clearml-docs/docs/integrations/transformers.md

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Transformers

HuggingFace's Transformers is a popular deep learning framework. You can seamlessly integrate ClearML into your Transformer's PyTorch Trainer code using the built-in ClearMLCallback. ClearML automatically logs Transformer's models, parameters, scalars, and more.

All you have to do is install and set up ClearML:

1. Install the clearml python package:

   pip install clearml
   

2. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:

   • Sign up for free to the ClearML Hosted Service
   • Set up your own server, see here.

3. Connect the ClearML SDK to the server by creating credentials (go to the top right in the UI to Settings > Workspace > Create new credentials), then execute the command below and follow the instructions:

   clearml-init
   

That's it! In every training run from now on, the ClearML experiment manager will capture:

• Source code and uncommitted changes
• Hyperparameters - PyTorch trainer parameters and TensorFlow definitions
• Installed packages
• Model files (make sure the CLEARML_LOG_MODEL environment variable is set to True)
• Scalars (loss, learning rates)
• Console output
• General details such as machine details, runtime, creation date etc.
• And more

All of this is captured into a ClearML Task. By default, a task called Trainer is created in the HuggingFace Transformers project. To change the task's name or project, use the CLEARML_PROJECT and CLEARML_TASK environment variables

:::tip project names ClearML uses / as a delimiter for subprojects: using example/sample as a name will create the sample task within the example project. :::

To log the models created during training, set the CLEARML_LOG_MODEL environment variable to True.

You can see all the captured data in the task's page of the ClearML WebApp.

Additionally, you can view all of your Transformers runs tracked by ClearML in the Experiments Table. Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model. You can also select multiple experiments and directly compare them.

See an example of Transformers and ClearML in action here.

Remote Execution

ClearML logs all the information required to reproduce an experiment on a different machine (installed packages, uncommitted changes etc.). The ClearML Agent listens to designated queues and when a task is enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the experiment manager.

Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running the following command on it:

clearml-agent daemon --queue <queues_to_listen_to> [--docker]

Use the ClearML Autoscalers to help you manage cloud workloads in the cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up and shuts down instances as needed, according to a resource budget that you set.

Cloning, Editing, and Enqueuing

Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task with the new configuration on a remote machine:

• Clone the experiment
• Edit the hyperparameters and/or other details
• Enqueue the task

The ClearML Agent executing the task will use the new values to override any hard coded values.

Hyperparameter Optimization

Use ClearML's HyperParameterOptimizer class to find the hyperparameter values that yield the best performing models. See Hyperparameter Optimization for more information.