{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/5"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"Provides NVIDIA's collective communication library (NCCL) runtime for GPU-accelerated all-reduce, all-gather, reduce, broadcast, and reduce-scatter operations optimized for CUDA 11.","skillfed_tags":["gpu-computing","distributed-training","cuda-runtime"],"use_cases":["Provide the NCCL runtime for distributed deep learning frameworks on CUDA 11 systems.","Support legacy production deployments that are locked to CUDA 11 and need the matching NCCL library.","Enable multi-GPU collective communication on systems using PCIe, NVLink, NVswitch, or InfiniBand.","Facilitate research or development on multi-GPU systems where collective communication is critical."],"what_it_does":"This package bundles NVIDIA's Collective Communication Library (NCCL) runtime for CUDA 11, implementing collective communication routines like all-reduce, all-gather, reduce, broadcast, and reduce-scatter. It has been optimized to achieve high bandwidth on any platform using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. The package is not a standalone library you call directly from Python; instead, it serves as a runtime dependency for frameworks that need to coordinate computation across multiple GPUs.\n\nBecause this is a platform-specific wheel (manylinux2014 x86_64 only) and the package has been dormant for 863 days with no recent maintenance, it is primarily useful for legacy projects already pinned to CUDA 11. New projects should evaluate whether a current CUDA version and its corresponding NCCL package better suit their needs.","worth_installing":"Yes, but only if you are locked to CUDA 11 and require NCCL for multi-GPU work. The package is dormant and will not receive updates, so it is suitable only for stable, legacy deployments. If you are starting a new project, prefer a current CUDA version and its corresponding NCCL package instead. Verify NVIDIA's licensing terms before production use."},"id":"nvidia-nccl-cu11","links":{"html":"https://skillfed.io/packages/nvidia-nccl-cu11","md":"https://skillfed.io/packages/nvidia-nccl-cu11.md","pypi":"https://pypi.org/project/nvidia-nccl-cu11/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-04-03","license_spdx":null,"license_treatment":"unclear","name":"nvidia-nccl-cu11","python_support":"supports_current","summary":"NVIDIA Collective Communication Library (NCCL) Runtime"},"popularity":{"monthly_downloads":1472815,"position":3865,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.21.5"}