Files

T

Will LinandClaude Fable 5 b65b37f73e [train] run ops: checkpoint start-step gating, LR reset on resume, W&B fallback, weights-only DCP export

- CheckpointConfig.start_step: suppress saves before a given step
- TrainingMethod.apply_configured_lrs(): re-apply YAML LRs after a DCP
  resume restores the checkpoint's optimizer/scheduler LRs
- tracker: if online W&B init fails at boot, retry offline, then run
  trackerless instead of crashing a multi-node job
- dcp_to_diffusers: --weights-only (skip optimizer state that OOMs a
  single-GPU conversion) and robust base-pipeline copytree
- GB200 sbatch: lustre HOME, NCCL/MNNVL env pins, boot watchdog,
  SP/HSDP topology env overrides; plus the 32-GPU test configs and the
  64-prompt held-out H3 validation set
(dataclass/parse plumbing for the new checkpoint fields lands with the
next commit, which touches the same config files)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

2026-08-14 02:07:51 +00:00

configs

[train] run ops: checkpoint start-step gating, LR reset on resume, W&B fallback, weights-only DCP export

2026-08-14 02:07:51 +00:00

scenario

[misc] yapf pass under CI's interpreter (3.12) + pin hook language_version (#1702 )

2026-08-08 22:24:17 -07:00

slurm

[train] run ops: checkpoint start-step gating, LR reset on resume, W&B fallback, weights-only DCP export

2026-08-14 02:07:51 +00:00

launch_minimax_h3_t2va_crush_smol_validation.sh

[feat] add Minimax H3 sft pipeline (#1688 )

2026-08-07 16:12:04 -07:00

README.md

[feat] add qat docs (#1621 )

2026-07-19 18:37:47 -07:00

run_slurm.sh

[feat] add Minimax H3 sft pipeline (#1688 )

2026-08-07 16:12:04 -07:00

run.sh

[feat] Add minimal LoRA finetuning support to the YAML training stack (#1242 )

2026-05-17 14:49:13 -07:00

setup_minimax_h3_t2va_crush_smol_single_sample.sh

[feat] add Minimax H3 sft pipeline (#1688 )

2026-08-07 16:12:04 -07:00

README.md

Training

Quick Start

Single-node

bash examples/train/run.sh <config.yaml> [--dotted.key value ...]

The script auto-detects available GPUs. Override with NUM_GPUS:

NUM_GPUS=4 bash examples/train/run.sh \
    examples/train/configs/fine_tuning/wan/t2v.yaml

Multi-node (Slurm)

bash examples/train/run_slurm.sh <config.yaml> <num_nodes> [--dotted.key value ...]

bash examples/train/run_slurm.sh \
    examples/train/configs/fine_tuning/wan/t2v.yaml 4 \
    --training.distributed.hsdp_shard_dim 8 \
    --training.distributed.hsdp_replicate_dim 4

Slurm environment variables:

Variable	Default	Description
`PARTITION`	`main`	Slurm partition
`NUM_GPUS`	`8`	GPUs per node
`CPUS_PER_TASK`	`128`	CPUs per task
`MEM`	`1440G`	Memory per node
`EXCLUDE`	`""`	Nodes to exclude

Overriding config values

Any config field can be overridden from the command line using dotted keys:

bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
    --training.optimizer.learning_rate 1e-5 \
    --training.loop.max_train_steps 1000 \
    --training.checkpoint.output_dir outputs/my_experiment

Resuming from a checkpoint

bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
    --training.checkpoint.resume_from_checkpoint outputs/my_experiment/checkpoint-500

W&B Logging

Training metrics and validation videos are logged to Weights & Biases. Set your API key before launching:

export WANDB_API_KEY=your_key_here

To disable W&B (e.g. for local debugging):

export WANDB_MODE=offline

The project name and run name are set in the config under training.tracker:

training:
  tracker:
    project_name: my_project
    run_name: my_run

Directory Layout

examples/train/
├── configs/      # Single-step training configs (by method and model)
├── scenario/     # Multi-step end-to-end training pipelines
├── run.sh        # Single-node launcher
└── run_slurm.sh  # Multi-node Slurm launcher

See configs/README.md and scenario/README.md for details.

The featured QAD Wan2.1 MixKit scenario runs Attn-QAT supervised fine-tuning, checkpoint export, and three-step DMD2. See the Attn-QAT training guide.