Creating metadata store for Google Cloud AI Platform?
I'm trying to do a simple basic Vertex Pipeline, and when I upload the json file, I get this:
Failed to create pipeline job. Error: Permission 'aiplatform.metadataStores.get' denied on resource '//aiplatform.googleapis.com/projects/399668206801/locations/us-central1/metadataStores/default' (or it may not exist).
I can't find how to create one - the docs (https://cloud.google.com/vertex-ai/docs/ml-metadata/configure) say it will be created for me. What do I need to do next?
** EDIT 1 ** Tried doing the same in the UI, similar bug:
** EDIT 2 ** Here's the json:
{
"pipelineSpec": {
"components": {
"comp-run-info-fn": {
"executorLabel": "exec-run-info-fn",
"inputDefinitions": {
"parameters": {
"run_id": {
"type": "STRING"
}
}
},
"outputDefinitions": {
"parameters": {
"run_info": {
"type": "STRING"
}
}
}
},
"comp-same-step-000-4538957a762e4c2ea30bb0f819345e25-fn": {
"executorLabel": "exec-same-step-000-4538957a762e4c2ea30bb0f819345e25-fn",
"inputDefinitions": {
"parameters": {
"input_context_path": {
"type": "STRING"
},
"metadata_url": {
"type": "STRING"
},
"run_info": {
"type": "STRING"
}
}
},
"outputDefinitions": {
"parameters": {
"output_context_path": {
"type": "STRING"
},
"output_context_path_2": {
"type": "STRING"
}
}
}
},
"comp-same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn": {
"executorLabel": "exec-same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn",
"inputDefinitions": {
"parameters": {
"input_context_path": {
"type": "STRING"
},
"metadata_url": {
"type": "STRING"
},
"run_info": {
"type": "STRING"
}
}
},
"outputDefinitions": {
"parameters": {
"output_context_path": {
"type": "STRING"
}
}
}
},
"comp-same-step-002-5cefd94e0a9c49cdb81a6c11a5c84ac9-fn": {
"executorLabel": "exec-same-step-002-5cefd94e0a9c49cdb81a6c11a5c84ac9-fn",
"inputDefinitions": {
"parameters": {
"input_context_path": {
"type": "STRING"
},
"metadata_url": {
"type": "STRING"
},
"run_info": {
"type": "STRING"
}
}
},
"outputDefinitions": {
"parameters": {
"output_context_path": {
"type": "STRING"
}
}
}
}
},
"deploymentSpec": {
"executors": {
"exec-run-info-fn": {
"container": {
"args": [
"--executor_input",
"{{$}}",
"--function_to_execute",
"run_info_fn"
],
"command": [
"sh",
"-c",
"\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'kfp' 'dill' 'kfp==1.8.12' && \"$0\" \"$@\"\n",
"sh",
"-ec",
"program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main --component_module_path \"$program_path/ephemeral_component.py\" \"$@\"\n",
"\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef run_info_fn(\n run_id: str,\n) -> NamedTuple(\"RunInfoOutput\", [(\"run_info\", str),]):\n from base64 import urlsafe_b64encode\n from collections import namedtuple\n import datetime\n import base64\n import dill\n import kfp\n\n client = kfp.Client(host=\"http://ml-pipeline:8888\")\n run_info = client.get_run(run_id=run_id)\n\n run_info_dict = {\n \"run_id\": run_info.run.id,\n \"name\": run_info.run.name,\n \"created_at\": run_info.run.created_at.isoformat(),\n \"pipeline_id\": run_info.run.pipeline_spec.pipeline_id,\n }\n\n # Track kubernetes resources associated wth the run.\n for r in run_info.run.resource_references:\n run_info_dict[f\"{r.key.type.lower()}_id\"] = r.key.id\n\n # Base64-encoded as value is visible in kubeflow ui.\n output = urlsafe_b64encode(dill.dumps(run_info_dict))\n\n return namedtuple(\"RunInfoOutput\", [\"run_info\"])(str(output, encoding=\"ascii\"))\n\n"
],
"image": "python:3.7"
}
},
"exec-same-step-000-4538957a762e4c2ea30bb0f819345e25-fn": {
"container": {
"args": [
"--executor_input",
"{{$}}",
"--function_to_execute",
"same_step_000_4538957a762e4c2ea30bb0f819345e25_fn"
],
"command": [
"sh",
"-c",
"\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'dill' 'requests' 'chart_studio' 'ipython' 'matplotlib' 'numpy' 'pandas' 'plotly' 'Requests' 'scipy' 'tensorflow' 'kfp==1.8.12' && \"$0\" \"$@\"\n",
"sh",
"-ec",
"program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main --component_module_path \"$program_path/ephemeral_component.py\" \"$@\"\n",
"\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef same_step_000_4538957a762e4c2ea30bb0f819345e25_fn(\n input_context_path: InputPath(str),\n output_context_path: OutputPath(str),\n run_info: str,\n metadata_url: str,\n) -> NamedTuple(\"StepOutput\", [(\"output_context_path\", str),]):\n from base64 import urlsafe_b64encode, urlsafe_b64decode\n from pathlib import Path\n import datetime\n import requests\n import tempfile\n import dill\n import os\n from collections import namedtuple\n\n # run_info = \"gAR9lC4=\"\n # metadata_url = \"\"\n\n input_context = None\n with Path(input_context_path.path).open(\"rb\") as reader:\n input_context = reader.read()\n\n # Helper function for posting metadata to mlflow.\n def post_metadata(json):\n if metadata_url == \"\":\n return\n\n try:\n req = requests.post(metadata_url, json=json)\n req.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print(f\"Error posting metadata: {err}\")\n\n # Move to writable directory as user might want to do file IO.\n # TODO: won't persist across steps, might need support in SDK?\n os.chdir(tempfile.mkdtemp())\n\n # Load information about the current experiment run:\n run_info = dill.loads(urlsafe_b64decode(run_info))\n\n # Post session context to mlflow.\n if len(input_context) > 0:\n input_context_str = urlsafe_b64encode(input_context)\n post_metadata(\n {\n \"experiment_id\": run_info[\"experiment_id\"],\n \"run_id\": run_info[\"run_id\"],\n \"step_id\": \"same_step_000\",\n \"metadata_type\": \"input\",\n \"metadata_value\": input_context_str,\n \"metadata_time\": datetime.datetime.now().isoformat(),\n }\n )\n\n # User code for step, which we run in its own execution frame.\n user_code = f\"\"\"\nimport dill\n\n# Load session context into global namespace:\nif { len(input_context) } > 0:\n dill.load_session(\"{ input_context_path }\")\n\n{dill.loads(urlsafe_b64decode(\"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\"))}\n\n# Remove anything from the global namespace that cannot be serialised.\n# TODO: this will include things like pandas dataframes, needs sdk support?\n_bad_keys = []\n_all_keys = list(globals().keys())\nfor k in _all_keys:\n try:\n dill.dumps(globals()[k])\n except TypeError:\n _bad_keys.append(k)\n\nfor k in _bad_keys:\n del globals()[k]\n\n# Save new session context to disk for the next component:\ndill.dump_session(\"{output_context_path}\")\n\"\"\"\n\n # Runs the user code in a new execution frame. Context from the previous\n # component in the run is loaded into the session dynamically, and we run\n # with a single globals() namespace to simulate top-level execution.\n exec(user_code, globals(), globals())\n\n # Post new session context to mlflow:\n with Path(output_context_path).open(\"rb\") as reader:\n context = urlsafe_b64encode(reader.read())\n post_metadata(\n {\n \"experiment_id\": run_info[\"experiment_id\"],\n \"run_id\": run_info[\"run_id\"],\n \"step_id\": \"same_step_000\",\n \"metadata_type\": \"output\",\n \"metadata_value\": context,\n \"metadata_time\": datetime.datetime.now().isoformat(),\n }\n )\n\n return namedtuple(\"StepOutput\", [\"output_context_path\"])(str(output_context_path, encoding=\"ascii\"))\n\n"
],
"image": "library/python:3.9-slim-buster"
}
},
"exec-same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn": {
"container": {
"args": [
"--executor_input",
"{{$}}",
"--function_to_execute",
"same_step_001_4847e5a4edc84257aa4ff6fe8aa0159b_fn"
],
"command": [
"sh",
"-c",
"\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'dill' 'requests' 'chart_studio' 'ipython' 'matplotlib' 'numpy' 'pandas' 'plotly' 'Requests' 'scipy' 'tensorflow' 'kfp==1.8.12' && \"$0\" \"$@\"\n",
"sh",
"-ec",
"program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main --component_module_path \"$program_path/ephemeral_component.py\" \"$@\"\n",
"\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef same_step_001_4847e5a4edc84257aa4ff6fe8aa0159b_fn(\n input_context_path: InputPath(str),\n output_context_path: OutputPath(str),\n run_info: str = \"gAR9lC4=\",\n metadata_url: str = \"\",\n):\n from base64 import urlsafe_b64encode, urlsafe_b64decode\n from pathlib import Path\n import datetime\n import requests\n import tempfile\n import dill\n import os\n\n input_context = None\n with Path(input_context_path.path).open(\"rb\") as reader:\n input_context = reader.read()\n\n # Helper function for posting metadata to mlflow.\n def post_metadata(json):\n if metadata_url == \"\":\n return\n\n try:\n req = requests.post(metadata_url, json=json)\n req.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print(f\"Error posting metadata: {err}\")\n\n # Move to writable directory as user might want to do file IO.\n # TODO: won't persist across steps, might need support in SDK?\n os.chdir(tempfile.mkdtemp())\n\n # Load information about the current experiment run:\n run_info = dill.loads(urlsafe_b64decode(run_info))\n\n # Post session context to mlflow.\n if len(input_context) > 0:\n input_context_str = urlsafe_b64encode(input_context)\n post_metadata(\n {\n \"experiment_id\": run_info[\"experiment_id\"],\n \"run_id\": run_info[\"run_id\"],\n \"step_id\": \"same_step_001\",\n \"metadata_type\": \"input\",\n \"metadata_value\": input_context_str,\n \"metadata_time\": datetime.datetime.now().isoformat(),\n }\n )\n\n # User code for step, which we run in its own execution frame.\n user_code = f\"\"\"\nimport dill\n\n# Load session context into global namespace:\nif { len(input_context) } > 0:\n dill.load_session(\"{ input_context_path }\")\n\n{dill.loads(urlsafe_b64decode(\"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\"))}\n\n# Remove anything from the global namespace that cannot be serialised.\n# TODO: this will include things like pandas dataframes, needs sdk support?\n_bad_keys = []\n_all_keys = list(globals().keys())\nfor k in _all_keys:\n try:\n dill.dumps(globals()[k])\n except TypeError:\n _bad_keys.append(k)\n\nfor k in _bad_keys:\n del globals()[k]\n\n# Save new session context to disk for the next component:\ndill.dump_session(\"{output_context_path}\")\n\"\"\"\n\n # Runs the user code in a new execution frame. Context from the previous\n # component in the run is loaded into the session dynamically, and we run\n # with a single globals() namespace to simulate top-level execution.\n exec(user_code, globals(), globals())\n\n # Post new session context to mlflow:\n with Path(output_context_path).open(\"rb\") as reader:\n context = urlsafe_b64encode(reader.read())\n post_metadata(\n {\n \"experiment_id\": run_info[\"experiment_id\"],\n \"run_id\": run_info[\"run_id\"],\n \"step_id\": \"same_step_001\",\n \"metadata_type\": \"output\",\n \"metadata_value\": context,\n \"metadata_time\": datetime.datetime.now().isoformat(),\n }\n )\n\n"
],
"image": "library/python:3.9-slim-buster"
}
},
"exec-same-step-002-5cefd94e0a9c49cdb81a6c11a5c84ac9-fn": {
"container": {
"args": [
"--executor_input",
"{{$}}",
"--function_to_execute",
"same_step_002_5cefd94e0a9c49cdb81a6c11a5c84ac9_fn"
],
"command": [
"sh",
"-c",
"\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip || python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'dill' 'requests' 'chart_studio' 'ipython' 'matplotlib' 'numpy' 'pandas' 'plotly' 'Requests' 'scipy' 'tensorflow' 'kfp==1.8.12' && \"$0\" \"$@\"\n",
"sh",
"-ec",
"program_path=$(mktemp -d)\nprintf \"%s\" \"$0\" > \"$program_path/ephemeral_component.py\"\npython3 -m kfp.v2.components.executor_main --component_module_path \"$program_path/ephemeral_component.py\" \"$@\"\n",
"\nimport kfp\nfrom kfp.v2 import dsl\nfrom kfp.v2.dsl import *\nfrom typing import *\n\ndef same_step_002_5cefd94e0a9c49cdb81a6c11a5c84ac9_fn(\n input_context_path: InputPath(str),\n output_context_path: OutputPath(str),\n run_info: str = \"gAR9lC4=\",\n metadata_url: str = \"\",\n):\n from base64 import urlsafe_b64encode, urlsafe_b64decode\n from pathlib import Path\n import datetime\n import requests\n import tempfile\n import dill\n import os\n\n input_context = None\n with Path(input_context_path.path).open(\"rb\") as reader:\n input_context = reader.read()\n\n # Helper function for posting metadata to mlflow.\n def post_metadata(json):\n if metadata_url == \"\":\n return\n\n try:\n req = requests.post(metadata_url, json=json)\n req.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print(f\"Error posting metadata: {err}\")\n\n # Move to writable directory as user might want to do file IO.\n # TODO: won't persist across steps, might need support in SDK?\n os.chdir(tempfile.mkdtemp())\n\n # Load information about the current experiment run:\n run_info = dill.loads(urlsafe_b64decode(run_info))\n\n # Post session context to mlflow.\n if len(input_context) > 0:\n input_context_str = urlsafe_b64encode(input_context)\n post_metadata(\n {\n \"experiment_id\": run_info[\"experiment_id\"],\n \"run_id\": run_info[\"run_id\"],\n \"step_id\": \"same_step_002\",\n \"metadata_type\": \"input\",\n \"metadata_value\": input_context_str,\n \"metadata_time\": datetime.datetime.now().isoformat(),\n }\n )\n\n # User code for step, which we run in its own execution frame.\n user_code = f\"\"\"\nimport dill\n\n# Load session context into global namespace:\nif { len(input_context) } > 0:\n dill.load_session(\"{ input_context_path }\")\n\n{dill.loads(urlsafe_b64decode(\"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\"))}\n\n# Remove anything from the global namespace that cannot be serialised.\n# TODO: this will include things like pandas dataframes, needs sdk support?\n_bad_keys = []\n_all_keys = list(globals().keys())\nfor k in _all_keys:\n try:\n dill.dumps(globals()[k])\n except TypeError:\n _bad_keys.append(k)\n\nfor k in _bad_keys:\n del globals()[k]\n\n# Save new session context to disk for the next component:\ndill.dump_session(\"{output_context_path}\")\n\"\"\"\n\n # Runs the user code in a new execution frame. Context from the previous\n # component in the run is loaded into the session dynamically, and we run\n # with a single globals() namespace to simulate top-level execution.\n exec(user_code, globals(), globals())\n\n # Post new session context to mlflow:\n with Path(output_context_path).open(\"rb\") as reader:\n context = urlsafe_b64encode(reader.read())\n post_metadata(\n {\n \"experiment_id\": run_info[\"experiment_id\"],\n \"run_id\": run_info[\"run_id\"],\n \"step_id\": \"same_step_002\",\n \"metadata_type\": \"output\",\n \"metadata_value\": context,\n \"metadata_time\": datetime.datetime.now().isoformat(),\n }\n )\n\n"
],
"image": "library/python:3.9-slim-buster"
}
}
}
},
"pipelineInfo": {
"name": "root-pipeline-compilation"
},
"root": {
"dag": {
"tasks": {
"run-info-fn": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-run-info-fn"
},
"inputs": {
"parameters": {
"run_id": {
"runtimeValue": {
"constantValue": {
"stringValue": "{{workflow.uid}}"
}
}
}
}
},
"taskInfo": {
"name": "run-info-fn"
}
},
"same-step-000-4538957a762e4c2ea30bb0f819345e25-fn": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-same-step-000-4538957a762e4c2ea30bb0f819345e25-fn"
},
"dependentTasks": [
"run-info-fn"
],
"inputs": {
"parameters": {
"input_context_path": {
"runtimeValue": {
"constantValue": {
"stringValue": ""
}
}
},
"metadata_url": {
"componentInputParameter": "metadata_url"
},
"run_info": {
"taskOutputParameter": {
"outputParameterKey": "run_info",
"producerTask": "run-info-fn"
}
}
}
},
"taskInfo": {
"name": "same-step-000-4538957a762e4c2ea30bb0f819345e25-fn"
}
},
"same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn"
},
"dependentTasks": [
"run-info-fn",
"same-step-000-4538957a762e4c2ea30bb0f819345e25-fn"
],
"inputs": {
"parameters": {
"input_context_path": {
"taskOutputParameter": {
"outputParameterKey": "output_context_path",
"producerTask": "same-step-000-4538957a762e4c2ea30bb0f819345e25-fn"
}
},
"metadata_url": {
"componentInputParameter": "metadata_url"
},
"run_info": {
"taskOutputParameter": {
"outputParameterKey": "run_info",
"producerTask": "run-info-fn"
}
}
}
},
"taskInfo": {
"name": "same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn"
}
},
"same-step-002-5cefd94e0a9c49cdb81a6c11a5c84ac9-fn": {
"cachingOptions": {
"enableCache": true
},
"componentRef": {
"name": "comp-same-step-002-5cefd94e0a9c49cdb81a6c11a5c84ac9-fn"
},
"dependentTasks": [
"run-info-fn",
"same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn"
],
"inputs": {
"parameters": {
"input_context_path": {
"taskOutputParameter": {
"outputParameterKey": "output_context_path",
"producerTask": "same-step-001-4847e5a4edc84257aa4ff6fe8aa0159b-fn"
}
},
"metadata_url": {
"componentInputParameter": "metadata_url"
},
"run_info": {
"taskOutputParameter": {
"outputParameterKey": "run_info",
"producerTask": "run-info-fn"
}
}
}
},
"taskInfo": {
"name": "same-step-002-5cefd94e0a9c49cdb81a6c11a5c84ac9-fn"
}
}
}
},
"inputDefinitions": {
"parameters": {
"context": {
"type": "STRING"
},
"metadata_url": {
"type": "STRING"
}
}
}
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.8.12"
},
"runtimeConfig": {
"parameters": {
"context": {
"stringValue": ""
},
"metadata_url": {
"stringValue": ""
}
}
}
}
And here's the python to attempt to run:
from google.cloud import aiplatform
job = aiplatform.PipelineJob(display_name="MY_DISPLAY_JOB", template_path=compiled_pipeline_path, project=project_id, pipeline_root=pipeline_root)
Solution
Suppose you are using vertexAI Notebook to run pipeline jobs.
The service account of the Notebook (actually a Compute Engine) should have permissions (reference A; reference B):
- roles/aiplatform.user;
- roles/storage.objectViewer;
- roles/storage.objectCreator;
- roles/iam.serviceAccountUser
One way is to do it in CloudShell (!not notebook terminal), for each role
gcloud projects add-iam-policy-binding <your_project_id> \
--member=serviceAccount:"<your_serviceaccount_email>" \
--role="roles/aiplatform.user"
The service account email looks like:
1234567890-compute@developer.gserviceaccount.com
P.S., keep in mind that the serviceAccount looks like an email, not the Unique ID of the service account.
P.P.S., you might get the error:
Policy members must be of the form ":"
If so, it is because --member requires serviceAccount:<service_account_name> or user:<user_name>, not the account name alone.
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