---

name: training description: "Operate ECCV2022-RIFE training preflight, Vimeo triplet data layout, distributed CUDA/NCCL launch, TensorBoard logs, checkpoints, and resource planning." disable-model-invocation: true metadata: disco-role: operating license: MIT

ECCV2022-RIFE training sub-skill

Use this sub-skill when the user asks how to train or reproduce ECCV2022-RIFE, prepare the Vimeo90K triplet layout, plan a distributed CUDA launch, interpret world_size and batch size, locate training checkpoints/logs, or diagnose TensorBoard, NCCL, CUDA, and OOM failures.

Route away from this sub-skill when the user asks for image/video interpolation CLI usage, official benchmark interpretation, or model-architecture changes beyond the public Model.update training contract.

Safety boundary

Full training is long-running, mutates the checkout, requires a Vimeo triplet dataset, and uses CUDA/NCCL. Do not start training automatically during routine assistance. Safe default actions are:

1. Validate the dataset layout with scripts/check_vimeo_triplet_layout.py.
2. Confirm CUDA-enabled PyTorch, TensorBoard, available GPUs, and the intended process count.
3. Build or review the launch command without executing it.
4. Ensure output directories and checkpoint-overwrite expectations are explicit.

Run the actual distributed command only after the user confirms data availability, GPU allocation, wall-time budget, and that writing train_log/flownet.pkl, train/, and validate/ is acceptable.

Primary references

• references/training.md — data layout, launch commands, arguments, checkpoint/log outputs, evaluation loop, and resource planning.
• references/troubleshooting.md — missing data, TensorBoard, CUDA/NCCL, rank/world-size, OOM, checkpoint overwrite, and long-run safety issues.
• scripts/check_vimeo_triplet_layout.py — safe no-training validator for vimeo_triplet list files and sampled im1.png/im2.png/im3.png sequence files.

Operating checklist

When handling a training request:

1. Clarify intent: preflight only, single-GPU smoke planning, full reproduction, resumed/continued experiment, or post-training benchmark use.
2. Check data: require vimeo_triplet/tri_trainlist.txt, vimeo_triplet/tri_testlist.txt, and vimeo_triplet/sequences/<clip>/im1.png, im2.png, im3.png for listed clips. The loader uses the first 95% of tri_trainlist.txt for training and the remaining 5% for validation.
3. Check runtime: training is CUDA-only in this checkout (torch.device("cuda"), NCCL process group, DDP when local_rank != -1). TensorBoard is required by train.py but is not listed in the base requirements file.
4. Plan launch: keep --world_size equal to the launcher process count. The README pattern is python3 -m torch.distributed.launch --nproc_per_node=4 train.py --world_size=4.
5. Plan resources: default --epoch 300, per-process --batch_size 16, and data-loader workers can make a run expensive. The README reports 16 CPUs, 4 GPUs, and about 20G memory for training.
6. Confirm side effects: checkpoints save to train_log/flownet.pkl after every epoch, overwriting the same filename; TensorBoard event files go to train/ and validate/.
7. Post-training routing: for metrics, use the evaluation sub-skill; for applying a produced checkpoint to images or videos, use the interpolation sub-skill.