Parallelization on Fabric using TF_on Spark

Thanks again for your answer, but there is an issue. Your code primarily disables GPU usage and lists the available devices for TensorFlow to confirm it's using the CPU. While it serves as a basic example to ensure TensorFlow is configured correctly on a local machine, it doesn't align with what I need for my use case.

My Objective

I am working on running multiple neural network models in parallel, specifically leveraging a Spark cluster in Azure Fabric to maximize cluster utilization. The goal is to:

1. Execute independent TensorFlow models on Spark executors in parallel.
2. Perform a proof-of-concept using a sample use case and extend it to more workloads.

Why the Code Falls Short

1. No Parallel Execution:

   • The provided code runs a simple main() function that prints "Hello, World!" and lists TensorFlow devices. It does not demonstrate any parallel execution or utilization of multiple models or Spark resources.

2. Local Execution Only:

   • The code is designed to run locally on a single machine and does not integrate with Spark or distribute tasks across a cluster.

3. No Spark Integration:

   • There is no use of Spark for distributing workloads or managing parallel execution, which is critical for maximizing cluster resource utilization.

What I’m Looking For

To align with my goal, I need:

1. Code that integrates TensorFlow with Spark to distribute the execution of multiple neural network models across the cluster.
2. A framework or approach that maximizes the use of Spark executors and ensures TensorFlow tasks efficiently utilize the allocated resources (e.g., CPU or GPU).

If you have suggestions or examples related to running TensorFlow models in parallel on a Spark cluster, I’d greatly appreciate your input!