# Training Models

## How to Train Deep Learning Models Using Deep Lake

Deep Lake provides [dataloaders](/examples/dl/dataloaders.md) 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](/examples/tql.md), 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.

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[Training an Image Classification Model in PyTorch](/examples/dl/tutorials/training-models/training-classification-pytorch.md)
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[Training an Object Detection and Segmentation Model in PyTorch](/examples/dl/tutorials/training-models/training-od-and-seg-pytorch.md)
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[Training Models Using PyTorch Lightning](/examples/dl/tutorials/training-models/training-lightning.md)
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[Splitting Datasets for Training](/examples/dl/tutorials/training-models/splitting-datasets-training.md)
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[Training on AWS SageMaker](/examples/dl/tutorials/training-models/training-sagemaker.md)
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[Training Models Using MMDetection](/examples/dl/tutorials/training-models/training-mmdet.md)
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[Training Reproducibility Using Deep Lake and Weights & Biases](/examples/dl/playbooks/training-reproducibility-wandb.md)
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[Querying, Training and Editing Datasets with Data Lineage](/examples/dl/playbooks/training-with-lineage.md)
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