": [],
       "source": [
         "PIPELINE_ROOT = urlparse(BUCKET_URI)._replace(path=\"pipeline_root\").geturl()\n",
-        "PIPELINE_PACKAGE = str(path(BUILD) / \"mlops_bqml_text_analyisis_pipeline.json\")\n",
+        "PIPELINE_PACKAGE = str(path(BUILD) / \"mlops_bqml_text_analyisis_pipeline.yaml\")\n",
         "REQUIREMENTS_URI = urlparse(BUCKET_URI)._replace(path=\"requirements.txt\").geturl()\n",
         "PYTHON_FILE_URI = urlparse(BUCKET_URI)._replace(path=\"src/ingest_pipeline.py\").geturl()\n",
         "MODEL_URI = urlparse(BUCKET_URI)._replace(path=\"swivel_text_embedding_model\").geturl()\n",
@@ -1447,7 +1446,7 @@
         "id": "b5c1b7b8b290"
       },
       "source": [
-        "Using the compiled json file, create Vertex AI Pipeline Job and run it by passing the `SERVICE_ACCOUNT` details configured earlier."
+        "Using the compiled yaml file, create Vertex AI Pipeline Job and run it by passing the `SERVICE_ACCOUNT` details configured earlier."
       ]
     },
     {