Align dir structure and URL structure with left nav hierarchy (#4843)

* Correct heading

Signed-off-by: Peeter Piegaze <[email protected]>

* Align directory and URL structure with left nav

Signed-off-by: Peeter Piegaze <[email protected]>

* Sentence case and logically named files

Signed-off-by: Peeter Piegaze <[email protected]>

---------

Signed-off-by: Peeter Piegaze <[email protected]>

docs/introduction/core_use_cases/analytics.md → docs/core_use_cases/analytics.md

@@ -13,7 +13,7 @@ Flyte is ideal for data cleaning, statistical summarization, and plotting
 because with `flytekit` you can leverage the rich Python ecosystem of data
 processing and visualization tools.
 
-## Cleaning Data
+## Cleaning data
 
 In this example, we are going to analyze some covid vaccination data:
 
@@ -41,7 +41,7 @@ As you can see, we're using `pandas` for data processing, and in the task
 below we use `plotly` to create a choropleth map of the percent of a country's
 population that has received at least one COVID-19 vaccination.
 
-## Rendering Plots
+## Rendering plots
 
 We can use {ref}`Flyte Decks <decks>` for rendering a static HTML report
 of the map. In this case, we normalize the `people_vaccinated` by the
@@ -158,7 +158,7 @@ logger.removeFilter(deck_filter)
 HTML(filename=cp_deck(deck_filter.deck_files["plot"]))
 ```
 
-## Custom Flyte Deck Renderers
+## Custom Flyte deck renderers
 
 You can also create your own {ref}`custom Flyte Deck renderers <getting_started_customer_renderers>`
 to visualize data with any plotting/visualization library of your choice, as

docs/introduction/core_use_cases/data_engineering.md → docs/core_use_cases/data_engineering.md

@@ -7,7 +7,7 @@ kernelspec:
 
 (getting_started_data_engineering)=
 
-# Data Engineering
+# Data engineering
 
 Flyte is well-suited for data engineering use cases, where you can interleave
 SQL queries with data processing logic implemented in Python with whichever
@@ -100,7 +100,7 @@ def load(df: pd.DataFrame) -> CSVFile:
     return CSVFile(path=csv_file)
 ```
 
-## ETL Workflow
+## ETL workflow
 
 Putting all the pieces together, we create an `etl_workflow` that produces a
 dataset based on the parameters you give it.
@@ -130,7 +130,7 @@ csv_file = etl_workflow(limit=5)
 pd.read_csv(csv_file)
 ```
 
-## Workflows as Reusable Components
+## Workflows as reusable components
 
 Because Flyte tasks and workflows are simply functions, we can embed
 `etl_workflow` as part of a larger workflow, where it's used to create a

docs/introduction/core_use_cases/index.md → docs/core_use_cases/index.md

@@ -1,8 +1,8 @@
 (getting_started_core_use_cases)=
 
-# Core Use Cases
+# Core use cases
 
-This section of the **Getting Started** documentation will take you through the
+This section of the documentation will take you through the
 core use cases for which Flyte is designed. Within the context of these guides,
 we're going to assume that the discipline of data science can be broken down into
 at least three specializations: data engineering, machine learning (or
@@ -16,9 +16,9 @@ practice.
 :header-rows: 0
 :widths: 10 30
 
-* - {doc}`🛠 Data Engineering <data_engineering>`
+* - {doc}`🛠 Data engineering <data_engineering>`
   - Create an ETL workflow for processing data with SQLAlchemy and Pandas.
-* - {doc}`🤖 Machine Learning <machine_learning>`
+* - {doc}`🤖 Machine learning <machine_learning>`
   - Train a classifier with Scikit-Learn and Pandas.
 * - {doc}`📈 Analytics <analytics>`
   - Develop a data cleaning and plotting pipeline with Plotly and Pandas.

docs/introduction/core_use_cases/machine_learning.md → docs/core_use_cases/machine_learning.md

@@ -7,13 +7,13 @@ kernelspec:
 
 (getting_started_machine_learning)=
 
-# Machine Learning
+# Machine learning
 
 Flyte can handle a full spectrum of machine learning workloads, from
 training small models to gpu-accelerated deep learning and hyperparameter
 optimization.
 
-## Getting the Data
+## Getting the data
 
 In this simple example, we train a binary classification model on the
 [wine dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-dataset)
@@ -34,7 +34,7 @@ def get_data() -> pd.DataFrame:
     return load_wine(as_frame=True).frame
 ```
 
-## Define a Training Workflow
+## Define a training workflow
 
 Then, we define `process_data` and `train_model` tasks along with a
 `training_workflow` to put all the pieces together for a model-training
@@ -76,7 +76,7 @@ type-safe launch forms on the Flyte UI. Learn more in the
 {ref}`Extending Flyte <customizing_flyte_types>` guide.
 ```
 
-## Computing Predictions
+## Computing predictions
 
 Executing this workflow locally, we can call the `model.predict` method to make
 sure we can use our newly trained model to make predictions based on some
@@ -88,7 +88,7 @@ X, _ = load_wine(as_frame=True, return_X_y=True)
 model.predict(X.sample(10, random_state=41))
 ```
 
-## Extending your ML Workloads
+## Extending your ML workloads
 
 There are many ways to extend your workloads:
 
@@ -119,7 +119,7 @@ There are many ways to extend your workloads:
     supports ScikitLearn, TensorFlow, and PyTorch.
 ```
 
-```{admonition} Learn More
+```{admonition} Learn more
 :class: important
 
 See the {ref}`Tutorials <tutorials>` for more machine learning examples.

docs/introduction/flyte_fundamentals/extending_flyte.md → docs/flyte_fundamentals/extending_flyte.md

@@ -19,7 +19,7 @@ the Flyte type system and Flyte tasks.
 
 (customizing_flyte_types)=
 
-## Customizing Flyte Types
+## Customizing Flyte types
 
 Flyte has a {ref}`rich type system <data_types_and_io>` that automatically
 handles the serialization and deserialization of objects so that when you pass
@@ -80,7 +80,7 @@ See the {ref}`User Guide <advanced_custom_types>` for an example of a custom
 type.
 ```
 
-## Customizing Flyte Tasks
+## Customizing Flyte tasks
 
 The easiest way to extend Flyte tasks is to use Python decorators. Since Flyte
 tasks are simply functions, you can wrap the task function in a custom
@@ -130,7 +130,7 @@ allows you to enable/disable services at the beginning/end of your workflows.
 See the {ref}`User Guide <decorating_workflows>` for more details.
 ```
 
-## The Plugin Hierarchy of Needs
+## The plugin hierarchy of needs
 
 The decorator approach is great for many surface-level use cases, but there are
 many more ways to customize Flyte tasks:
@@ -154,7 +154,7 @@ many more ways to customize Flyte tasks:
     external services like Sagemaker, Snowflake, BigQuery, etc.
 ```
 
-## What's Next?
+## What's next?
 
 Congratulations! 🎉 You've just completed the Flyte Fundamentals tour.
 

docs/introduction/flyte_fundamentals/index.md → docs/flyte_fundamentals/index.md

@@ -1,6 +1,6 @@
 (getting_started_fundamentals)=
 
-# Flyte Fundamentals
+# Flyte fundamentals
 
 This section of the documentation will take you through the
 fundamental concepts of Flyte: tasks, workflows, and launch plans.
@@ -15,16 +15,16 @@ use cases.
 :header-rows: 0
 :widths: 20 30
 
-* - {doc}`🔀 Tasks, Workflows and LaunchPlans <tasks_and_workflows>`
+* - {doc}`🔀 Tasks, workflows and launch plans <tasks_workflows_and_launch_plans>`
   - Create tasks as building blocks, compose them into workflows, and schedule
-    them with launchplans.
-* - {doc}`🗄 Registering Workflows <package_register>`
+    them with launch plans.
+* - {doc}`🗄 Registering workflows <registering_workflows>`
   - Develop and deploy workflows to a local Flyte demo cluster.
-* - {doc}`⏱ Running and Scheduling Workflows <run_schedule>`
+* - {doc}`⏱ Running and scheduling workflows <running_and_scheduling_workflows>`
   - Execute workflows programmatically and schedule them as cron jobs.
-* - {doc}`📊 Visualizing Task Input and Output <visualizing_task_input_and_output>`
+* - {doc}`📊 Visualizing task input and output <visualizing_task_input_and_output>`
   - Create rich, customizable static reports for increased visibility into tasks.
-* - {doc}`🏎 Optimizing Tasks <optimizing_tasks>`
+* - {doc}`🏎 Optimizing tasks <optimizing_tasks>`
   - Make tasks scalable, performant, and robust to unexpected failures.
 * - {doc}`🔌 Extending Flyte <extending_flyte>`
   - Customize Flyte types and tasks to fit your needs.
@@ -42,9 +42,9 @@ cluster, see the {ref}`Deployment Guide <deployment>`.
 :maxdepth: -1
 :hidden:
 
-tasks_and_workflows
-package_register
-run_schedule
+tasks_workflows_and_launch_plans
+registering_workflows
+running_and_scheduling_workflows
 visualizing_task_input_and_output
 optimizing_tasks
 extending_flyte

docs/introduction/flyte_fundamentals/optimizing_tasks.md → docs/flyte_fundamentals/optimizing_tasks.md

@@ -7,7 +7,7 @@ kernelspec:
 
 (getting_started_optimizing_tasks)=
 
-# Optimizing Tasks
+# Optimizing tasks
 
 There are many ways to optimize your tasks and workflows in Flyte, and this guide
 will take you through some of the common methods for doing so.
@@ -94,7 +94,7 @@ def compute_mean(data: List[float]) -> float:
 Notice that the `timeout` argument takes a built-in Python
 {py:class}`~datetime.timedelta` object.
 
-## Map Tasks
+## Map tasks
 
 If you need to parallelize a task, you can use the {py:func}`~flytekit.map_task`
 construct. A mappable task is one that takes in a single argument and produces
@@ -145,7 +145,7 @@ def parallelized_compute_mean(data: List[float], n_partitions: int = 10) -> floa
 parallelized_compute_mean(data=[float(x) for x in range(10_000)])
 ```
 
-## Resource Allocation
+## Resource allocation
 
 As one of the core features of Flyte, workflows can be composed of tasks that
 potentially have heterogeneous resource requirements. You can express this with
@@ -165,7 +165,7 @@ def heavy_task() -> float:
     ...
 ```
 
-## Multi-image Workflows
+## Multi-image workflows
 
 In addition to task-level resource configuration, you can
 also specify different images per task. This is particularly useful if some
@@ -211,7 +211,7 @@ You can also configure the container images dynamically. See the
 ```
 
 
-## Declarative Infrastructure
+## Declarative infrastructure
 
 Finally, staying with the theme of Flyte's ability to handle heterogeneous
 workloads at the most granular level, you can configure tasks to leverage
@@ -252,7 +252,7 @@ so its plugin system enables you to call out to third-party services and
 infrastructure when needed so that you can embed existing workloads into the
 Flyte programming paradigm.
 
-## What's Next?
+## What's next?
 
 In this guide, you learned the various ways in which you can optimize your
 tasks and workflows to make them more scalable and robust. In the final stop of

docs/introduction/flyte_fundamentals/package_register.md → docs/flyte_fundamentals/registering_workflows.md

@@ -1,6 +1,6 @@
 (getting_started_package_register)=
 
-# Registering Workflows
+# Registering workflows
 
 In this guide, you'll learn how to package and register (that is, deploy) your tasks and
 workflows to a Flyte cluster. This will enable you to scale your workloads with
@@ -17,7 +17,7 @@ This guide assumes that you:
   guide to create a minimal Flyte project.
 ```
 
-## Custom Dependencies
+## Custom dependencies
 
 If you have custom Python dependencies, update the `requirements.txt` file that
 ships with the {ref}`project template <getting_started_python_dependencies>`
@@ -27,7 +27,7 @@ You can also update the Dockerfile (if using a Dockerfile) or the [ImageSpec con
 want to use a different base image or if the additional Python dependencies
 require installing binaries or packages from other languages.
 
-# Registration Patterns
+## Registration patterns
 
 There are different methods of registering your workflows to a Flyte cluster
 where each method fulfills a particular use case during the workflow development
@@ -45,7 +45,7 @@ The following diagram provides a summarized view of the different registration p
 
 (getting_started_register_pyflyte_run)=
 
-## Iterating on a Single Task or Workflow
+## Iterating on a single task or workflow
 
 The quickest way to register a task or workflow to a Flyte cluster is with the
 `pyflyte run` CLI command. Assuming that you're inside the `my_project` directory
@@ -80,7 +80,7 @@ contain additional tasks or workflows.
 
 (getting_started_pyflyte_register)=
 
-### Iterating on a Flyte Project
+### Iterating on a Flyte project
 
 One of Flyte's benefits is its functional design, which means that you can
 import and reuse tasks and workflows like you would Python functions when you
@@ -137,7 +137,7 @@ Once you've successfully registered your workflows, you can execute them by
 going to the Flyte console. If you're using a local Flyte demo cluster, you can
 go to the browser at `localhost:30080/console` and do the following:
 
-  - Navigate to the **flytesnacks** > **development** domain.
+- Navigate to the **flytesnacks** > **development** domain.
 - Click on the **Workflows** section of the left-hand sidebar.
 - Click on the **workflows.example.wf** card on the workflows list.
 - Click on the **Launch Workflow** button on the top-right corner.
@@ -149,7 +149,7 @@ go to the browser at `localhost:30080/console` and do the following:
 In the next guide you'll learn about how to run your workflows programmatically.
 ```
 
-#### Fast Registration
+#### Fast registration
 
 `pyflyte register` packages up your code through a mechanism called
 **fast registration**. Fast registration is useful when you already have a
@@ -182,7 +182,7 @@ You can do so by specifying these files in a `.gitignore` or `.dockerignore`
 file in the root directory of your project.
 ```
 
-### Productionizing your Workflows
+### Productionizing your workflows
 
 Flyte's core design decision is to make workflows reproducible and repeatable.
 One way it achieves this is by providing a way for you to bake-in user-defined
@@ -200,7 +200,7 @@ are immutable.
 
 (containerizing_your_project)=
 
-#### Containerizing your Project
+#### Containerizing your project
 
 Flyte relies on OCI-compatible containers to package up your code and third-party
 dependencies. When you invoke `pyflyte init`, the resulting template project
@@ -242,7 +242,7 @@ docker login ghcr.io
 docker push <tag>
 ```
 
-```{admonition} Pulling Private Images
+```{admonition} Pulling private images
 :class: important
 
 For many projects it's convenient to make your images public, but in the case
@@ -252,7 +252,7 @@ metadata/configuration, it's more secure if they're private.
 Learn more about how to pull private image in the {ref}`User Guide <private_images>`.
 ```
 
-#### Package your Project with `pyflyte package`
+#### Package your project with `pyflyte package`
 
 You can package your project with the `pyflyte package` command like so:
 
@@ -350,7 +350,7 @@ two GitHub actions that facilitates this:
   of Flyte packages, for example, the `.tgz` archives that are created by
   `pyflyte package`.
 
-## What's Next?
+## What's next?
 
 In this guide, you learned about the Flyte demo cluster, Flyte configuration, and
 the different registration patterns you can leverage during the workflow