wehub-resource-sync
78 lines
2.2 KiB
Markdown
78 lines
2.2 KiB
Markdown
Train a [LeRobot](https://github.com/huggingface/lerobot) ACT policy using Rerun's experimental PyTorch dataloader, streaming trajectory data directly from a Rerun catalog.
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For an explanation of the dataloader API and how the example fits together, see the [Train PyTorch models with the Rerun dataloader](https://rerun.io/docs/howto/train) how-to guide.
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## Run the code
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### 1. Install dependencies
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This example has its own `uv` project, separate from the workspace `.venv`, because LeRobot requires
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Python >=3.12 while the workspace supports older versions.
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**Standalone** (sparse-checkout of just this directory, no local Rerun build):
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```bash
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uv sync --no-sources --no-dev
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```
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**Monorepo dev** (full repo checkout, editable local `rerun-sdk`):
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```bash
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cd examples/python/dataloader
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RERUN_ALLOW_MISSING_BIN=1 uv sync
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uv pip install ../../../rerun_py/rerun_dev_fixup
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```
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Then either `source .venv/bin/activate` or prefix subsequent commands with `uv run`.
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### 2. Start a local Rerun server
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In a separate terminal:
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```bash
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rerun server
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```
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This serves a Rerun server at `rerun+http://127.0.0.1:51234` (the default used by the scripts).
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### 3. Prepare and register the dataset
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Downloads a LeRobot dataset from HuggingFace, splits it into per-episode RRDs, and registers them as a dataset in the catalog:
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```bash
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uv run python prepare_dataset.py
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```
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Pass `--repo-id user/other_lerobot_ds` to use a different dataset, or `--catalog-url ""` to skip registration and only write local RRDs.
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### 4. Train
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```bash
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uv run python train.py
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```
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The script streams batches from the catalog, trains an ACT policy for a few epochs, and saves a checkpoint to `act_checkpoint/`.
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It accepts a few CLI flags (run `uv run python train.py --help` for the full list):
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```bash
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uv run python train.py \
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--catalog-url rerun+http://127.0.0.1:51234 \
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--dataset rerun_so101-pick-and-place \
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--num-segments 3 \
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--epochs 5 \
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--batch-size 8 \
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--num-workers 8 \
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--lr 1e-5 \
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--checkpoint-dir act_checkpoint \
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--dataset-style iterable # or "map"
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```
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Pass `--num-segments 0` to train on all segments in the dataset.
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### Training with traces
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```sh
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TELEMETRY_ENABLED=true OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4317 uv run python train.py
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```
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