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69 lines
3.5 KiB
Markdown
69 lines
3.5 KiB
Markdown
---
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title: PyTorch Distributed
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---
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The [pytorch_distributed_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_distributed_example.py)
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script demonstrates integrating **ClearML** into code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html)
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(`torch.distributed`).
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The script initializes a main Task and spawns subprocesses, each for an instance of that Task.
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The Task in each subprocess trains a neural network over a partitioned dataset (the torchvision built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
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dataset), and reports (uploads) the following to the main Task:
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* Artifacts - A dictionary containing different key-value pairs.
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* Scalars - Loss reported as a scalar during training in each Task in a subprocess.
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* Hyperparameters - Hyperparameters created in each Task are added to the hyperparameters in the main Task.
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Each Task in a subprocess references the main Task by calling [Task.current_task](../../references/sdk/task#taskcurrent_task), which always returns
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the main Task.
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When the script runs, it creates an experiment named `test torch distributed`, which is associated with the `examples` project
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in the **ClearML Web UI**.
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## Artifacts
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The example uploads a dictionary as an artifact in the main Task by calling the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact)
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method on [`Task.current_task`](../../references/sdk/task.md#taskcurrent_task) (the main Task). The dictionary contains the [`dist.rank`](https://pytorch.org/docs/stable/distributed.html#torch.distributed.get_rank)
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of the subprocess, making each unique.
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Task.current_task().upload_artifact(
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'temp {:02d}'.format(dist.get_rank()), artifact_object={'worker_rank': dist.get_rank()})
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All of these artifacts appear in the main Task under **ARTIFACTS** **>** **OTHER**.
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## Scalars
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Loss is reported to the main Task by calling the [Logger.report_scalar](../../references/sdk/logger#report_scalar)
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method on `Task.current_task().get_logger`, which is the logger for the main Task. Since `Logger.report_scalar` is called
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with the same title (`loss`), but a different series name (containing the subprocess' `rank`), all loss scalar series are
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logged together.
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Task.current_task().get_logger().report_scalar(
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'loss', 'worker {:02d}'.format(dist.get_rank()), value=loss.item(), iteration=i)
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The single scalar plot for loss appears in **RESULTS** **>** **SCALARS**.
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## Hyperparameters
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**ClearML** automatically logs the argparse command line options. Since the [Task.connect](../../references/sdk/task#connect)
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method is called on `Task.current_task`, they are logged in the main Task. A different hyperparameter key is used in each
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subprocess, so they do not overwrite each other in the main Task.
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param = {'worker_{}_stuff'.format(dist.get_rank()): 'some stuff ' + str(randint(0, 100))}
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Task.current_task().connect(param)
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All the hyperparameters appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**.
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## Log
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Output to the console, including the text messages printed from the main Task object and each subprocess appear in **RESULTS** **>** **LOG**.
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