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annotate docs: install lerobot from main (post-merge wording)

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The example already pins '@main'; update the doc step and the script
docstring from 'the branch under test' to 'lerobot (from main)' now that
the pipeline is merging to main.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Pepijn
2026-06-04 11:45:38 +02:00
parent eba3ab3741
commit c6f682b3f4
2 changed files with 2 additions and 2 deletions
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docs/source/annotation_pipeline.mdx
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@@ -57,7 +57,7 @@ HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
[`examples/annotations/run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py) [`examples/annotations/run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
spawns a single-GPU `h200` job (scale up to `h200x4` for larger datasets) that: spawns a single-GPU `h200` job (scale up to `h200x4` for larger datasets) that:
1. installs the branch under test plus the annotation extras, 1. installs `lerobot` (from `main`) plus the annotation extras,
2. boots one vLLM server per GPU (in the `vllm/vllm-openai` image) for the 2. boots one vLLM server per GPU (in the `vllm/vllm-openai` image) for the
chosen model, which the pipeline drives over the OpenAI-compatible API, chosen model, which the pipeline drives over the OpenAI-compatible API,
3. runs the `plan` / `interjections` / `vqa` modules across the dataset 3. runs the `plan` / `interjections` / `vqa` modules across the dataset
examples/annotations/run_hf_job.py
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@@ -17,7 +17,7 @@
Spawns one single-GPU ``h200`` job that: Spawns one single-GPU ``h200`` job that:
1. installs ``lerobot`` plus the annotation extras, 1. installs ``lerobot`` from ``main`` plus the annotation extras,
2. boots one vllm server with Qwen3.6-27B (dense VLM), 2. boots one vllm server with Qwen3.6-27B (dense VLM),
3. runs the plan / interjections / vqa modules across the dataset 3. runs the plan / interjections / vqa modules across the dataset
in free-form mode (each episode generates its own subtasks + in free-form mode (each episode generates its own subtasks +