---

name: training description: "Construct and debug OpenCLIP task-era and legacy training commands, data loaders, task/loss wrappers, compile, FSDP, precision, optimizer, scheduler, and checkpoint options." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Training

Use this sub-skill when an agent needs to build or troubleshoot OpenCLIP training commands, data layouts, task/loss wrappers, optimizer/scheduler settings, precision, torch.compile, distributed/FSDP, or checkpoint/resume behavior.

Route Here

• Launch training with python -m open_clip_train.main or legacy compatibility training with python -m open_clip_train.legacy_main.
• Choose --dataset-type among csv, webdataset, synthetic, webdataset-audio, synthetic-audio, or auto.
• Explain dict batch contracts such as {"image": ..., "text": ...}, {"audio": ..., "text": ...}, nested NaFlex patch/audio dicts, and optional text_valid masks.
• Select CLIPTask, SigLIPTask, CoCaTask, DistillCLIPTask, CLAPTask, GenLipTask, or GenLapTask behavior via model and CLI flags.
• Tune optimizer, scheduler, precision, gradient accumulation, loss options, torch.compile, FSDP2, full/sharded checkpoints, resume/latest, and remote sync.
• Run safe parser and CSV data smoke checks without training.

Read First

• references/cli-reference.md for command construction, default parser facts, and option families.
• references/data-formats.md for CSV, WebDataset, synthetic, audio, dict batch, and validation contracts.
• references/task-api.md for TrainingTask wrappers, loss creation, batch preparation, and dummy batches.
• references/checkpoints-distributed.md for resume, checkpoint, FSDP, compile, precision, optimizer, and scheduler decisions.
• references/troubleshooting.md for common failures and fixes.

Safe Helpers

• scripts/training_arg_report.py imports open_clip_train.params.parse_args, parses OpenCLIP training args, and prints selected defaults/options without constructing models, data loaders, or training.
• scripts/validate_csv_dataset.py validates CSV/TSV image path and caption columns, optional separators/keys, numeric-caption stringification, and image path existence without loading OpenCLIP or starting training.

Quick Starts

Inspect parser defaults and a planned command:

python sub-skills/training/scripts/training_arg_report.py -- --dataset-type csv --train-data DATA/train.tsv --model ViT-B-32

Validate a TSV dataset before training:

python sub-skills/training/scripts/validate_csv_dataset.py DATA/train.tsv --image-key filepath --caption-key title --separator tab

Minimal CPU-safe synthetic smoke command shape:

python -m open_clip_train.main --dataset-type synthetic --train-num-samples 16 --batch-size 4 --epochs 1 --workers 0 --device cpu --model RN50

Boundaries

• Pure model loading, tokenization, preprocessing, embeddings, and inference route to ../model-inference/SKILL.md.
• CLAP audio model setup, audio zero-shot evaluation, and audio-specific data details route to ../audio-clap/SKILL.md.
• NaFlex token-budget tuning, patch dictionary details, GenLIP/GenLAP generation behavior, and NaFlex scripts route to ../naflex-generative/SKILL.md.
• Evaluation metrics, zero-shot/retrieval reporting, checkpoint conversion, export, and Hugging Face publishing route to ../evaluation-conversion/SKILL.md.