README - Rerun — ContextQMD

Train a LeRobot ACT policy using Rerun's experimental PyTorch dataloader, streaming trajectory data directly from a Rerun catalog.

For an explanation of the dataloader API and how the example fits together, see the Train PyTorch models with the Rerun dataloader how-to guide.

Run the code

1. Install dependencies

This example has its own uv project, separate from the workspace .venv, because LeRobot pins an incompatible rerun-sdk. From the repo root:

bash

cd examples/python/dataloader
RERUN_ALLOW_MISSING_BIN=1 uv sync  # builds local rerun-sdk + installs lerobot into ./.venv

Then either source .venv/bin/activate or prefix subsequent commands with uv run.

2. Start a local Rerun server

In a separate terminal:

bash

rerun server

This serves a Rerun server at rerun+http://127.0.0.1:51234 (the default used by the scripts).

3. Prepare and register the dataset

Downloads a LeRobot dataset from HuggingFace, splits it into per-episode RRDs, and registers them as a dataset in the catalog:

bash

uv run python prepare_dataset.py

Pass --repo-id user/other_lerobot_ds to use a different dataset, or --catalog-url "" to skip registration and only write local RRDs.

4. Train

bash

uv run python train.py

The script streams batches from the catalog, trains an ACT policy for a few epochs, and saves a checkpoint to act_checkpoint/.

It accepts a few CLI flags (run uv run python train.py --help for the full list):

bash

uv run python train.py \
    --catalog-url rerun+http://127.0.0.1:51234 \
    --dataset rerun_so101-pick-and-place \
    --num-segments 3 \
    --epochs 5 \
    --batch-size 8 \
    --num-workers 8 \
    --lr 1e-5 \
    --checkpoint-dir act_checkpoint \
    --dataset-style iterable  # or "map"

Pass --num-segments 0 to train on all segments in the dataset.

Training with traces

TELEMETRY_ENABLED=true OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4317 uv run python train.py