README.md

Nomad Cluster for Tensorflow Distributed Model Training IN-A-BOX.

A simple framework to deploy clusters for training of TensorFlow deep learning AI models.

• Terraform for deployment of cluster nodes on AWS (GCP, Azure, and On-Site pending)

• HashiCorp Nomad scheduler ensures fast and secure execution of TensorFlow code.

• HashiCorp Consul maintains cluster state and key/value store for cluster.

• Nomad is lighter, faster, and easier than Kubernetes for distributed model training.

Architecture

• TensorFlow Chief Node ("Worker 0")
• Workers Nodes
• Parameter Server Node
• Evaluator Node
• Consul as KV store

How to Use

• Set your cloud provider authentication environment variables

• Set variables in variables.tf, and execute terraform init & terraform apply

• Cluster will immediately start training model, as per Git-commited Tensorflow code.

• Trained model is saved upon completion to parameterized location (default is S3).

• Nomad cluster node instance type is set in variables.tf, and determines hardware available for training.

• train directory contains the Python code each cluster node type will execute.

Tensorflow Cluster Nodes / Docker Containers / Nomad Exec

• Nomad workers run the Tensorflow code, either directly on machine via Exec, or with Docker Containers.
  • Both Nomad Exec and Nomad Docker workers are supported
  • Nomad Exec eliminates the Docker/Container layer, and thus may have performance improvements.
  • Nomad Docker is available, for existing containerized developer/pipeline workflows
  • Tensorflow cluster nodes (Python via Exec or Docker) run 1:1 on Nomad cluster nodes (Cloud VM Instances)
    • Each Tensorflow cluster node has a noncompeting connection to each Nomad cluster node hardware.

References

• https://www.tensorflow.org/guide/keras/distributed_training

• https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/distributed_training.ipynb

• https://www.tensorflow.org/guide/distributed_training

• https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy

• https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras

• https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy

• https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy

• https://towardsdatascience.com/distributed-training-in-tf-keras-with-w-b-ccf021f9322e

• https://theaisummer.com/distributed-training/

TODO

• GitAction pipelines to initiate model training upon tensorflow code commits