# Efficiency for calling Julia from python and purely run Julia

**URL:** <https://discourse.julialang.org/t/efficiency-for-calling-julia-from-python-and-purely-run-julia/81484>\
**Category:** General Usage\
**Created:** [May 23, 2022, 3:16am UTC](https://discourse.julialang.org/t/efficiency-for-calling-julia-from-python-and-purely-run-julia/81484 "2022-05-23T03:16:01Z")\
**Posts on this page:** 1\
**Showing post:** 14

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**Author:** ![Geositta](https://avatars.discourse-cdn.com/v4/letter/g/e5b9ba/32.png) [@Geositta](https://discourse.julialang.org/u/Geositta)\
**Post date:** [May 25, 2022, 3:01am UTC](https://discourse.julialang.org/t/efficiency-for-calling-julia-from-python-and-purely-run-julia/81484/14 "2022-05-25T03:01:53Z")

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Thank you so much.

> julia --trace-compile=my\_precompile\_recipe.jl precompile\_plots.jl

works. Then,

> using PackageCompiler  
> create\_sysimage([“LoopVectorization, TensorOperations”], sysimage\_path=“sys\_image.so”, precompile\_execution\_file=“my\_precompile\_recipe.jl”)

after I entered julia, the second step led to

```julia
ERROR: package(s) LoopVectorization, TensorOperations not in project
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:33
 [2] check_packages_in_project(ctx::Pkg.Types.Context, packages::Vector{String})
   @ PackageCompiler ~/.julia/packages/PackageCompiler/wpsGv/src/PackageCompiler.jl:108
 [3] create_sysimage(packages::Vector{String}; sysimage_path::String, project::String, precompile_execution_file::String, precompile_statements_file::Vector{String}, incremental::Bool, filter_stdlibs::Bool, cpu_target::String, script::Nothing, sysimage_build_args::Cmd, include_transitive_dependencies::Bool, base_sysimage::Nothing, julia_init_c_file::Nothing, version::Nothing, soname::Nothing, compat_level::String, extra_precompiles::String)
   @ PackageCompiler ~/.julia/packages/PackageCompiler/wpsGv/src/PackageCompiler.jl:445
 [4] top-level scope
   @ REPL[2]:1

```

it’s weird that `using LoopVectorization, TensorOperations` is at the top of `precompile_plots.jl` ☹ (I have commented out `#BenchmarkTools`, and `#@btime test7(A) setup=(n=30; A=rand(Float64,(n,n,n,n)))`

1. I profiled. Using `snakeviz ` for profiling gives me many many information. I also used `time.time()` to check my python code. The conclusion is, the series of permutations and additions of arrays is the bottleneck (besides some possible improvement). I used transpose in `numpy`. But julia’s @tensor has some memory cache management, that outperform numpy by a factor of 2 ~ 3, [Is there any way to optimize array additions and multiplications with transposes?](https://discourse.julialang.org/t/is-there-any-way-to-optimize-array-additions-and-multiplications-with-transposes/80268), some of the discussions are summarized in the #6 reply in this question.

It would be great to start from julia. So far, the main python code has many other calculations. I think migrate into julia is bit complicated and prefer to improve the bottleneck by interfacing with julia.

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