Transformer Lab SDK
The Transformer Lab Python SDK provides a way for ML scripts to integrate with Transformer Lab.
Install
pip install transformerlab
Usage
from lab import lab
# Initialize with experiment ID
lab.init("my-experiment")
lab.log("Job initiated")
config_artifact_path = lab.save_artifact(<config_file>, "training_config.json")
lab.log(f"Saved training config: {config_artifact_path}")
lab.update_progress(1)
...
lab.update_progress(99)
model_path = lab.save_model(<training_output_dir>, name="trained_model")
lab.log("Saved model file to {model_path}")
lab.finish("Training completed successfully")
Reporting metrics
Pass a dict of named metrics to lab.finish(score=...). The dict shape is required —
metrics are stored under job_data.score and surfaced by lab job list (Score column)
and lab job info, and read by sweep flows for optimization.
lab.finish(message="Done!") # success, no score
lab.finish(message="Done!", score={"accuracy": 0.78}) # success with one metric
lab.finish(score={"accuracy": 0.78, "f1": 0.83}) # multiple metrics
Do not pass a scalar (e.g. lab.finish(score=0.78)) — wrap it in a dict instead:
lab.finish(score={"score": 0.78}).
Sample scripts can be found at https://github.com/transformerlab/transformerlab-app/tree/main/lab-sdk/scripts/examples
Development
The code for this can be found in the lab-sdk directory of
https://github.com/transformerlab/transformerlab-app
To develop locally in editable mode and run automated tests:
cd lab-sdk
uv venv
uv pip install -e .
uv run pytest # Run tests
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