Appliances
Course stanford
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.
Course
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers
Course stanford
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 software stack is optimized for running on CPU.
Course
Tensorflow
Pytorch
Keras
Python
Machine learning
A pre-configured and fully integrated software stack with TensorFlow, an open source software library for machine learning, Python 2.7, and Jupiter Notebook, a browser-based interactive notebook for programming, mathematics, and data science. The stack is designed for research and development tasks and optimized for running on NVidia GPU.
Tensorflow
Python
Jupyter notebook
Cuda
Cudnn
Nvidia drivers
Selfmanagement
Preset
Machine learning
A pre-configured and fully integrated software stack with TensorFlow, an open source software library for machine learning, Python 2.7, and Jupiter Notebook, a browser-based interactive notebook for programming, mathematics, and data science. The stack is designed for research and development tasks and optimized for running on CPU.
Tensorflow
Python
Jupyter notebook
Selfmanagement
Preset
Machine learning
A pre-configured and fully integrated software stack with MXNet, an open-source deep learning framework, 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 NVidia GPU.
Mxnet
Python
Cuda
Cudnn
Nvidia drivers
Opencv
Preset
Machine learning
A pre-configured and fully integrated software stack with MXNet, an open-source deep learning framework, 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.
Mxnet
Python
Cuda
Cudnn
Nvidia drivers
Opencv
Preset
Machine learning
A pre-configured and fully integrated software stack with MXNet, an open-source deep learning framework, 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.
Mxnet
Python
Opencv
Preset
Machine learning
A pre-configured and fully integrated software stack with MXNet, an open-source deep learning framework, 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.
Mxnet
Python
Opencv
Preset
Caffe2
A pre-configured and fully integrated software stack with Caffe2, a lightweight, modular, and scalable deep learning framework. 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.
Caffe2
Python
Cuda
Cudnn
Nvidia drivers
Opencv
Preset