Shareable Jupyter Notebooks That Run on Free Cloud GPUs

https://blog.paperspace.com/paperspace-launches-gradient-community-notebooks/

by hsikka • 7 years ago

199 49 7 years ago

blog.paperspace.com

hsikka• 7 years ago

Hey HN, really thrilled to see this launch. I was a Research Fellow at the Paperspace Advanced Technologies Group this summer and saw this project develop. Gradient Notebooks were an indispensable tool in the work I was doing, and had several advantages for my work over other notebook services. As a researcher, it’s great to see an emphasis being placed on starting projects with no fuss or issues around setting up infrastructure and sharing/forking models.

Giving anyone access to free GPUs and powerful tooling seems like an incredible opportunity. I'd love to hear what you all think!

ArtWomb• 7 years ago

It's a home run in my book. Perfect for "Intro to ML" courses and allows students instant access to real env. Personally can use it for art & design experiments like GAN Breeder ;)

Am wondering what to use for the data tier? Is there a dedicated backing cloud store supporting BigQuery style syntax? Import form S3 datasets?

DTE6• 7 years ago

Hey there ! I'm one of the co-founders of Paperspace. We currently have a couple of options for data ingest (and more coming soon!). The current system provides a single persistent mount that you can access from any notebook/experiment/etc in the /storage directory. We also mount a special directory called /artifacts where you can pipe out any models, files, etc and they will be pushed to an S3-compatible object store.

Checkout the docs here -> https://docs.paperspace.com/gradient/data/storage

And happy to answer any other questions

minxomat• 7 years ago

Suspiciously absent is a comparison with Google Colab, which not only includes free GPU support (T4) and TPU support, but supports pretty much everything I've seen here. It allows access to the underlying VM system and has native integration with Google Drive to store and retrieve huge files (like checkpoints) and GitHub to version notebooks.

Other similar services include Kaggle (way more locked down) and Baidu's free GPU powered notebook platform (only open to Chinese citizens)

solidasparagus2• 7 years ago

Sharing free GPUs is amazing! GPU costs are such a huge blocker for people learning deep learning.

But as a cloud provider, I would be worried about abuse by cryptojackers. I hope that's not a problem and this is sustainable.

applecrazy• 7 years ago

Google has run a similar service for a while (Colab) and they have methods to detect crypto mining within their instances. I'm sure Paperspace does too.

fierarul• 7 years ago

I don't understand this obsession with cloud-based deep learning for beginners. It creates this hyper-focus on cost: always remember to shut down your instances! With the occasional slippage which causes psychological (and / or financial) pain... The mood is all wrong for somebody entering a new (work-)field.

Google Colab is decent and free.

But you can do computing on cheaper hardware too. The CPU is good enough for learning and a GPU is not outside the budget of many people that presumably already own laptops and such.

I know a local group that shares an i9 / dual GTX "server" and are learning on this shared hardware. I think it's great!

I had a small budget for this learning curiosity and bought a Ryzen and a GTX with only 4GB of RAM. Got a job offer after a while which I had to turn down as it seemed to actually kill my interest in the field. Doing some small personal project now without much fuss to rekindle the fire. And using the CPU for it since it's so small.

doctorpangloss• 7 years ago

Google Collaboratory works pretty well as an alternative to this. In fact it works so well, like there’s so little time spent in IT, my total usage of it is quite low, because so much of what I’ve been communicating or seen communicated with notebooks is low quality in the first place.

llampx• 7 years ago

so much of what I’ve been communicating or seen communicated with notebooks is low quality in the first place.

Do you think there's a format which is better suited to sharing data stories?

jfim2• 7 years ago

I tried browsing the documentation for several minutes, but it does not answer this question: which Jupyter kernels are supported? Is it only Python like Google colab?

DTE6• 7 years ago

Because everything is running in a docker container behind the scenes we support any kernel you would like. We have a handful of pre-built containers and you can also add a custom container very easily or build one off of a base template such as the Jupyter R stack. Here is a list of some of the container we provide by default https://docs.paperspace.com/gradient/notebooks/notebook-cont...

jaredscheib• 7 years ago

I've gone ahead and added this response to the docs here: https://docs.paperspace.com/gradient/notebooks/about#contain... :)

Traster• 7 years ago

Sorry, I'm very new to this, how do notebooks/shared notebooks play with version control?

psv1• 7 years ago

Badly - under the hood jupyter notebooks are json which stores not only the code but all of the metadata as well. I know that there are tools that help with integrating jupyter and git but I just end up going back and forth between .py files in VSCode and notebooks in jupyter lab depending on what I'm working on.

jimmyvalmer4• 7 years ago

Aye, notebooks truly represent a wrong turn in scientific computing. The absence of version control alone is a showstopper, but it's insane generally to launch long-running estimations interactively via browser-based notebook interfaces, with outputs that are not readily greppable. But corporate philistines continue to ooh and ah over jupyter's visuals, and companies like Gradient are more than happy to cater to their FOMO.

mkutsovsky• 7 years ago

There are some tools in the jupyter ecosystem that are aware of the .ipynb json format & make diffing + version control easier to manage (https://github.com/jupyter/nbdime). Another option is by converting the notebook file to something (.py,.md,.html) that existing git tools are better at working with (https://github.com/mwouts/jupytext, https://github.com/jupyter/nbconvert)

schmudde• 7 years ago

It's possible but requires some foresight and strategy. Images are the first problem - they're stored in blobs. Small changes in graphics can create meaningless commits.

Data is the second problem - you usually can't run a notebook without it. If the notebook transforms data over time, it can be a real issue.

Here's a pretty complete run down of the first problem, the .ipynb itself: https://nextjournal.com/schmudde/how-to-version-control-jupy...

An integrated solution that versions data, the notebook, and the computational environment (collaborators will make changes over time) is the ideal collaborative platform.

solidasparagus2• 7 years ago

Can someone clarify how this works in terms of data? I see "5GB of persistent data", but that's nowhere near enough for my training data. Is there also a larger amount of non-persistent storage that I can download data to?

DTE6• 7 years ago

The 5GB of persistent data is available for by default for all free accounts running on the free instances. You can easily upgrade your storage up to 1TB by upgrading you subscription within the console. We can also provide up to 4TB by opening a ticket.

snrji2• 7 years ago

Does anyone now if this system supports Google Docs-like live editing? I'm struggling to find such a service.

lrem3• 7 years ago

Google Colab?

Disclaimer: I use Colab regularly as part of my job in Google.

mjn• 7 years ago

I don't believe Colab is real-time live in the way that Google Docs is. I'm using it to teach a class and would love that kind of functionality, but unless I'm missing some option, when I make changes to the notebook, students who have it open don't see the changes immediately, only when they reload.

snrji2• 7 years ago

With which option! I have used Colab myself and I don't see live editing. I would like to see the cursor of the collaborators in real time while they edit. Also, the option to share the runtime.

kvlr• 7 years ago

Can't say if it does but at https://nextjournal.com we do. We also support running on GPUs.

zerop• 7 years ago

Question: Why use this over Google colab?

sabalaba• 7 years ago

Hey HN, just wanted to post here to say that if you need more than 6 hours training (free tier limit) we offer a 4x GPU machine that is only $1.5/hour. Just wanted to let everybody know what kind of competition is out there while you're doing your price comparisons:

https://lambdalabs.com/service/gpu-cloud

JonathanFly• 7 years ago

I'd be interested in whatever you could offer that's lowest specs in every respect except GPU ram. So 1 GPU, moderate to low everything else, but 24GB of RAM. Right now there's no great way to run those cheaply, even if I want to do something that might only take a few hours.

foobarbecue• 7 years ago

Does a card like that exist? Last time I shopped for them (a few years ago) it didn't.

darkmighty• 7 years ago

Perhaps you could try CPU training. I believe it should take about 10x longer on a many-core machine, but still might be worth it (if you require lots of RAM and can afford to wait). There are also various techniques to reduce memory usage.

dkobran2• 7 years ago

Paperspace team here. We offer a 24GB GPU option with minimal RAM. Check out the P6000 here: https://gradient.paperspace.com/instances Hope that's what you're looking for :)

tastroder• 7 years ago

fyi: your chatbot makes a "popping" noise when scrolling down and puts "(2) New Messages" in the tab title. Both distract from your content.

Shareable Jupyter Notebooks That Run on Free Cloud GPUs

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