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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. Grad

by hsikka 7y 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 7y 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?
- DTE 7y 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 https://docs.paperspace.com/gradient/data/storage And happy to answer any other questions
  - MarkMMullin 7y ago
    OK, did a quick scan on free and paid tiers and wondering about ability to bring in specialized python libraries- in my case, https://github.com/opencog/link-grammar https://github.com/opencog/link-grammar which almost always requires hand building and migrating, and some custom packages on top of my own even more horrible C++ . - I have to run a jupyterhub ami on an ec2 instance right now
    - DTE 7y ago
      Yes, one of the differences in how we handle notebooks is that everything is actually run in a container behind the scenes. This means that is isn't just the .ipynb that we are hosting and if you install and dependencies, libraries, etc it will actually persist all of it inside of a container. This makes it much easier to share your work with others so that i.e. I could fork your notebook if you made it public and get all of the installed libraries and compiled dependencies by default. Hope that helps! Edit: we also have another tool called GradientCI (https://docs.paperspace.com/gradient/projects/gradientci https://docs.paperspace.com/gradient/projects/gradientci) that might also be of interest. Basically it lets you connect a GitHub repo directly to a project and you can use it to build your container automatically.
      - MarkMMullin 7y ago
        Gotcha - yah, I can containerize, it's not like I'm screwing with drivers or whatnot on the AMI - I'll keep an eye on you, not ready for GPU yet, as I can't even rationally define the vectors I'm extracting from the Link stuff. Best of luck to you, lot of people piling into that battleground, and the Dunning-Kreuger effect is rampant. :-) Side question, if you're willing to entertain it. Tired me tells a notebook to checkpoint, wanders off to bed, comes back next day and wonders why its taking an aeon to open the notebook..... oh yeah, damn, I've got gigs of images and panda crap in it . -- do you wrangle this problem, i.e. please don't save your notebooks as gihugic files representing a mental state you can't possibly remember ?
        DTE 7y ago
        I should also mention you can just as easily run these on CPU-backed instances as well. The GPU is not a hard requirement. As for checkpointing data, that is still a relatively difficult problem to solve and our current recommendation is to use a combination of the persitent /storage directory and the notebook home directory. There are definitely issues with doing 100K+ of small files and committing those to the primary docker layer. When you get to testing it out don't hesitate to reach out to use and we can try to see what the best solution is for your particular project. To date there isn't a "one size fits all" solution but we are working hard on making more intelligent choices behind the scenes to unblock some of these IO constraints.
  - lostmsu 7y ago
    Hi! Do you guys have a trademark for the name Gradient? I am developing a C#/.NET binding to TensorFlow with the same name: https://losttech.software/gradient.html https://losttech.software/gradient.html If you do, do you mind that clash of names? Secondly, would you be interested to invest in providing .NET-powered notebooks? I just got it working on Azure Notebooks for F# ( http://ml.blogs.losttech.software/What-New-In-Preview-6.4/ http://ml.blogs.losttech.software/What-New-In-Preview-6.4/ ), but I feel like there would be more interest from C# developers. There is a good C# Jupyter kernel out there.
minxomat 7y 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)
solidasparagus 7y 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 7y 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 7y 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.

by hsikka 7y ago

ArtWomb 7y 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?

DTE 7y 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 https://docs.paperspace.com/gradient/data/storage And happy to answer any other questions
- MarkMMullin 7y ago
  OK, did a quick scan on free and paid tiers and wondering about ability to bring in specialized python libraries- in my case, https://github.com/opencog/link-grammar https://github.com/opencog/link-grammar which almost always requires hand building and migrating, and some custom packages on top of my own even more horrible C++ . - I have to run a jupyterhub ami on an ec2 instance right now
  - DTE 7y ago
    Yes, one of the differences in how we handle notebooks is that everything is actually run in a container behind the scenes. This means that is isn't just the .ipynb that we are hosting and if you install and dependencies, libraries, etc it will actually persist all of it inside of a container. This makes it much easier to share your work with others so that i.e. I could fork your notebook if you made it public and get all of the installed libraries and compiled dependencies by default. Hope that helps! Edit: we also have another tool called GradientCI (https://docs.paperspace.com/gradient/projects/gradientci https://docs.paperspace.com/gradient/projects/gradientci) that might also be of interest. Basically it lets you connect a GitHub repo directly to a project and you can use it to build your container automatically.
    - MarkMMullin 7y ago
      Gotcha - yah, I can containerize, it's not like I'm screwing with drivers or whatnot on the AMI - I'll keep an eye on you, not ready for GPU yet, as I can't even rationally define the vectors I'm extracting from the Link stuff. Best of luck to you, lot of people piling into that battleground, and the Dunning-Kreuger effect is rampant. :-) Side question, if you're willing to entertain it. Tired me tells a notebook to checkpoint, wanders off to bed, comes back next day and wonders why its taking an aeon to open the notebook..... oh yeah, damn, I've got gigs of images and panda crap in it . -- do you wrangle this problem, i.e. please don't save your notebooks as gihugic files representing a mental state you can't possibly remember ?
      - DTE 7y ago
        I should also mention you can just as easily run these on CPU-backed instances as well. The GPU is not a hard requirement. As for checkpointing data, that is still a relatively difficult problem to solve and our current recommendation is to use a combination of the persitent /storage directory and the notebook home directory. There are definitely issues with doing 100K+ of small files and committing those to the primary docker layer. When you get to testing it out don't hesitate to reach out to use and we can try to see what the best solution is for your particular project. To date there isn't a "one size fits all" solution but we are working hard on making more intelligent choices behind the scenes to unblock some of these IO constraints.
- lostmsu 7y ago
  Hi! Do you guys have a trademark for the name Gradient? I am developing a C#/.NET binding to TensorFlow with the same name: https://losttech.software/gradient.html https://losttech.software/gradient.html If you do, do you mind that clash of names? Secondly, would you be interested to invest in providing .NET-powered notebooks? I just got it working on Azure Notebooks for F# ( http://ml.blogs.losttech.software/What-New-In-Preview-6.4/ http://ml.blogs.losttech.software/What-New-In-Preview-6.4/ ), but I feel like there would be more interest from C# developers. There is a good C# Jupyter kernel out there.

minxomat 7y 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)

solidasparagus 7y 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 7y 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 7y 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.