Installing packages

A number of Julia packages is used in this lesson. These can be installed on-the-fly during a workshop, but you can also follow the instructions below to install all packages in your global Julia environment.

Creating an environment (optional)

Copy-paste the following text into a file called Project.toml, which you can for example place under a new directory julia in your home directory:

name = "Julia-for-HPDA"
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
DataDrivenDiffEq = "2445eb08-9709-466a-b3fc-47e12bd697a2"
DataDrivenSparse = "5b588203-7d8b-4fab-a537-c31a7f73f46b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
GLMNet = "8d5ece8b-de18-5317-b113-243142960cc6"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b"
LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
LineSearches = "d3d80556-e9d4-5f37-9878-2ab0fcc64255"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
MLBase = "f0e99cf1-93fa-52ec-9ecc-5026115318e0"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
MLJScikitLearnInterface = "5ae90465-5518-4432-b9d2-8a1def2f0cab"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
PalmerPenguins = "8b842266-38fa-440a-9b57-31493939ab85"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
VegaDatasets = "0ae4a718-28b7-58ec-9efb-cded64d6d5b4"
VegaLite = "112f6efa-9a02-5b7d-90c0-432ed331239a"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

Then open a Julia REPL and specify the location of the Project.toml file:

$ julia --project=/path/to/Project.toml

Then copy-paste the following code into your Julia session:

using Pkg
Pkg.instantiate()

It could take a couple of minutes to download and install the packages. After it completes you should be able to list all installed packages:

Pkg.status()
Status `~/julia/Project.toml`
[6e4b80f9] BenchmarkTools v1.6.0
[336ed68f] CSV v0.10.15
[aaaa29a8] Clustering v0.15.8
[35d6a980] ColorSchemes v3.27.1
[5ae59095] Colors v0.12.11
[b0b7db55] ComponentArrays v0.15.22
[2445eb08] DataDrivenDiffEq v1.5.0
[5b588203] DataDrivenSparse v0.1.2
[a93c6f00] DataFrames v1.7.0
[864edb3b] DataStructures v0.18.20
[7806a523] DecisionTree v0.12.4
[aae7a2af] DiffEqFlux v4.1.0
[0c46a032] DifferentialEquations v7.15.0
[b4f34e82] Distances v0.10.12
[31c24e10] Distributions v0.25.117
[7a1cc6ca] FFTW v1.8.0
[587475ba] Flux v0.16.1
[38e38edf] GLM v1.9.0
[8d5ece8b] GLMNet v0.7.4
[cd3eb016] HTTP v1.10.15
[7073ff75] IJulia v1.26.0
[4138dd39] JLD v0.13.5
[682c06a0] JSON v0.21.4
[b1bec4e5] LIBSVM v0.8.1
[b964fa9f] LaTeXStrings v1.4.0
[d3d80556] LineSearches v7.3.0
[b2108857] Lux v1.5.1
[f0e99cf1] MLBase v0.9.2
[add582a8] MLJ v0.20.7
[c6f25543] MLJDecisionTreeInterface v0.4.2
[5ae90465] MLJScikitLearnInterface v0.7.0
[961ee093] ModelingToolkit v9.61.0
[b8a86587] NearestNeighbors v0.4.21
[7f7a1694] Optimization v4.0.5
[36348300] OptimizationOptimJL v0.4.1
[42dfb2eb] OptimizationOptimisers v0.3.7
[1dea7af3] OrdinaryDiffEq v6.90.1
[8b842266] PalmerPenguins v0.1.4
[91a5bcdd] Plots v1.40.9
[54e16d92] PrettyPrinting v0.4.2
[d330b81b] PyPlot v2.11.5
[ce6b1742] RDatasets v0.7.7
[1ed8b502] SciMLSensitivity v7.72.0
[860ef19b] StableRNGs v1.0.2
[10745b16] Statistics v1.11.1
[2913bbd2] StatsBase v0.34.4
[0ae4a718] VegaDatasets v2.1.1
[112f6efa] VegaLite v3.3.0
[e88e6eb3] Zygote v0.6.75
[ade2ca70] Dates v1.11.0
[37e2e46d] LinearAlgebra v1.11.0
[9a3f8284] Random v1.11.0
[2f01184e] SparseArrays v1.11.0

Activating your environment in VS Code

Open VS Code from terminal

If you run VS Code from a terminal you can activate your enviroment as follows. Open a terminal and go to the directory where the Project.toml file resides. Now start VS Code with code .

Change environment in VS Code

When using Visual Studio Code for julia development and using your own environment, you can activate it as follows. Start a Julia REPL in VS Code, for example by runnig a script. Start the package mode by typing ]. Typically you will be in the stardard environment v1.9:

(@v1.11) pkg>

To activate another environment, type

(@v1.11) pkg>activate path-to-projectfile

where you specify the path to the directory where you put your Project.toml file.

The same procedure applies when running Julia from the terminal and you want to switch enviroments. For example, if you start Julia from the terminal by simply typing julia (without the --project argument) you will end up in the standard environment and can switch enviroment as described above.

Manual installation and updates

It is convenient also to add packages as they are needed. This can be done in several ways. For instance, to install the package Plots one may do as follows. Open the Julia REPL as above and type:

using Pkg
Pkg.add("Plots")

Alternatively we may enter the package mode in the REPL by typing ] and then add the package:

(@v1.11) pkg> add Plots

To update all your packages, you can type up in the package mode in REPL:

(@v1.11) pkg> up

Installing JupyterLab and a Julia kernel

One way to use Julia is through Jupyter notebooks. Jupyter notebooks can be installed via the Python package manager pip:

pip install jupyterlab

Also, JupyterLab can most easily be installed through the full Anaconda distribution of Python packages or the minimal Miniconda distribution.

To install Anaconda, visit https://www.anaconda.com/products/individual , download an installer for your operating system and follow the instructions. JupyterLab and an IPython kernel are included in the distribution.

To install Miniconda, visit https://docs.conda.io/en/latest/miniconda.html , download an installer for your operating system and follow the instructions. After activating a conda environment in your terminal, you can install JupyterLab with the command conda install jupyterlab.

Add Julia to JupyterLab

To be able to use a Julia kernel in a Jupyter notebook you need to install the IJulia Julia package. Open the Julia REPL and type:

using Pkg
Pkg.add("IJulia")

Create a Julia notebook

Now you should be able to open up a JupyterLab session by typing jupyter-lab in a terminal, and create a Julia notebook by clicking on Julia in the JupyterLab Launcher or by selecting File > New > Notebook and selecting a Julia kernel in the drop-down menu that appears.