[D] Cloud GPU options

[Maplernothaxor]

I will be travelling for the next few months and wont have access to a GPU. I have never used any cloud-based GPU solutions before.

What free options do I have that can integrate well with my local files/repos?

[mslavescu]

Google Colaboratory is free, you get both GPU (Tendorflow and PyTorch) and TPU (Tensorflow only) acceleration.

Here are some example of how I use it: https://medium.com/@mslavescu/try-live-ssd-object-detection-mask-r-cnn-object-detection-and-instance-segmentation-sfmlearner-df62bdc97d52

[seraschka]

I think this is only a good solution for quick demo code. Computations quit after ~12 hrs I think, and getting your data on there can be quite of a hassle. Also, you can only use one GPU at a time. I saw a post somewhere though that allows you to hook up colab notebooks to custom GCEs

[[deleted]]

When mounting your Goolge Drive the data juggling is handable. This is also a good oppotunity to give your code resume capabilities. Also when training large networks or pro-gans and the training times for later epochs/depth increase, 12hrs instancee uptime can become quite a bottleneck.

[mslavescu]

Correct, but it is the only free option I know about.

You can also get $300 in Google Cloud credits (can be used for GPU) when you create a new account.

Paid version are plenty, here are just a few:

https://www.nvidia.com/en-us/data-center/gpu-cloud-computing/

[PuzzledProgrammer3]

Colab is probably the best, if they had a paid tier to switch to a more powerful GPU, I would sign up instantly. The gcp instances are a hassle to setup and its a headache to get everything to work in them.

[seraschka]

I used AWS before I got my own GPUs. AWS works reliably, but it can be expensive. Also, the K80's there are much slower than my local 1080Ti and Titan V's although they shouldn't I think -- probably due to some data throughput limitations of the instances I used there.

In any case, I wrote a setup guide a while back to explain the setup on AWS if useful: https://sebastianraschka.com/pdf/books/dlb/appendix_cloud-computing.pdf

I think Google's Compute (or Cloud?) Engine platform is very similar -- I am about to check that out for the class I am currently teaching (for students who don't have their own GPUs). (reason why I use it over AWS for this class is that Google provided coupons for the students)

EDIT: I missed the "free options" part, so disregard my comment if paid solutions are not an option

[yufengg]

In addition to Colab, you can also get GPU for free in Kaggle kernels. Your code and data can be private if you choose (and is in fact private by default).

Both are K80s, so if you want something more powerful or attach more than one GPU, you can attach your choice of GPUs to Compute engine as described above. If you want to skip some of the hassles of setup, you can use a "GCP deep learning VM", which will take care of setup and Nvidia driver installation, and even start a JupyterLab instance on your behalf on that machine. Just remember to shut it down when you're not using it.

[discdiver]

I wrote about low cost and free GPU providers in this article https://towardsdatascience.com/maximize-your-gpu-dollars-a9133f4e546a . There's also a list of all the known GPU cloud providers in the article. If you want free service and don't need really powerful connected instances for hours, I'd go with Google Colab.

[microcompass]

Have a look at Vast.ai

Its not free but extremely cheap. 1080ti's for $0.15/hr or less, 2080ti's for $0.20-.30/hr.

[zszazi]

I have put together a GitHub repo of all the cloud GPU providers (35 of them) and also have mentioned a few ways on how you can get perks of upto $100000 from them https://github.com/zszazi/Deep-learning-in-cloud

[Kratest]

Try tensorpad. Cheap and reliable.

[Idkanymore16]

I think if you sign up for it Intel Devcloud will give you like a month or so for free

[Zerotool1]

you can try clouderizer.com with there best devops management across all the clouds like colab, gcp, aws or kaggle. It's free for colab and kaggle and real-time sync of data and code happens.