Julia in Colab

General Usage
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That still wouldn’t fix the bigger problem Valentin raised which is

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I assumed some sort of depot did exist, but I didn’t check myself

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Sounds right to me. Indeed it would be fantastic if Colab packaged a precompilation cache, regardless of what packages sit in the base environment.

However one worry I have here is cache invalidation from new packages—note that Colab does not update the packages that frequently IIRC. I think artifacts would be tricky also, in case the new packages require new artifacts. I think a larger base env (with frozen versions), while it isn’t perfect, does mean you can get a start working quicker. Similar to Python—it can take a while to install PyTorch, so I don’t mind that Colab freezes the version at a slightly older value.

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Oops! My brain said “I’m using the +(::CuArray, ::CuArray); I’m good to go.”, but timing just the kernel launch strikes again! I thought it was weird that the GPU line was flat lol; I chocked it up to the GPU might not be saturating even at those longer arrays (which it probably definitely should at 10^8 elements). Thanks for catching that!

I have edited my original post to disclose this and linked to your post!

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I believe you can have Javascript run by display(MIME("application/javascript"), yourjs_string), so that can be a “listener” to a message…I was experimenting with this a bit for WGLMakie support but probably wasn’t using the IJulia interface correctly.

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There is an issue opened : Support Pluto notebooks for Julia · Issue #5167 · googlecolab/colabtools · GitHub

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Any plans to support the latest stable version of Julia ? (Currently 1.11.3).

On the roadmap, but a little ways out.

We did just upgrade to 1.10.9 following its release a couple days ago. We also pre-install CUDA.jl now (configured to use system-installed CUDA), to save some time.

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This is an interesting idea. It’s slightly different than our current experience as @MilesCranmer mentioned, so not sure where it will land, but one we’re thinking on.

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This seems a bit concerning to me and probably worth filing an issue with a minimal repro.

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This worked for me! Thank you very much for the “hacky”.

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Would really love to see this, especially a smooth UX for mount Google Drive!

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Hi guys, I don’t think I’m able to get Reactant to find any Nvidia GPUs when I select a GPU runtime on Colab. E.g. when I select A100 or the L4 GPU, and then try to run:

Reactant.set_default_backend(“gpu”)

I get the following error:

No GPU client foundStacktrace: [1] error(s::String) @ Base ./error.jl:35 
[2] client(backend::String) @ Reactant.XLA ~/.julia/packages/Reactant/cTiTU/src/xla/XLA.jl:82 
[3] set_default_backend @ ~/.julia/packages/Reactant/cTiTU/src/xla/XLA.jl:104 [inlined] 
[4] set_default_backend(backend::String) @ Reactant ~/.julia/packages/Reactant/cTiTU/src/Reactant.jl:293 
[5] top-level scope @ In[15]:1
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This thread has been about the inclusion of Julia as a backend for colab. Because your question is about using Julia in colab, I think it would be more appropriate as its own topic under the colab tag.

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Would you mind opening an issue in Reactant.jl?

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Sure thing!

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Thanks so much for setting up the julia colab, this is great! Similarly struggling with very slow precompilation times for custom set-ups, I opened an issue with some MWE: Julia pkg loading and compilation slow · Issue #5608 · googlecolab/colabtools · GitHub

1 Like