20 ms·
Colab Pro
- minimaxir 7y agoA 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. 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.
- ampdepolymerase 7y agoTo clarify, does that mean you can continuously train a model for e.g. hundred of hours or are you still limited by the 24 hours notebook limit?
- anidh 7y agoThe page said "Longer running notebooks and fewer idle timeouts mean you disconnect less often." I'm guessing there is a limit but it has been increased for the pro customers. In contrast on GCE you can train for 22 hours if you want.
- piadodjanho 7y agoThe page says its twice the traditional Cloud.
- eru 7y agoYou can work around that by saving your model and reloading.
- ampdepolymerase 7y agoCan you elaborate on this?
- PeterisP 7y agoHave model training make checkpoints every five minutes. When the training gets interrupted, just restart it on a new instance from the last checkpoint. Repeat as necessary until hundreds of hours of training is completed.
- eru 7y agoYes, exactly. To add one more detail: mount your Google Drive and save the checkpoint there.
- PeterisP 7y agoCloud storage bucket works better than Google Drive for both training data and checkpoints; and if you're doing training on TPUs, then I believe that you must do the data exchange via cloud storage.
- eru 7y agoOK, that also works. Just don't rely solely on the storage that comes with your notebook instance.
- sillysaurusx 7y agoHas anyone here actually tried to use preemptible GPUs on GCE? You're lucky if you can get the VM to stay up for more than 10 minutes. Litany of failure here: https://twitter.com/theshawwn/status/1174480402779648000 https://twitter.com/theshawwn/status/1174480402779648000 Perhaps I just got unlucky and the situation is improved now. But it was a waste of a day for me.
- minimaxir 7y agoI haven't in awhile; when I used them more heavily, I got the full 24 hours. It's possible that it's region-dependent; IIRC I had better luck with us-east1 than us-central1. I should test more at some point.
- boulos 7y agoDisclosure: I work on Google Cloud. Sorry about that. We (I) had screwed up and allowed users to smash themselves into the wall of "Huh, you keep getting preempted, but there's technically a slot right here". We've re-enabled some backpressure to keep you from getting hit over and over again, and instead see a stockout error when you should probably try another (less full) zone.
- sillysaurusx 7y agoHey, neat! Thanks for your work. I was hoping that this was the case, and I haven't tried since September. But I'll give it another shot now.
- boulos 7y agoFeel free to send me an email (contact info in profile), if you want to go back and forth.
- dwiel 7y agoI killed a lot of time with this exact issue a couple months ago as well. Quite frustrating, and it was even worse trying to include the preemptible GPUs in a kubernetes cluster. It would often take so long to add the GPU to the cluster that it would get killed before it even ran any real workload. This cycle would repeat for an hour before a machine stayed up long enough to actually schedule kubernetes pods on them.
- fulafel 7y agoThis seems to be a hosted Jupyter service, right mybinder, is that right?
- LegitShady 7y agoGoogle Colab is a hosted jupyter notebooks service and is free. It includes the use of a Tesla k80. Runtimes reset after 12 hours This is the pro/paid offering with fewer limitations and better resources.
- freediver 7y agoGood idea, but it's the first premium product that I've seen where the pitch is 'you may 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.
- Confiks 7y agoA fair use policy, which you seem to be referring to [1], is pretty standard fare for many 'invididual user' products to exclude heavy-use groups. However, they could at least define expected and minimum capacity. They might omit it because the business in this – aside from capturing users in their ecosystem – is arbitraging wholesale GPU price against consumer monthly needs, along with scaling the free tier. [1] "Why aren't resources guaranteed in Colab Pro?" on https://colab.research.google.com/signup https://colab.research.google.com/signup
- londons_explore 7y agoI would be happy with an explanation of "none of our service is guaranteed, but if we don't manage to give you your chosen GPU more than 80 percent of the time, you are free to cancel, and we will refund your final months subscription payment".
- asdfasgasdgasdg 7y agoSince the subscription is monthly with no commitment, I think the viewpoint is probably something like "if it's not meeting your needs then unsubscribe." A refund of $10 is not going to be economically meaningful to folks who want to subscribe to this service. "Chosen GPU more than 80% of the time" sounds nice but it opens you up to adverse selection. They aren't looking for customers who want to pay ten bucks a month to rent 80% of a high performance GPU. That's not nearly enough money for that kind of commitment.
- dr_kiszonka 7y ago
- iddan 7y agoColab is the best notebook I've ever used. It is a real game-changer and I can totally understand why people who use daily would pay for it.
- bitxbit 7y agoWe should really thank Mathematica for the notebook format.
- anidh 7y agoDid mathematica invent the notebook format? Just curious..
- acidburnNSA 7y agoLooks like a yes according to this page at least: https://en.wikipedia.org/wiki/Notebook_interface https://en.wikipedia.org/wiki/Notebook_interface
- mistrial9 7y agoMathematica certainly pushed the Notebook format as a fundamental feature, with WYSIWIG math that no one else had ..."Invented" is pretty hard to say honestly, but the popularization and technical polish, absolutely yes
- jamesbrock 7y agoYes, my understanding is that Theodore Gray invented the "computational notebook" for Mathematica in the 90s. Here is the best writeup of the history of the notebook that I've ever read. https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/ https://www.theatlantic.com/science/archive/2018/04/the-scie...
- mistrial9 7y agoquick look at Wikipedia shows Mathematica: "Initial release June 23, 1988;" Mathematica took at least a year or more to write.. so that goes back perhaps to 1986.. 90s is way off...
- dankle 7y ago> For now, Colab Pro is only available in the US.
- mkl 7y agoYes, they should have led with that instead of finished with it.
- bitxbit 7y agoThis is so much better than buying your own hardwares.
- zapf 7y agoThere'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.
- jonbaer 7y agoI wish they would connect Colab under https://script.google.com https://script.google.com so you can run a notebook at interval times, something akin to what https://github.com/TensorTom/colabctl https://github.com/TensorTom/colabctl does.
- ccarpenterg 7y agoI've been using Colab for over a year now. I train deep learning models on NLP and medical imaging datasets. 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. 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. 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.
- bhl 7y agoCurious to how you've been training with Colab. I tried running 50 epochs on a UNet3D model, each taking about 20 minutes to run; it was a pain in the ass because the session kept disconnecting.
- mistrial9 7y agoin medical research you are saying $10 / month is too much.. this might be a study in consumer psychology! Here in California, biotech secretaries drive a mercedes...
- eru 7y agoA big Mercedes, but share three people to a bedroom.
- vicmiclovich 7y agodo you do this training for work or personal gain? (Usually if it is a company, they would be perfectly fine expensifying this as a business cost)
- deleted 7y ago[deleted]
- lewis1028282 7y agoI mean when modern GPUs are $1000 I'm curious how you think $9.99 is too much.
- ludwigschubert 7y agoCan 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.) 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. There’s so much more potential, too; they could offer whole clusters on demand, with really simple Python integrations say using dask, or ray.
- ianhowson 7y ago$10/mo doesn't pay for much compute in Google Cloud. Colab Pro is offering GPUs and high memory instances, presumably under the assumption that interactive users will be mostly idle.
- londons_explore 7y agoUnder the assumption that many users on one GPU eventually end up needing more compute and migrating along the easiest cost path to GCE and TPU's, which they earn lots from.
- minimaxir 7y agoThe Deep Learning VM (available as a native option when creating a new GCE VM) has a bunch of DL tools, and starts a JupyterLab instance on launch: https://cloud.google.com/deep-learning-vm https://cloud.google.com/deep-learning-vm There is an easy native command for port forwarding to Jupyter: https://cloud.google.com/ai-platform/deep-learning-vm/docs/jupyter https://cloud.google.com/ai-platform/deep-learning-vm/docs/j...
- leoh 7y agoBecause they're trying to introduce a different paradigm for computing. Notebooks are portable, purpose-oriented, and allow Google to make decisions about the underlying implementation allowing for a $10 monthly price point and an incredible level of performance not unlike what they've done for BigQuery.
- TaylorAlexander 7y ago
- mark_l_watson 7y agoI am tempted to sign up. Colab is very usable on Safari for iOS/iPad. 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. $10/month for better GPUs and longer sessions seems like a good deal.
- fibrennan 7y agoJust wanted to share a Colab alternative I work on called Gradient[0] (also includes a free GPU). Some of the key differences: - 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. - Notebooks are fully persistent. With Colab, you need to re-install everything every time you start your Notebook. - Colab instances can be shutdown (preempted) in the middle of a session leading to potential loss of work. Gradient will guarantee the entire session. - 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. - 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. Those are the main pieces but happy to elaborate on any of this or other questions! [0] https://gradient.paperspace.com https://gradient.paperspace.com
- m0zg 7y agoAfter spending 5 minutes on the site, I could not figure out what an offering comparable to Colab Pro would cost me (i.e. single instance of a notebook hooked up to P100), and bailed. In contrast, Google puts pricing front and center, which makes me feel better about their value prop in spite of the fine print around limits and preemptibility.
- dragonsh 7y agoI have been a user of colab and recommended to many friends. As mentioned in the other thread the session time of colab notebook is too low, so increasing it for pro is good, but still if one has a large data for testing and training, dependency on google drive or products becomes one another bottleneck, but not that bad. My biggest concern with google product is, it might be shut down any time when any small and medium company begins to depend on it. This is the legacy of google in which anything which cannot generate revenues like it's advertising business it's shut down. So although my team use from time to time google colab, we keep an alternative always open. Hopefully with this 9.99/month charge it might become viable business but will not be as significant as the google's primary business of advertising, so there will always be sword at the neck of this product.
- bhl 7y agoI 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.
- m0zg 7y agoNot 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? 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. 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.
- lupire 7y ago--
- jrockway 7y agoProbably because it's easier to buy something that costs $10/month instead of "maybe it will be $0, maybe it will be $300,000, we'll see at the end of the month!" When you're using your own money to pay for cloud resources, that unbounded worst case is pretty scary.
- eru 7y agoGoogle Cloud offers tools to limit your worst case expenses upfront.
- deleted 7y ago[deleted]
- hobofan 7y agoI hate to be the boy who cries "Google will cancel this service", but this offering just seems strange. 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.
- zelly 7y agoLike others said, subscription products are very profitable. The target demographic for this has disposable income and would see this as an investment. A lot of people will sign up and never use it. Don't forget that the alternative was just free. For all we know this is the way they're breaking even on Colab to prevent shutting it down.
- Tenoke 7y agoUnlike most other products it won't matter as much here if they cancel it. It's hard to be too dependant on an in-browser stateless tool for prototyping. You aren't going to be running service off it or even have a permanent set up so if they cancel it you'd have very little to transition from.
- dannyw 7y agoColab Pro is probably the "we want to give verified non-bots more access, but need a small monetary cost to actually stop abuse". The collection of a credit card isn't just monetary: it also provides some sort of anti-abuse as most people have a limited number of credit cards; and even a bulk credit farm operation has a monetary cost per card (I read the going rate for a hacked card even is $100 ish). Aged Google Accounts go for $1-2 from account farmers these days.
- agluszak 7y agoI wonder how long it takes until Google shuts down the free service. It's so easy to abuse it. And what Google gets in return?
- panchicore3 7y agoFor example learning about the type of abuse and how to sort out issues while free, so product future version can be sold as tested?
- gizmodo59 7y agoSome content in their github repo: https://github.com/googlecolab https://github.com/googlecolab
- wildermuthn 7y agoA 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).