The HuggingFace Transformers example
demonstrates how to integrate ClearML into your Transformer's Trainer
code. The HuggingFace Trainer automatically uses the built-in
clearml package is already installed, to log Transformers models, parameters, scalars, and more.
In the example, ClearML is installed and set up in the training environment. This way ClearML can log models, parameters, scalars, and more.
When the example runs, it creates a ClearML task called
Trainer in the
HuggingFace Transformers project. To change
the task's name or project, use the
CLEARML_TASK environment variables respectively.
For more information about integrating ClearML into your Transformers code, see HuggingFace Transformers.
ClearML automatically captures all the Trainer parameters.
Notice in the code example that only a few of the
TrainingArguments are explicitly set:
training_args = TrainingArguments(
ClearML captures all of the
TrainingArguments passed to the Trainer.
View these parameters in the experiment's CONFIGURATION tab > Hyperparameters section.
In order for ClearML to log the models created during training in this example, the
variable is set to
ClearML automatically captures the model snapshots created by the Trainer, and saves them as artifacts. View the snapshots in the experiment's ARTIFACTS tab.
ClearML automatically captures the Trainer's scalars, which can be viewed in the experiment's Scalars tab.