👉 Mesa is an open-source framework for deploying and managing distributed machine learning models across multiple GPUs or nodes in a cluster, designed to simplify the process of scaling deep learning tasks. It provides a unified API that abstracts away the complexities of data parallelism, model parallelism, and synchronization, enabling developers to focus on building and training models efficiently. Mesa supports both synchronous and asynchronous training methods, making it versatile for various deployment scenarios. It also integrates seamlessly with popular deep learning libraries like TensorFlow, PyTorch, and Horovod, allowing users to leverage their existing codebases while benefiting from Mesa's distributed computing capabilities. This flexibility and ease of use make Mesa a popular choice for researchers and practitioners aiming to accelerate machine learning workflows on large-scale datasets and high-performance hardware.
