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.

pageTraining an Image Classification Model in PyTorchpageTraining an Object Detection and Segmentation Model in PyTorchpageTraining Models Using PyTorch LightningpageSplitting Datasets for TrainingpageTraining on AWS SageMakerpageTraining Models Using MMDetectionpageTraining Reproducibility Using Deep Lake and Weights & BiasespageQuerying, Training and Editing Datasets with Data Lineage