We’ve aggregated together hardware, performance, and cost data for both current cloud offerings and peer rentals into one place to make it easier to compare and rank. If you’re interested in reducing your training costs you can rent a consumer machine and get about 3 to 5 times more performance per dollar.
Edit: The fastai $20 credit promotion all been claimed. New users still receive credit sufficient 5 to 10 hours of GPU time, aka $1.
We support the fast.ai docker image provided by paperspace - just select it from the list when renting an instance. (We also fixed a minor issue with the base container that prevented lesson 6 from working - let us know if you find any others!)
To back up the 3x to 5x lower cost claim, we can use an example from Stanford’s Dawnbench CIFAR10 competition. The winning entry uses a single V100 to train in 6:45 for a cost of $0.26 on Paperspace. Using the same code as the 2nd place entry (bkj), you can train in ~13 minutes on a single 1080Ti, for a cost of ~$0.05 on our service.
To reproduce that result, select the pytorch/pytorch image, connect to the jupyter notebook, open a terminal (new => terminal), and then run the following commands in the container:
apt install git; git clone https://github.com/bkj/basenet.git; cd basenet; git checkout 49b2b61; pip install -r requirements.txt; python setup.py install; cd examples; python cifar10.py --download
And finally, if you happen to have a powerful deep learning rig that you aren’t using very much, you could use our service to rent it out.
Feedback much appreciated. We are also available on our discord server.