Wangda Tan and Vinod Kumar Vavilapalli show how to control TensorFlow jobs with YARN:
YARN has been used successfully to run all sorts of data applications. These applications can all coexist on a shared infrastructure managed through YARN’s centralized scheduling.
With TensorFlow, one can get started with deep learning without much knowledge about advanced math models and optimization algorithms.
If you have GPU-equipped hardware, and you want to run TensorFlow, going through the process of setting up hardware, installing the bits, and optionally also dealing with faults, scaling the app up and down etc. becomes cumbersome really fast. Instead, integrating TensorFlow to YARN allows us to seamlessly manage resources across machine learning / deep learning workloads and other YARN workloads like MapReduce, Spark, Hive, etc.
Read on for more details, including a demo video.