Fixes to the TaskRunner API tutorial

README.md

@@ -38,11 +38,7 @@ For more installation options check out the [online documentation](https://openf
 OpenFL supports two APIs to set up a Federated Learning experiment:
 
 - [Task Runner API](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html):
-Define an experiment and distribute it manually. All participants can verify model code and [FL plan](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html#federated-learning-plan-fl-plan-settings) prior to execution. The federation is terminated when the experiment is finished. This API is meant for enterprise-grade FL experiments, including support for mTLS-based communication channels and TEE-ready nodes (based on Intel® SGX).
-
-The quickest way to start testing the [TaskRunner API](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html) for managing and automating your tasks efficiently is to follow the steps outlined in the documentation. The TaskRunner API provides a simple and flexible interface to define, execute, and monitor tasks, making it an ideal choice for users looking to quickly integrate task automation into their projects.
-<br/>
-Read the [GitHub README File](https://github.com/securefederatedai/openfl/tree/develop/openfl-workspace/torch_cnn_mnist/README.md) explaining steps to train a model with OpenFL. <br/>
+Define an experiment and distribute it manually. All participants can verify model code and [FL plan](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html#federated-learning-plan-fl-plan-settings) prior to execution. The federation is terminated when the experiment is finished. This API is meant for enterprise-grade FL experiments, including support for mTLS-based communication channels and TEE-ready nodes (based on Intel® SGX). Follow the [Quick Start Guide](https://openfl.readthedocs.io/en/latest/get_started/quickstart.html#quick-start) for launching your first FL experiment locally. Then, refer to the [TaskRunner API Tutorial](https://github.com/securefederatedai/openfl/tree/develop/openfl-workspace/torch_cnn_mnist/README.md) for customizing the example workspace to your specific needs. <br/>
 
 - [Workflow API](https://openfl.readthedocs.io/en/latest/about/features_index/workflowinterface.html) ([*experimental*](https://openfl.readthedocs.io/en/latest/developer_guide/experimental_features.html)):
 Create complex experiments that extend beyond traditional horizontal federated learning. This API enables an experiment to be simulated locally, then seamlessly scaled to a federated setting. See the [experimental tutorials](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/workflow/) to learn how to coordinate [aggregator validation after collaborator model training](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/experimental/workflow/102_Aggregator_Validation.ipynb), [perform global differentially private federated learning](https://github.com/psfoley/openfl/tree/experimental-workflow-interface/openfl-tutorials/experimental/workflow/Global_DP), measure the amount of private information embedded in a model after collaborator training with [privacy meter](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/workflow/Privacy_Meter/readme.md), or [add a watermark to a federated model](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/workflow/301_MNIST_Watermarking.ipynb).

openfl-workspace/torch_cnn_mnist/README.md

@@ -13,7 +13,7 @@ pip install openfl
 
 ```
 cd ~/openfl-quickstart
-fx workspace create --template torch_template --prefix fl_workspace
+fx workspace create --template torch_cnn_mnist --prefix fl_workspace
 cd ~/openfl-quickstart/fl_workspace
 ```