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updating for vr
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jpmorgan98 committed Oct 15, 2024
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -17,25 +17,25 @@ Our documentation on installation, contribution, and a brief user guide is on [R

## Installation

We recommend using [`conda`](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) or some other environment manager to manage the MC/DC installation.
We recommend using [Python virtual environments (venv)](https://docs.python.org/3/library/venv.html) or some other environment manager (e.g. conda) to manage the MC/DC installation.
This avoids the need for admin access when installing MC/DC's dependencies and allows greater configurability for developers.
For most users working on a single machine of which they are administrators, MC/DC can be installed via pip:
For most users working in a venv, MC/DC can be installed via pip:
```bash
pip install mcdc
```
For developers or users on HPC machines, we recommend that you *not* use the pip distribution and instead install MC/DC and its dependencies via the included [install script](https://mcdc.readthedocs.io/en/latest/install.html), which builds `mpi4py` from source and uses conda to manage your environment. *This is the most reliable way to install and configure MC/DC*. It also takes care of the [Numba patch]() and can configure the [continuous energy data library](), if you have access.
For developers or users on HPC machines, mpi4py is often distributed as part of an HPC machines given venv.

### Common issues with `mpi4py`

The `pip mpi4py` distribution commonly has errors when building due to incompatible local MPI dependencies it builds off of. While pip does have some remedy for this, we recommend the following:
* **Mac users:** we recommend `openmpi` is [installed via homebrew](https://formulae.brew.sh/formula/open-mpi) (note that more reliable mpi4py distribution can also be [found on homebrew](https://formulae.brew.sh/formula/mpi4py)), alternatively you can use `conda` if you don't have admin privileges;
* **Linux users:** we recommend `openmpi` is installed via a root package manager if possible (e.g. `sudo apt install openmpi`) or a conda distribution (e.g. `conda install openmpi`)
* **HPC users and developers on any system:** On HPC systems in particular, `mpi4py` must be built using the system's existing `mpi` installation. Installing MC/DC using the [install script](https://mcdc.readthedocs.io/en/latest/install.html) we've included will handle that for you by installing dependencies using conda rather than pip. It also takes care of the [Numba patch]() and can configure the [continuous energy data library](), if you have access.
* **HPC users and developers on any system:** On HPC systems that do not supply a suitable venv, `mpi4py` may need to be built using the system's existing `mpi` installation. Installing MC/DC using the [install script](https://mcdc.readthedocs.io/en/latest/install.html) we've included will handle that for you by installing dependencies using conda rather than pip. It also takes care of the [Numba patch](https://github.com/CEMeNT-PSAAP/MCDC/blob/main/patch_numba.sh) and can configure the [continuous energy data library](https://github.com/CEMeNT-PSAAP/MCDC/blob/main/config_cont_energy.sh), if you have access.

### Numba Config

Running MC/DC performantly in [Numba mode](#numba-mode) requires a patch to a single Numba file. If you installed MC/DC with the [install script](https://mcdc.readthedocs.io/en/latest/install.html), this patch has already been taken care of. If you installed via pip, we have a patch script will make the necessary changes for you:
1. Download the `patch.sh` file [here]() (If you've cloned MC/DC's GitHub repository, you already have this file in your MCDC/ directory).
1. Download the `patch.sh` file [here](https://github.com/CEMeNT-PSAAP/MCDC/blob/main/patch_numba.sh) (If you've cloned MC/DC's GitHub repository, you already have this file in your MCDC/ directory).
2. In your active conda environment, run `bash patch_numba.sh`.
*If you manage your environment with conda, you will not need admin privileges*.

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3 changes: 2 additions & 1 deletion pyproject.toml
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Expand Up @@ -4,7 +4,7 @@ build-backend = "hatchling.build"

[project]
name = "mcdc"
version = "0.10.0"
version = "0.11.0"
authors = [
{ name="Ilham Variansyah", email="variansi@oregonstate.edu" },
{ name="Sam Pasmann", email="spasmann@nd.edu" },
Expand All @@ -27,6 +27,7 @@ authors = [

maintainers = [
{ name="Ilham Variansyah", email = "variansi@oregonstate.edu"},
{ name="Braxton Cuneo", email="bcuneo@seattleu.edu" },
{ name="Kayla Clements", email = "clemekay@oregonstate.edu"},
{ name="Joanna Piper Morgan", email = "morgajoa@oregonstate.edu"},
{ name="Kyle E. Niemeyer", email = "kyle.niemeyer@oregonstate.edu"},
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