Simple Devcontainer vscode for Julia SciML + PyCall

General Usage
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1

I am exploring SciML with Julia and found that the best way to experiment with Julia advanced feature without touchingt the core OS was using Devcontainers via vscode. This could be useful for beginners.

This is is simplest Devcontainer I was able to build in 47 lines, and includes:

  • Julia
  • Quarto
  • JupyterLab
  • Essential SciML libraries
  • Python 3 (to test PyCall)
  • Examples

Requirements

  1. Docker
  2. VS Code
  3. VSCode Extensions: Dev Containers

Files

devcontainer.json

{
	"name": "JuliaQuartoPycall",
	"image": "mcr.microsoft.com/devcontainers/base:ubuntu",

	// Features to add to the dev container. More info: https://containers.dev/features.
	"features": {
		// A Feature to install Julia via juliaup. More info: https://github.com/JuliaLang/devcontainer-features/tree/main/src/julia.
		"ghcr.io/julialang/devcontainer-features/julia:1": {
			"channel": "release"
		},
        "ghcr.io/devcontainers/features/common-utils:2": {
            "installZsh": "true",
			"configureZshAsDefaultShell": true,
            "username": "vscode",
            "upgradePackages": "true"
        },
    "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {
		"version": "latest",
		"installTinyTex": false,
		"installChromium": false
		},
    // A comma separated list of Linux packages
    "ghcr.io/rocker-org/devcontainer-features/apt-packages:1": {
		"packages": "libpython3-dev,python3-dev,python3-pip,qpdf"
	  }		
	},
	
	"onCreateCommand": "pip install --no-cache-dir jupyterlab jupyterlab-git jupyterlab-lsp notebook nbclassic nbconvert",
	"postCreateCommand": "julia ./.devcontainer/pkgs.jl",
	
	// Use 'forwardPorts' to make a list of ports inside the container available locally.
	// "forwardPorts": [],

	// Configure tool-specific properties.
    "customizations": {
        "vscode": {
            "extensions": [
                "julialang.language-julia",
                "tamasfe.even-better-toml",
				"ms-python.python",
				"ms-toolsai.jupyter"
            ]
        }
    }	
	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
	// "remoteUser": "root"
}

pkgs.jl

using Pkg
# Pkg.add("BandedMatrices")
# Pkg.add("BenchmarkTools")
# Pkg.add("BlockBandedMatrices")
Pkg.add("ComponentArrays")
Pkg.add("Conda")
# Pkg.add("CSV")
# Pkg.add("Dates")
Pkg.add(["DataDrivenDiffEq", "DataDrivenSparse"])
# Pkg.add("DataFrames")
# Pkg.add("DelimitedFiles")
Pkg.add("DifferentialEquations")
# Pkg.add("Distributions")
# Pkg.add("Flux")
Pkg.add("ForwardDiff")
# Pkg.add("GLM")
# Pkg.add("HypothesisTests")
# Pkg.add(["Images", "ImageMagick"])
Pkg.add("IJulia")
# Pkg.add("IterativeSolvers")
# Pkg.add("JLD")
# Pkg.add("KernelDensity")
# Pkg.add("KrylovKit")
# Pkg.add("Libdl")
Pkg.add("LinearAlgebra")
Pkg.add("LineSearches")
# Pkg.add("LsqFit")
Pkg.add("Lux")
Pkg.add("MAT")
# Pkg.add("MatrixDepot")
# Pkg.add("Measurements")
# Pkg.add("Metalhead")
# Pkg.add("MLBase")
Pkg.add("ModelingToolkit")
Pkg.add("NonlinearSolve")
Pkg.add(["Optimization", "OptimizationNLopt", "OptimizationPolyalgorithms"])
Pkg.add(["OptimizationOptimisers", "OptimizationOptimJL"])
Pkg.add("OrdinaryDiffEq")
Pkg.add("NPZ")
Pkg.add("Plots")
# Pkg.add("Primes")
Pkg.add("PyCall")
# Pkg.add("Printf")
# Pkg.add("RData")
# Pkg.add("RDatasets")
Pkg.add("SciMLSensitivity")
# Pkg.add("SparseArrays")
Pkg.add("StableRNGs")
Pkg.add("Statistics")
# Pkg.add("StatsBase")
# Pkg.add("StatsPlots")
Pkg.add("SymPy")
# Pkg.add("Tables")
# Pkg.add("Test")
# Pkg.add("TSVD")
Pkg.add("UnicodePlots")
# Pkg.add("Unitful")
# Pkg.add("XLSX")
Pkg.add("Zygote")

# ENV["PYCALL_DEBUG_BUILD"] = "yes"

Pkg.build("IJulia")
Pkg.build("PyCall")
Pkg.precompile()

File, folder structure

image

Installation

  1. Create a folder, let’s say julia-quarto-pycall
  2. Create a sub-folder .devcontainer
  3. Copy the two files devcontainer.json and pkgs.jl under the sub-folder .devcontainer
  4. Open the folder julia-quarto-pycall with VS Code
  5. Press the green button on the bottom left corner of the IDE, and select “Reopen in Container”. That will start the building process.
  6. Run any Julia file (.jl) or Julia Jupyter notebook (.ipynb) from the IDE.
  7. That’s it

Link: Dropbox

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