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docs/fundamentals/artifacts.md
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@ -21,7 +21,7 @@ frameworks:
* [Fast.ai](../integrations/fastai.md)
* [MegEngine](../integrations/megengine.md)
* [CatBoost](../integrations/catboost.md)
* [MONAI](../integrations/monai.md))
* [MONAI](../integrations/monai.md)
When a supported framework loads a weights file, the running task will be automatically updated, with its input model
pointing directly to the original training task's model.

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docs/integrations/pytorch.md
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@ -92,7 +92,7 @@ additional tools, like argparse, TensorBoard, and matplotlib:
* [PyTorch TensorBoardX](../guides/frameworks/pytorch/pytorch_tensorboardx.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoardX's `SummaryWriter`
* [PyTorch Abseil](../guides/frameworks/pytorch/pytorch_abseil.md) - Demonstrates ClearML automatically logging PyTorch models and `absl.flags` parameters
* [PyTorch Model Updating](../guides/frameworks/pytorch/model_updating.md) - Demonstrates training, logging, and updating a PyTorch model using ClearML's [OutputModel](../references/sdk/model_outputmodel.md) class
* [PyTorch Distributed](../guides/frameworks/pytorch/pytorch_distributed_example.md) - Demonstrates using ClearML with the [PyTorch Distributed Communications Package (torch.distributed)](https://pytorch.org/tutorials/beginner/dist_overview.html)
* [PyTorch Distributed](../guides/frameworks/pytorch/pytorch_distributed_example.md) - Demonstrates using ClearML with the [PyTorch Distributed Communications Package (`torch.distributed`)](https://pytorch.org/tutorials/beginner/dist_overview.html)
## Remote Execution
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,