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pollfly
2021-12-05 11:29:59 +02:00
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@@ -603,20 +603,24 @@ providing the `output_uri` parameter allows you to specify the location in which
For example, to store model checkpoints (snapshots) in `/mnt/shared/folder`:
task = Task.init(project_name, task_name, output_uri="/mnt/shared/folder")
```python
task = Task.init(project_name, task_name, output_uri="/mnt/shared/folder")
```
ClearML will copy all stored snapshots into a subfolder under `/mnt/shared/folder`. The subfolder's name will contain
the experiment's ID. If the experiment's ID is `6ea4f0b56d994320a713aeaf13a86d9d`, the following folder will be used:
`/mnt/shared/folder/task.6ea4f0b56d994320a713aeaf13a86d9d/models/`
```
/mnt/shared/folder/task.6ea4f0b56d994320a713aeaf13a86d9d/models/
```
ClearML supports other storage types for `output_uri`, including:
```python
# AWS S3 bucket
task = Task.init(project_name, task_name, output_uri="s3://bucket-name/folder")
# AWS S3 bucket
task = Task.init(project_name, task_name, output_uri="s3://bucket-name/folder")
# Google Cloud Storage bucket
task = Task.init(project_name, task_name, output_uri="gs://bucket-name/folder")
# Google Cloud Storage bucket
task = Task.init(project_name, task_name, output_uri="gs://bucket-name/folder")
```
To use Cloud storage with ClearML, configure the storage credentials in your `~/clearml.conf`. For detailed information,
see [ClearML Configuration Reference](configs/clearml_conf.md).