transformerlab-internal 0.1.47

Python SDK for Transformer Lab

Transformer Lab SDK

The Transformer Lab Python SDK provides a way for ML scripts to integrate with Transformer Lab.

Install

pip install transformerlab-internal

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 / autoresearch 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-platform/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-platform

To develop locally in editable mode and run automated tests:

cd lab-sdk
uv venv
uv pip install -e .
uv run pytest  # Run tests