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111 lines
4.1 KiB
Markdown
111 lines
4.1 KiB
Markdown
---
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title: Artifacts Reporting
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---
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The [artifacts.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py) example demonstrates
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uploading objects (other than models) to storage as experiment artifacts.
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These artifacts include:
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* Pandas DataFrames
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* Local files, dictionaries
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* Folders
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* Numpy objects
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* Image files
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* Folders.
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Artifacts can be uploaded and dynamically tracked, or uploaded without tracking.
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<a name="configure_artifact_storage" class="tr_top_negative"></a>
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Configure **ClearML** for uploading artifacts to any of the supported types of storage, which include local and shared folders,
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S3 buckets, Google Cloud Storage, and Azure Storage ([debug sample storage](../../references/sdk/logger.md#set_default_upload_destination)
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is different). Configure **ClearML** in any of the following ways:
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* In the configuration file, set [default_output_uri](../../configs/clearml_conf.md#sdkdevelopment).
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* In code, when [initializing a Task](../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
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* In the **ClearML Web UI**, when [modifying an experiment](../../webapp/webapp_exp_tuning.md#output-destination).
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When the script runs, it creates an experiment named `artifacts example`, which is associated with the `examples` project.
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**ClearML** reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab.
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## Dynamically Tracked Artifacts
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Currently, **ClearML** supports uploading and dynamically tracking Pandas DataFrames. Use the [Task.register_artifact](../../references/sdk/task.md#register_artifact)
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method. If the Pandas DataFrame changes, **ClearML** uploads the changes. The updated artifact is associated with the experiment.
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For example:
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df = pd.DataFrame(
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{
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'num_legs': [2, 4, 8, 0],
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'num_wings': [2, 0, 0, 0],
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'num_specimen_seen': [10, 2, 1, 8]
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},
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index=['falcon', 'dog', 'spider', 'fish']
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)
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# Register Pandas object as artifact to watch
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# (it will be monitored in the background and automatically synced and uploaded)
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task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69}))
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By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts)
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method to retrieve it, we can see that **ClearML** tracked the change.
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# change the artifact object
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df.sample(frac=0.5, replace=True, random_state=1)
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# or access it from anywhere using the Task's get_registered_artifacts()
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Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace=True, random_state=1)
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## Artifacts Without Tracking
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**ClearML** supports several types of objects that can be uploaded and are not tracked. Use the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact)
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method.
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Artifacts without tracking include:
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* Pandas DataFrames
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* Local files
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* Dictionaries (stored as a JSONs)
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* Numpy objects (stored as NPZ files)
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* Image files (stored as PNG files)
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* Folders (stored as a ZIP files)
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* Wildcards (stored as a ZIP files)
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### Pandas DataFrames
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# add and upload pandas.DataFrame (onetime snapshot of the object)
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task.upload_artifact('Pandas', artifact_object=df)
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### Local Files
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# add and upload local file artifact
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task.upload_artifact('local file', artifact_object=os.path.join('data_samples', 'dancing.jpg'))
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### Dictionaries
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# add and upload dictionary stored as JSON)
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task.upload_artifact('dictionary', df.to_dict())
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### Numpy Objects
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# add and upload Numpy Object (stored as .npz file)
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task.upload_artifact('Numpy Eye', np.eye(100, 100))
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### Image Files
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# add and upload Image (stored as .png file)
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im = Image.open(os.path.join('data_samples', 'dancing.jpg'))
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task.upload_artifact('pillow_image', im)
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### Folders
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# add and upload a folder, artifact_object should be the folder path
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task.upload_artifact('local folder', artifact_object=os.path.join('data_samples'))
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### Wildcards
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# add and upload a wildcard
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task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg')) |