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name: training description: "Train, resume, smoke-test, or troubleshoot BigGAN-PyTorch GAN runs, including BigGAN and BigGANdeep architectures, CUDA parallelism, gradient accumulation, EMA, spectral normalization, orthogonal regularization, precision modes, checkpoints, datasets, and the checked-in launch recipes." disable-model-invocation: true metadata: disco-role: operating license: MIT

BigGAN-PyTorch training

Use this sub-skill as the training router for the BigGAN-PyTorch repository. It covers the executable training path (train.py and train_fns.py), the model and numerical building blocks (BigGAN.py, BigGANdeep.py, layers.py, losses.py, and sync_batchnorm/), and the bookkeeping/data helpers in utils.py.

Route by intent

• Start a run or choose a recipe: read references/workflows.md.
• Choose flags, understand tensor/optimizer contracts, or modify a model: read references/api-reference.md.
• Diagnose a failed help command, data path, OOM, resume, precision, or multi-GPU run: read references/troubleshooting.md.

Do not launch a production-scale run until the CUDA, dataset, output-root, and experiment-name gates in workflows.md pass. The code is designed for CUDA; train.py assigns device = 'cuda', and the sampling/standing-stat utilities also create tensors on CUDA. A CPU-only run is not a supported fallback.

Non-negotiable facts

1. The historical README targets PyTorch 1.0.1 plus tqdm, numpy, scipy, and h5py; this is an old research codebase, so validate the installed PyTorch/torchvision pair before a long run.
2. Supported dataset keys are I32, I64, I128, I256, their _hdf5 ImageNet forms, C10, and C100. The key determines resolution, class count, and the child name appended below data_root; do not substitute an arbitrary directory name without updating the metadata dictionaries.
3. --parallel uses torch.nn.DataParallel around the combined G_D module, not distributed training. Select visible GPUs with CUDA_VISIBLE_DEVICES.
4. The default loss is hinge loss. The training closure averages each accumulation loss before backward(), then takes one optimizer step for the corresponding network.
5. --ema creates a non-optimizing generator copy. --use_ema affects sampling/metrics only when EMA is enabled; it does not change which weights are optimized. Standing-stat accumulation is a separate option.
6. --G_fp16/--D_fp16 cast the corresponding modules (and the relevant data) to half. The *_mixed_precision switches select the repository's naive Adam16; they are not a modern AMP/Tensor-Core implementation. The README reports early collapse risk and no Tensor-Core activation for this path.
7. Checkpoints are a family of files under <weights_root>/<experiment_name>/: G, D, both optimizer states, state_dict, and optionally G_ema. Resume with the same configuration-derived or explicitly supplied experiment name; use --load_weights for suffixes such as best0 or copy0.
8. On Python 3.11, python train.py --help can crash before showing help with ValueError: unsupported format character '#'. The literal %#.#f/%#.#e text in the --logstyle help string is interpreted by argparse's % formatter. Escape the literal percent signs in that help string as %%#.#f/%%#.#e (or replace the text with words); keep the runtime --logstyle value such as %3.3e unchanged. The same failure is observable on newer Python versions. This is a parser-help bug, not a CUDA diagnosis.

For a short, bounded verification run, use the temporary ImageFolder smoke harness in workflows.md; it bypasses the repository's unconditional Inception startup, avoids metric evaluation and checkpoint sample sheets, uses one tiny batch, and keeps all artifacts outside the repository.