# PyTorch and Julia

**URL:** <https://discourse.julialang.org/t/pytorch-and-julia/22211>\
**Category:** Machine Learning\
**Created:** [March 22, 2019, 6:26pm UTC](https://discourse.julialang.org/t/pytorch-and-julia/22211 "2019-03-22T18:26:40Z")\
**Posts on this page:** 1\
**Showing post:** 9

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**Author:** ![tkf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tkf/32/17635_2.png) [@tkf](https://discourse.julialang.org/u/tkf)\
**Post date:** [March 24, 2019, 3:39am UTC](https://discourse.julialang.org/t/pytorch-and-julia/22211/9 "2019-03-24T03:39:08Z")

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I think the shortest path for making PyJulia easier to use is [RFC: a "workaround" for the multi-project precompilation cache problem without long-term code debt](https://discourse.julialang.org/t/rfc-a-workaround-for-the-multi-project-precompilation-cache-problem-without-long-term-code-debt/22233) with [Make PyCall.jl AOT-compilable by tkf · Pull Request #651 · JuliaPy/PyCall.jl · GitHub](https://github.com/JuliaPy/PyCall.jl/pull/651). (The idea is to create a system image for PyJulia and let Julia automatically use precompilation cache dedicated to PyJulia.) Maybe you can help me in [that thread](https://discourse.julialang.org/t/22233) 🙂

I also added Julia option support recently (available only in master). You can use `Julia(compiled_modules=False)` to workaround the compilation cache problem: [Troubleshooting — PyJulia 0.5.8.dev documentation](https://pyjulia.readthedocs.io/en/latest/troubleshooting.html#turn-off-compilation-cache) It would make PyJulia setup plug-and-play _if_ you are OK with waiting for precompilation for each Python process (yeah, I know this is not super practical for all purposes).

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