Recommendation Model Implementation by using PyTorch

Under this problem setup, the goal is to leverage existing configuration data to make the overall optimization process as efficient as possible.

We use the Titanic dataset to implement machine learning and deep learning. Preprocessing data, visualizing, building models, and ensembling are practiced in the ML section; PyTorch basics, PyTorchLightning framework, and RayTune hyperparameter-tuning are in the DL section.

Simple example of hyperparameter optimization using Raytune (with PyTorch ignite)

Examples scripts demonstrating raytune and comet-ml usage.

Python wrapper for starting distributed Ray clusters with LSF job submission