Terox

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Terox is an open source tiny Deep Learning System based on Python, Cython and CUDA.

Terox is a tiny Python package that provides some features:

• Support automatic differentiation.
• Provides convenient tensor calculation.
• Control the parameters and the model.
• Provides common computing functions for deep learning.
• Provides common deep learning components.
• Provides deep learning model optimizer.
• Accelerate computing on CPU and GPU.
• Support distributed computing.

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Setup

Terox requires Python 3.8 or higher. To check your version of Python, run either:

python --version # expect python version >= 3.8

The next step is to install packages. There are several packages used throughout Terox, and you can install them in your enviroment by running:

python -m pip install -r requirements.txt

As a final step, you can run the following command to package Terox and install it in your environment:

python -m pip install -Ue .

Make sure that everything is installed by running python and then checking. If your output is Terox v0.1 by Tokisakix., the installation was successful:

import terox
print(terox.__version__) # expect output: "Terox v0.1 by Tokisakix."

Test

You can test the correctness of the project by running pytest in the root directory of the project:

python -m pytest

Pytest tests all modules by default, but you can also run the following commands to do some testing:

python -m pytest -m <test-name>

Where <test-name> can select the following test module name:

# autodiff test
test_function
test_scalar
test_scalar_opts
test_scalar_overload
test_backward

# module test
test_module

project

You can find the accompanying demonstration project under the /project path, which demonstrates some of the uses of Terox.

You can run the sample code by going to the project path under '/project' and running the following command:

python run.py

Examples of projects currently available are:

scalar