Yep! You can use the conda search command to see what versions of the NVIDIA CUDA Toolkit are available from the default channels. Are NVIDIA libraries available via Conda?
I will assume a basic familiarity with Conda so if you haven’t hear of Conda or are just getting started I would encourage you to read my other articles Getting started with Conda, and Managing your data science projects with Conda.
#Nvidia cuda toolkit license how to
In this post I am going to show you how to manage project specific versions of the NVIDIA CUDA Toolkit, NCCL, and cuDNN using Conda. But then what do you do if you need different versions of these new libraries for different projects? You could install a bunch of different versions of NVIDIA CUDA Toolkit, NCCL, and cuDNN system wide and then use environment variables to control the “active” versions for each project but this is cumbersome and error prone. If you go to the NVIDIA developer website you will find loads of documentation and instructions for how to install these libraries system wide. In particular, there is quite a bit of unfamiliar additional software, such as NVIDIA CUDA Toolkit, NVIDIA Collective Communications Library (NCCL), and NVIDIA Deep Neural Network Library (cuDNN) to download and install. Transitioning your data science projects from CPU to GPU can seem like a daunting task. Conda greatly simplifies starting new, GPU accelerated data science projects.
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