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27 lines
1.0 KiB
Python
27 lines
1.0 KiB
Python
import wandb
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def log_artifact(artifact_name, artifact_type, artifact_description, filename, wandb_run):
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"""
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Log the provided filename as an artifact in W&B, and add the artifact path to the MLFlow run
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so it can be retrieved by subsequent steps in a pipeline
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:param artifact_name: name for the artifact
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:param artifact_type: type for the artifact (just a string like "raw_data", "clean_data" and so on)
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:param artifact_description: a brief description of the artifact
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:param filename: local filename for the artifact
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:param wandb_run: current Weights & Biases run
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:return: None
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"""
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# Log to W&B
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artifact = wandb.Artifact(
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artifact_name,
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type=artifact_type,
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description=artifact_description,
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)
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artifact.add_file(filename)
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wandb_run.log_artifact(artifact)
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# We need to call this .wait() method before we can use the
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# version below. This will wait until the artifact is loaded into W&B and a
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# version is assigned
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artifact.wait()
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