{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/3"}],"enrichment":{"capability":"Provides per-step fault tolerance for PyTorch distributed training, allowing jobs to recover from worker failures without restarting the entire training process.","skillfed_tags":["distributed-training","fault-tolerance","pytorch"],"use_cases":["Training large models like Llama 3 70B on multi-node clusters where worker failures are expected and full restart is costly.","Implementing fault-tolerant DDP training loops that gracefully handle node failures without losing training progress.","Scaling HSDP training across heterogeneous hardware with automatic recovery from individual worker outages.","Running parameter server training with reconfigurable process groups that adapt to membership changes.","Distributed training on preemptible or spot instances where interruptions are frequent and expected."],"what_it_does":"torchft-nightly is a PyTorch library that adds fault tolerance to distributed training by detecting and recovering from worker failures at the training step level, rather than requiring full job restart. It provides coordination primitives, fault-tolerant process groups, and checkpoint transports built on a Lighthouse server that tracks worker health via heartbeating. The library includes out-of-the-box algorithms for Fault Tolerant DDP, Fault Tolerant HSDP (combining FSDP, tensor parallelism, and DDP), LocalSGD, and DiLoCo.\n\nThe package depends on torch, opentelemetry-api, opentelemetry-sdk, and opentelemetry-exporter-otlp-proto-http for observability. It is designed for large-scale training scenarios where worker failures are common and restarting the entire job is expensive. Integration requires wrapping your model, optimizer, and process group with torchft's Manager and fault-tolerant wrappers, then running a Lighthouse server to coordinate membership changes across replica groups.","worth_installing":"Yes, with conditions. Install if you are training large distributed models on infrastructure where worker failures are common and you need per-step recovery without full job restart. The active maintenance, recent releases, and zero known vulnerabilities are positive signals. However, verify the BSD 3-Clause license applies to your use case (metadata is unclear), and be aware that medium install friction (Rust compilation, system dependencies) and nightly-only availability mean this is best suited for teams with infrastructure expertise rather than casual users."},"id":"torchft-nightly","links":{"html":"https://skillfed.io/packages/torchft-nightly","md":"https://skillfed.io/packages/torchft-nightly.md","pypi":"https://pypi.org/project/torchft-nightly/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"unclear","name":"torchft-nightly","python_support":"supports_current","summary":null},"popularity":{"monthly_downloads":102918,"position":12836,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.8.14"}