• Switched to Python3
• Included the latest nvidia driver (384.145)
• Upgraded to CUDA 9.0 and cuDNN 7.0
• Upgraded to nvidia-docker2
• Added support for CNTK (cntk-gpu v2.5.1)
• Upgraded to keras v2.2.0, tensorflow-gpu v1.8.0
• Docker 18.03

This repository contains data and a sample notebook to build a simple time series model using an LSTM network. In order to build and train the model, we're using the Keras framework on top of the Tensorflow library. The code is executed on GPUs through nvidia-docker for efficiency purposes.

Although the sample data and the model are trivial and hence don't require GPUs, this should give a starting point for more elaborate models and larger datasets.

Please note that NC-series (GPU enabled instances) are not available in all regions, please keep that in mind when selection the location

This basically provisions an N-series instance running Ubuntu on Azure. The machine has nvidia-docker installed and starts the Jupyter notebooks with a sample notebook of how to build an LSTM model using Keras. You can access Jupyter through the VM's DNS name, and/or connect to the machine through SSH.

Everything is setup to utilize the GPU of the machine for the training. Note that the sample notebook only utilizes a single GPU; with Keras currently you can only do model parallelization (training multiple models and averaging outcomes). If you need to do data parallelization, you might want to consider the Horovod project.