fix versions

Author: shlbatra

pipeline.yaml

@@ -276,24 +276,34 @@ deploymentSpec:
           )\n\n    # 1. Initialize Vertex AI and find blessed model\n    aiplatform.init(project=project_id,\
           \ location=location)\n\n    client = aiplatform_v1.ModelServiceClient(\n\
           \        client_options={\"api_endpoint\": f\"{location}-aiplatform.googleapis.com\"\
-          }\n    )\n    request = {\n        \"parent\": f\"projects/{project_id}/locations/{location}\"\
-          ,\n        \"filter\": f\"display_name={model_name}\"\n    }\n\n    models\
-          \ = list(client.list_models(request=request))\n    blessed_model = None\n\
-          \n    print(f\"Found {len(models)} models with name {model_name}\")\n\n\
-          \    for model in models:\n        print(f\"Model: {model.name}, Aliases:\
-          \ {list(model.version_aliases)}\")\n        if \"blessed\" in model.version_aliases:\n\
-          \            blessed_model = model\n            break\n\n    if not blessed_model:\n\
-          \        raise ValueError(f\"No blessed version found for model {model_name}.\
-          \ Available models: {[(m.name, list(m.version_aliases)) for m in models]}\"\
-          )\n\n    print(f\"Found blessed model: {blessed_model.name}\")\n    print(f\"\
-          Model URI: {blessed_model.artifact_uri}\")\n\n    # 2. Download joblib model\
-          \ from blessed version\n    gcs_uri = blessed_model.artifact_uri\n    if\
-          \ not gcs_uri.startswith('gs://'):\n        raise ValueError(f\"Expected\
-          \ GCS URI, got: {gcs_uri}\")\n\n    bucket_name = gcs_uri.replace('gs://',\
-          \ '').split('/')[0]\n    model_path = '/'.join(gcs_uri.replace('gs://',\
-          \ '').split('/')[1:])\n\n    print(f\"Downloading model from gs://{bucket_name}/{model_path}\"\
-          )\n\n    storage_client = storage.Client()\n    bucket = storage_client.bucket(bucket_name)\n\
-          \n    # Download and validate the model\n    model_blob_path = f\"{model_path}/model.joblib\"\
+          }\n    )\n\n    print(f\"Searching for blessed model with name: {model_name}\"\
+          )\n\n    # Use the high-level aiplatform library to list all model versions\n\
+          \    models = aiplatform.Model.list(filter=f\"display_name={model_name}\"\
+          )\n    blessed_model = None\n\n    print(f\"Found {len(models)} model versions\
+          \ with name {model_name}\")\n\n    # Search through all model versions (each\
+          \ item in models is already a version)\n    for parent_model in models:\n\
+          \        print(f\"Checking parent model: projects/57434141298/locations/{location}/models/{parent_model.name}\"\
+          )\n\n        # List all versions of this model\n        versions_request\
+          \ = {\"name\": parent_model.name}\n        versions = list(client.list_model_versions(request=versions_request))\n\
+          \n        print(f\"Found {len(versions)} versions for this model\")\n\n\
+          \        for version in versions:\n            print(f\"Version {version.version_id}:\
+          \ Aliases = {list(version.version_aliases)}\")\n            if \"blessed\"\
+          \ in version.version_aliases:\n                blessed_model = version\n\
+          \                print(f\"Found blessed version: {version.version_id}\"\
+          )\n                break\n\n        if blessed_model:\n            break\n\
+          \n    if not blessed_model:\n        available_versions = [(m.resource_name,\
+          \ m.version_id, list(m.version_aliases)) for m in models]\n        raise\
+          \ ValueError(f\"No blessed version found for model {model_name}. Available\
+          \ versions: {available_versions}\")\n\n    print(f\"Found blessed model:\
+          \ {blessed_model.name}\")\n    print(f\"Model URI: {blessed_model.artifact_uri}\"\
+          )\n\n    # 2. Download joblib model from blessed version\n    gcs_uri =\
+          \ blessed_model.artifact_uri\n    if not gcs_uri.startswith('gs://'):\n\
+          \        raise ValueError(f\"Expected GCS URI, got: {gcs_uri}\")\n\n   \
+          \ bucket_name = gcs_uri.replace('gs://', '').split('/')[0]\n    model_path\
+          \ = '/'.join(gcs_uri.replace('gs://', '').split('/')[1:])\n\n    print(f\"\
+          Downloading model from gs://{bucket_name}/{model_path}\")\n\n    storage_client\
+          \ = storage.Client()\n    bucket = storage_client.bucket(bucket_name)\n\n\
+          \    # Download and validate the model\n    model_blob_path = f\"{model_path}/model.joblib\"\
           \n    blob = bucket.blob(model_blob_path)\n\n    if not blob.exists():\n\
           \        raise ValueError(f\"Model file not found at gs://{bucket_name}/{model_blob_path}\"\
           )\n\n    with tempfile.NamedTemporaryFile(suffix='.joblib', delete=False)\

src/ml_pipelines_kfp/iris_xgboost/pipelines/components/deploy.py

@@ -19,6 +19,7 @@ def deploy_blessed_model_to_fastapi(
     service_endpoint: Output[Artifact]
 ):
     from google.cloud import aiplatform, aiplatform_v1, run_v2, storage
+    from google.auth import default
     import joblib
     import tempfile
     import os
@@ -28,30 +29,49 @@ def deploy_blessed_model_to_fastapi(
     print(f"Starting FastAPI deployment for blessed model: {model_name}")
     print(f"Service name: {service_name}")
 
-    # 1. Initialize Vertex AI and find blessed model
+    # 1. Initialize Vertex AI and get credentials
     aiplatform.init(project=project_id, location=location)
 
+    # Get default credentials
+    credentials, _ = default()
+    
+    # Create client with explicit credentials
     client = aiplatform_v1.ModelServiceClient(
+        credentials=credentials,
         client_options={"api_endpoint": f"{location}-aiplatform.googleapis.com"}
     )
-    request = {
-        "parent": f"projects/{project_id}/locations/{location}",
-        "filter": f"display_name={model_name}"
-    }
 
-    models = list(client.list_models(request=request))
+    print(f"Searching for blessed model with name: {model_name}")
+    
+    # Use the high-level aiplatform library to list all model versions
+    models = aiplatform.Model.list(filter=f"display_name={model_name}")
     blessed_model = None
 
-    print(f"Found {len(models)} models with name {model_name}")
+    print(f"Found {len(models)} model versions with name {model_name}")
 
-    for model in models:
-        print(f"Model: {model.name}, Aliases: {list(model.version_aliases)}")
-        if "blessed" in model.version_aliases:
-            blessed_model = model
+    # Search through all model versions (each item in models is already a version)
+    for parent_model in models:
+        print(f"Checking parent model: projects/57434141298/locations/{location}/models/{parent_model.name}")
+        
+        # List all versions of this model
+        versions_request = {"name": parent_model.name}
+        versions = list(client.list_model_versions(request=versions_request))
+        
+        print(f"Found {len(versions)} versions for this model")
+        
+        for version in versions:
+            print(f"Version {version.version_id}: Aliases = {list(version.version_aliases)}")
+            if "blessed" in version.version_aliases:
+                blessed_model = version
+                print(f"Found blessed version: {version.version_id}")
+                break
+        
+        if blessed_model:
             break
 
     if not blessed_model:
-        raise ValueError(f"No blessed version found for model {model_name}. Available models: {[(m.name, list(m.version_aliases)) for m in models]}")
+        available_versions = [(m.resource_name, m.version_id, list(m.version_aliases)) for m in models]
+        raise ValueError(f"No blessed version found for model {model_name}. Available versions: {available_versions}")
 
     print(f"Found blessed model: {blessed_model.name}")
     print(f"Model URI: {blessed_model.artifact_uri}")