clearml-docs/docs/guides/ui/tuning_exp.md

title
Tuning Tasks

In this tutorial, learn how to tune a task. The task that will be tuned is created by the pytorch_mnist.py example script.

Prerequisites

• Clone the clearml repository.
• Install the requirements for the TensorFlow examples.
• Have ClearML Agent installed and configured.

Step 1: Run the Task

In the examples/frameworks/pytorch directory, run the task script:

python pytorch_mnist.py

Step 2: Clone the Task

Clone the task to create an editable copy for tuning.

1. In the ClearML WebApp (UI), on the Projects page, click the examples project card.

2. In the task table, right-click the task pytorch mnist train.

3. In the context menu, click Clone > CLONE. The newly cloned task appears and its info panel slides open.

Step 3: Tune the Cloned Task

To demonstrate tuning, change two hyperparameter values.

1. In the info panel, CONFIGURATION > HYPERPARAMETERS > Args > Hover and click EDIT.

2. Change the value of batch_size from 64 to 32.

3. Change the value of lr from 0.01 to 0.025.

4. Click SAVE.

Step 4: Run a Worker Daemon Listening to a Queue

To execute the cloned task, use a ClearML Agent.

Run the agent on the local development machine:

1. Open a terminal session.

2. Run the following clearml-agent command which runs a worker daemon listening to the default queue:

   clearml-agent daemon --queue default
   

   The response to this command is information about the configuration, the worker, and the queue. For example:

   Current configuration (clearml_agent v0.16.0, location: /home/<username>/clearml.conf):
   ----------------------
   agent.worker_id =
   agent.worker_name = LAPTOP-PPTKKPGK
   agent.python_binary =
   agent.package_manager.type = pip
   .
   .
   .
   sdk.development.worker.report_period_sec = 2
   sdk.development.worker.ping_period_sec = 30
   sdk.development.worker.log_stdout = true
   
   Worker "LAPTOP-PPTKKPGK:0" - Listening to queues:
   + ---------------------------------+---------+-------+
   | id                               | name    | tags  |
   + ---------------------------------+---------+-------+
   | 2a03daf5ff9a4255b9915fbd5306f924 | default |       |
   + ---------------------------------+---------+-------+
   
   Running CLEARML-AGENT daemon in background mode, writing stdout/stderr to /home/<username>/.clearml_agent_daemon_outym6lqxrz.txt
   

Step 5: Enqueue the Tuned Task

Enqueue the tuned task.

1. In the ClearML WebApp > task table, right-click the task Clone Of pytorch mnist train.

2. In the context menu, click Enqueue.

3. Select Default queue.

4. Click ENQUEUE. The task's status becomes Pending. When the worker fetches the task from the queue, the status becomes Running. The progress of the task can be viewed in the info panel. When the status becomes Completed, continue to the next step.

Step 6: Compare the Tasks

To compare the original and tuned tasks:

1. In the ClearML WebApp (UI), on the Projects page, click the examples project.

2. In the task table, select the checkboxes for the two tasks: pytorch mnist train and Clone Of pytorch mnist train.

3. On the menu bar at the bottom of the task table, click COMPARE. The task comparison window appears. All differences appear with a different background color to highlight them.

   The task comparison window is organized in the following tabs:

   • DETAILS - The ARTIFACTS section, including input and output models with their network designs, and other artifacts; the EXECUTION section execution, including source code control, installed Python packages and versions, uncommitted changes, and the Docker image name which, in this case, is empty.
   • HYPERPARAMETERS - The hyperparameters and their values.
   • SCALARS - Scalar metrics with the option to view them as charts or values.
   • PLOTS - Plots of any data with the option to view them as charts or values.
   • DEBUG SAMPLES - Media including images, audio, and video uploaded by your task shown as thumbnails.

4. Examine the differences.

   1. Compare the hyperparameters. In the HYPERPARAMETERS tab, expand ARGS. The hyperparameters batch_size and lr are shown with a different background color. The values are different.
   2. Compare the metrics. In the SCALARS tab, to the right of Add Task, select the plot or value comparison:
      • Graph - The scalar metrics plots show pytorch mnist train and Clone of pytorch mnist train.
      • Last Values - Expand a metric and variant.

Next Steps

• For more information about editing tasks, see modifying tasks.