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65 lines
2.4 KiB
Markdown
65 lines
2.4 KiB
Markdown
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---
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title: PyTorch MNIST
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---
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The [pytorch_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py) example
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demonstrates the integration of **ClearML** into code that uses PyTorch.
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The example script does the following:
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* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/docs/stable/torchvision/datasets.html#mnist)
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dataset.
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* Uses **ClearML** automatic logging.
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* Calls the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method to demonstrate explicit reporting and explicit reporting,
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which allows adding customized reporting to the code.
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* Creates an experiment named `pytorch mnist train`, which is associated with 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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In the `test` method, 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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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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These scalars can be visualized in plots, which appear in the **ClearML web UI**, in the experiment's
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page **>** **RESULTS** **>** **SCALARS**.
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## Hyperparameters
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**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
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## Log
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **LOG**.
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in
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the info panel of the **MODELS** tab.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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The model info panel contains the model details, including:
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* Model URL
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* Framework
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* Snapshot locations.
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