feat: Vertex AI Experiments GA #1410
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sararob
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| class _VertexResourceArtifactResolver: | ||
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| _resource_to_artifact_type = {models.Model: "google.VertexModel"} |
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IIUC, this resolver would automatically link a resource to a MLMD artifact? And if so, is Model the only one we would support for GA? How about managed dataset?
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I opened a ticket and added TODO for managed dataset. Let me sync with the service team to see if we can get this in before GA.
sararob
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Jun 9, 2022
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| @staticmethod | ||
| def _get_experiment( | ||
| experiment: Optional[Union[experiment_resources.Experiment, str]] = None, |
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Flagging since in the docstring this is called experiment_name and is required.
sararob
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Jun 10, 2022
| @@ -25,7 +25,7 @@ | |||
| from google.auth import credentials | |||
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| from google.cloud.aiplatform import initializer | |||
| from google.cloud.aiplatform.metadata.metadata import metadata_service | |||
| from google.cloud.aiplatform.metadata.metadata import _experiment_tracker | |||
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Here's the error when I try to run test_initializer:
__________________________________________________________________________________________________________ ERROR collecting tests/unit/aiplatform/test_initializer.py __________________________________________________________________________________________________________
ImportError while importing test module '/Users/sararob/Dev/sara-fork/python-aiplatform/tests/unit/aiplatform/test_initializer.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/aiplatform/test_initializer.py:28: in <module>
from google.cloud.aiplatform.metadata.metadata import _experiment_tracker
E ImportError: cannot import name '_experiment_tracker' from 'google.cloud.aiplatform.metadata.metadata' (/Users/sararob/Library/Python/3.8/lib/python/site-packages/google/cloud/aiplatform/metadata/metadata.py)
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| for key, value in metadata.items(): | ||
| # Note: This only support nested dictionaries one level deep | ||
| if isinstance(value, collections.Mapping): | ||
| gca_resource.metadata[key].update(value) |
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What is the proposed way to remove keys from a nested dictionary?
P.S. This also seems to fail when the key does not already exist in the metadata.
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