Model Developer Guide# Model Developer Quickstart This quickstart helps first-time users get started. Distributed Training Learn how to perform optimized distributed training with Determined to speed up the training of a single trial. Preparing Container Environment Resources for preparing your container environment. Preparing Data What is the best way to load data into your ML models? This depends on several factors... Using a Training API Learn how to work with Training APIs and configure your distributed training experiments. Core API Learn how to use the flexible Core API to train any deep learning model. Hyperparameter Tuning Conceptual information about why hyperparameter tuning can be challenging and why it's important. Submitting Experiment Find out how to run an experiment by providing a launcher. Managing the Job Queue After submitting an experiment, you can manage the job queue. Managing Models Model management involves using and deleting checkpoints, archiving experiments, and managing trained models. Best Practices General tips for the trial definition, and best practices for separating configuration from code. Batch Inference Try the experimental Batch Processing API for batch inference.
