{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/4"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"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 cuDNN runtime libraries for GPU-accelerated deep neural network operations on NVIDIA CUDA 11 hardware.","skillfed_tags":["gpu-acceleration","cuda","deep-learning"],"use_cases":["Ensuring cuDNN primitives are available in containerized deep learning environments.","Providing GPU-accelerated neural network operations for research or production ML pipelines.","Bundling with custom CUDA applications that directly call cuDNN functions.","Supporting deep learning frameworks that require cuDNN on CUDA 11 hardware."],"what_it_does":"nvidia-cudnn-cu11 is a runtime library distribution that bundles NVIDIA's cuDNN primitives for GPU-accelerated machine learning workloads on CUDA 11 hardware. It is a thin packaging layer that delivers precompiled cuDNN binaries\u2014you import it to ensure the runtime libraries are available to downstream frameworks that depend on cuDNN for their GPU operations.\n\nThe package targets developers building or deploying deep learning applications on NVIDIA GPUs. It handles the complexity of distributing platform-specific binary libraries (separate wheels for Linux and Windows) and manages the dependency chain through nvidia-cublas-cu11. Installation requires a compatible NVIDIA GPU and drivers; the package itself is not a high-level API but rather a prerequisite that other libraries consume.","worth_installing":"Yes, if you are building or deploying deep learning workloads on NVIDIA GPUs with CUDA 11 and your framework requires cuDNN. No, if you do not have compatible NVIDIA hardware or are using a different CUDA version. Be aware: the proprietary license terms are unclear in the fact sheet, so verify NVIDIA's licensing requirements for your use case before production deployment. The aging maintenance status (434 days) warrants checking whether CUDA 11 is still actively supported."},"id":"nvidia-cudnn-cu11","links":{"html":"https://skillfed.io/packages/nvidia-cudnn-cu11","md":"https://skillfed.io/packages/nvidia-cudnn-cu11.md","pypi":"https://pypi.org/project/nvidia-cudnn-cu11/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-06-06","license_spdx":null,"license_treatment":"unclear","name":"nvidia-cudnn-cu11","python_support":"supports_current","summary":"cuDNN runtime libraries"},"popularity":{"monthly_downloads":2488865,"position":3044,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"9.10.2.21"}