https://github.com/JuliaGPU/XLA.jl

Julia on TPUs
https://github.com/JuliaGPU/XLA.jl

deep-learning going-faster julia-language machine-learning peanut-butter tpu xla

Last synced: about 2 months ago
JSON representation

Julia on TPUs

• Host: GitHub
• URL: https://github.com/JuliaGPU/XLA.jl
• Owner: JuliaGPU
• License: other
• Archived: true
• Created: 2018-04-02T20:21:15.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-12-18T13:32:42.000Z (almost 4 years ago)
• Last Synced: 2024-06-11T17:31:50.125Z (3 months ago)
• Topics: deep-learning, going-faster, julia-language, machine-learning, peanut-butter, tpu, xla
• Language: Julia
• Homepage:
• Size: 7.94 MB
• Stars: 224
• Watchers: 19
• Forks: 20
• Open Issues: 19
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.bib

Awesome Lists containing this project

README

        
# XLA.jl - Compiling Julia to XLA

NOTE: We're in the process of adding better instructions. Check back in a bit.

# Getting started on TPUs

## Running on Colab

Google currently offers free access to Cloud TPUs through its Colab notebook

service. Colab does not officially support julia at the moment, but it is

possible to install julia by manually installing it into the runtime (though

this has to be done every time the runtime gets reset). By this mechanism,

you can get access to TPUs through the notebook interface. Start with the installation

notebook in `docs/colab/InstallJuliaXLA.ipynb`:




Run in Google Colab



Afterwards, any JuliaTPU notebook should work if opened without resetting the runtime

in between.

## Running on GCP

The process for setting up this repository to run against TPUs is much 

the same as the process for setting up the repository locally. However, since

there is additional steps involved in launching the actual TPU, we are providing

a tutorial to walk you through all the steps. It is recommended for those new to

Julia and/or TPUs. If you're already familiar with both, you may skip the tutorial

and just use the setup guide below. The tutorial will open in Google Cloud Shell,

by clicking the button below:









# Getting started (CPU/GPU backend)

- Grab julia on branch [kf/tpu3](https://github.com/JuliaLang/julia/tree/kf/tpu3) (Prebuilt Linux x86_64 binaries with TPU support are available [here](https://storage.googleapis.com/julia-tpu-binaries/julia.v1.1.0-kf.tpu3.x86_64-linux-gnu.tar.gz))

- Instantiate this repo

- `julia> using TensorFlow`

- Get yourself an `xrt_server` (either running it locally via ``run(`$(joinpath(dirname(pathof(TensorFlow)),"..","deps","downloads","bin","xrt_server"))`)`` or by spinning up a Google Cloud TPU and starting an SSH tunnel to expose its port to the world) and connect it to localhost:8470

- Run the script in `examples/vgg_forward.jl`