Jacinle

Jacinle is a personal python toolbox. It contains a range of utility functions for python development, including project configuration, file IO, image processing, inter-process communication, etc.

• Website: https://jacinle.jiayuanm.com

• Github: vacancy/Jacinle

• Examples: vacancy/Jacinle

• Jacinle Reference: http://jacinle.jiayuanm.com/reference/jacinle.html

• JacLearn Reference: http://jacinle.jiayuanm.com/reference/jaclearn.html

• JacTorch Reference: http://jacinle.jiayuanm.com/reference/jactorch.html

Installation

Clone the Jacinle package (be sure to clone all submodules), and add the bin path to your PATH environment.

git clone https://github.com/vacancy/Jacinle --recursive
export PATH=<path_to_jacinle>/bin:$PATH

Optionally, you may need to install third-party packages specified in requirements.txt

Command Line

1. jac-run xxx.py Jacinle comes with a command line to replace the python command.

In short, this command will automatically add the Jacinle packages into PYTHONPATH, as well as adding a few vendor Python packages into PYTHONPATH (for example, (vacancy/JacMLDash)). Using this command to replace python xxx.py is the best practice to manage dependencies.

Furthremore, this command also supports a configuration file specific to projects. The command will search for a configuration file named jacinle.yml in the current working directory and its parent directories. This file specifies additional environmental variables to add, for example.

project_root: true  # tell the script that the folder containing this file is the root of a project. The directory will be added to PYTHONPATH.
system:
    envs:
        CUDA_HOME: /usr/local/cuda-10.0  # set needed environment variables here.
path:
    bin:  # will be prepended to $PATH
        /usr/local/bin
    python:  # will be prepended to $PYTHONPATH
        /Users/jiayuanm/opt/my_python_lib
vendors:  # load additional Python packages (root paths will be added to PYTHONPATH)
    pybullet_tools:
        root: /Users/jiayuanm/opt/pybullet/utils
    alfred:
        root: /Users/jiayuanm/opt/alfred

2. jac-crun <gpu_ids> xxx.py The same as jac-run, but takes an additional argument, which is a comma-separated list of gpu ids, following the convension of CUDA_VISIBLE_DEVICES.

3. jac-debug xxx.py The same as jac-run, but sets the environment variable JAC_DEBUG=1 before running the command. By default, in the debug mode, an ipdb interface will be started when an exception is raised.

4. jac-cdebug <gpu_ids> xxx.py The combined jac-debug and jac-crun.

5. jac-update Update the Jacinle package (and all dependencies inside vendors/).

6. jac-inspect-file xxx.json yyy.pkl Start an IPython interface and loads all files in the argument list. The content of the files can be accessed via f1, f2, …

Python Libraries

Jacinle contains a collection of useful packages. Here is a list of commonly used packages, with links to the documentation.

• jacinle.* (https://jacinle.jiayuanm.com/reference/jacinle.io.html): frequently used utility functions, such as jacinle.JacArgumentParser, jacinle.TQDMPool, jacinle.get_logger, jacinle.cond_with, etc.

• jacinle.io.* (https://jacinle.jiayuanm.com/reference/jacinle.io.html): IO functions. Two of the mostly used ones are: jacinle.io.load(filename) and jacinle.io.dump(filename, obj)

• jacinle.random.* (https://jacinle.jiayuanm.com/reference/jacinle.random.html): almost the same as numpy.random.*, but with a few additional utility functions and RNG state management functions.

• jacinle.web.* (https://jacinle.jiayuanm.com/reference/jacinle.web.html): the old jacweb package, which is a customized wrapper around the [tornado](https://www.tornadoweb.org/en/stable/) web server.

• jaclearn.* (https://jacinle.jiayuanm.com/reference/jaclearn.html): machine learning modules.

• jactorch.* (https://jacinle.jiayuanm.com/reference/jactorch.html): a collection of PyTorch functions in addition to the torch.* functions.

Indices and tables

• Index

• Module Index

• Search Page