{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Adds fault tolerance, checkpointing, and performance monitoring to PyTorch distributed training, detecting hung ranks, straggler GPUs, and enabling automatic recovery without reallocating compute resources.","skillfed_tags":["distributed-training","fault-tolerance","gpu-monitoring"],"use_cases":["Detect and recover from hung ranks in multi-GPU training without manual job restart or SLURM reallocation.","Identify straggler GPUs that slow down synchronous training and trigger corrective action.","Implement frequent asynchronous checkpointing to minimize training loss when hardware fails mid-job.","Monitor system-wide GPU and CPU health during long-running distributed training runs.","Integrate fault tolerance into PyTorch Lightning training loops with minimal code changes."],"what_it_does":"NVIDIA Resiliency Extension (NVRx) is a modular toolkit for making large-scale PyTorch distributed training more resilient to hardware faults and performance degradation. It detects hung training ranks, identifies slower GPUs that slow down the entire job, and enables fast recovery without needing to reallocate compute nodes\u2014critical for long-running training on shared infrastructure. The package provides asynchronous and local checkpointing frameworks to minimize work loss, health monitoring across GPU and CPU, and integration hooks for PyTorch Lightning and NeMo frameworks.\n\nThe package is actively maintained but explicitly experimental, with frequent breaking changes expected. It depends on a substantial stack including torch, grpcio, protobuf, numpy, and system-level NVIDIA monitoring tools. Installation requires specific Python, PyTorch, CUDA, and driver versions, making it suitable for managed training environments rather than casual experimentation.","worth_installing":"Yes, if you run distributed PyTorch training at scale on shared infrastructure where hardware faults or performance variance are real concerns and you can meet the strict version requirements (Python >=3.10, <3.13; PyTorch >=2.5.1; CUDA >=12.8). The experimental status and frequent breaking changes mean you should pin versions carefully and monitor releases. Not recommended for single-GPU or development-only use cases."},"id":"nvidia-resiliency-ext","links":{"html":"https://skillfed.io/packages/nvidia-resiliency-ext","md":"https://skillfed.io/packages/nvidia-resiliency-ext.md","pypi":"https://pypi.org/project/nvidia-resiliency-ext/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-14","license_spdx":null,"license_treatment":"permissive","name":"nvidia-resiliency-ext","python_support":"supports_current","summary":"NVIDIA Resiliency Package"},"popularity":{"monthly_downloads":124594,"position":11862,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.0"}