In a distributed environment with multiple NVIDIA GPUs, optimizing workload distribution and GPU utilization requires tools that enable efficient computation and communication:
* NVIDIA CUDA(A) is a foundational parallel computing platform that allows developers to harness GPU power for general-purpose computing, including machine learning. It's essential for programming GPUs and optimizing workloads in a distributed setup.
* NVIDIA NCCL(D) (NVIDIA Collective Communications Library) is designed for multi-GPU and multi-node communication, providing optimized primitives (e.g., all-reduce, broadcast) for collective operations in deep learning. It ensures efficient data exchange between GPUs, maximizing utilization in distributed training.
* NVIDIA NGC(B) is a hub for GPU-optimized containers and models, useful for deployment but not directly responsible for workload distribution or GPU utilization optimization.
* TensorFlow Serving(C) is a framework for deploying machine learning models for inference, not for optimizing distributed training or GPU utilization during model development.
* Keras(E) is a high-level API for building neural networks, but it lacks the low-level control needed for distributed workload optimization-it relies on backends like TensorFlow or CUDA.
Thus, CUDA (A) and NCCL (D) are the best choices for this scenario.