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

---
title: Remote Execution
---

The [execute_remotely_example](https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py)
script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method. 

:::note
Make sure to have at least one [ClearML Agent](../../clearml_agent.md) 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. 
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 
   to the `default` queue, as specified in the `queue_name` parameter.
1. 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` 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` in the `examples` project.


## Scalars

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

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

In the script's `test` function, the code explicitly reports `loss` and `accuracy` scalars.

```python
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 **SCALARS** tab. 

![Experiment Scalars](../../img/examples_pytorch_mnist_07.png)

## Hyperparameters

ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **Args**.

![Experiment hyperparameters](../../img/examples_pytorch_mnist_01.png)

## Console

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

![Experiment console log](../../img/examples_pytorch_mnist_06.png)

## 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. 

![Experiment artifacts](../../img/examples_remote_execution_artifacts.png)