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fix(docs): resolve residual Ahrefs content issues (#831)
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api-reference/overview.mdx

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Retrieve the complete OpenAPI specification for client generation, request validation, or tooling integration.
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See the [interactive API documentation endpoint reference](/api-reference/docs/GET/docs) to open the browser-based reference directly.
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<CodeGroup>
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```bash cURL

community-solutions/copyparty-file-manager/overview.mdx

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icon: "party-horn"
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---
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# Setting up CopyParty on Runpod
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## Set up CopyParty on Runpod
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CopyParty provides a webbased GUI that makes file management simple on Runpod instances. With its intuitive interface, you can browse directories, upload/download files, preview images and videos, and manage your Pod's filesystem without complex command-line operations.
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community-solutions/overview.mdx

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---
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title: "Introduction"
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title: "Community solutions"
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description: "Community-created tools and solutions by and for Runpod users. Review setup and usage guidance for this Runpod community solution."
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icon: "flask"
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---

get-started/connect-to-runpod.mdx

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Runpod offers multiple ways to access and manage your compute resources. Choose the method that best fits your workflow:
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If you are deciding between Pods, Serverless, Public Endpoints, and Clusters, start with the [Runpod product overview](/get-started/products).
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## Runpod console
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The Runpod console provides an intuitive web interface to manage <PodsTooltip /> and <EndpointTooltip />s, access Pod terminals, send endpoint requests, monitor resource usage, and view billing and usage history.
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Every Pod comes pre-installed with the `runpodctl` command and includes a Pod-scoped API key for seamless command-line management.
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[Learn more about runpodctl →](/runpodctl/overview)
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## Coding agents
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Use the [agent onboarding guide](/agent-setup) to install Runpod skills and connect the Runpod MCP server to a supported coding agent.

instant-clusters.mdx

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<Card title="Axolotl fine-tuning" href="/instant-clusters/axolotl" icon="screwdriver" horizontal>
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Fine-tune LLMs across multiple GPUs.
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</Card>
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<Card title="Distributed inference with Ray and vLLM" href="/instant-clusters/ray-vllm" icon="network-wired" horizontal>
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Serve models across multiple nodes with Ray and vLLM.
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</Card>
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## How it works

instant-clusters/axolotl.mdx

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Now that you've successfully deployed and tested an Axolotl distributed training job on an Instant Cluster, you can:
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* Review the [fine-tuning guide](/fine-tune) for a complete Axolotl workflow and deployment path.
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* **Fine-tune your own models** by modifying the configuration files in Axolotl to suit your specific requirements.
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* **Scale your training** by adjusting the number of Pods in your cluster (and the size of their containers and volumes) to handle larger models or datasets.
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* **Try different optimization techniques** such as DeepSpeed, FSDP (Fully Sharded Data Parallel), or other distributed training strategies.

serverless/development/local-testing.mdx

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description: "Test your Serverless handlers locally before deploying to production. Review configuration and operations guidance for Runpod Serverless."
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Testing your handler locally before deploying saves time and helps you catch issues early. The Runpod SDK provides multiple ways to test your handler function without consuming cloud resources.
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Testing your handler locally before deploying saves time and helps you catch issues early. The Runpod SDK provides multiple ways to test your handler function without consuming cloud resources. See the [Serverless development overview](/serverless/development/overview) for the complete development lifecycle.
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## Basic testing
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- Starts the local API server on port 8080.
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- Uses 4 concurrent workers.
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- Sets the log level to `DEBUG` for maximum information.
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- Enables the debugger for troubleshooting.
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- Enables the debugger for troubleshooting.

serverless/vllm/openai-compatibility.mdx

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| Authentication error | Use your Runpod API key, not an OpenAI key. |
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| Timeout errors | Increase client timeout for large models. |
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| Unexpected response format | Set `RAW_OPENAI_OUTPUT=1`. |
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## Integrate with workflow tools
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Follow the [n8n integration guide](/integrations/n8n-integration) to connect an OpenAI-compatible Runpod endpoint to an automated workflow.

tutorials/introduction/overview.mdx

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<Card title="Generate images with ComfyUI" href="/tutorials/serverless/comfyui" icon="images" horizontal>
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Deploy ComfyUI and generate images using JSON workflows.
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</Card>
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<Card title="Run Ollama on Serverless CPU" href="/tutorials/serverless/run-ollama-inference" icon="microchip" horizontal>
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Deploy an Ollama server on CPU workers and send an inference request.
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</Card>
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## Flash
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Chain multiple Public Endpoints to generate videos from text.
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</Card>
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## More resources
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<CardGroup cols={2}>
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<Card title="Container tutorials" href="/containers" icon="container-storage" horizontal>
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Learn container fundamentals, Dockerfile creation, persistence, and common Docker commands.
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</Card>
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<Card title="Community video resources" href="/references/video-resources" icon="circle-play" horizontal>
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Browse community tutorials about Runpod workflows and supporting Linux tools.
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</Card>
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</CardGroup>

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