![]() ![]() They "probably" suggest conda because it's the easiest method to get a working install and minimizes debugging users' install issues, per above. ![]() These repos probably suggest conda because they are used to it I don't want that to be a reason I hesitate to try a new package (or consider taking on a new dependency), nor do package authors want potential new users to be so discouraged. I'm also glad to avoid the pain of properly building and linking compiled dependencies even once. Of course one "should," but once you need to deploy an environment to multiple machines (especially where you can't install system deps), need to set up CI, or want any other person (including your future self) to be able to reproduce your environment, then clearly this is not a reasonable solution. You should be able to use pip and figure out any system dependencies as you go PyTorch: Uses CUDA 11.3, ships with conda provided cuda-toolkit.TensorFlow & MxNet: Uses CUDA 11.2 Prefer to install with pip as conda is always lagging behind with the latest version.You'll get the latest drivers and can install multiple versions of cuda in parallel, not problem whatsoever.ĭepending on the library I still use the system-provided cuda-toolkit, or the one provided by conda. as I said just use the PPA nvidia provides themselves. Only issue is that you have to wait 2 years for next upgrade from Nvidia.īut you don't have to. (the latest one for Ubuntu is 510.47.03)īut I am running the cudatoolkit that comes from apt repository, and it's working good enough. You still need to have a compatible driver for it to work. The problem in your case might actually be the following: conda can provide the cuda-toolkit, but not the driver. Just to be clear: You can use conda provided cudatoolkit with nvidia provided cuda/driver installation with no problems whatsoever. Introduction to Programming with Python (from Microsoft Virtual Academy). ![]()
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