Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks

This chapter focuses on technical solutions to set up popular deep learning frameworks. First, we provide solutions to set up a stable and flexible environment on local machines and with cloud solutions. Next, all popular Python deep learning frameworks are discussed in detail:

• Setting up a deep learning environment
• Launching an instance on Amazon Web Services (AWS)
• Launching an instance on Google Cloud Platform (GCP)
• Installing CUDA and cuDNN
• Installing Anaconda and libraries
• Connecting with Jupyter Notebook on a server
• Building state-of-the-art, production-ready models with TensorFlow
• Intuitively building networks with Keras
• Using PyTorch's dynamic computation graphs for RNNs
• Implementing high-performance models with CNTK
• Building efficient models with MXNet
• Defining networks using simple and efficient code with Gluon