Installation
Please note that, unlike most code provided in papers, the Benco repository is not intended to be used via methods like git clone
, as the code involves numerous components required for engineering development. The expected method of usage is through pip install
, treating it as a Python library.
Regular Users
Installing IMDL-BenCo is very straightforward. Currently, it is managed via PyPI, so you can install it with the following command:
pip install imdlbenco
Developers
If you are attempting to develop new features for the IMDL-BenCo Python Library locally and contribute to the repository, it is recommended that you first uninstall any existing IMDL-BenCo installations in your environment. Then, clone your forked IMDL-BenCo repository, switch to the dev
branch to get the latest "development version," and use the special command pip install -e .
to complete the local installation. This will ensure that your current Python environment always executes the IMDL-BenCo library scripts based on the files in your local directory, automatically updating behavior when files are modified, making it convenient for debugging and development.
# Uninstall any existing IMDL-BenCo library
pip uninstall imdlbenco
# Clone your forked IMDL-BenCo repository from GitHub
git clone https://github.com/your_name/IMDL-BenCo.git
# Enter the project directory
cd IMDL-BenCo
# Switch to the dev branch
git checkout dev
# Perform a local development installation using `pip install -e .`
pip install -e .
# Verify the installation
pip show imdlbenco
If the installation is successful, after executing pip list
, you should see something like this:
Package Version Editable project location
----------------------- ------------------ ------------------------------------------------------
...
IMDLBenCo 0.1.10 /mnt/data0/xiaochen/workspace/IMDLBenCo_pure/IMDLBenCo
...
The presence of a corresponding path in the Editable project location
column indicates that any modifications to the Python scripts in this path will take effect directly in the Python environment without the need for reinstallation. This is very convenient for debugging.