Introduction

Keras, a high-level neural networks API, is integrated with TensorFlow and is widely used for building and training models. However, as models grow in complexity and data size, running these models on a single GPU or CPU is often insufficient. Parallel computing on a cluster can provide the necessary resources to manage training efficiently. OAR2, a resource manager for cluster computing, is an excellent tool for scheduling jobs and leveraging multiple nodes for training multiple models concurrently. This article discusses how to run multiple Keras models on a cluster managed by OAR2, enabling efficient utilization of computational resources.

Understanding Keras and OAR2

Keras allows for easy and fast prototyping through user-friendly, extensible, and modular specifications. When combined with TensorFlow's capabilities, it becomes a powerful tool for deep learning practitioners. OAR2, on the other hand, is designed to help users manage cluster resources by scheduling jobs, managing resource allocations, and ensuring efficient job execution.

Setting Up the Cluster Environment

Before running multiple Keras models on an OAR2-managed cluster, you need to ensure that your environment is properly set up:

1. Cluster Access: Make sure you have access to a cluster managed by OAR2. Coordinate with your institution or organization’s IT department to obtain the necessary credentials and permissions.
2. Software Installation: Ensure that Python, TensorFlow, and Keras are installed on all nodes within the cluster. You might use a shared environment such as Anaconda or Docker to ensure consistency:

• `#OAR -l /nodes=1/core=4,walltime=02:00:00`: Request 1 node with 4 cores and set the walltime to 2 hours.
• `module load`: Load necessary software modules (e.g., Python and TensorFlow).
• `source activate keras_environment`: Activate the virtual environment containing Keras and TensorFlow.
• Log output to a file associated with the OAR job ID.
• Run the `train_model.py` script, which contains the Keras model definition and training code.
• Each `model_x.py` contains the necessary code to define and train respective Keras models.
• Concurrency and Resource Management: It's crucial to balance the load among nodes and cores. Overloading resources may lead to job failures or inefficient execution.
• Data Storage: Ensure shared storage is available to all nodes for accessing datasets and logging results.
• Network Bandwidth: Large data transfers between nodes can affect performance. Consider network topology and bandwidth usage when designing your job configurations.