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Same situation for me, luckily it looks like archive.org snagged a backup while it was up! https://web.archive.org/web/20200907164516/https://timdettmers.com/2

Same situation for me, luckily it looks like archive.org snagged a backup while it was up! https://web.archive.org/web/20200907164516/https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/ https://web.archive.org/web/20200907164516/https://timdettme... This post looks very thorough, and came just in time for me. I'm looking to snag an upgrade from my GTX 970 for a mix of flight sim 2020 and digging into Fast.ai's course part 2. The 970 has been my big hold-up, right now even simple models take a really long time to work with.

dgellow 6y ago
Have you tried using Google Colab and other online platforms? I started the course a few days ago and so far Colab works well (I don’t really like Jupiter but that’s a detail...). It’s free and you have the choice between a CPU, a GPU, and a TPU.
- PNWChris 6y ago
  Disclosure (since Colab is a Google product): I work at Google, but everything I say is my personal opinion and experience. I really dig the overall idea of cloud notebooks. Back when I did fast.ai part 1, I used Paperspace Gradient. It was a pretty good experience, but moving files around was a bit of a hassle. For example, getting the images for the Planet Labs exercise took a round trip of downloading from Kaggle to my computer and re-uploading into Jupyter to do analysis. Because of all those moving parts, I decided to give a try running things locally. To my surprise, setup was super easy and I was quickly productive! I really dig how customizable a local Jupyter server is, too. I do use Colab, it's particularly great for collaboration/sharing notebooks, but my past experience has me hooked on the idea of a capable ML machine at home. Plus: I can pitch it to myself and my spouse as an investment in personal development that happens to be able to game :D
  - fxtentacle 6y ago
    Go for it! I have my own Jupyter docker image that I run on a server in the basement. PyCharm can even do code completion for TensorFlow inside a remote docker container. So it's instant, reproducible, and I don't hear a thing in my office :)
g42gregory 6y ago
Thank you for posting the archived link. I read it of this archive page, and it's great. This article is so good, that it made to the HN front page without even being available!

dgellow 6y ago

Have you tried using Google Colab and other online platforms? I started the course a few days ago and so far Colab works well (I don’t really like Jupiter but that’s a detail...). It’s free and you have the choice between a CPU, a GPU, and a TPU.

PNWChris 6y ago
Disclosure (since Colab is a Google product): I work at Google, but everything I say is my personal opinion and experience. I really dig the overall idea of cloud notebooks. Back when I did fast.ai part 1, I used Paperspace Gradient. It was a pretty good experience, but moving files around was a bit of a hassle. For example, getting the images for the Planet Labs exercise took a round trip of downloading from Kaggle to my computer and re-uploading into Jupyter to do analysis. Because of all those moving parts, I decided to give a try running things locally. To my surprise, setup was super easy and I was quickly productive! I really dig how customizable a local Jupyter server is, too. I do use Colab, it's particularly great for collaboration/sharing notebooks, but my past experience has me hooked on the idea of a capable ML machine at home. Plus: I can pitch it to myself and my spouse as an investment in personal development that happens to be able to game :D
- fxtentacle 6y ago
  Go for it! I have my own Jupyter docker image that I run on a server in the basement. PyCharm can even do code completion for TensorFlow inside a remote docker container. So it's instant, reproducible, and I don't hear a thing in my office :)

g42gregory 6y ago

Thank you for posting the archived link. I read it of this archive page, and it's great. This article is so good, that it made to the HN front page without even being available!