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47 lines
2.0 KiB
Markdown
47 lines
2.0 KiB
Markdown
---
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title: Using Artifacts
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---
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The [using_artifacts_example](https://github.com/allegroai/clearml/blob/master/examples/reporting/using_artifacts_example.py)
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script demonstrates uploading a data file to a task as an artifact and then accessing and utilizing the artifact in a different task.
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When the script runs it creates two tasks, `create artifact` and `use artifact from other task`, both of which are associated
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with the `examples` project. The first task creates and uploads the artifact, and the second task accesses the first task's
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artifact and utilizes it.
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## Task 1: Uploading an Artifact
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The first task uploads a data file as an artifact using [`Task.upload_artifact()`](../../references/sdk/task.md#upload_artifact),
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and inputting the artifact's name and the location of the file.
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```python
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task1.upload_artifact(name='data file', artifact_object='data_samples/sample.json')
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```
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The task is then closed, using [`Task.close()`](../../references/sdk/task.md#close), so another task can be
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initialized in the same script.
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Artifact details (location and size) can be viewed in ClearML's **web UI > experiment details > ARTIFACTS tab > OTHER section**.
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## Task 2: Accessing an Artifact
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After the second task is initialized, the script uses the [`Task.get_task`](../../references/sdk/task.md#taskget_task)
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class method to get the first task and access its artifacts, specifically the `data file` artifact. The `get_local_copy`
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method downloads the files and returns a path.
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:::info Cache
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ClearML manages a cache of all downloaded content, so the code won't download the
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same data multiple times. See [Caching](../../integrations/storage.md#caching) for configuration options.
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:::
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```python
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preprocess_task = Task.get_task(task_name='create artifact', project_name='examples')
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local_json = preprocess_task.artifacts['data file'].get_local_copy()
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```
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Task 2 then reads the contents of the downloaded artifact and prints it to screen.
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