TensorFlow is an Python-based open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.
See official TensorFlow GitHub page (https://github.com/tensorflow/tensorflow).
This TensorFlow-GPU-Distributed recipe contains information on how to run distributed TensorFlow job across multiple GPU nodes with BatchAI.
This TensorFlow-GPU recipe contains information on how to run TensorFlow job on a GPU node with BatchAI.
This Horovod recipe contains information on how to run Horovod distributed training job for Tensorflow on a GPU cluster with Batch AI.
If you have any problems or questions, you can reach the Batch AI team at AzureBatchAITrainingPreview@service.microsoft.com or you can create an issue on GitHub.
We also welcome your contributions of additional sample notebooks, scripts, or other examples of working with Batch AI.