I’ve used FloydHub, Crestle and Paperspace.
These are my preliminary thoughts.
FloydHub has a unique structure that takes getting used to.
You create a new ‘Project’ and attach data to it.
Then, you can have a series of ‘jobs’ via .py scripts or Jupyter Notebooks.
The workflow is not very intuitive IMO.
Their Pricing is a bit higher compared to the other two ($0.75/h for a K80 even at their highest $100/mo plan).
Regardless, they have good funding (Y-Combinator), are on an aggressive growth spurt (improvements every few weeks), and have two dedicated co-founders in @sai and @narenst.
Things can only get better.
Crestle, on the other hand, scores a 10/10 on the ‘intuitiveness’ scale.
It literally cannot get any easier to do Deep Learning on the cloud.
You just sign-up and BOOM - there’s a Jupyter Notebook staring at you with unlimited home storage.
Two of the things apart from the simplicity, that I like about them, is that:
You can switch between CPU and GPU using a neat little toggle switch. This saves you a ton of money considering the fact that most of your time will be spent coding and debugging. Very little time is spent on the ‘training’. This makes it easier to tinker with your code without worrying too much about the cost.
You get your own home directory, unlike FloydHub. This is much closer to what you actually would have in your local setup. It makes it easier to share files between notebooks and save data in a folder without having to worry about ‘mounting’ it. Whereas in Floyd, if you need to download a dataset (say from Kaggle using the CLI tool), you need to first run a job. Then you’ll have to ‘mount’ the output of this ‘job’ which contains this data every single time you want to use it.
Created by our own @anurag, Crestle gives the same amount of compute (K80) at about half the price ($0.34/h vs FloydHub’s $0.75/h at best).
The only downside is the storage costs - $0.014/GB/day. So, if you’re using ~50GB (say), it’ll end up costing you ~$20/mo.
Again, this depends if you intend to persist the data (which for most purposes you won’t need to).
Beware, though that both FloydHub and Crestle use EFS for handling file storage
This means, for instance, that you will have a painful time handling a large number of small files (which is what most datasets in Deep Learning/CV have).
I took me an hour and a half to extract the StateFarm dataset on both platforms!
Paperspace alleviates most of these problems.
You get a full Desktop experience (not just a CLI).
They have a P4000 GPU (comparable to the K80) at $0.4/h. But also, you can have a P5000 ($0.65/h), a P6000 ($0.9/h almost 3x the performance of the K80 - comparable to the 1080Ti).
They also have the latest V100 (only AWS and Paperspace have them as of now) at $2.3/h!
You can also subscribe monthly at a discount although Hetzner would be much more cost-effective in that case.
They currently have 3 data centres (2 in the US and 1 in Amsterdam) and are expanding.
Apart from the straightforward, familiar experience and workflow (it’s like your own computer - only not local), the big upside for me is the SSD storage (not EFS unlike the other two). StateFarm only took a few seconds to extract - much like my local build (which is what you expect).
Also, the storage starts at $5/mo for 50GB (against ~$20/mo in Crestle’s case) and increases in $1 increments up to 250GB. The maximum is 2TB for $40/mo.
Paperspace also has a dedicated VM specifically for FastAI.
In summary, if you’re just getting started with Deep Learning, I’d recommend Crestle for its sheer simplicity.
If you want a full cloud desktop and SSD, use Paperspace.
I would recommend against using FloydHub for now.
If you ask me, personally, Paperspace is the winner for me. A personal cloud desktop which I can customize according to my will and treat just like my local build seals the deal. Also, they have multiple GPU options and datacentres and lower pricing.