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

title
Tuning Experiments

In this tutorial, learn how to tune an experiment. The experiment 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 Experiment

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

python pytorch_mnist.py

Step 2: Clone the Experiment

Clone the experiment to create an editable copy for tuning.

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

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

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

Step 3: Tune the Cloned Experiment

To demonstrate tuning, change two hyperparameter values.

1. In the info panel, CONFIGURATION > HYPER PARAMETERS > 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 experiment, use a worker that can run a worker daemon listening to a queue.

:::note For more information about workers, worker daemons, and queues, see Agents and queues. :::

Run the worker daemon 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 Experiment

Enqueue the tuned experiment.

1. In the ClearML Web-App (UI), experiments table, right-click the experiment Clone Of pytorch mnist train.

2. In the context menu, click Enqueue.

3. Select Default queue.

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

Step 6: Compare the Experiments

To compare the original and tuned experiments:

1. In the ClearML Web-App (UI), on the Projects page, click the examples project.

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

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

   The experiment 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.
   • HYPER PARAMETERS - 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 experiment shown as thumbnails.

4. Examine the differences.

   1. Compare the hyperparameters. In the HYPER PARAMETERS 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 Experiment, 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 experiments, see modify experiments in the User Interface section.