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docs/clearml_sdk/task_sdk.md
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@ -63,10 +63,10 @@ After invoking `Task.init` in a script, ClearML starts its automagical logging,
* Command Line Parsing - ClearML captures any command line parameters passed when invoking code that uses standard python packages, including:
* [click](../integrations/click.md)
* [argparse](../guides/reporting/hyper_parameters.md#argparse-command-line-options)
* [Python Fire](https://github.com/allegroai/clearml/tree/master/examples/frameworks/fire)
* [Python Fire](../integrations/python_fire.md)
* [LightningCLI](../integrations/pytorch_lightning.md)
* TensorFlow Definitions (`absl-py`)
* [Hydra](../integrations/hydra.md) - the OmegaConf which holds all the configuration files, as well as overridden values.
* [Hydra](../integrations/hydra.md) - ClearML logs the OmegaConf which holds all the configuration files, as well as values overridden during runtime.
* **Models** - ClearML automatically logs and updates the models and all snapshot paths saved with the following frameworks:
* [TensorFlow](../integrations/tensorflow.md)
* [Keras](../integrations/keras.md)

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docs/fundamentals/hyperparameters.md
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@ -22,16 +22,16 @@ and tracks hyperparameters of various types, supporting automatic logging and ex
Once a ClearML Task has been [initialized](../references/sdk/task.md#taskinit) in a script, ClearML automatically captures and tracks
the following types of parameters:
* Command line parsing - command line parameters passed when invoking code that uses standard python packages, including:
* [click](https://click.palletsprojects.com) - see code example [here](https://github.com/allegroai/clearml/blob/master/examples/frameworks/click/click_multi_cmd.py).
* [argparse](https://docs.python.org/3/library/argparse.html) - see code example [here](../guides/frameworks/pytorch/pytorch_tensorboardx.md).
* [Python Fire](https://github.com/google/python-fire) - see code examples [here](https://github.com/allegroai/clearml/tree/master/examples/frameworks/fire).
* [LightningCLI](https://lightning.ai/docs/pytorch/stable/cli/lightning_cli.html#lightning-cli) - see code example [here](https://github.com/allegroai/clearml/blob/master/examples/frameworks/jsonargparse/pytorch_lightning_cli.py).
* [click](../integrations/click.md).
* [argparse](../guides/reporting/hyper_parameters.md#argparse-command-line-options).
* [Python Fire](../integrations/python_fire.md).
* [LightningCLI](../integrations/pytorch_lightning.md).
* TensorFlow Definitions (`absl-py`). See examples of ClearML's automatic logging of TF Defines:
* [TensorFlow MNIST](../guides/frameworks/tensorflow/tensorflow_mnist.md)
* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md)
* [Hydra](https://github.com/facebookresearch/hydra) - ClearML logs the `OmegaConf` which holds all the configuration files,
as well as values overridden during runtime. See code example [here](https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py).
* [Hydra](../integrations/hydra.md) - ClearML logs the `OmegaConf` which holds all the configuration files,
as well as values overridden during runtime.
:::tip Disabling Automatic Logging
Automatic logging can be disabled. See this [FAQ](../faq.md#controlling_logging).
:::

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docs/fundamentals/task.md
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@ -96,7 +96,7 @@ Most importantly, ClearML also logs experiments' input and output models as well
#### Logging Artifacts
ClearML provides an explicit logging interface that supports manually reporting a variety of artifacts. Any type of
artifact can be logged to a task using the [`Task.upload_artifact`](../references/sdk/task.md#upload_artifact) method.
artifact can be logged to a task using [`Task.upload_artifact()`](../references/sdk/task.md#upload_artifact).
See more details in the [Artifacts Reporting example](../guides/reporting/artifacts.md).
ClearML can be configured to upload artifacts to any of the supported types of storage, which include local and shared

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docs/guides/frameworks/pytorch/model_updating.md
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@ -67,7 +67,7 @@ model.update_design(config_dict=model_config_dict)
```
## Updating Models
To update a model, use the [OutputModel.update_weights](../../../references/sdk/model_outputmodel.md#update_weights) method.
To update a model, use [`OutputModel.update_weights()`](../../../references/sdk/model_outputmodel.md#update_weights).
This uploads the model to the set storage destination (see [Setting Upload Destination](../../../fundamentals/artifacts.md#setting-upload-destination)),
and registers that location to the task as the output model.