clearml-docs/docs/guides/reporting/artifacts.md

---
title: Artifacts Reporting
---

The [artifacts.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/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
* Folders. 
  
Artifacts can be uploaded and dynamically tracked, or uploaded without tracking. 

<a name="configure_artifact_storage" class="tr_top_negative"></a>

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](../../references/sdk/logger.md#set_default_upload_destination) 
is different). Configure **ClearML** in any of the following ways:

* In the configuration file, set [default_output_uri](../../configs/clearml_conf.md#sdkdevelopment).
* In code, when [initializing a Task](../../references/sdk/task.md#taskinit), use the `output_uri` parameter.
* In the **ClearML Web UI**, when [modifying an experiment](../../webapp/webapp_exp_tuning.md#output-destination).

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.

![image](../../img/examples_reporting_03.png)

## Dynamically Tracked Artifacts

Currently, **ClearML** supports uploading and dynamically tracking Pandas DataFrames. Use the [Task.register_artifact](../../references/sdk/task.md#register_artifact)
method. If the Pandas DataFrame changes, **ClearML** uploads the changes. The updated artifact is associated with the experiment.

For example:

    df = pd.DataFrame(
        {
            'num_legs': [2, 4, 8, 0],
            'num_wings': [2, 0, 0, 0],
            'num_specimen_seen': [10, 2, 1, 8]
        },
        index=['falcon', 'dog', 'spider', 'fish']
    )

    # Register Pandas object as artifact to watch
    # (it will be monitored in the background and automatically synced and uploaded)
    task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69}))

By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts) 
method to retrieve it, we can see that **ClearML** tracked the change.

    # change the artifact object
    df.sample(frac=0.5, replace=True, random_state=1)
    # or access it from anywhere using the Task's get_registered_artifacts()
    Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace=True, random_state=1)

## Artifacts Without Tracking

**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) 
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)

### Pandas DataFrames

    # add and upload pandas.DataFrame (onetime snapshot of the object)
    task.upload_artifact('Pandas', artifact_object=df)

### Local Files

    # add and upload local file artifact
    task.upload_artifact('local file', artifact_object=os.path.join('data_samples', 'dancing.jpg'))

### Dictionaries

    # add and upload dictionary stored as JSON)
    task.upload_artifact('dictionary', df.to_dict())

### Numpy Objects

    # add and upload Numpy Object (stored as .npz file)
    task.upload_artifact('Numpy Eye', np.eye(100, 100))

### Image Files

    # add and upload Image (stored as .png file)
    im = Image.open(os.path.join('data_samples', 'dancing.jpg'))
    task.upload_artifact('pillow_image', im)

### Folders

    # add and upload a folder, artifact_object should be the folder path
    task.upload_artifact('local folder', artifact_object=os.path.join('data_samples'))

### Wildcards

    # add and upload a wildcard
    task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))