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fixing typos reported by community user (#16457)

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4
docs/source-app/levels/basic/build_a_lightning_component.rst
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@ -51,7 +51,7 @@ Organizing your code into Lightning components offers these benefits:
Lightning embeds the best practices of building production-ready full stack AI apps into your Lightning embeds the best practices of building production-ready full stack AI apps into your
coding experience. You can write code like you normally do, and the Lightning structure coding experience. You can write code like you normally do, and the Lightning structure
ensures your code is implicitely production ready... even if you're just doing research. ensures your code is implicitly production ready... even if you're just doing research.
.. collapse:: For experts: Scale with full control .. collapse:: For experts: Scale with full control
@ -65,7 +65,7 @@ Organizing your code into Lightning components offers these benefits:
| |
Lightning components are self-contained pieces of funcionality. Add them to your current workflow Lightning components are self-contained pieces of functionality. Add them to your current workflow
tools to quickly fill in gaps in your ML workflow such as monitoring drift, training LLMs and more. tools to quickly fill in gaps in your ML workflow such as monitoring drift, training LLMs and more.
You can (optionally) use the Lightning App to integrate components into a cohesive workflow. You can (optionally) use the Lightning App to integrate components into a cohesive workflow.

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docs/source-app/levels/basic/real_lightning_component_implementations.rst
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@ -24,7 +24,7 @@ This example shows how to train PyTorch with the Lightning trainer on your machi
or cloud GPUs without code changes. or cloud GPUs without code changes.
.. lit_tabs:: .. lit_tabs::
:descriptions: import Lightning; We're using a demo LightningModule; Move your training code here (usually your main.py); Pass your component to the multi-node executor (it works on CPU or single GPUs also); Select the number of machines (nodes). Here we choose 2.; Choose from over 15+ machine types. This one has 4 v100 GPUs.; Initialize the App object that executes the component logic. :descriptions: import Lightning; We're using a demo LightningModule; Move your training code here (usually your main.py); Pass your component to the multi-node executor (it works on CPU or single GPUs also); Select the number of machines (nodes). Here we choose 4.; Choose from over 15+ machine types. This one has 4 v100 GPUs.; Initialize the App object that executes the component logic.
:code_files: /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; :code_files: /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py; /levels/basic/hello_components/pl_multinode.py;
:highlights: 2; 4; 9-11; 14-17; 16; 17; 19 :highlights: 2; 4; 9-11; 14-17; 16; 17; 19
:enable_run: true :enable_run: true