TensorFlow With YARN

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.

Related Posts

Plotting ML Results In R

Bernardo Lares shows off the plots he creates in R to compare ML models: Split and compare quantiles This parameter is the easiest to sell to the C-level guys. “Did you know that with this model, if we chop the worst 20% of leads we would have avoided 60% of the frauds and only lose […]

Read More

Building TensorFlow Neural Networks On Spark With Keras

Jules Damji has an example of using the PyCharm IDE to use Keras to build TensorFlow neural network models on the Databricks MLflow library: Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and […]

Read More

Categories

March 2017
MTWTFSS
« Feb Apr »
 12345
6789101112
13141516171819
20212223242526
2728293031