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pollfly
2024-03-06 15:00:50 +02:00
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16 changed files with 102 additions and 78 deletions
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docs/guides/reporting/artifacts.md
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@@ -18,7 +18,7 @@ Configure ClearML for uploading artifacts to any of the supported types of stora
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#config_default_output_uri).
* In the configuration file, set [`default_output_uri`](../../configs/clearml_conf.md#config_default_output_uri).
* 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).
@@ -26,12 +26,12 @@ When the script runs, it creates an experiment named `artifacts example` in the
ClearML reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab.
![image](../../img/examples_reporting_03.png)
![Experiment artifacts](../../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.
ClearML supports uploading and dynamically tracking Pandas DataFrames. Use [`Task.register_artifact()`](../../references/sdk/task.md#register_artifact)
to add a DataFrame to a task. If the DataFrame is modified, ClearML will automatically update the changes.
For example:
@@ -47,11 +47,15 @@ df = pd.DataFrame(
# 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}))
task.register_artifact(
name='train',
artifact=df,
metadata={'counting': 'legs', 'max legs': 68}
)
```
By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts)
method to retrieve it, you can see that ClearML tracked the change.
By modifying the artifact, and calling [`Task.get_registered_artifacts()`](../../references/sdk/task.md#get_registered_artifacts)
to retrieve it, you can see ClearML tracking the changes:
```python
# change the artifact object
@@ -78,7 +82,7 @@ Artifacts without tracking include:
### Pandas DataFrames
```python
# add and upload pandas.DataFrame (onetime snapshot of the object)
task.upload_artifact('Pandas', artifact_object=df)
task.upload_artifact(name='Pandas', artifact_object=df)
```
### Local Files
@@ -86,7 +90,7 @@ task.upload_artifact('Pandas', artifact_object=df)
```python
# add and upload local file artifact
task.upload_artifact(
'local file',
name='local file',
artifact_object=os.path.join(
'data_samples',
'dancing.jpg'
@@ -97,31 +101,31 @@ task.upload_artifact(
### Dictionaries
```python
# add and upload dictionary stored as JSON
task.upload_artifact('dictionary', df.to_dict())
task.upload_artifact(name='dictionary', artifact_object=df.to_dict())
```
### Numpy Objects
```python
# add and upload Numpy Object (stored as .npz file)
task.upload_artifact('Numpy Eye', np.eye(100, 100))
task.upload_artifact(name='Numpy Eye', artifact_object=np.eye(100, 100))
```
### Image Files
```python
# add and upload Image (stored as .png file)
im = Image.open(os.path.join('data_samples', 'dancing.jpg'))
task.upload_artifact('pillow_image', im)
task.upload_artifact(name='pillow_image', artifact_object=im)
```
### Folders
```python
# add and upload a folder, artifact_object should be the folder path
task.upload_artifact('local folder', artifact_object=os.path.join('data_samples'))
task.upload_artifact(name='local folder', artifact_object=os.path.join('data_samples'))
```
### Wildcards
```python
# add and upload a wildcard
task.upload_artifact('wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
task.upload_artifact(name='wildcard jpegs', artifact_object=os.path.join('data_samples', '*.jpg'))
```