TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015. For more information visit the official TensorFlow page.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras contains numerous implementations of commonly used neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier. In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. For more information visit the official Keras page.
The complete machine learning environment consists of the following components: Jupyter, Keras and TensorFlow utilizing the power of a GPU card.
Important: To use this demo, your virtual machine must have an NVIDIA GPU card. If you want to modify the CUDA driver, you can safely customize it in the install-cuda.sh script in the nodes/cloud_init_jupyter_server_gpu.yaml file.
User manual and installation guide: