Fix typos from docs updates (zenml-io#2287)

* Fix typos

* CI runs with markdown changes

* revert CI markdown deletion

* isolate the spellcheck

.github/workflows/setup-python-environment.yml

@@ -93,13 +93,6 @@ jobs:
           OS: ${{ inputs.os }}
         run: |
           bash scripts/lint.sh
-      - name: Spelling checker
-        uses: crate-ci/typos@v1.17.0
-        with:
-          files: .
-          config: ./.typos.toml
-        # Only run the spell check on ubuntu-latest and python 3.8
-        if: ${{ inputs.os == 'ubuntu-dind-runners' && inputs.python-version == '3.8' }}
       - name: Docstring check
         run: bash scripts/docstring.sh
         # Only run the docstring check on ubuntu-latest and python 3.8

.github/workflows/spellcheck.yml

@@ -0,0 +1,27 @@
+---
+name: Spellcheck
+on:
+  workflow_dispatch:
+  workflow_call:
+  push:
+    branches: [main]
+    paths-ignore: [docs/**, docker/**, '*', '!pyproject.toml']
+  pull_request:
+    types: [opened, synchronize, ready_for_review]
+    paths-ignore: [docs/**, docker/**, '*', '!pyproject.toml']
+concurrency:
+  # New commit on branch cancels running workflows of the same branch
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+jobs:
+  spellcheck:
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-dind-runners
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4.1.1
+      - name: Spelling checker
+        uses: crate-ci/typos@v1.17.0
+        with:
+          files: .
+          config: ./.typos.toml

docs/book/user-guide/advanced-guide/configuring-zenml/ci-cd.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Managing the lifecycle of a ZenML pipeline with Continous Integration and Delivery
+  Managing the lifecycle of a ZenML pipeline with Continuous Integration and Delivery
 ---
 
 # CI/CD with ZenML pipelines

docs/book/user-guide/advanced-guide/configuring-zenml/configuring-zenml.md

@@ -4,6 +4,6 @@ description: Configuring ZenML's default behavior
 
 # Configuring ZenML
 
-There are various ways to adapt how ZenML behaves in certain sitations. This guide walks users through how to configure certain aspects of ZenML.
+There are various ways to adapt how ZenML behaves in certain situations. This guide walks users through how to configure certain aspects of ZenML.
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>

docs/book/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines.md

@@ -121,7 +121,7 @@ if __name__=="__main__":
     addition_pipeline()
 ```
 
-The method `write_run_configuration_template` genreates a config template (at path `run.yaml` in this case) that includes all configuration options for this specific pipeline and your active stack. Let's run the pipeline:
+The method `write_run_configuration_template` generates a config template (at path `run.yaml` in this case) that includes all configuration options for this specific pipeline and your active stack. Let's run the pipeline:
 
 ```python
 python run.py

docs/book/user-guide/advanced-guide/pipelining-features/pipeline-settings.md

@@ -64,7 +64,7 @@ Or in a YAML like this:
 ```yaml
 settings:
   docker:
-    requrements:
+    requirements:
       - pandas
 ```
 

docs/book/user-guide/production-guide/cloud-orchestration.md

@@ -166,7 +166,7 @@ Now that we have our orchestrator and container registry registered, we can [reg
 {% tabs %}
 {% tab title="CLI" %}
 ```shell
-zenml stack register miminal_cloud_stack -o skypilot_orchestrator -a cloud_artifact_store -c cloud_container_registry
+zenml stack register minimal_cloud_stack -o skypilot_orchestrator -a cloud_artifact_store -c cloud_container_registry
 ```
 {% endtab %}
 {% tab title="Dashboard" %}
@@ -178,7 +178,7 @@ Now, using the [code from the previous chapter](understand-stacks.md#run-a-pipel
 we can run a training pipeline. First, set the minimal cloud stack active:
 
 ```shell
-zenml stack set miminal_cloud_stack
+zenml stack set minimal_cloud_stack
 ```
 
 and then, run the training pipeline: