Build Options

To make customization of the build options easier we have the LegatePreferences.jl package to generate the LocalPreferences.toml which is read by the build script to determine which build option to use. LegatePreferences.jl will also enforce that Julia is restarted for changes to take effect.

Default Build (jlls)

pkg> add Legate

If you previously used a custom build or conda build and would like to revert back to using prebuilt JLLs, run the following command in the directory containing the Project.toml of your environment.

using LegatePreferences; LegatePreferences.use_jll_binary()

LegatePreferences is a separate module so that it can be used to configure the build settings before Legate.jl is added to your environment. To install it separately run

pkg> add LegatePreferences

Developer mode

This gives the most flexibility in installs. It is meant for developing on Legate.jl.

We support using a custom install version of Legate. See https://docs.nvidia.com/legate/latest/installation.html for details about different install configurations, or building Legate from source.

We require that you have a g++ capable compiler of C++ 20, and a recent version CMake >= 3.26.

To use developer mode,

using LegatePreferences; LegatePreferences.use_developer_mode(; use_legate_jll=true, legate_path=nothing)

By default use_legate_jll will be set to true. However, you can set a custom branch and/or use a custom path of legate. By setting use_legate_jll=false, you can set legate_path to your custom install.

using LegatePreferences; LegatePreferences.use_developer_mode(;use_legate_jll=false, legate_path="/path/to/legate/root")

Right now, building Legate with CUDA support requires both the CUDA driver library (libcuda.so) and the CUDA runtime library (libcudart.so) to be discoverable on the system library path. Automatic detection and use of CUDA versions provided via JLL packages is not yet implemented and remains a TODO.

In deps/cpp_wrapper.log, you will a CUDAToolkit version that is not self-contained with our JLL distributions.

Link Against Existing Conda Environment

This feature is not passing our CI currently. Please use with caution. We are failing to currently match proper versions of .so libraries together. Our hope is to get this functional for users already using Legate within conda.

Note, you need conda >= 24.1 to install the conda package. More installation details are found here.

# with a new environment
conda create -n myenv -c conda-forge -c legate
# into an existing environment
conda install -c conda-forge -c legate

Once you have the conda package installed, you can activate here.

conda activate [conda-env-with-legate]

To update LocalPreferences.toml so that a local conda environment is used as the binary provider for cupynumeric run the following command. conda_env should be the absolute path to the conda environment (e.g., the value of CONDA_PREFIX when your environment is active). For example, this path is: /home/JuliaLegate/.conda/envs/legate-gpu.

using LegatePreferences; LegatePreferences.use_conda("conda-env-with-legate");
Pkg.build()