A brief tutorial about tips and tricks for packaging a python gui linked to cpp code, and how to release it through conda-forge.
- Install Visual Studio Code extensions: Remote SSH, Python, C/C++
- Setup passthrough HECC Knowledgebase and enable multiplexing
- Connect using:
ssh -XY pfe
and add the following function topfe:$~/.bashrc
:
gpu() {qsub -X -I -q v100@pbspl4 -l walltime=$2:00:00,select=1:model=sky_gpu:ncpus=16:ngpus=$1:mem=180g,place=vscatter:shared -W group_list=[ADD_GROUP]}
- Request node using
gpu 1 24
and add the following inlocal:$~/.ssh/config
(modify HostName according to node assigned)
Host pfe-intgpu
HostName r101i0n1
ProxyJump pfe
ForwardAgent yes
ForwardX11 yes
ControlMaster auto
ServerAliveInterval 120
ServerAliveCountMax 2
User ADD_USERNAME
- Connect to node from VS Code (N.B. no internet connection inside node!)
- Request access to Anaconda Nucleus to create conda environments on NAS
Git is a powerful tool for keeping version control of your package. To create a new repository and push it to Github run:
mkdir python_packaging101
cd python_packaging101
git init
# create necessary files ...
git remote add origin https://github.com/fsemerar/python_packaging101.git
git branch -M main
git status
git add -A
git commit -m 'Initial commit'
git push origin main
To clone an existing repository use: git clone https://github.com/fsemerar/python_packaging101.git
Conda is a cross-platform package manager that enables the creation of independent environments on a system and install the dependencies you need (not only python!).
Add the following to your pfe:$~/.bashrc
:
module use -a /swbuild/analytix/tools/modulefiles
module load miniconda3/v4
export CONDA_ENVS_PATH=/nobackup/$USER/.conda/envs
export CONDA_PKGS_DIRS=/nobackup/$USER/.conda/pkgs
conda config --append envs_dirs /nobackup/$USER/.conda/envs
source activate
Then you can run this to create and activate the environment:
conda env create --file environment.yaml
conda activate packaging101
Use cmd+shift+p
to select the interpreter on VS Code.
We can install the package using:
conda activate packaging101
cd packaging101
pip install .
We can now use packaging101
both calling the gui from the terminal directly or by importing it as a python package.
To test the code we can use the test.ipynb notebook from within VS Code.
└── 📁packaging101
└── __init__.py
└── 📁gui
└── __init__.py
└── main.py
└── main_window.py
└── 📁utils
└── __init__.py
└── fastfactorial.cpp
└── slowfactorial.py
setup.py
environment.yaml
README.md
LICENSE
.gitignore
Here is the docs (sphinx) file structure to be built on readthedocs:
└── 📁docs
└── Makefile
└── 📁source
└── conf.py
└── index.md
└── 📁placeholders
└── 📁template
└── sphinx_env.yml
.readthedocs.yml
Here is the file structure for testing packaging101 every time that a push happens to main:
└── 📁tests
└── test_utils.py
└── 📁workflows
└── test-packaging101.yml
Lots of resources can be found here. These are the main steps to release an already open-source and released package (note that every dependency has to be already available through conda-forge):
- Fork staged-recipes repo
- Create conda-forge recipe by creating a new folder under
staged-recipes/recipes
, like shown here - Create a tag to your package and update the url field in the meta.yaml (need a link to the .tar.gz tag)
- Update sha256 field in meta.yaml, which you can find by running:
curl -sL https://github.com/fsemerar/python_packaging101/archive/refs/tags/v1.0.0.tar.gz | openssl sha256`)
- Open Pull Request (PR) between your fork and conda-forge's staged-recipes (example) --> builds package on Azure Pipelines
- Once build is successfull, ping the conda-forge developers for a review
- Once approved, a packaging101-feedstock repo will be created under conda-forge (PuMA example)
- The package is now installable using:
conda install conda-forge::packaging101
- Maintain package as explained here