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78 lines
3.0 KiB
Markdown
78 lines
3.0 KiB
Markdown
---
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title: Remote Execution
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---
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The [execute_remotely_example](https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py)
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script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method.
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:::note
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Make sure to have at least one [ClearML Agent](../../clearml_agent.md) running and assigned to listen to the `default` queue
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```
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clearml-agent daemon --queue default
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```
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:::
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## Execution Flow
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The script trains a simple deep neural network on the PyTorch built-in MNIST dataset. The following describes the code's
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execution flow:
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1. The training runs for one epoch.
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1. The code uses [`Task.execute_remotely()`](../../references/sdk/task.md#execute_remotely), which terminates the local execution of the code and enqueues the task
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to the `default` queue, as specified in the `queue_name` parameter.
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1. An agent listening to the queue fetches the task and restarts task execution remotely. When the agent executes the task,
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the `execute_remotely` is considered no-op.
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An execution flow that uses `execute_remotely` is especially helpful when running code on a development machine for a few iterations
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to debug and to make sure the code doesn't crash, or to set up an environment. After that, the training can be
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moved to be executed by a stronger machine.
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During the execution of the example script, the code does the following:
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* Uses ClearML's automatic and explicit logging.
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* Creates an experiment named `Remote_execution PyTorch MNIST train` in the `examples` project.
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## Scalars
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In the example script's `train` function, the following code explicitly reports scalars to ClearML:
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```python
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Logger.current_logger().report_scalar(
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"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
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)
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```
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In the script's `test` function, the code explicitly reports `loss` and `accuracy` scalars.
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```python
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Logger.current_logger().report_scalar(
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"test", "loss", iteration=epoch, value=test_loss
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)
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Logger.current_logger().report_scalar(
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"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
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)
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```
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These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's **SCALARS** tab.
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## Hyperparameters
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ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **Args**.
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## Console
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Text printed to the console for training progress, as well as all other console output, appear in **CONSOLE**.
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## Artifacts
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Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks models
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and any snapshots created using PyTorch.
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