Upgrade dataset port script to paralllel and distributed proccesing a…

[eDeveloperOZ]

⚡️ Distributed + parallel v2.1→v3 converter with manifest orchestration (--orchestrate) and safe benchmarking (--no-push)

Addresses: [lerobot#1998](#1998)
Label: (⚡️ Performance)

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What this does

This PR upgrades src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py to support parallel and distributed conversion of large LeRobot datasets from v2.1 to v3.0, while preserving the existing single-process behavior.

Highlights

• Manifest-based orchestration (--orchestrate)

  • Plans the dataset into batches (user-tunable episodes per batch) and writes a work manifest to disk.
  • A pool of workers leases batches and converts them in parallel, writing temp shards.
  • The main process packs shards into the final v3 layout with the exact same file-size policies as the original script.
  • Workers read & write independently and concurrently (reads and writes happen at the same time, safely).

• Deterministic v3 packing (unchanged layout)

  • Data: per-episode parquet files are packed into data/chunk-XXX/file-YYY.parquet.
  • Videos: per-camera MP4s are concatenated into videos/<camera>/chunk-XXX/file-YYY.mp4.
  • Meta: meta/episodes rebuilt with accurate (chunk_index, file_index) and per-camera from/to timestamps.

• New CLI flags

  • --orchestrate – enable manifest-based distributed flow.
  • --episodes-per-batch <int> – batch size for planning.
  • --num-workers <int> – parallel workers on this machine.
  • --work-dir <path> – optional external work directory (keeps _work out of the cache tree).
  • --no-push – skip Hub mutations (no delete/commit/tag/push), ideal for local benchmarking/CI.

• Backward compatible by default

  • Running without --orchestrate keeps the current single-process flow (including Hub updates).
  • Thresholds (--data-file-size-in-mb, --video-file-size-in-mb) and file layout remain the same.

• Robust orchestration

  • Manifest records pending/leased/done per batch for safe multi-worker execution and resumability.
  • Temp output isolated in _work/; final swap at the dataset root is atomic.
  • --no-push prevents permissions issues during local tests.

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How it was tested

Environment: macOS (Apple Silicon M1), 16 GB RAM, Python via uv venv.
Dataset: lerobot/svla_so101_pickplace (revision v2.1).
Auth: authenticated; used --no-push for benchmarks.

Commands

Baseline (sequential):

/usr/bin/time -l python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 \
  --repo-id lerobot/svla_so101_pickplace \
  --no-push \
  2> bench_logs/baseline_time.log | tee bench_logs/baseline.log

Single-machine parallel (no manifest; intra-process fan-out):

/usr/bin/time -l python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 \
  --repo-id lerobot/svla_so101_pickplace \
  --max-workers 2 \
  --no-push \
  2> bench_logs/parallel_time.log | tee bench_logs/parallel.log

Orchestrated (distributed-style with manifest):

/usr/bin/time -l python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 \
  --repo-id lerobot/svla_so101_pickplace \
  --orchestrate --episodes-per-batch 10 \
  --num-workers 4 \
  --no-push \
  2> bench_logs/orch_time.log | tee bench_logs/orch.log

We also used psrecord/hyperfine, but time -l covers the core metrics for this PR.

Results (this small dataset)

Mode	real	Max RSS	Notes
Baseline (sequential)	7.70 s	449 MB	Reference
Single-machine parallel (--max-workers 2)	4.51 s	431 MB	~1.7× faster
Orchestrated (--orchestrate, 10 ep/batch, 4 workers)	9.84 s	415 MB	Small DS → orchestration overhead dominates

Why the orchestrator looks slower here: with only ~50 episodes and default thresholds, you end up with one v3 data file and one MP4 per camera. The orchestrator introduces process startup, manifest I/O, and a final pack pass—overhead that is amortized on large datasets where many final file-00X outputs are produced and workers keep the writer busy.

Correctness checks performed:

• v3 layout present under cache:

  ~/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace/{data,meta,videos}
  

• meta/info.json contains "codebase_version": "v3.0".

• meta/episodes/chunk-000/file-000.parquet has correct mappings for data/* and per-camera videos/* columns.

• For this DS: data/chunk-000/file-000.parquet, and per camera videos/.../chunk-000/file-000.mp4 as expected.

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How to checkout & try? (for the reviewer)

Install & auth

pip install psutil psrecord hyperfine   # optional
huggingface-cli login                   # only needed if you intend to push; otherwise use --no-push

Run sequential (baseline)

/usr/bin/time -l python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 \
  --repo-id lerobot/svla_so101_pickplace --no-push

Run single-machine parallel

/usr/bin/time -l python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 \
  --repo-id lerobot/svla_so101_pickplace --max-workers 2 --no-push

Run orchestrated (distributed-style)

/usr/bin/time -l python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 \
  --repo-id lerobot/svla_so101_pickplace \
  --orchestrate --episodes-per-batch 10 --num-workers 2 --no-push

Tip: add --work-dir /tmp/lerobot_work/svla_so101_pickplace to keep _work/manifest and temp shards outside the dataset cache.
For large datasets, tune --episodes-per-batch and consider lowering --data-file-size-in-mb / --video-file-size-in-mb to force multiple output files and expose more parallelism.

Outputs sanity

# Final v3 files
ls -R ~/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace/{data,meta,videos} | head -n 50

# Info + thresholds
cat ~/.cache/huggingface/lerobot/lerobot/svla_so101_pickplace/meta/info.json

Multi-host usage (optional)
Point multiple machines at a shared --work-dir (e.g., NFS / object-storage mount). Each process leases batches from the manifest and works independently, enabling safe conversion of TB-scale datasets.

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Design notes & decisions

• Manifest rationale: deterministic planning, safe lease-based scheduling for many workers, and resumability via pending/leased/done.
• Concurrency model: workers prepare shards in parallel while the main process writes final outputs; this keeps workers isolated and the writer authoritative for the v3 layout.
• Compatibility: strictly preserves the v3 naming/layout and default thresholds (data 100 MB, video 500 MB).
• Safety: --no-push mode supports read-only benchmarking and avoids Hub permission errors; default mode keeps the current push behavior.

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Benchmarks snapshot (MBP M1, 16 GB RAM)

Mode	Command (abridged)	real	Notes
Baseline	convert_dataset_v21_to_v30 --no-push	7.70 s	Sequential
Parallel	--max-workers 2 --no-push	4.51 s	~1.7× speedup
Orchestrated	--orchestrate --episodes-per-batch 10 --num-workers 4 --no-push	9.84 s	Small DS ⇒ overhead dominates; shines on large DS

Expect larger speedups on big datasets where many final file-00X outputs are produced; orchestration overlaps read/pack work and amortizes per-batch overhead.

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Tiny housekeeping

• Add bench_logs/ to .gitignore (local benchmarking artifacts).

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Thanks for reviewing! Happy to adjust defaults, add tests, or extend CI coverage for the orchestrator path if that helps.

[eDeveloperOZ]

Update: bug fix + additional benchmarks & validation

TL;DR: My hypothesis is now CONFIRMED: the orchestrator outperforms the baseline as dataset size scale.

Bug fix

• Fixed a regression in the new orchestrator path where convert_tasks wasn’t referenced correctly (raised a NameError), which caused the writer to fail before producing the final output.

  The orchestrator now calls the same convert_tasks logic as the sequential path and completes successfully.

• Hardened local benchmarking mode: --no-push reliably skips all Hub mutations while performing the full local conversion.

Benchmark setup

• Dataset: unitreerobotics/G1_Brainco_GraspOreo_Dataset (201 episodes, ~9GB)

• Machine: MacBook Pro (Apple M1, 16GB RAM, local SSD)

• Mode: local, --no-push

• Metric: wall-clock “real” time from /usr/bin/time -l

Results

Baselines

• Baseline A (sequential, cold-ish): 31.87 s

• Orchestrator grid (episodes-per-batch × workers)

  Mode/Config	Episodes/Batch	Workers	Real	Speedup
  Baseline (Reference)	—	—	31.87	1.00×
  Orchestrator	25	2	30.86	1.03×
  Orchestrator	25	4	31.60	1.01×
  Orchestrator	25	6	106.68	0.30×
  Orchestrator	50	2	28.10	1.13×
  Orchestrator	50	4	28.82	1.11×
  Orchestrator	50	6	30.07	1.06×
  Orchestrator	75	2	26.38	1.21×
  Orchestrator	75	4	28.28	1.13×
  Orchestrator	75	6	29.80	1.07×
  Orchestrator	100	2	27.10	1.18×
  Orchestrator	100	4	28.35	1.12×
  Orchestrator	100	6	29.69	1.07×
  Orchestrator	75	2	28.03	1.14×

  Highlights

  • Best observed: 26.38 s (episodes-per-batch=75, workers=2) → ~1.21× vs 31.87 baseline (~1.11× vs 29.15).

  • Too many local workers (e.g., 6) degraded performance on this single-node SSD setup (contention & overhead).

  • Larger output file sizes (512/2048 MB) improved over baseline but weren’t the single-node best here.

  
  

  Validation (correctness)

  • We ran a baseline sequential conversion and the orchestrator conversion, both --no-push.

  • We asserted final directories match (ignoring the orchestrator’s transient _work/ and OS metadata).

    Additionally, we normalized and compared meta/stats.json—no meaningful differences after normalization.

  
  

  Conclusion

  • Our original small-dataset (50 episodes) test showed the sequential path slightly faster, so we treated “orchestrator wins” as a hypothesis.

  • On a larger dataset (201 episodes), that hypothesis is now confirmed: the orchestrator outperforms the baseline (up to ~1.21× here).

  • On single-node, fast-SSD workloads of this size, both paths are largely I/O-bound, so gains are modest locally.

    We expect larger speedups on bigger datasets and/or multi-node or network storage, where overlapping read/compute/write and true distribution remove sequential choke points.

  
  

[adlai]

• Machine: MacBook Pro (Apple M1, 16GB RAM, local SSD)

I don't have 16GB RAM let alone half that, although 823x338 looks like a reasonable diff... should I bother reading?

[eDeveloperOZ]

@adlai I think so, you could just try it our with smaller DS.

[adlai]

I believe it is good Python style to include at least one blank line before lines of reduced indentation.