Command Line Interface (CLI)¶
Distilabel offers a CLI to explore and re-run existing Pipeline dumps, meaning that an existing dump can be explored to see the steps, how those are connected, the runtime parameters used, and also re-run it with the same or different runtime parameters, respectively.
Available commands¶
The only available command as of the current version of distilabel is distilabel pipeline.
$ distilabel pipeline --help
Usage: distilabel pipeline [OPTIONS] COMMAND [ARGS]...
Commands to run and inspect Distilabel pipelines.
╭─ Options ───────────────────────────────────────────────────────────────────────────────╮
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ──────────────────────────────────────────────────────────────────────────────╮
│ info Get information about a Distilabel pipeline. │
│ run Run a Distilabel pipeline. │
╰─────────────────────────────────────────────────────────────────────────────────────────╯
So on, distilabel pipeline has two subcommands: info and run, as described below. Note that for testing purposes we will be using the following dataset.
distilabel pipeline info¶
$ distilabel pipeline info --help
Usage: distilabel pipeline info [OPTIONS]
Get information about a Distilabel pipeline.
╭─ Options ───────────────────────────────────────────────────────────────────────────╮
│ * --config TEXT Path or URL to the Distilabel pipeline configuration file. │
│ [default: None] │
│ [required] │
│ --help Show this message and exit. │
╰─────────────────────────────────────────────────────────────────────────────────────╯
As we can see from the help message, we need to pass either a Path or a URL. This second option comes handy for datasets stored in Hugging Face Hub, for example:
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml"
If we take a look:
The pipeline information includes the steps used in the Pipeline along with the Runtime Parameter that was used, as well as a description of each of them, and also the connections between these steps. These can be helpful to explore the Pipeline locally.
distilabel pipeline run¶
We can also run a Pipeline from the CLI just pointing to the same pipeline.yaml file or an URL pointing to it and calling distilabel pipeline run:
$ distilabel pipeline run --help
Usage: distilabel pipeline run [OPTIONS]
Run a Distilabel pipeline.
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --config TEXT Path or URL to the Distilabel pipeline configuration file. │
│ [default: None] │
│ [required] │
│ --param PARSE_RUNTIME_PARAM [default: (dynamic)] │
│ --ignore-cache --no-ignore-cache Whether to ignore the cache and re-run the pipeline from │
│ scratch. │
│ [default: no-ignore-cache] │
│ --repo-id TEXT The Hugging Face Hub repository ID to push the resulting │
│ dataset to. │
│ [default: None] │
│ --commit-message TEXT The commit message to use when pushing the dataset. │
│ [default: None] │
│ --private --no-private Whether to make the resulting dataset private on the Hub. │
│ [default: no-private] │
│ --token TEXT The Hugging Face Hub API token to use when pushing the │
│ dataset. │
│ [default: None] │
│ --help Show this message and exit. │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
To specify the runtime parameters of the steps we will need to use the --param option and the value of the parameter in the following format:
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml" \
--param load_dataset.repo_id=distilabel-internal-testing/instruction-dataset-mini \
--param load_dataset.split=test \
--param generate_with_gpt35.llm.generation_kwargs.max_new_tokens=512 \
--param generate_with_gpt35.llm.generation_kwargs.temperature=0.7 \
--param to_argilla.dataset_name=text_generation_with_gpt35 \
--param to_argilla.dataset_workspace=admin
Again, this helps with the reproducibility of the results, and simplifies sharing not only the final dataset but also the process to generate it.