clearml-docs/docs/guides/advanced/execute_remotely.md

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Remote Execution

The execute_remotely_example script demonstrates the use of the execute_remotely method.

The script does the following:

• Trains a simple deep neural network on the PyTorch built-in MNIST dataset.
• Uses ClearML's automatic and explicit logging.
• Creates an experiment named remote_execution pytorch mnist train, which is associated with the examples project.

Execution Flow

The following describes the code's execution flow:

1. The training runs for one epoch.
2. The code passes the execute_remotely method which terminates the local execution of the code.
3. Execution switches to remote execution by the agent listening to queue specified in the queue_name parameter of the method.

The execute_remotely method is especially helpful when running code on a development machine for a few iterations to debug and to make sure the code doesn't crash, or setting up an environment. After that, the training can be moved to be executed by a stronger machine.

Scalars

In the example script's train function, the following code explicitly reports scalars to ClearML:

Logger.current_logger().report_scalar(
    "train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())

In the test method, the code explicitly reports loss and accuracy scalars.

Logger.current_logger().report_scalar(
    "test", "loss", iteration=epoch, value=test_loss)
Logger.current_logger().report_scalar(
    "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))

These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's page > RESULTS > SCALARS.

Hyperparameters

ClearML automatically logs command line options defined with argparse. They appear in CONFIGURATIONS > HYPER PARAMETERS > Args.

Console

Text printed to the console for training progress, as well as all other console output, appear in RESULTS > CONSOLE.

Artifacts

Model artifacts associated with the experiment appear in the info panel of the EXPERIMENTS tab and in the info panel of the MODELS tab.