mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-12 12:32:02 +00:00
Pepijn
68fa5d80b0
- examples/scaling/train_streaming_multinode.py: Accelerate-based distributed/ resumable streaming training (no DistributedSampler; rank/world_size auto-resolved), checkpoints the dataset stream state, and supports a --dummy pure-dataloading path with throughput logging. SLURM launcher in slurm/train_streaming_robocasa.sh. - benchmarks/streaming/benchmark_streaming.py: dummy-consumer dataloading benchmark (single / sarm frame modes) emitting frames/s/node, p50/p95/p99 sample latency, first-batch latency, and VideoDecoderCache reuse stats as JSON + CSV. SLURM launcher + README documenting the source/node/mode matrix and manual bucket prewarming. - VideoDecoderCache: add hit/miss/eviction counters and a stats() method so the benchmark can surface decoder thrash (no new cache, no eviction-policy change). - tests/datasets/test_streaming_distributed.py: accelerate-launch smoke test asserting per-rank disjointness; skips (does not false-pass) when <2 processes spawn. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
50 lines
1.7 KiB
Bash
50 lines
1.7 KiB
Bash
#!/bin/bash
|
|
#SBATCH --job-name=stream_robocasa
|
|
#SBATCH --nodes=2
|
|
#SBATCH --ntasks-per-node=1
|
|
#SBATCH --gpus-per-node=8
|
|
#SBATCH --cpus-per-task=96
|
|
#SBATCH --exclusive
|
|
#SBATCH --time=24:00:00
|
|
#SBATCH --output=logs/%x-%j.out
|
|
|
|
# Multinode streaming training over a large HF-hosted RoboCasa dataset (never touches local disk).
|
|
# Launches examples/scaling/train_streaming_multinode.py with Accelerate. Each rank streams a disjoint
|
|
# set of shards via split_dataset_by_node (auto-resolved from the Accelerate state), so per-node
|
|
# throughput scales independently. For an even split, ensure n_shards % (nodes * gpus_per_node) == 0.
|
|
#
|
|
# Submit with: sbatch slurm/train_streaming_robocasa.sh
|
|
|
|
set -euo pipefail
|
|
|
|
REPO_ID=${REPO_ID:-pepijn223/robocasa_pretrain_human300_v4}
|
|
GPUS_PER_NODE=8
|
|
NUM_PROCESSES=$((SLURM_NNODES * GPUS_PER_NODE))
|
|
|
|
# Rendezvous: use the first node in the allocation as the main process.
|
|
MAIN_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n1)
|
|
MAIN_PORT=${MAIN_PORT:-29500}
|
|
|
|
export HF_HOME=${HF_HOME:-$SCRATCH/hf_home}
|
|
# Avoid each rank fighting over the tokenizers' internal thread pool.
|
|
export TOKENIZERS_PARALLELISM=false
|
|
|
|
srun --kill-on-bad-exit=1 bash -c '
|
|
accelerate launch \
|
|
--num_machines '"$SLURM_NNODES"' \
|
|
--num_processes '"$NUM_PROCESSES"' \
|
|
--machine_rank $SLURM_NODEID \
|
|
--main_process_ip '"$MAIN_ADDR"' \
|
|
--main_process_port '"$MAIN_PORT"' \
|
|
--mixed_precision bf16 \
|
|
--dynamo_backend no \
|
|
examples/scaling/train_streaming_multinode.py \
|
|
--repo_id '"$REPO_ID"' \
|
|
--batch_size 64 \
|
|
--num_workers 12 \
|
|
--buffer_size 4000 \
|
|
--steps 200000 \
|
|
--save_freq 2000 \
|
|
--log_freq 50
|
|
'
|