10 Python Frameworks for Parallel and Distributed Machine Learning Tasks
Python Libraries that Enable Capabilities to Distribute and Parallelize ML Tasks
Nowadays, Neural network models are very deep and complicated with so many weights to learn. Training such models is very challenging. Data scientists need to set up distributed training, checkpointing, etc. Even after that, data scientists may not achieve the desired performance and convergence rate. Training large models is even more challenging in that the model easily runs out of memory.
In this article, we will see a list of Python Frameworks that allow us to Distribute and Parallelize the Deep Learning models.
1. Elephas
Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Spark. Elephas intends to keep the simplicity and high usability of Keras, thereby allowing for fast prototyping of distributed models, which can be run on massive data sets.
Elephas currently supports a number of applications, including:
