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

title
Remote Execution

The execute_remotely_example script demonstrates the use of the Task.execute_remotely method.

:::note Make sure to have at least one ClearML Agent running and assigned to listen to the default queue

clearml-agent daemon --queue default

:::

Execution Flow

The script trains a simple deep neural network on the PyTorch built-in MNIST dataset. 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 and enqueues the task to the default queue, as specified in the queue_name parameter.
3. An agent listening to the queue fetches the task and restarts task execution remotely. When the agent executes the task, the execute_remotely is considered no-op.

An execution flow that uses 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 to set up an environment. After that, the training can be moved to be executed by a stronger machine.

During the execution of the example script, the code does the following:

• Uses ClearML's automatic and explicit logging.
• Creates an experiment named Remote_execution PyTorch MNIST train, which is associated with the examples project.

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

Models created by the experiment appear in the experiment’s ARTIFACTS tab. ClearML automatically logs and tracks models and any snapshots created using PyTorch.