Artifacts Reporting
The artifacts.py example demonstrates uploading objects (other than models) to storage as experiment artifacts.
These artifacts include:
- Pandas DataFrames
- Local files, dictionaries
- Folders
- Numpy objects
- Image files
Artifacts can be uploaded and dynamically tracked, or uploaded without tracking.
Configure ClearML for uploading artifacts to any of the supported types of storage, which include local and shared folders, S3 buckets, Google Cloud Storage, and Azure Storage (debug sample storage is different). Configure ClearML in any of the following ways:
- In the configuration file, set default_output_uri.
- In code, when initializing a Task, use the
output_uri
parameter. - In the ClearML Web UI, when modifying an experiment.
When the script runs, it creates an experiment named artifacts example
, which is associated with the examples
project.
ClearML reports artifacts in the ClearML Web UI > experiment details > ARTIFACTS tab.
#
Dynamically Tracked ArtifactsCurrently, ClearML supports uploading and dynamically tracking Pandas DataFrames. Use the Task.register_artifact method. If the Pandas DataFrame changes, ClearML uploads the changes. The updated artifact is associated with the experiment.
For example:
By changing the artifact, and calling the Task.get_registered_artifacts method to retrieve it, we can see that ClearML tracked the change.
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Artifacts Without TrackingClearML supports several types of objects that can be uploaded and are not tracked. Use the Task.upload_artifact method.
Artifacts without tracking include:
- Pandas DataFrames
- Local files
- Dictionaries (stored as a JSONs)
- Numpy objects (stored as NPZ files)
- Image files (stored as PNG files)
- Folders (stored as a ZIP files)
- Wildcards (stored as a ZIP files)