# Julia on Google Colab: Free GPU-Accelerated Shareable Notebooks
**URL:** https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319
**Category:** GPU
**Tags:** announcement, notebooks
**Created:** [September 21, 2018, 11:00pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319 "2018-09-21T23:00:02Z")
**Posts on this page:** 20
**Page:** 1
### Author:  [@jekbradbury](https://discourse.julialang.org/u/jekbradbury)
#### Post date: [September 21, 2018, 11:00pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/1 "2018-09-21T23:00:02Z")
(Disclosure: While I work for Google, this is not an official Google product or announcement.)
tl;dr: For Julia on Colab with GPUs, first open [this notebook](https://colab.research.google.com/drive/1zAkadxc_iJE_oj-TOdwHpfLU2oaNHJOS) and run the cell (takes ~15-20 minutes), then open [this one](https://colab.research.google.com/drive/1AYj_TIE_zhy4LlY8Zc8PKBrkJkFcOrlf) to start using Julia.
Recently, @denizyuret brought up on Slack that it would be nice if Google Colab supported Julia, especially for GPUs. I can’t promise anything about official or built-in support, but it turns out it’s possible to get this working entirely from the outside.
First, install CUDA 9.0 (the most recent version supported by the NVIDIA driver in the Colab GPU container) and Julia 1.0 using IPython line magic. This can be done by running the contents of [this Colab notebook](https://colab.research.google.com/drive/1zAkadxc_iJE_oj-TOdwHpfLU2oaNHJOS) or by executing this code in a cell of a new notebook with the GPU runtime (either way, it takes 15-20 minutes):
```julia
!wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
!dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64-deb
!apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
!apt update -q
!apt install cuda gcc-6 g++-6 -y -q
!ln -s /usr/bin/gcc-6 /usr/local/cuda/bin/gcc
!ln -s /usr/bin/g++-6 /usr/local/cuda/bin/g++
!curl -sSL "https://julialang-s3.julialang.org/bin/linux/x64/1.0/julia-1.0.0-linux-x86_64.tar.gz" -o julia.tar.gz
!tar -xzf julia.tar.gz -C /usr --strip-components 1
!rm -rf julia.tar.gz*
!julia -e 'using Pkg; pkg"add IJulia; add CuArrays; add Flux; precompile"'
```
Feel free to add additional packages; you’ll also still be able to install them normally later.
Then open a new notebook in the same container (i.e., the same session) that declares `julia-1.0` as its kernel. Unfortunately you can’t do this purely using the web UI; you must either use `File -> Upload notebook` and provide the following JSON as a `.ipynb`:
```julia
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Julia on Colab.ipynb",
"version": "0.3.2",
"provenance": []
},
"kernelspec": {
"name": "julia-1.0",
"display_name": "Julia 1.0"
},
"accelerator": "GPU"
},
"cells": [
{
"metadata": {
"id": "oMSuTc3pDlHv",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
```
or you can simply open [this notebook](https://colab.research.google.com/drive/1AYj_TIE_zhy4LlY8Zc8PKBrkJkFcOrlf). If a saved notebook stops working with Julia (i.e., starts trying to run things in Python) you’ll have to run the installation notebook again.
Credits go to the intrepid folks who managed to get something similar working with node.js and R in [this thread](https://github.com/googlecolab/colabtools/issues/13).
---
### Author:  [@denizyuret](https://discourse.julialang.org/u/denizyuret)
#### Post date: [September 22, 2018, 2:06am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/2 "2018-09-22T02:06:18Z")
This is excellent news, thanks James! Does this install CUDA and Julia in your personal workspace? If you shared a Julia colab notebook, would the recipient need to go through the same process?
---
### Author:  [@wookyoung](https://discourse.julialang.org/u/wookyoung)
#### Post date: [September 22, 2018, 3:09am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/3 "2018-09-22T03:09:39Z")
wow, it works quite well with Julia.
drop here my playground [notebook](https://colab.research.google.com/github/wookay/colab/blob/master/sunyata.ipynb).
thanks James!
---
### Author:  [@denizyuret](https://discourse.julialang.org/u/denizyuret)
#### Post date: [September 22, 2018, 6:17am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/4 "2018-09-22T06:17:54Z")
I ported the Julia/Knet deep learning tutorial using @jekbradbury’s hack, so anyone can run them on a gpu using colab if they first initialize with the [installation notebook](https://colab.research.google.com/drive/1zAkadxc_iJE_oj-TOdwHpfLU2oaNHJOS). Here is the [Google Drive link](https://drive.google.com/drive/folders/19D-R31unxZV_PUYYYpCfd-gnbdUiZfNb?usp=sharing). You can also run directly from github adding the right prefix as @ekinakyurek mentioned on slack, for example: [Google Colab](https://colab.research.google.com/github/denizyuret/Knet.jl/blob/6b114cb771c8ec779612730feafc8c5fba552664/tutorial/03.lin.ipynb)
---
### Author:  [@Phil\_Tomson](https://discourse.julialang.org/u/Phil_Tomson)
#### Post date: [October 2, 2018, 11:58pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/5 "2018-10-02T23:58:47Z")
Is it possible to run Julia with TPU acceleration on Google Colab?
---
### Author:  [@GasserAkila](https://discourse.julialang.org/u/GasserAkila)
#### Post date: [November 15, 2018, 11:43pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/6 "2018-11-15T23:43:27Z")
I tried running the first notebook and I managed to install Julia correctly and I can run it using “ **julia** ”. But, I can not start a new notebook with Julia preinstalled on it.
Can you tell me if this option still works or not?
---
### Author:  [@ImreSamu](https://discourse.julialang.org/u/ImreSamu)
#### Post date: [November 22, 2018, 11:01am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/7 "2018-11-22T11:01:59Z")
> [@Phil\_Tomson](#):
>
> Is it possible to run Julia with TPU acceleration on Google Colab?
see:
- [GitHub - JuliaGPU/XLA.jl: Julia on TPUs](https://github.com/JuliaTPU/XLA.jl#running-on-colab)
---
### Author:  [@GasserAkila](https://discourse.julialang.org/u/GasserAkila)
#### Post date: [January 6, 2019, 7:54pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/8 "2019-01-06T19:54:03Z")
I finally managed to run it !
You will need to hyper-link the new notebook “julia-based” in the installation notebook to be able to open the notebook in the same container/session.
---
### Author:  [@Jon\_Norberg](https://discourse.julialang.org/u/Jon_Norberg)
#### Post date: [January 7, 2019, 8:17am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/9 "2019-01-07T08:17:43Z")
How did you hyperlink this?
---
### Author:  [@GasserAkila](https://discourse.julialang.org/u/GasserAkila)
#### Post date: [January 9, 2019, 9:53pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/10 "2019-01-09T21:53:40Z")
You add a new text cell in the installation notebook and add a hyper link (you will find this option in the editor) for the julia-based notebook
---
### Author:  [@Alexander-Barth](https://discourse.julialang.org/u/Alexander-Barth)
#### Post date: [March 6, 2019, 8:26pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/11 "2019-03-06T20:26:16Z")
Now the command `apt install cuda` install cuda 10, which produces the following error when loading `CuArrays`:
```julia
using CuArrays
┌ Info: Precompiling CuArrays [3a865a2d-5b23-5a0f-bc46-62713ec82fae]
└ @ Base loading.jl:1186
ERROR: LoadError: LoadError: UndefVarError: CUBLAS not defined
```
It is sufficient to run the commands in a Julia notebook to downgrade the CUDA version:
```julia
;apt-get install cuda-9-0
]build CuArrays
```
Alternatively, in the installation notebook one can directly specify version 9.0 by replacing
```julia
!apt install cuda gcc-6 g++-6 -y -q
```
with
```julia
!apt install cuda-9-0 gcc-6 g++-6 -y -q
```
In any case, thanks a lot to @jekbradbury for sharing this info about the julia notebook!
---
### Author:  [@findmyway](https://discourse.julialang.org/u/findmyway)
#### Post date: [April 30, 2019, 8:05am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/12 "2019-04-30T08:05:43Z")
> [@Alexander-Barth](#):
>
> !apt install cuda-9-0 gcc-6 g+±6 -y -q
I just tried this. However, I have another error while `using CuArrays`
```julia
BoundsError(a=Array{Core.Compiler.BasicBlock, (32,)}[
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=1, last=7), preds=Array{Int64, (1,)}[32], succs=Array{Int64, (1,)}[2]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=8, last=14), preds=Array{Int64, (1,)}[1], succs=Array{Int64, (2,)}[5, 3]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=15, last=20), preds=Array{Int64, (1,)}[2], succs=Array{Int64, (1,)}[4]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=21, last=21), preds=Array{Int64, (1,)}[3], succs=Array{Int64, (1,)}[7]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=22, last=28), preds=Array{Int64, (1,)}[2], succs=Array{Int64, (1,)}[6]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=29, last=29), preds=Array{Int64, (1,)}[5], succs=Array{Int64, (1,)}[7]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=30, last=47), preds=Array{Int64, (2,)}[4, 6], succs=Array{Int64, (2,)}[9, 8]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=48, last=49), preds=Array{Int64, (1,)}[7], succs=Array{Int64, (0,)}[]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=50, last=50), preds=Array{Int64, (1,)}[7], succs=Array{Int64, (1,)}[10]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=51, last=52), preds=Array{Int64, (1,)}[9], succs=Array{Int64, (1,)}[11]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=53, last=53), preds=Array{Int64, (1,)}[10], succs=Array{Int64, (1,)}[12]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=54, last=54), preds=Array{Int64, (1,)}[11], succs=Array{Int64, (1,)}[13]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=55, last=56), preds=Array{Int64, (1,)}[12], succs=Array{Int64, (1,)}[14]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=57, last=65), preds=Array{Int64, (1,)}[13], succs=Array{Int64, (1,)}[15]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=66, last=71), preds=Array{Int64, (1,)}[14], succs=Array{Int64, (2,)}[17, 16]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=72, last=74), preds=Array{Int64, (1,)}[15], succs=Array{Int64, (0,)}[]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=75, last=82), preds=Array{Int64, (1,)}[15], succs=Array{Int64, (2,)}[19, 18]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=83, last=84), preds=Array{Int64, (1,)}[17], succs=Array{Int64, (0,)}[]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=85, last=85), preds=Array{Int64, (1,)}[17], succs=Array{Int64, (1,)}[20]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=86, last=87), preds=Array{Int64, (1,)}[19], succs=Array{Int64, (1,)}[21]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=88, last=88), preds=Array{Int64, (1,)}[20], succs=Array{Int64, (1,)}[22]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=89, last=89), preds=Array{Int64, (1,)}[21], succs=Array{Int64, (1,)}[23]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=90, last=90), preds=Array{Int64, (1,)}[22], succs=Array{Int64, (1,)}[24]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=91, last=92), preds=Array{Int64, (1,)}[23], succs=Array{Int64, (1,)}[25]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=93, last=93), preds=Array{Int64, (1,)}[24], succs=Array{Int64, (1,)}[26]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=94, last=94), preds=Array{Int64, (1,)}[25], succs=Array{Int64, (1,)}[27]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=95, last=95), preds=Array{Int64, (1,)}[26], succs=Array{Int64, (2,)}[29, 28]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=96, last=98), preds=Array{Int64, (1,)}[27], succs=Array{Int64, (0,)}[]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=99, last=102), preds=Array{Int64, (1,)}[27], succs=Array{Int64, (2,)}[31, 30]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=103, last=103), preds=Array{Int64, (1,)}[29], succs=Array{Int64, (1,)}[32]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=104, last=104), preds=Array{Int64, (1,)}[29], succs=Array{Int64, (1,)}[32]),
Core.Compiler.BasicBlock(stmts=Core.Compiler.StmtRange(first=105, last=107), preds=Array{Int64, (2,)}[30, 31], succs=Array{Int64, (1,)}[1])], i=(0,))
rec_backtrace at /buildworker/worker/package_linux64/build/src/stackwalk.c:94
record_backtrace at /buildworker/worker/package_linux64/build/src/task.c:246
jl_throw at /buildworker/worker/package_linux64/build/src/task.c:577
jl_bounds_error_ints at /buildworker/worker/package_linux64/build/src/rtutils.c:187
getindex at ./array.jl:731
jfptr_getindex_1600.clone_1 at /usr/lib/julia/sys.so (unknown line)
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2182
replace_code_newstyle! at ./compiler/ssair/legacy.jl:80
optimize at ./compiler/optimize.jl:208
typeinf at ./compiler/typeinfer.jl:35
typeinf_ext at ./compiler/typeinfer.jl:567
typeinf_ext at ./compiler/typeinfer.jl:604
jfptr_typeinf_ext_1.clone_1 at /usr/lib/julia/sys.so (unknown line)
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2182
jl_apply at /buildworker/worker/package_linux64/build/src/julia.h:1536 [inlined]
jl_apply_with_saved_exception_state at /buildworker/worker/package_linux64/build/src/rtutils.c:257
jl_type_infer at /buildworker/worker/package_linux64/build/src/gf.c:275
jl_compile_method_internal at /buildworker/worker/package_linux64/build/src/gf.c:1784 [inlined]
jl_fptr_trampoline at /buildworker/worker/package_linux64/build/src/gf.c:1828
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2182
jl_apply at /buildworker/worker/package_linux64/build/src/julia.h:1536 [inlined]
start_task at /buildworker/worker/package_linux64/build/src/task.c:268
unknown function (ip: 0xffffffffffffffff)
```
Does anyone know how to use CuArrays in colab?
---
### Author:  [@Alexander-Barth](https://discourse.julialang.org/u/Alexander-Barth)
#### Post date: [May 14, 2019, 9:30am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/13 "2019-05-14T09:30:42Z")
I never had this issue. I recently updated the notebook to Julia 1.1 and all worked fine. Here is these updated notebook (Julia 1.1 but still using cuda 9.0):
> **[Google Colaboratory](https://colab.research.google.com/drive/1p0Vo5Uh5ZQo3XSC4y7LNfKLEkXwkpG1-)**
To get a notebook in Julia, download the following and re-upload in the notebook window where Julia was installed (otherwise you will get an error message that Julia in an unknown language).
> **[Google Colaboratory](https://colab.research.google.com/drive/15eOu-ft5XP2CbR931dHRa-Cr9LwhgkHS)**
---
### Author:  [@Bion\_Howard](https://discourse.julialang.org/u/Bion_Howard)
#### Post date: [May 17, 2019, 10:35pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/14 "2019-05-17T22:35:37Z")
hi, i would love to use julia kernel with colab.
I also wish it were possible to embed Colab as a react component
I also wish it were possible to run Colab notebooks offline by transpiling Python/Julia/R/Scala etc into Chromium V8 code … but that might be difficult. Anyone else interested to push Colab into more languages and offline/embedded environments?
---
### Author:  [@merckxiaan](https://discourse.julialang.org/u/merckxiaan)
#### Post date: [July 21, 2019, 8:55am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/15 "2019-07-21T08:55:02Z")
I really like the ability to use colab, thanks a lot!
Would it also be possible to use Julia in Kaggle kernels?
---
### Author:  [@lazarusA](https://discourse.julialang.org/u/lazarusA)
#### Post date: [July 29, 2019, 1:53pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/16 "2019-07-29T13:53:28Z")
Hello, I do have the same error. Do you have any updates on this regard?
---
### Author:  [@sdanisch](https://discourse.julialang.org/u/sdanisch)
#### Post date: [July 29, 2019, 2:38pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/17 "2019-07-29T14:38:09Z")
I have no idea about that bug, but if you want to try out running Julia online with a GPU, you can also remix this article:
> **[Flux](https://nextjournal.com/julia/flux-template)**
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [July 29, 2019, 3:22pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/18 "2019-07-29T15:22:00Z")
Interesting! Maybe we can get a diffeq example up and running on NextJournal.
---
### Author:  [@sdanisch](https://discourse.julialang.org/u/sdanisch)
#### Post date: [July 29, 2019, 3:53pm UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/19 "2019-07-29T15:53:37Z")
Sure, lets add it to [https://nextjournal.com/julia](https://nextjournal.com/julia)! 🙂
Could also try to create an ahead of time compiled image with PackageCompiler.
---
### Author:  [@ennvvy](https://discourse.julialang.org/u/ennvvy)
#### Post date: [September 21, 2019, 8:17am UTC](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319/21 "2019-09-21T08:17:13Z")
I am using google colab for the first time. The first link you have shared works without any errors. However, the second one, when I try to upload the notebook, the previous session gets terminated and it throws the following error:
File [“”](https://8f7tg8sbarj-b5278c2458b95a70-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20190919-085300-RC00_270050738#), line 1
> using Pkg
> ^
> SyntaxError: invalid syntax
[Next page](https://discourse.julialang.org/t/julia-on-google-colab-free-gpu-accelerated-shareable-notebooks/15319.md?page=2)