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Colab Pro

colab.research.google.com

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Re: Colab Pro

#21

Post by ianhowson »

ludwigschubert wrote: post #20

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.) 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 clust…

$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.

Re: Colab Pro

#22

Post by minimaxir »

ludwigschubert wrote: post #20

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.) 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 clust…

The 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

There is an easy native command for port forwarding to Jupyter: https://cloud.google.com/ai-platform/deep-learning-vm/docs/j...

Re: Colab Pro

#23

Post by leoh »

ludwigschubert wrote: post #20

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.) 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 clust…

Because 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.

Re: Colab Pro

#24

Post by TaylorAlexander »

ludwigschubert wrote: post #20

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.) 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 clust…

Perhaps this is more profitable. I’ve heard that Colab has become very popular for people doing certain kinds of deep learning, but that the wait for GPU instances has been frustrating. It seems a paid tier to get priority is directly targeting those users.

Re: Colab Pro

#25

Post by dzhiurgis »

TaylorAlexander wrote: post #24
ludwigschubert wrote: post #20

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.) 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 clust…

Perhaps this is more profitable. I’ve heard that Colab has become very popular for people doing certain kinds of deep learning, but that the wait for GPU instances has been frustrating. It seems a paid tier to get priority is directly targeting those users.

I've recently used Colab to run some pretty cool StyleGAN notebooks. Finally I can replicate so many cool projects without bending my head how to setup gcp, virtualbox or install tensorflow into geforce macbook.

Doubt I'll pickup deep learning as a profession by this, but it's a step forward.

Re: Colab Pro

#26

Post by mark_l_watson »

I 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.

Re: Colab Pro

#27

Post by sebasmurphy »

ludwigschubert wrote: post #20

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.) 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 clust…

You could alternatively use the google AI Platform Notebooks which are basically the same thing and billed per hour. You can now Prebake a docker images with all of the libraries that you need.

Re: Colab Pro

#28

Post by bhl »

ccarpenterg wrote: post #19

I'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…

Curious 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.

Re: Colab Pro

#29

Post by fibrennan »

Just 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

Re: Colab Pro

#30

Post by bhl »

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.
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