The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes latest versions of Python 3, TensorFlow, and PyTorch. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.

The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes latest versions of Python 2, TensorFlow, and PyTorch. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.

The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes original (old) versions of Python, TensorFlow, and PyTorch, used in the course. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.

The pre-configured and ready-to-use runtime environment for the Open Machine Learning Course, 2018. It includes Python 3.6, TensorFlow 1.4, Keras 2, XGBoost, LightGBM and Vowpal Wabbit. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.

A standard one-click install solution for Redmine 2.6, a free and open source, web-based project management and issue tracking tool.

A standard one-click install solution for Redmine 3.3, a free and open source, web-based project management and issue tracking tool with PostgreSQL as a database back-end.

A one-click install solution for Redmine 3.3, a free and open source, web-based project management and issue tracking tool, using MyRocks MySQL with RocksDB storage engine.

A standard one-click install solution for Redmine 3.3, a free and open source, web-based project management and issue tracking tool.