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‎.cursor/rules/google-style-guide.mdc

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---
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description:
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globs:
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alwaysApply: false
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---
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---
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description:
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globs:
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alwaysApply: true
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---
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---
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description: |
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Enforce Google's Developer Style Guide principles for technical documentation.
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These rules guide the AI to create clear, consistent, and user-friendly documentation.
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globs:
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- "*.md"
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- "*.mdx"
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- "*.txt"
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---
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# Google Developer Style Guide for Technical Documentation
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## Document Structure
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- Always use sentence case for all Markdown headings (e.g., '# This is a heading' not '# This Is A Heading').
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- Begin each main section with a brief one or two sentence overview that summarizes the section's content.
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- Organize content into logical sections with clear and concise headings and subheadings.
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- Structure the documentation in a hierarchical manner, using heading levels (# for main titles, ## for sections, ### for subsections).
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## Lists and Formatting
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- Use Markdown numbered lists (1., 2., etc.) for sequential steps or ordered procedures.
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- Use Markdown unordered lists (-, *, etc.) for collections of related items that don't have a specific order.
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- Format code-related text using Markdown code blocks with the appropriate language identifier for syntax highlighting:
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```python
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def example_function():
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return "Hello, world!"
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```
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- Format UI elements such as button labels and menu items using bold Markdown syntax (**UI Element**).
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- Use italic text (*text*) sparingly, primarily for emphasis, terms, or book titles.
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- Present pairs of related data (like terms and definitions) using description lists or bold terms followed by their explanations.
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- Use unambiguous date formatting, preferably YYYY-MM-DD.
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## Language and Tone
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- Always address the reader using the second person pronoun "you" instead of "we" or "us".
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- Prefer active voice in sentences. For example, instead of "The file was saved by the system," write "The system saved the file."
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- Maintain a friendly, conversational, and helpful tone, similar to explaining a concept to a colleague.
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- Use standard American English spelling and punctuation consistently.
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- Avoid highly technical jargon without providing clear explanations or definitions.
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- Be mindful of using idioms or culturally specific references that might not be universally understood.
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- Avoid unnecessary repetition of adjectives and adverbs.
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- Write in a clear, concise, and factual manner, avoiding overly casual or promotional language.
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## Links and References
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- When creating hyperlinks using Markdown, ensure the link text clearly describes the target page (e.g., [Learn more about the API](mdc:url)).
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- Prioritize linking to official documentation, well-established technical websites, or academic resources.
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- For fundamental concepts crucial to understanding the current topic, provide a brief explanation within the documentation rather than immediately linking externally.
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- Reserve external links for more detailed or supplementary information.
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## Code Examples
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- Always enclose code examples in Markdown code blocks using triple backticks (```) and specify the programming language.
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- Precede every code block with a brief paragraph explaining its context and purpose.
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- Follow the code block with an explanation of its key parts and expected output.
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- Provide substantial, real-world code examples that demonstrate complete or significant functionality rather than isolated snippets.
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- If the code example pertains to a specific file or directory, mention its location relative to the project root.
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## Images and Diagrams
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- When including images or diagrams, use Markdown image syntax and provide descriptive alt text: ![Alt text describing the image](mdc:image.png)
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- Prefer PNG format for diagrams and illustrations, and WebP format for other images where appropriate.
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- Ensure all images serve a purpose and enhance understanding of the content.
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## Warnings, Notes, and Important Information
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- Format warnings using Markdown blockquotes with a clear prefix:
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> :::warning
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This action cannont be undone.
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:::
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- Format notes using Markdown blockquotes:
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> :::warning
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Additional configuration may be required for custom installations.
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:::
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- Keep warning, note, and important information messages brief and to the point, focusing on essential information.
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## Step-by-Step Instructions
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- Present step-by-step instructions using Markdown numbered lists.
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- Begin each step with a clear action verb (e.g., "Click", "Open", "Enter").
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- Ensure each step represents a single, actionable task.
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- Provide sufficient detail for the target audience to understand and execute each action without requiring additional assumptions.

‎docs/get-started/connect-to-runpod.md

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## Web interface
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The [RunPod web interface](https://runpod.io/console/home) provides an intuitive way to:
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- Launch and manage Pods.
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- Monitor resource usage.
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- Access Pod terminals.
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## VSCode
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Follow [this tutorial](/tutorials/pods/connect-to-vscode) to learn how to connect your VSCode environment to a Pod for remote development.
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Follow [this tutorial](/tutorials/pods/connect-to-vscode) to learn how to connect your VSCode environment to a Pod for remote development.

‎docs/instant-clusters/index.md

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The following environment variables are available in all Pods:
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| Environment Variable | Description |
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| ------------------------------ | ------------------------------------------------------------ |
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| `PRIMARY_ADDR` / `MASTER_ADDR` | The address of the primary Pod. |
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| `PRIMARY_PORT` / `MASTER_PORT` | The port of the primary Pod (all ports are available). |
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| `NODE_ADDR` | The static IP of this Pod within the cluster network. |
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| Environment Variable | Description |
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| ------------------------------ | ----------------------------------------------------------------------------- |
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| `PRIMARY_ADDR` / `MASTER_ADDR` | The address of the primary Pod. |
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| `PRIMARY_PORT` / `MASTER_PORT` | The port of the primary Pod (all ports are available). |
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| `NODE_ADDR` | The static IP of this Pod within the cluster network. |
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| `NODE_RANK` | The Cluster (i.e., global) rank assigned to this Pod (0 for the primary Pod). |
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| `NUM_NODES` | The number of Pods in the Cluster. |
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| `NUM_TRAINERS` | The number of GPUs per Pod. |
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| `HOST_NODE_ADDR` | Defined as `PRIMARY_ADDR:PRIMARY_PORT` for convenience. |
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| `WORLD_SIZE` | The total number of GPUs in the Cluster (`NUM_NODES` * `NUM_TRAINERS`). |
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| `NUM_NODES` | The number of Pods in the Cluster. |
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| `NUM_TRAINERS` | The number of GPUs per Pod. |
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| `HOST_NODE_ADDR` | Defined as `PRIMARY_ADDR:PRIMARY_PORT` for convenience. |
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| `WORLD_SIZE` | The total number of GPUs in the Cluster (`NUM_NODES` * `NUM_TRAINERS`). |
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Each Pod receives a static IP (`NODE_ADDR`) on the overlay network. When a Cluster is deployed, the system designates one Pod as the primary node by setting the `PRIMARY_ADDR` and `PRIMARY_PORT` environment variables. This simplifies working with multiprocessing libraries that require a primary node.
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Let's look at the code in our `main.py` file:
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```python
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```python
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import os
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import torch
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import torch.distributed as dist
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def init_distributed():
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"""Initialize the distributed training environment"""
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# Initialize the process group
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dist.init_process_group(backend="nccl")
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# Get local rank and global rank
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local_rank = int(os.environ["LOCAL_RANK"])
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global_rank = dist.get_rank()
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world_size = dist.get_world_size()
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# Set device for this process
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device = torch.device(f"cuda:{local_rank}")
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torch.cuda.set_device(device)
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return local_rank, global_rank, world_size, device
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"""Initialize the distributed training environment"""
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# Initialize the process group
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dist.init_process_group(backend="nccl")
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# Get local rank and global rank
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local_rank = int(os.environ["LOCAL_RANK"])
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global_rank = dist.get_rank()
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world_size = dist.get_world_size()
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# Set device for this process
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device = torch.device(f"cuda:{local_rank}")
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torch.cuda.set_device(device)
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return local_rank, global_rank, world_size, device
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def cleanup_distributed():
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"""Clean up the distributed environment"""
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dist.destroy_process_group()
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"""Clean up the distributed environment"""
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dist.destroy_process_group()
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def main():
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# Initialize distributed environment
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local_rank, global_rank, world_size, device = init_distributed()
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print(f"Running on rank {global_rank}/{world_size-1} (local rank: {local_rank}), device: {device}")
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# Your code here
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# Clean up distributed environment when done
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cleanup_distributed()
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# Initialize distributed environment
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local_rank, global_rank, world_size, device = init_distributed()
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print(f"Running on rank {global_rank}/{world_size-1} (local rank: {local_rank}), device: {device}")
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# Your code here
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# Clean up distributed environment when done
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cleanup_distributed()
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if __name__ == "__main__":
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main()
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main()
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```
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This is the minimal code necessary for initializing a distributed environment. The `main()` function prints the local and global rank for each GPU process (this is also where you can add your own code). `LOCAL_RANK` is assigned dynamically to each process by PyTorch. All other environment variables are set automatically by RunPod during deployment.

‎docs/overview.md

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- Queue jobs efficiently **without managing infrastructure**.
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### Get started with Serverless
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- [Build your first Serverless app](/serverless/get-started)
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- [Run any LLM as an endpoint using vLLM workers](/serverless/workers/vllm/get-started)
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- [Tutorial: Create a Serverless endpoint with Stable Diffusion](/tutorials/serverless/gpu/run-your-first)
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- [Create and manage your account](/get-started/manage-accounts)
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- [Add funds to your account](/get-started/billing-information)
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- [Complete your first tutorial](/tutorials/introduction/overview)
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- [Complete your first tutorial](/tutorials/introduction/overview)

‎docs/pods/manage-pods.md

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```bash
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sleep 2h; runpodctl stop pod $RUNPOD_POD_ID &
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```
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This command uses sleep to wait for 2 hours before executing the `runpodctl stop pod` command to stop the Pod.
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The `&` at the end runs the command in the background, allowing you to continue using the SSH session.
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nohup bash -c "sleep 2h; runpodctl stop pod $RUNPOD_POD_ID" &
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`nohup` ensures the process continues running if you close the web terminal window.
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</TabItem>
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</Tabs>
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Pods start as soon as they are created, but you can resume a Pod that has been stopped.
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runpodctl get pod
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```
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```

‎docusaurus.config.js

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async: true,
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defer: true,
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},
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{
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src: "https://kit.fontawesome.com/4b9ba14b0f.js",
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crossOrigin: "anonymous",
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},
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// {
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// async: true,

‎src/components/HomepageFeatures/index.js

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import Heading from "@theme/Heading";
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import clsx from "clsx";
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import React from 'react';
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import styles from "./styles.module.css";
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const FeatureList = [
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{
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title: "Serverless",
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Svg: require("@site/static/img/home_2.svg").default,
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url: "/serverless/overview", // Add URL here
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description: (
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<>
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<a href="/serverless/overview">
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<b>Serverless</b>
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</a>{" "}
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service provides pay-per-second serverless computing with autoscaling, quick start times, and robust security in
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its Secure Cloud.
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</>
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),
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},
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{
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title: "Pods",
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Svg: require("@site/static/img/home_1.svg").default,
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url: "/pods/overview", // Add URL here
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description: (
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<>
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<a href="/pods/overview">
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<b>Pods</b>
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</a>{" "}
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offer fast deployment of container-based GPU instances, with Secure Cloud for high reliability and security, and
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Community Cloud for a secure peer-to-peer network.
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</>
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),
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},
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{
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title: "vLLM",
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Svg: require("@site/static/img/home_3.svg").default,
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url: "/serverless/workers/vllm/overview",
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description: (
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<>
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<a href="/serverless/workers/vllm/overview">
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<b>vLLM Workers</b>
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</a>{" "}
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are blazingly fast OpenAI-compatible serverless endpoints for any LLM.
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</>
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),
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},
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];
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function Feature({ Svg, title, description, url }) {
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<div className={clsx("col col--4")}>
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<div className="text--center">
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<a href={url}>
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<Svg className={styles.featureSvg} role="img" />
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</a>
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</div>
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<div className="text--center padding-horiz--md">
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<Heading as="h2">{title}</Heading>
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<p>{description}</p>
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</div>
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</div>
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);
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}
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export default function HomepageFeatures() {
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return (
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<section className={styles.features}>
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<div className="container">
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<div className="row">
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{FeatureList.map((props, idx) => <Feature key={idx} {...props} />)}
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</div>
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</div>
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</section>
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);
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return null;
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}

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