Just wanted to share a Colab alternative I work on called Gradient[0] (also includes a free GPU).<p>Some of the key differences:<p>- Faster storage. Colab uses Google Drive which is convenient to use but very slow. For example, training datasets often contain a large amount of small files (eg 50k images in the sample TensorFlow and PyTorch datasets). Colab will start to crawl when it tries to ingest these files which is a really standard workflow for ML/DL. It's great for toy projects eg training MNIST but not for training more interesting models that are popular in the research/professional communities today.<p>- Notebooks are fully persistent. With Colab, you need to re-install everything every time you start your Notebook.<p>- Colab instances can be shutdown (preempted) in the middle of a session leading to potential loss of work. Gradient will guarantee the entire session.<p>- Gradient offers the ability to add more storage and higher-end dedicated GPUs from the same environment. If you want to train a more sophisticated model that requires say a day or two of training and maybe a 1TB dataset, that's all possible. You could even use the 1-click deploy option to make your model available as an API endpoint. The free GPU tier is just an entrypoint into a full production-ready ML pipeline. With Colab, you would need to take your model somewhere else to accomplish these more advanced tasks.<p>- A large repository of ML templates that include all the major frameworks eg the obvious TensorFlow and PyTorch but also MXNet, Chainer, CNTK, etc. Gradient also includes a public datasets repository with a growing list of common datasets freely available to use in your projects.<p>Those are the main pieces but happy to elaborate on any of this or other questions!<p>[0] <a href="https://gradient.paperspace.com" rel="nofollow">https://gradient.paperspace.com</a>
Good idea, but it's the first premium product that I've seen where the pitch is 'you <i>may</i> get certain features if you subscribe'. In another words there is no guarantee and a premium subscriber may still end up with same GPU as a free user. You may end up with a high-end V100 (not available to free) might be a better pitch.
I am tempted to sign up. Colab is very usable on Safari for iOS/iPad.<p>I invested 18 months ago in a GPU setup for home. Really convenient but I somewhat regret the purchase. I used to spin up GCP GPU instanced when needed and that was not convenient. Colab is very convenient.<p>$10/month for better GPUs and longer sessions seems like a good deal.
I hate to be the boy who cries "Google will cancel this service", but this offering just seems strange.<p>With a very low price point coupled with not that huge of a user base, this will end up making how much for Google? $1MM/month? $10MM/month? Either would be negligible for them.
Can anyone see a reason why they wouldn’t just allow you to provision (and pay for) a persistent Google Cloud VM instead?
(I currently do that manually and need port forwarding to a machine that runs Jupyter.)<p>It’s hard for me to understand why Colab would build such a vague pro tier instead of the simplest possible solution: let me pay for my compute.<p>There’s so much more potential, too; they could offer whole clusters on demand, with really simple Python integrations say using dask, or ray.
Not sure who this is geared towards. People mostly use Colab to share GPU-dependent work from what I can tell. How would that work on a paid subscription? Do others need to pay to run the notebooks you shared? Can they use their "free" account?<p>As far as utility for research, as a researcher, I _already have_ several local GPUs at my disposal, and I only use notebooks to kick the tires on things and visualize. The moment something starts to look like it's useful, I move it to a real *.py file where it's more maintainable and diffable.<p>Edit: actually I now think I know who this is geared towards. It's geared towards people who aren't going to really use it, and don't mind to pay $120/yr (+tax) for something they don't use. Which, IMO, is pretty smart.
A preemptible P100 + VM on Google Compute Engine is about ~$0.45/hr, so to exceed that value with Colaboratory Pro (ignoring conveience factors) you'd need to train for more than 22 hours in a month. Which, for deep learning, is not too unreasonable.<p>Reading between the lines of both the signup page and up-to-date FAQ, it seems like the free TPU in Colab notebooks will be depreciated, which isn't too surprising.
I wonder who made the decision to spin this out into a commercial product; maybe it has to do with Google's push into the cloud further? I always thought Colab was just an experimental tool; it's still under the research.google domain.
I wish they would connect Colab under <a href="https://script.google.com" rel="nofollow">https://script.google.com</a> so you can run a notebook at interval times, something akin to what <a href="https://github.com/TensorTom/colabctl" rel="nofollow">https://github.com/TensorTom/colabctl</a> does.
I've been using Colab for over a year now. I train deep learning models on NLP and medical imaging datasets.<p>It's a great tool and it lets you focus on the code and the models, instead of the hardware and OS. But $9.99/month is a little expensive for my taste.<p>You can't customize it and if they change something you have to install software by hand sometimes. It should be $1.99/month, that's the kind of price I'd pay for this basic cloud computing service.<p>edit: I use Colab to play with ML models. I really don't think it's possible, for instance, to train a model on Imagenet using Colab. So Colab is similar to the microwave, if you want to cook a serious recipe you should use a real kitchen.
A lot of comments are missing the value here: cheap and easy TPU access for hobbyist use of deep learning models that need TPUs for fine-tuning and/or inference (GPT-2, I’m looking at you).
Some content in their github repo: <a href="https://github.com/googlecolab" rel="nofollow">https://github.com/googlecolab</a>
There's so much data in this universe, people don't know what to do with it. When people don't know what to do, an industry grows to let them "feel" they are doing something useful.