Training Models

Workflows for training models using Deep Lake datasets

How to Train Deep Learning Models Using Deep Lake

Deep Lake provides dataloaders that can be used as a drop-in replacements in existing training scripts. The benefits of Deep Lake dataloaders is their data streaming speed and compatibility with Deep Lakes query engine, which enables users to rapidly filter their data and connect it to their GPUs.

Below is a series of tutorials for training models using Deep Lake.

Training an Image Classification Model in PyTorch Training an Object Detection and Segmentation Model in PyTorch Training Models Using PyTorch Lightning Splitting Datasets for Training Training on AWS SageMaker Training Models Using MMDetection Training Reproducibility Using Deep Lake and Weights & Biases Querying, Training and Editing Datasets with Data Lineage

PreviousCreating Video Datasets NextSplitting Datasets for Training