v0.2.0

pixtreme-train v0.2.0

New training components in pixtreme.train.common:

• write_text and staging_directory replace a file or a directory only after the new content is fully written.
• training_signals turns SIGINT and SIGTERM into a stop request for a training loop, with defer() for sections that must finish.
• worker_signals and stop_loader_workers handle signals in DataLoader worker processes and stop and reap the workers of an iterator without waiting for prefetched batches.
• rng_state and restore_rng_state save and restore the Python, NumPy, torch CPU and CUDA, and named torch.Generator random states.
• warmup_cosine_factor, update_ema, check_finite_loss, check_finite_gradients, release_gpu_memory and EndlessSampler provide small training parts.

Importing pixtreme.train.common still does not load torch; each torch-based name loads its module on first access.

The wheel attached here is the reference copy for the PyPI distribution of pixtreme-train. Verify it with sha256sum -c SHA256SUMS.