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Remote Execution

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


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.


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

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

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

"test", "loss", iteration=epoch, value=test_loss)
"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.



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



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



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