Small edits (#785)

docs/clearml_sdk/task_sdk.md

@@ -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)

docs/fundamentals/hyperparameters.md

@@ -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).
+  * [click](../integrations/click.md).
-  * [argparse](https://docs.python.org/3/library/argparse.html) - see code example [here](../guides/frameworks/pytorch/pytorch_tensorboardx.md).
+  * [argparse](../guides/reporting/hyper_parameters.md#argparse-command-line-options).
-  * [Python Fire](https://github.com/google/python-fire) - see code examples [here](https://github.com/allegroai/clearml/tree/master/examples/frameworks/fire).
+  * [Python Fire](../integrations/python_fire.md).
-  * [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).
+  * [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, 
+* [Hydra](../integrations/hydra.md) - 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).
+  as well as values overridden during runtime.
-    
+
 :::tip Disabling Automatic Logging
 Automatic logging can be disabled. See this [FAQ](../faq.md#controlling_logging).
 :::

docs/fundamentals/task.md

@@ -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 

docs/guides/frameworks/pytorch/model_updating.md

@@ -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.