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I have heard this criticism of Nvidia w.r.t. Linux before, so I feel like I must be missing something. Working with Nvidia cards has been easy for the last ~5 y

I have heard this criticism of Nvidia w.r.t. Linux before, so I feel like I must be missing something. Working with Nvidia cards has been easy for the last ~5 years -- literally just install their drivers, `apt install nvidia-cuda-toolkit` and CuDNN and everything just works. In comparison, I've never been able to figure out how to get Tensorflow or PyTorch to run on Windows. What are your use cases that the Nvidia drivers fail for? EDIT: I think I misunderstood, and your complaint wasn't about the quality of their proprietary drivers, rather their refusal to work with open source developers. I was reacting mainly to "no option to actually make an Nvidia card useful"

edgyquant 5y ago
I can’t get cuda to work for me on my nvidia 1660x. I’ve followed their steps straight from nvidia, am using the Ubuntu LTS, etc. I’m sure I must be doing something wrong but I always get a dependency issue (cuda depends on X which isn’t going to be installed) or when I try to import tensorflow it claims it can’t find the .so files. I’ve added the cuda path in opt to the LD path with no luck. I bought nvidia specifically to be able to use tensorflow (had an amd prior) and I’m just kinda upset about it.
- disabled 5y ago
  You may want to try this guide: https://medium.com/@dun.chwong/the-ultimate-guide-ubuntu-18-04-37bae511efb0 https://medium.com/@dun.chwong/the-ultimate-guide-ubuntu-18-... I also had to resort to looking at guides on Medium.com for installing CUDA, as the install is not as straightforward as it looks. Alternatively, you can search on DuckDuckGo (for privacy reasons) with this as the query: "NVIDIA" AND "CUDA" AND "install" site:medium.com
  - deleted 5y ago
    [deleted]
- rahimnathwani 5y ago
  The fast.ai student community forums have good tips for people having trouble getting CUDA to work.
- kwertzzz 5y ago
  Installing CUDA (with kernel driver, CuDNN, Tensorflow/PyTorch) has not been easy for me either. Usually you want to have the latest version of all of them. But sometimes the very latest version of these packages is not yet supported by the other. This is where it get difficult. Also the fact that the CUDA instruction from TensorFlow [1] still are at Ubuntu 18.04 does not help. In my experience installing PyTorch with CUDA is easier than TensorFlow (and much better documented). The best experience, in my opinion, is however with CUDA and Flux.jl or Knet.jl on Julia. All you need there is the kernel driver (and julia). CUDA and CuDNN will be installed per default if necessary. I am wondering why CUDA is not simply a pip-installable package (like it is in Julia or in Conda). [1] https://www.tensorflow.org/install/gpu https://www.tensorflow.org/install/gpu
  - eigenspace 5y ago
    My understanding is that the dependency analysis in Pip just isn’t very good, so it’s easy to end up with incompatible versions.

edgyquant 5y ago

I can’t get cuda to work for me on my nvidia 1660x. I’ve followed their steps straight from nvidia, am using the Ubuntu LTS, etc. I’m sure I must be doing something wrong but I always get a dependency issue (cuda depends on X which isn’t going to be installed) or when I try to import tensorflow it claims it can’t find the .so files. I’ve added the cuda path in opt to the LD path with no luck. I bought nvidia specifically to be able to use tensorflow (had an amd prior) and I’m just kinda upset about it.

disabled 5y ago
You may want to try this guide: https://medium.com/@dun.chwong/the-ultimate-guide-ubuntu-18-04-37bae511efb0 https://medium.com/@dun.chwong/the-ultimate-guide-ubuntu-18-... I also had to resort to looking at guides on Medium.com for installing CUDA, as the install is not as straightforward as it looks. Alternatively, you can search on DuckDuckGo (for privacy reasons) with this as the query: "NVIDIA" AND "CUDA" AND "install" site:medium.com
- deleted 5y ago
  [deleted]
rahimnathwani 5y ago
The fast.ai student community forums have good tips for people having trouble getting CUDA to work.
kwertzzz 5y ago
Installing CUDA (with kernel driver, CuDNN, Tensorflow/PyTorch) has not been easy for me either. Usually you want to have the latest version of all of them. But sometimes the very latest version of these packages is not yet supported by the other. This is where it get difficult. Also the fact that the CUDA instruction from TensorFlow [1] still are at Ubuntu 18.04 does not help. In my experience installing PyTorch with CUDA is easier than TensorFlow (and much better documented). The best experience, in my opinion, is however with CUDA and Flux.jl or Knet.jl on Julia. All you need there is the kernel driver (and julia). CUDA and CuDNN will be installed per default if necessary. I am wondering why CUDA is not simply a pip-installable package (like it is in Julia or in Conda). [1] https://www.tensorflow.org/install/gpu https://www.tensorflow.org/install/gpu
- eigenspace 5y ago
  My understanding is that the dependency analysis in Pip just isn’t very good, so it’s easy to end up with incompatible versions.