Role
Appliances
Machine learning
A pre-configured and fully integrated software stack with PyTorch, an open source machine learning library, and Python 3.6. It provides a stable and tested execution environment for training, inference, or running as an API service. The stack can be easily integrated into continuous integration and deployment workflows. It is designed for short and long-running high-performance tasks and optimized for running on CPU.
Pytorch
Python
Selfmanagement
Preset
Machine learning
A pre-configured and fully integrated software stack with PyTorch, an open source machine learning library, and Python 2.7. It provides a stable and tested execution environment for training, inference, or running as an API service. The stack can be easily integrated into continuous integration and deployment workflows. It is designed for short and long-running high-performance tasks and optimized for running on NVidia GPU.
Pytorch
Python
Cuda
Cudnn
Nvidia drivers
Selfmanagement
Preset
Machine learning
A pre-configured and fully integrated software stack with PyTorch, an open source machine learning library, and Python 2.7. It provides a stable and tested execution environment for training, inference, or running as an API service. The stack can be easily integrated into continuous integration and deployment workflows. It is designed for short and long-running high-performance tasks and optimized for running on CPU.
Pytorch
Python
Selfmanagement
Preset
Course udacity
The pre-configured and ready-to-use runtime environment for the Udacity's Deep Learning Nanodegree Foundation program (nd101). It includes Python 3.5, TensorFlow 1.0.0 and tflearn 0.30. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Cuda
Cudnn
Nvidia drivers
Course udacity
The pre-configured and ready-to-use runtime environment for the Udacity's Machine Learning Engineer Nanodegree program (nd009t). It includes Python 3.5, TensorFlow 1.0.0 and Keras 2.0.2. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Python
Cuda
Cudnn
Nvidia drivers
Course udacity
The pre-configured and ready-to-use runtime environment for the Udacity's Machine Learning Engineer Nanodegree program (nd009t). It includes Python 2.7, TensorFlow 1.0.0 and Keras 2.0.2. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Python
Cuda
Cudnn
Nvidia drivers
Course stanford
The pre-configured and ready-to-use runtime environment for the Stanford's CS224n course: Natural Language Processing with Deep Learning. It includes Python 3.6 and TensorFlow 1.4.1. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Python
Cuda
Cudnn
Nvidia drivers
Course stanford
The pre-configured and ready-to-use runtime environment for the Stanford's CS224n course: Natural Language Processing with Deep Learning. It includes Python 2.7 and TensorFlow 1.4.1. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Python
Cuda
Cudnn
Nvidia drivers
Course stanford
The pre-configured and ready-to-use runtime environment for the Stanford's CS20 course: Tensorflow for Deep Learning Research. It includes Python 3.6 and TensorFlow 1.4.1. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Cuda
Cudnn
Nvidia drivers
Course mit
The pre-configured and ready-to-use runtime environment for the MIT 6.S094 course: Deep Learning for Self-Driving Cars, 2017. It includes Python 2.7, TensorFlow 0.12.1 and OpenCV 3.3.0. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Cuda
Cudnn
Nvidia drivers