Improve clarity in code and docs

docs/environment_setup.md

@@ -30,6 +30,12 @@ In this setup guide, let's run the `examples/basics` project.
 
 ```{prompt} bash
 git clone https://github.com/flyteorg/flytesnacks
+
+# or if your SSH key is registered on GitHub:
+git clone git@github.com:flyteorg/flytesnacks.git
+
+# or if you use the `gh` tool:
+gh repo clone flyteorg/flytesnacks
 cd flytesnacks/examples/basics
 pip install -r requirements.txt
 ```
@@ -67,8 +73,9 @@ pyflyte run basics/hello_world.py my_wf
 ```
 
 :::{note}
-The first couple arguments of `pyflyte run` is in the form of `path/to/script.py <workflow_name>`, where
-`<workflow_name>` is the function decorated with `@workflow` that you want to run.
+The first two arguments to `pyflyte run` have the form of 
+`path/to/script.py <workflow_name>`, where `<workflow_name>` is the function 
+decorated with `@workflow` that you want to run.
 :::
 
 To run the workflow on the demo Flyte cluster, all you need to do is supply the `--remote` flag:
@@ -103,7 +110,11 @@ option as `--arg-name`.
 
 ## Visualizing Workflows
 
-Workflows can be visualized as DAGs on the UI. However, you can visualize workflows on the browser and in the terminal by *just* using your terminal.
+Workflows can be visualized as DAGs in the UI. You can also visualize workflows
+from your terminal that will be displayed in your default web browser. This
+visualization uses the service at graph.flyte.org to render Graphviz diagrams,
+and hence shares your DAG (but not your data or code) with an outside party 
+(security hint 🔐).
 
 To view workflow on the browser:
 
@@ -127,15 +138,20 @@ flytectl get workflows \
     basics.basic_workflow.my_wf
 ```
 
-Replace `<version>` with version from console UI, it may look something like `BLrGKJaYsW2ME1PaoirK1g==`
+Replace `<version>` with the base64-encoded version shown in the console UI,
+that looks something like `BLrGKJaYsW2ME1PaoirK1g==`.
 
 :::{tip}
-Running most of the examples in the **User Guide** only requires the default Docker image that ships with Flyte.
-Many examples in the {ref}`tutorials` and {ref}`integrations` section depend on additional libraries, `sklearn`,
-`pytorch`, or `tensorflow`, which will not work with the default docker image used by `pyflyte run`.
 
-These examples will explicitly show you which images to use for running these examples by passing in the docker
-image you want to use with the `--image` option in `pyflyte run`.
+Running most of the examples in the **User Guide** only requires the default
+Docker image that ships with Flyte. Many examples in the {ref}`tutorials` and
+{ref}`integrations` section depend on additional libraries such as `sklearn`,
+`pytorch`, or `tensorflow`, which will not work with the default docker image 
+used by `pyflyte run`.
+
+These examples will explicitly show you which images to use for running these
+examples by passing in the docker image you want to use with the `--image`
+option in `pyflyte run`.
 :::
 
 🎉 Congrats! Now you can run all the examples in the {ref}`userguide` 🎉

docs/getting_started/package_register.md

@@ -269,7 +269,7 @@ By default, the `docker_build.sh` script:
 - Uses the `PROJECT_NAME` specified in the `pyflyte init` command, which in
   this case is `my_project`.
 - Will not use any remote registry.
-- Uses the git sha to version your tasks and workflows.
+- Uses the git revision SHA1 to version your tasks and workflows.
 ```
 
 You can override the default values with the following flags:
@@ -367,7 +367,7 @@ Let's break down what each flag is doing here:
 - `--archive`: This argument allows you to pass in a package file, which in
   this case is `flyte-package.tgz`.
 - `--version`: This is a version string that can be any string, but we recommend
-  using the git sha in general, especially in production use cases.
+  using the git revision in general, especially in production use cases.
 
 ### Using `pyflyte register` versus `pyflyte package` + `flytectl register`
 

docs/index.md

@@ -33,8 +33,8 @@ on your local machine.
 :title: text-muted
 :animate: fade-in-slide-down
 
-The introduction below is also available on a hosted sandbox environment, where
-you can get started with Flyte without installing anything locally.
+Union.ai provides a hosted sandbox environment, free of charge, where you can
+get started with Flyte without installing anything locally.
 
 ```{link-button} https://sandbox.union.ai/
 ---
@@ -73,39 +73,48 @@ First install [flytekit](https://pypi.org/project/flytekit/), Flyte's Python SDK
 pip install flytekit flytekitplugins-deck-standard scikit-learn
 ```
 
-Then install [flytectl](https://docs.flyte.org/projects/flytectl/en/latest/),
+Next install [flytectl](https://docs.flyte.org/projects/flytectl/en/latest/),
 which the command-line interface for interacting with a Flyte backend.
 
-````{tabbed} Homebrew
+````{tabbed} Homebrew (macOS)
 
 ```{prompt} bash $
 brew install flyteorg/homebrew-tap/flytectl
 ```
 
 ````
 
-````{tabbed} Curl
+````{tabbed} Curl (Unix-like)
 
 ```{prompt} bash $
 curl -sL https://ctl.flyte.org/install | sudo bash -s -- -b /usr/local/bin
 ```
 
 ````
 
+````{tabbed} Windows
+
+```{prompt} C:\>
+TODO
+```
+
+````
+
+
 ## Creating a Workflow
 
 The first workflow we'll create is a simple model training workflow that consists
 of three steps that will:
 
 1. 🍷 Get the classic [wine dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#wine-recognition-dataset)
    using [sklearn](https://scikit-learn.org/stable/).
-2. 📊 Process the data that simplifies the 3-class prediction problem into a
-   binary classification problem by consolidating class labels `1` and `2` into
-   a single class.
-3. 🤖 Train a `LogisticRegression` model to learn a binary classifier.
+2. 📊 Process the data by simplifying its 3-class prediction problem into a binary
+   classification problem by consolidating class labels 1 and 2 into a single
+   class.
+3. 🤖 Train a `LogisticRegression` model to create a binary classifier.
 
-First, we'll define three tasks for each of these steps. Create a file called
-`example.py` and copy the following code into it.
+Let's define three tasks, corresponding to each of these steps. Create a
+file called example.py and copy the following code into it.
 
 ```{code-cell} python
 :tags: [remove-output]
@@ -126,7 +135,9 @@ def get_data() -> pd.DataFrame:
 @task
 def process_data(data: pd.DataFrame) -> pd.DataFrame:
     """Simplify the task from a 3-class to a binary classification problem."""
-    return data.assign(target=lambda x: x["target"].where(x["target"] == 0, 1))
+    df = data.copy()
+    df.loc[df.target == 0, "target"] = 1
+    return df
 
 @task
 def train_model(data: pd.DataFrame, hyperparameters: dict) -> LogisticRegression:
@@ -139,10 +150,11 @@ def train_model(data: pd.DataFrame, hyperparameters: dict) -> LogisticRegression
 As we can see in the code snippet above, we defined three tasks as Python
 functions: `get_data`, `process_data`, and `train_model`.
 
-In Flyte, **tasks** are the most basic unit of compute and serve as the building
-blocks 🧱 for more complex applications. A task is a function that takes some
-inputs and produces an output. We can use these tasks to define a simple model
-training workflow:
+In Flyte, **tasks** are the most basic "unit of compute" (per Kubernetes
+jargon) and serve as the building blocks 🧱 for more complex applications. 
+At its core, a task is simply a function: it takes inputs and produces and 
+output. We can use these tasks to define a simple model training workflow:
+
 
 ```{code-cell} python
 @workflow
@@ -165,15 +177,15 @@ is typically written with inputs and outputs.
 A **workflow** is also defined as a Python function, and it specifies the flow
 of data between tasks and, more generally, the dependencies between tasks 🔀.
 
-::::{dropdown} {fa}`info-circle` The code above looks like Python, but what do `@task` and `@workflow` do exactly?
+::::{dropdown} {fa}`info-circle` This looks like typical Python, but what do `@task` and `@workflow` do?
 :title: text-muted
 :animate: fade-in-slide-down
 
 Flyte `@task` and `@workflow` decorators are designed to work seamlessly with
 your code-base, provided that the *decorated function is at the top-level scope
 of the module*.
 
-This means that you can invoke tasks and workflows as regular Python methods and
+This means that you can invoke tasks and workflows as regular Python functions and
 even import and use them in other Python modules or scripts.
 
 :::{note}
@@ -202,16 +214,19 @@ pyflyte run example.py training_workflow \
 :animate: fade-in-slide-down
 
 If you're using Bash, you can ignore this 🙂
-You may need to add .local/bin to your PATH variable if it's not already set,
-as that's not automatically added for non-bourne shells like fish or xzsh.
-
-To use pyflyte, make sure to set the /.local/bin directory in PATH
+You may need to add .local/bin to your PATH variable if it's not already set;
+it may not automatically get added for non-bourne shells.  For example, if you 
+use `fish` or `csh`, you can set this with:
 
 :::{code-block} fish
-set -gx PATH $PATH ~/.local/bin
+set -gx PATH $PATH ~/.local/bin  # fish
+:::
+
+:::{code-block} csh
+set path = ($path $HOME/.local/bin)  # csh/tcsh
 :::
-:::::
 
+:::::
 
 
 :::::{dropdown} {fa}`info-circle` Why use `pyflyte run` rather than `python example.py`?
@@ -223,7 +238,9 @@ set -gx PATH $PATH ~/.local/bin
 
 Keyword arguments can be supplied to ``pyflyte run`` by passing in options in
 the format ``--kwarg value``, and in the case of ``snake_case_arg`` argument
-names, you can pass in options in the form of ``--snake-case-arg value``.
+names, you can optionally spell them as "kebab case," for example as
+``--snake-case-arg value``.
+
 
 ::::{note}
 If you want to run a workflow with `python example.py`, you would have to write
@@ -347,8 +364,8 @@ There are a few features about FlyteConsole worth pointing out in the GIF above:
 ## What's Next?
 
 Follow the rest of the sections in the documentation to get a better
-understanding of the key constructs that make Flyte such a powerful
-orchestration tool 💪.
+understanding of the key constructs that make Flyte a powerful orchestration
+tool 💪.
 
 ```{admonition} Recommendation
 :class: tip

examples/basics/basics/hello_world.py

@@ -12,19 +12,22 @@
 from flytekit import task, workflow
 
 # %% [markdown]
-# You can change the signature of the workflow to take in an argument like this:
-
+# You can change the signature of the task to take in an argument like this:
+# def say_hello(name: str) -> str: 
+#     return f"hello {name}"
 # %%
 @task
 def say_hello() -> str:
     return "hello world"
 
 
 # %% [markdown]
-# You can treat the outputs of a task as you normally would a Python function. Assign the output to two variables
-# and use them in subsequent tasks as normal. See {py:func}`flytekit.workflow`
+# You can treat the outputs of a task as you normally would a Python function. 
+# Assign the output to two variables and use them in subsequent tasks as normal. 
+# See {py:func}`flytekit.workflow`
 # You can change the signature of the workflow to take in an argument like this:
-
+# def my_wf(name: str) -> str:
+#     ...
 # %%
 @workflow
 def my_wf() -> str:
@@ -49,5 +52,5 @@ def my_wf() -> str:
 
 
 # %% [markdown]
-# In the next few examples you'll learn more about the core ideas of Flyte, which are tasks, workflows, and launch
-# plans.
+# In the next few examples you'll learn more about the core ideas of Flyte, 
+# which are tasks, workflows, and launch plans.