I have read that. I even re-read it before making my post.
That implementation requires starting individual tasks on each node in your cluster.
>To create a cluster, you start one TensorFlow server per task in the cluster. Each task typically runs on a different machine, but you can run multiple tasks on the same machine (e.g. to control different GPU devices).
I'm used to using tools that can roll out to a cluster with more finesse than that. The Spark wrapper seems to provide some capability to do this automatically, but even the Spark wrapper requires installing python libraries on each node.
That implementation requires starting individual tasks on each node in your cluster.
>To create a cluster, you start one TensorFlow server per task in the cluster. Each task typically runs on a different machine, but you can run multiple tasks on the same machine (e.g. to control different GPU devices).
I'm used to using tools that can roll out to a cluster with more finesse than that. The Spark wrapper seems to provide some capability to do this automatically, but even the Spark wrapper requires installing python libraries on each node.