PyCallJLD2 Tutorial

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Overview

This demo shows how to get started with PyCallJLD2.jl. Because its usage is very similar, much of the code is taken directly from the example for PyCallJLD as a direct comparison.

Setup

First, you must have your PyCall environment setup in the correct way. Here, we will point to the default Python installation internal to Julia

ENV["PYTHON"] = ""
using Pkg
Pkg.build("PyCall")

    Building Conda ─→ `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/8c86e48c0db1564a1d49548d3515ced5d604c408/build.log`
    Building PyCall → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/43d304ac6f0354755f1d60730ece8c499980f7ba/build.log`

Next, we load our dependencies. To use this package, you must load PyCall, JLD2, and PyCallJLD2 in the context that you intend to do model saving and loading:

# Load the modules into the current context
using
    PyCall,     # for PyObjects
    JLD2,       # for saving and loading
    PyCallJLD2  # for telling JLD2 how to save and load PyObjects

Create some `PyObject`s

Next, we load a scikit-learn module with the @pyimport macro

@pyimport sklearn.linear_model as lm

We can finally create some Python objects:

m1 = lm.LinearRegression()

LinearRegression()

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and

m2 = lm.ARDRegression()

ARDRegression()

Save and Load

We should first declare where we are saving the file rather than typing it out repeatedly:

model_file = "models.jld2"

"models.jld2"

To save, we use the normal JLD2.save usage:

JLD2.save(model_file, "mods", [m1, m2])

To load, we also use the normal JLD2.load usage:

models = JLD2.load(model_file, "mods")

2-element Vector{PyCall.PyObject}:
 PyObject LinearRegression()
 PyObject ARDRegression()

And voila! We have back our two models, ready for use.

Note

When loading the object, you must be sure that the definition for the unpacked data is in the current workspace (i.e., if you change terminal sessions here, you must remember to reimport @pyimport sklearn.linear_model as lm before loading the model file).

For the sake of this script, we will clean up after ourselves and remove the model:

rm(model_file)

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