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Workflows for training models using Deep Lake datasets |
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.
{% content-ref url="training-classification-pytorch.md" %} training-classification-pytorch.md {% endcontent-ref %}
{% content-ref url="training-od-and-seg-pytorch.md" %} training-od-and-seg-pytorch.md {% endcontent-ref %}
{% content-ref url="training-lightning.md" %} training-lightning.md {% endcontent-ref %}
{% content-ref url="splitting-datasets-training.md" %} splitting-datasets-training.md {% endcontent-ref %}
{% content-ref url="training-sagemaker.md" %} training-sagemaker.md {% endcontent-ref %}
{% content-ref url="training-mmdet.md" %} training-mmdet.md {% endcontent-ref %}
{% content-ref url="../../playbooks/training-reproducibility-wandb.md" %} training-reproducibility-wandb.md {% endcontent-ref %}
{% content-ref url="../../playbooks/training-with-lineage.md" %} training-with-lineage.md {% endcontent-ref %}