fix(docs): add missing page descriptions

api-reference-v2/openapi.json

@@ -8415,6 +8415,7 @@
       "get": {
         "operationId": "listDelegations",
         "summary": "List all ECR delegations",
+        "description": "Returns every AWS ECR delegation owned by the authenticated user, including repository, image tag, AWS region, and registry URI details.",
         "tags": [
           "Registries"
         ],
@@ -8463,6 +8464,7 @@
       "post": {
         "operationId": "createDelegation",
         "summary": "Register an ECR delegation",
+        "description": "Creates an AWS ECR delegation for the specified repository ARN so Runpod can access images from the delegated private repository.",
         "tags": [
           "Registries"
         ],
@@ -8534,6 +8536,7 @@
       "delete": {
         "operationId": "revokeDelegation",
         "summary": "Revoke an ECR delegation",
+        "description": "Revokes an AWS ECR delegation by ID, removing Runpod access to images from the associated private repository and returning no response body.",
         "tags": [
           "Registries"
         ],

containers.mdx

@@ -1,5 +1,6 @@
 ---
 title: "Containers"
+description: "Explore Runpod container tutorials for Dockerfiles, persistence, common Docker commands, application packaging, and Serverless deployment."
 ---
 
 <Card href="/tutorials/introduction/containers" horizontal>

flash/cli/app.mdx

@@ -1,6 +1,7 @@
 ---
 title: "app"
 sidebarTitle: "app"
+description: "Create, inspect, list, and delete Flash applications that organize deployment environments, build artifacts, and configuration."
 ---
 
 Manage Flash applications. An app is the top-level container that groups your deployment environments, build artifacts, and configuration.
@@ -20,7 +21,7 @@
 
 ---
 
 ## app list
 
 Show all Flash apps under your account.
 
@@ -42,7 +43,7 @@
 
 ---
 
 ## app create
 
 Register a new Flash app on Runpod's backend.
 
@@ -58,10 +59,10 @@
 
 ### What it creates
 
 This command registers a Flash app in Runpod's backend—essentially creating a namespace for your environments and builds. It does not:
 
 - Create local files (use `flash init` for that).
 - Provision cloud resources (endpoints, volumes, etc.).
 - Deploy any code.
 
 The app is just a container that groups environments and builds together.
@@ -76,7 +77,7 @@
 
 ---
 
 ## app get
 
 Get detailed information about a Flash app.
 
@@ -123,7 +124,7 @@
 
 ---
 
 ## app delete
 
 Delete a Flash app and all its associated resources.
 
@@ -139,8 +140,8 @@
 
 ### Process
 
 1. Shows app details and resources to be deleted.
 2. Prompts for confirmation (required).
 3. Deletes all environments and their resources.
 4. Deletes all builds.
 5. Deletes the app.
@@ -159,7 +160,7 @@
 
 ## Auto-detection
 
 Flash CLI automatically detects the app name from your current directory:
 
 ```bash
 cd /path/to/APP_NAME

flash/cli/build.mdx

@@ -1,9 +1,10 @@
 ---
 title: "build"
 sidebarTitle: "build"
+description: "Build a deployment-ready Flash application artifact without deploying, with controls for dependencies, exclusions, and output files."
 ---
 
 Build a deployment-ready artifact for your Flash application without deploying. Use this for more control over the build process or to inspect the artifact before deploying.
 
 ```bash
 flash build [OPTIONS]
@@ -49,17 +50,17 @@
 
 ## What happens during build
 
 1. **Python version resolution**: Resolves the target Python version from CLI flag, resource configs, or your local interpreter.
 2. **Function discovery**: Finds all `@Endpoint` decorated functions.
 3. **Grouping**: Groups functions by their endpoint configuration.
 4. **Manifest generation**: Creates `.flash/flash_manifest.json` with endpoint definitions.
 5. **Source fingerprinting**: Computes a SHA-256 fingerprint of your source files to detect code changes between deployments.
 6. **Dependency installation**: Installs Python packages for Linux x86_64.
 7. **Packaging**: Bundles everything into `.flash/artifact.tar.gz`.
 
 ## Built-in ignore patterns
 
 Flash automatically excludes certain files and directories from deployment artifacts. These patterns cover common development files that shouldn't be deployed to production.
 
 | Category | Patterns |
 |----------|----------|
@@ -78,9 +79,9 @@
 
 ### Local modules and the ignore filter
 
 Local (non-pip) modules that your endpoints import are bundled automatically, provided they pass the ignore filter: your `.gitignore` plus the built-in patterns listed above. During the build, Flash resolves the transitive local-import closure of every shipped Python file and checks it against the files the ignore filter already selected.
 
 If shipped code imports a local module that an ignore rule excludes (for example, a `test_*.py` sibling or a file under `tests/`), the build fails with a `LocalModuleResolutionError` instead of silently overriding your ignore rules or shipping a broken artifact. Flash names each excluded file and the file that imports it:
 
 ```
 Shipped code imports local modules that the build ignore rules (.gitignore or built-in defaults) exclude:
@@ -89,7 +90,7 @@
 Shipping them would silently override a deliberate exclusion, and omitting them would break the worker with ModuleNotFoundError. Remove the matching ignore pattern or stop importing these modules from shipped code.
 ```
 
 If an `@Endpoint` file has a local import Flash can't resolve at all (a broken relative import, or a file outside your project root), the build also fails with a clear error. A file that fails resolution but doesn't define an endpoint is skipped with a warning and the build continues. For details on how local imports are resolved and bundled, see [Import local modules](/flash/create-endpoints#import-local-modules).
 
 ## Build artifacts
 
@@ -99,22 +100,22 @@
 |----------------|-------------|
 | `.flash/artifact.tar.gz` | Deployment package ready for Runpod |
 | `.flash/flash_manifest.json` | Service discovery configuration |
 | `.flash/.build/` | Build directory (kept for inspection) |
 
 ## Cross-platform builds
 
 Flash automatically handles cross-platform builds:
 
 - **Automatic platform targeting**: Dependencies are installed for Linux x86_64, regardless of your build platform.
 - **Binary wheel enforcement**: Only pre-built wheels are used, preventing compilation issues.
 
 ### Python version in deployed workers
 
 Flash workers support Python 3.10, 3.11, 3.12, and 3.13. The target version is determined by:
 
 1. **CLI flag:** The `--python-version` flag takes precedence.
 2. **Resource config:** The `python_version` parameter on your endpoint configs.
 3. **Local interpreter:** Your local Python version (from `sys.version_info`) when neither is specified.
 
 All resources in a Flash app must use the same Python version because Flash ships a single tarball for the entire app. If resources declare conflicting versions, the build fails.
 
@@ -127,19 +128,19 @@
 | 3.12 | No overhead | PyTorch pre-installed in base image |
 | 3.13 | +~7 GB on GPU | Alternative Python install required |
 | 3.11 | +~7 GB on GPU | Alternative Python install required |
 | 3.10 | +~7 GB on GPU | EOL 2026-10-31; consider migrating to 3.11+ |
 
 If your local Python version is not supported (for example, 3.9 or 3.14), the build fails with an actionable error message listing the supported versions.
 
 Image tags follow the pattern `py{version}-{tag}` (for example, `runpod/flash:py3.12-latest`).
 
 ## Managing deployment size
 
 Runpod Serverless has a **1.5GB deployment limit**. Flash automatically excludes packages that are pre-installed in the base image:
 
 - `torch`, `torchvision`, `torchaudio`, `triton`
 
 These packages are excluded at archive time, so you don't need to specify them manually.
 
 ### Manual exclusions
 
@@ -179,7 +180,7 @@
 
 ### Archive is too large
 
 Some CUDA packages (`torch`, `torchvision`, `torchaudio`, `triton`) are auto-excluded. If the archive is still too large, use `--exclude` to skip additional packages or `--no-deps` to skip transitive dependencies:
 
 ```bash
 flash build --exclude scipy,pandas
@@ -189,12 +190,12 @@
 
 If a package doesn't have Linux x86_64 wheels:
 
 1. Ensure standard pip is installed: `python -m ensurepip --upgrade`
 2. Check PyPI for Linux wheel availability.
 
 ### Need to examine generated files
 
 The build directory is kept after building. Inspect it with:
 
 ```bash
 ls .flash/.build/
@@ -202,7 +203,7 @@
 
 ## Related commands
 
 - [`flash deploy`](/flash/cli/deploy) - Build and deploy in one step (includes `--preview` option for local testing)
 - [`flash dev`](/flash/cli/dev) - Start development server
 - [`flash env`](/flash/cli/env) - Manage environments
 

flash/cli/deploy.mdx

@@ -1,9 +1,10 @@
 ---
 title: "deploy"
 sidebarTitle: "deploy"
+description: "Build and deploy a Flash application to Runpod Serverless, select an environment, preview the build, and control packaged dependencies."
 ---
 
 Build and deploy your Flash application to Runpod Serverless endpoints in one step. This is the primary command for getting your application running in the cloud.
 
 ```bash
 flash deploy [OPTIONS]
@@ -11,7 +12,7 @@
 
 ## Examples
 
 Build and deploy a Flash app from the current directory (auto-selects environment if only one exists):
 
 ```bash
 flash deploy
@@ -67,7 +68,7 @@
 
 ## What happens during deployment
 
 1. **Build phase**: Creates the deployment artifact (same as `flash build`).
 2. **Environment resolution**: Detects or creates the target environment.
 3. **Upload**: Sends the artifact to Runpod storage.
 4. **Provisioning**: Creates or updates Serverless endpoints.
@@ -75,7 +76,7 @@
 
 ## Rolling releases for code changes
 
 When you run `flash deploy` on an already-deployed application, Flash compares your current build against the previous deployment to determine what needs updating.
 
 Flash triggers a rolling release when your source code changes, even if your resource configuration stays the same. During the build phase, Flash computes a fingerprint of your source files. If this fingerprint differs from the previous deployment, Flash treats it as a configuration change and initiates a rolling update to your endpoints.
 
@@ -153,7 +154,7 @@
 
 ### Default environment
 
 If no app or environment exists and none is specified, Flash creates the app with a `production` environment by default.
 
 ## Post-deployment
 
@@ -184,7 +185,7 @@
         -d '{"input": {"input_data": {"message": "Hello from the GPU"}}}'
 ```
 
 Each endpoint is independent with its own URL and can be called directly.
 
 ### Authentication
 
@@ -216,7 +217,7 @@
 
 ## Managing deployment size
 
 Runpod Serverless has a **1.5GB limit**. Flash automatically excludes packages that are pre-installed in the base image (`torch`, `torchvision`, `torchaudio`, `numpy`, `triton`).
 
 If the deployment is still too large, use `--exclude` to skip additional packages:
 
@@ -226,7 +227,7 @@
 
 See [`flash build` - Managing deployment size](/flash/cli/build#managing-deployment-size) for more details.
 
 ## flash dev vs flash deploy
 
 See [`flash dev`](/flash/cli/dev#flash-dev-vs-flash-deploy) for a detailed comparison of local development vs production deployment.
 
@@ -254,7 +255,7 @@
 
 ### Authentication fails
 
 Ensure your API key is set:
 
 ```bash
 echo $RUNPOD_API_KEY

flash/cli/dev.mdx

@@ -1,6 +1,7 @@
 ---
 title: "dev"
 sidebarTitle: "dev"
+description: "Run a local Flash development server, test Endpoint functions on Runpod Serverless, and configure provisioning, ports, and updates."
 ---
 
 Start the Flash development server for local testing with automatic updates. A local development server provides a unified interface for testing while `@Endpoint` functions execute on Runpod Serverless.
@@ -56,7 +57,7 @@
 Flash extracts the first line of each function's docstring and uses it in two places:
 
 - **Startup table**: The "Description" column shows the docstring when the server starts.
 - **Swagger UI**: The endpoint summary in the API explorer at `/docs`.
 
 Add docstrings to your `@Endpoint` functions to make your API self-documenting:
 
@@ -108,14 +109,14 @@
 
 - A local development server provides a convenient testing interface at `localhost:8888`.
 - `@Endpoint` functions deploy to Runpod Serverless with `live-` prefix to distinguish from production.
 - Code changes are picked up automatically without restarting the server.
 - The development server routes requests to appropriate remote endpoints.
 
 This differs from `flash deploy`, where all endpoints run on Runpod without a local server.
 
 ## Auto-provisioning
 
 By default, endpoints are provisioned lazily on first `@Endpoint` function call. Use `--auto-provision` to provision all endpoints at server startup:
 
 ```bash
 flash dev --auto-provision
@@ -124,7 +125,7 @@
 ### How it works
 
 1. **Discovery**: Scans your app for `@Endpoint` decorated functions.
 2. **Deployment**: Deploys resources concurrently (up to 3 at a time).
 3. **Confirmation**: Asks for confirmation if deploying more than 5 endpoints.
 4. **Caching**: Stores deployed resources in `.flash/resources.pkl` for reuse.
 5. **Updates**: Recognizes existing endpoints and updates if configuration changed.
@@ -133,7 +134,7 @@
 
 - **Zero cold start**: All endpoints ready before you test them.
 - **Faster development**: No waiting for deployment on first HTTP call.
 - **Resource reuse**: Cached endpoints are reused across server restarts.
 
 ### When to use
 
@@ -143,9 +144,9 @@
 
 ## Provisioning modes
 
 | Mode | When endpoints are deployed |
 |------|----------------------------|
 | Default (lazy) | On first `@Endpoint` function call |
 | `--auto-provision` | At server startup |
 
 ## Testing your API
@@ -171,18 +172,18 @@
 Queue-based endpoints require the `{"input": {...}}` wrapper format to match deployed endpoint behavior. Load-balanced endpoints accept direct JSON payloads.
 </Note>
 
 Open http://localhost:8888/docs for the interactive API explorer.
 
 ## Requirements
 
 - `RUNPOD_API_KEY` must be set in your `.env` file or environment.
 - A valid Flash project structure (created by `flash init` or manually).
 
 ## flash dev vs flash deploy
 
 | Aspect | `flash dev` | `flash deploy` |
 |--------|-------------|----------------|
 | Local development server | Yes (http://localhost:8888) | No |
 | `@Endpoint` functions run on | Runpod Serverless | Runpod Serverless |
 | Endpoint persistence | Temporary (`live-` prefix) | Persistent |
 | Code updates | Automatic reload | Manual redeploy |

flash/cli/env.mdx

@@ -1,9 +1,10 @@
 ---
 title: "env"
 sidebarTitle: "env"
+description: "Create, inspect, list, and delete isolated Flash deployment environments for development, staging, and production applications."
 ---
 
 Manage deployment environments for Flash applications. Environments are isolated deployment contexts (like `dev`, `staging`, `production`) within a Flash app.
 
 ```bash Command
 flash env <subcommand> [OPTIONS]
@@ -20,7 +21,7 @@
 
 ---
 
 ## env list
 
 Show all available environments for an app.
 
@@ -58,7 +59,7 @@
 
 ---
 
 ## env create
 
 Create a new deployment environment.
 
@@ -102,7 +103,7 @@
 
 ---
 
 ## env get
 
 Show detailed information about a deployment environment.
 
@@ -162,7 +163,7 @@
 
 ---
 
 ## env delete
 
 Delete a deployment environment and all its associated resources.
 
@@ -194,8 +195,8 @@
 
 ### Process
 
 1. Shows environment details and resources to be deleted.
 2. Prompts for confirmation (required).
 3. Undeploys all associated endpoints.
 4. Removes all associated network volumes.
 5. Deletes the environment from the app.

flash/cli/init.mdx

@@ -1,6 +1,7 @@
 ---
 title: "init"
 sidebarTitle: "init"
+description: "Initialize a Flash project with a FastAPI server, example GPU and CPU workers, configuration files, and a ready-to-run directory structure."
 ---
 
 Create a new Flash project with a ready-to-use template structure including a FastAPI server, example GPU and CPU workers, and configuration files.
@@ -59,18 +60,18 @@
 
 ### Template contents
 
 - **lb_worker.py**: load-balanced endpoint with HTTP routes. Contains `@Endpoint` functions with custom HTTP methods and paths (e.g., `POST /process`, `GET /health`). Multiple routes can share the same endpoint.
 - **gpu_worker.py**: GPU queue-based endpoint. Contains an `@Endpoint` function that runs on GPU hardware. Provides `/run` or `/runsync` routes for job submission. Creates one Serverless endpoint when deployed.
 - **cpu_worker.py**: CPU queue-based endpoint. Contains an `@Endpoint` function that runs on CPU-only instances. Provides `/run` or `/runsync` routes for job submission. Creates one Serverless endpoint when deployed.
 - **.env**: Template for environment variables including `RUNPOD_API_KEY`.
 
 ### AI coding agent files
 
 Flash generates context files that help AI coding assistants (Claude Code, Cursor, GitHub Copilot, Codex, Aider, and others) use Flash correctly. These files tell agents to use Flash CLI commands instead of raw Runpod API calls.
 
 | File | Purpose |
 |------|---------|
 | `AGENTS.md` | CLI-first rules for AI coding tools (Cursor, Codex, Aider, Amp, Jules, and others) |
 | `CLAUDE.md` | Symlink to `AGENTS.md` so Claude Code picks up the same rules |
 
 Flash writes these files only when they don't already exist. If you have your own `AGENTS.md` or `CLAUDE.md`, Flash leaves them alone.
@@ -102,7 +103,7 @@
 
 After initialization:
 
 1. Copy `.env.example` to `.env` (if needed) and add your `RUNPOD_API_KEY`.
 2. Install dependencies: `pip install -r requirements.txt`
 3. Start the development server: `flash dev`
 4. Open http://localhost:8888/docs to explore the API.

flash/cli/login.mdx

@@ -1,9 +1,10 @@
 ---
 title: "login"
 sidebarTitle: "login"
+description: "Authenticate the Flash CLI with Runpod, save your API key locally, and configure browser-based authorization options and timeouts."
 ---
 
 Authenticate with Runpod and save your API key for all Flash operations, including CLI commands and standalone `@Endpoint` functions.
 
 ```bash
 flash login [OPTIONS]
@@ -11,13 +12,13 @@
 
 ## Example
 
 Authenticate with Runpod (opens browser automatically):
 
 ```bash
 flash login
 ```
 
 The command opens your default browser to the Runpod authorization page. After you approve the request, your API key is saved locally for future CLI operations.
 
 ## How it works
 
@@ -42,9 +43,9 @@
 
 ## Credential storage
 
 After successful login, your API key is saved to `~/.runpod/config.toml`. This file is used by:
 
 - All Flash CLI commands (`flash dev`, `flash deploy`, etc.)
 - Standalone Python scripts using `@Endpoint` functions
 - Any code using the Flash SDK
 
@@ -53,7 +54,7 @@
 </Note>
 
 
 ## Alternative: Environment variable authentication
 
 Instead of using `flash login`, you can set your API key directly as an environment variable:
 
@@ -61,7 +62,7 @@
 export RUNPOD_API_KEY=your_api_key_here
 ```
 
 Or add it to your project's `.env` file for local CLI use:
 
 ```bash
 RUNPOD_API_KEY=your_api_key_here
@@ -74,10 +75,10 @@
 Flash checks for credentials in this order:
 
 1. `RUNPOD_API_KEY` environment variable
 2. `RUNPOD_API_KEY` in a `.env` file (loaded automatically at startup)
 3. Credentials file (`~/.runpod/config.toml`)
 
 The first valid credential found is used. Environment variables take precedence over the credentials file, so you can override per-project or per-session without modifying stored credentials.
 
 <Warning>
 Values in your `.env` file are only used for local CLI commands and development. They are **not** passed to deployed endpoints. To set environment variables on deployed endpoints, use the `env` parameter in your endpoint configuration. See [Endpoint parameters](/flash/configuration/parameters#env) for details.

flash/cli/undeploy.mdx

@@ -1,6 +1,7 @@
 ---
 title: "undeploy"
 sidebarTitle: "undeploy"
+description: "List or delete Runpod Serverless endpoints created by Flash, including individual deployments and local development resources."
 ---
 
 Manage and delete Runpod Serverless endpoints deployed via Flash. Use this command to clean up endpoints created during local development with `flash dev`.
@@ -43,8 +44,8 @@
 
 - **Name**: Endpoint name
 - **Endpoint ID**: Runpod endpoint identifier
 - **Status**: Current health status (Active/Inactive/Unknown)
 - **Type**: Resource type (Live Serverless, Cpu Live Serverless, etc.)
 
 **Status indicators:**
 
@@ -72,7 +73,7 @@
 
 ### Undeploy all
 
 Delete all tracked endpoints (requires double confirmation):
 
 ```bash
 flash undeploy --all
@@ -82,7 +83,7 @@
 
 1. Shows total count of endpoints.
 2. First confirmation: Yes/No prompt.
 3. Second confirmation: Type "DELETE ALL" exactly.
 4. Deletes all endpoints from Runpod.
 5. Removes all from tracking.
 
@@ -104,7 +105,7 @@
 flash undeploy --cleanup-stale
 ```
 
 Use this when endpoints were deleted via the Runpod console or API (not through Flash). The local tracking file (`.flash/resources.pkl`) becomes stale, and this command cleans it up.
 
 ## Flags
 
@@ -126,7 +127,7 @@
 Name of the endpoint to undeploy. Use `list` to show all endpoints.
 </ResponseField>
 
 ## undeploy vs env delete
 
 | Command | Scope | When to use |
 |---------|-------|-------------|
@@ -137,13 +138,13 @@
 
 ## How tracking works
 
 Flash tracks deployed endpoints in `.flash/resources.pkl`. Endpoints are added when you:
 
 - Run `flash dev --auto-provision`
 - Run `flash dev` and call `@Endpoint` functions
 - Run `flash deploy`
 
 The tracking file is in `.gitignore` and should never be committed. It contains local deployment state.
 
 ## Common workflows
 
@@ -186,7 +187,7 @@
 
 ### Endpoint shows as "Inactive"
 
 The endpoint was deleted via Runpod console or API. Clean up:
 
 ```bash
 flash undeploy --cleanup-stale
@@ -208,6 +209,6 @@
 
 ## Related commands
 
 - [`flash dev`](/flash/cli/dev) - Development server (creates endpoints)
 - [`flash deploy`](/flash/cli/deploy) - Deploy to Runpod
 - [`flash env delete`](/flash/cli/env) - Delete entire environment

flash/cli/update.mdx

@@ -1,9 +1,10 @@
 ---
 title: "update"
 sidebarTitle: "update"
+description: "Update the Flash CLI to the latest or a selected version, understand automatic update checks, and configure when those checks run."
 ---
 
 Update the Flash CLI to the latest version or a specific version. The command fetches version information from PyPI and installs using uv (when available) or pip.
 
 ```bash
 flash update [OPTIONS]
@@ -11,7 +12,7 @@
 
 ## Examples
 
 Update to the latest version:
 
 ```bash
 flash update
@@ -38,11 +39,11 @@
   Run 'flash update' to upgrade.
 ```
 
 This check runs at most once every 24 hours and is cached locally to `~/.config/runpod/update_check.json`.
 
 ### Excluded commands
 
 The background check does not run for:
 
 - `flash dev` - Long-running development server where the notice would appear at an unpredictable time.
 - `flash update` - Already managing versions directly.
@@ -55,7 +56,7 @@
 export FLASH_NO_UPDATE_CHECK=1
 ```
 
 Update checks are also skipped automatically in CI environments (when the `CI` environment variable is set) and in non-interactive sessions (when neither stdout nor stderr is a TTY).
 
 ## Related commands
 

instant-clusters/axolotl.mdx

@@ -1,25 +1,26 @@
 ---
 title: "Deploy an Instant Cluster with Axolotl"
 sidebarTitle: "Axolotl"
+description: "Deploy a Runpod Cluster with Axolotl and configure multi-GPU distributed training for fine-tuning large language models."
 ---
 import { TrainingTooltip, InferenceTooltip } from "/snippets/tooltips.jsx";
 
 This tutorial demonstrates how to use Instant Clusters with [Axolotl](https://axolotl.ai/) to fine-tune large language models (LLMs) across multiple GPUs. By leveraging PyTorch's distributed <TrainingTooltip /> capabilities and Runpod's high-speed networking infrastructure, you can significantly accelerate your training process compared to single-GPU setups.
 
 Follow the steps below to deploy a cluster and start training your models efficiently.
 
 ## Step 1: Deploy an Instant Cluster
 
 1. Open the [Instant Clusters page](https://www.console.runpod.io/cluster) on the Runpod web interface.
 2. Click **Create Cluster**.
 3. Use the UI to name and configure your Cluster. For this walkthrough, keep **Pod Count** at **2** and select the option for **16x H100 SXM** GPUs. Keep the **Pod Template** at its default setting (Runpod PyTorch).
 4. Click **Deploy Cluster**. You should be redirected to the Instant Clusters page after a few seconds.
 
 ## Step 2: Set up Axolotl on each Pod
 
 1. Click your cluster to expand the list of Pods.
 
 2. Click on a Pod, for example `CLUSTERNAME-pod-0`, to expand the Pod.
 
 3. Click **Connect**, then click **Web Terminal**.
 
@@ -50,7 +51,7 @@
 
 Repeat these steps for **each Pod** in your cluster.
 
 ## Step 3: Start the training process on each Pod
 
 Run this command in the web terminal of **each Pod**:
 
@@ -91,7 +92,7 @@
 Congratulations! You've successfully trained a model using Axolotl on an Instant Cluster. Your fine-tuned model has been saved to the `./outputs/lora-out` directory. You can now use this model for <InferenceTooltip /> or continue training with different parameters.
 </Check>
 
 ## Step 4: Clean up
 
 If you no longer need your cluster, make sure you return to the [Instant Clusters page](https://www.console.runpod.io/cluster) and delete your cluster to avoid incurring extra charges.
 
@@ -106,7 +107,7 @@
 Now that you've successfully deployed and tested an Axolotl distributed training job on an Instant Cluster, you can:
 
 * **Fine-tune your own models** by modifying the configuration files in Axolotl to suit your specific requirements.
 * **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.
 * **Try different optimization techniques** such as DeepSpeed, FSDP (Fully Sharded Data Parallel), or other distributed training strategies.
 
 For more information on fine-tuning with Axolotl, refer to the [Axolotl documentation](https://github.com/axolotl-ai-cloud/axolotl).

instant-clusters/pytorch.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Deploy an Instant Cluster with PyTorch"
 sidebarTitle: "PyTorch"
+description: "Deploy a Runpod Cluster with PyTorch and configure distributed processing across multiple Pods for multi-GPU workloads."
 ---
 
 import { PyTorchTooltip } from "/snippets/tooltips.jsx";
@@ -12,18 +13,18 @@
 - A Runpod account with sufficient credits for a multi-node cluster
 - Basic familiarity with PyTorch and distributed training concepts
 
 ## Step 1: Deploy an Instant Cluster
 
 1. Open the [Instant Clusters page](https://www.console.runpod.io/cluster) on the Runpod web interface.
 2. Click **Create Cluster**.
 3. Use the UI to name and configure your Cluster. For this walkthrough, keep **Pod Count** at **2** and select the option for **16x H100 SXM** GPUs. Keep the **Pod Template** at its default setting (Runpod PyTorch).
 4. Click **Deploy Cluster**. You should be redirected to the Instant Clusters page after a few seconds.
 
 ## Step 2: Clone the PyTorch demo into each Pod
 
 1. Click your cluster to expand the list of Pods.
 
 2. Click on a Pod, for example `CLUSTERNAME-pod-0`, to expand the Pod.
 
 3. Click **Connect**, then click **Web Terminal**.
 
@@ -37,7 +38,7 @@
 
 ## Step 3: Examine the main.py file
 
 Let's look at the code in our `main.py` file:
 
 ```python main.py
 import os
@@ -79,7 +80,7 @@
    main()
 ```
 
 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).
 
 <Accordion title="Instant Cluster environment variables">
 
@@ -98,7 +99,7 @@
 
 </Accordion>
 
 ## Step 4: Start the PyTorch process on each Pod
 
 Run this command in the web terminal of **each Pod** to start the PyTorch process:
 
@@ -114,7 +115,7 @@
 torch-demo/main.py
 ```
 
 This command launches eight `main.py` processes per node (one per GPU in the Pod).
 
 <Accordion title="NCCL network configuration details">
 
@@ -144,17 +145,17 @@
 Running on rank 10/15 (local rank: 2), device: cuda:2
 ```
 
 The first number refers to the global rank of the thread, spanning from `0` to `WORLD_SIZE-1` (`WORLD_SIZE` = the total number of GPUs in the cluster). In our example there are two Pods of eight GPUs, so the global rank spans from 0-15. The second number is the local rank, which defines the order of GPUs within a single Pod (0-7 for this example).
 
 The specific number and order of ranks may be different in your terminal, and the global ranks listed will be different for each Pod.
 
 This diagram illustrates how local and global ranks are distributed across multiple Pods:
 
 <Frame>
   <img src="/images/79e746f3-instant-clusters-rank-diagram.png" />
 </Frame>
 
 ## Step 5: Clean up
 
 If you no longer need your cluster, make sure you return to the [Instant Clusters page](https://www.console.runpod.io/cluster) and delete your cluster to avoid incurring extra charges.
 
@@ -166,12 +167,12 @@
 
 ## Next steps
 
 Now that you've successfully deployed and tested a PyTorch distributed application on an Instant Cluster, you can:
 
 * **Adapt your own PyTorch code** to run on the cluster by modifying the distributed initialization in your scripts.
 * **Scale your training** by adjusting the number of Pods in your cluster to handle larger models or datasets.
 * **Try different frameworks** like [Axolotl](/instant-clusters/axolotl) for fine-tuning large language models.
 * **Optimize performance** by experimenting with different distributed training strategies like Data Parallel (DP), Distributed Data Parallel (DDP), or Fully Sharded Data Parallel (FSDP).
 * **Review the [configuration reference](/instant-clusters/configuration)** for detailed information on environment variables, network interfaces, and troubleshooting.
 
 For more information on distributed training with PyTorch, refer to the [PyTorch Distributed Training documentation](https://pytorch.org/tutorials/beginner/dist_overview.html).

instant-clusters/slurm.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Deploy an Instant Cluster with Slurm (unmanaged)"
 sidebarTitle: "Slurm (unmanaged)"
+description: "Deploy and configure an unmanaged Runpod Cluster with Slurm to schedule distributed workloads across multiple GPU nodes."
 ---
 
 import { SlurmTooltip } from "/snippets/tooltips.jsx";
@@ -19,19 +20,19 @@
 * You have basic familiarity with Linux command line.
 * You're comfortable working with [Pods](/pods/overview) and understand the basics of [Slurm](https://slurm.schedmd.com/).
 
 ## Step 1: Deploy an Instant Cluster
 
 1. Open the [Instant Clusters page](https://www.console.runpod.io/cluster) on the Runpod web interface.
 2. Click **Create Cluster**.
 3. Use the UI to name and configure your cluster. For this walkthrough, keep **Pod Count** at **2** and select the option for **16x H100 SXM** GPUs. Keep the **Pod Template** at its default setting (Runpod PyTorch).
 4. Click **Deploy Cluster**. You should be redirected to the Instant Clusters page after a few seconds.
 
 ## Step 2: Clone demo and install Slurm on each Pod
 
 To connect to a Pod:
 
 1. On the Instant Clusters page, click on the cluster you created to expand the list of Pods.
 2. Click on a Pod, for example `CLUSTERNAME-pod-0`, to expand the Pod.
 
 **On each Pod:**
 
@@ -49,26 +50,26 @@
    apt update && apt install -y slurm-wlm slurm-client munge
    ```
 
 ## Step 3: Overview of Slurm demo scripts
 
 The repository contains several essential scripts for setting up Slurm. Let's examine what each script does:
 
 * `create_gres_conf.sh`: Generates the Slurm Generic Resource (GRES) configuration file that defines GPU resources for each node.
 * `create_slurm_conf.sh`: Creates the main Slurm configuration file with cluster settings, node definitions, and partition setup.
 * `install.sh`: The primary installation script that sets up MUNGE authentication, configures Slurm, and prepares the environment.
 * `test_batch.sh`: A sample Slurm job script for testing cluster functionality.
 
 ## Step 4: Install Slurm on each Pod
 
 Now run the installation script **on each Pod**, replacing `[MUNGE_SECRET_KEY]` with any secure random string (like a password). The secret key is used for authentication between nodes, and must be identical across all Pods in your cluster.
 
 ```bash
 ./install.sh "[MUNGE_SECRET_KEY]" node-0 node-1 10.65.0.2 10.65.0.3
 ```
 
 This script automates the complex process of configuring a two-node Slurm cluster with GPU support, handling everything from system dependencies to authentication and resource configuration. It implements the necessary setup for both the primary (i.e. master/control) and secondary (i.e compute/worker) nodes.
 
 ## Step 5: Start Slurm services
 
 <Tip>
 
@@ -76,7 +77,7 @@
 
 </Tip>
 
 1. **On the primary node** (`node-0`), run both Slurm services:
 
    ```bash
    slurmctld -D
@@ -88,7 +89,7 @@
    slurmd -D
    ```
 
 3. **On the secondary node** (`node-1`), run:
 
    ```bash
    slurmd -D
@@ -96,9 +97,9 @@
 
 After running these commands, you should see output indicating that the services have started successfully. The `-D` flag keeps the services running in the foreground, so each command needs its own terminal.
 
 ## Step 6: Test your Slurm Cluster
 
 1. Run this command **on the primary node** (`node-0`) to check the status of your nodes:
 
    ```bash
    sinfo
@@ -114,17 +115,17 @@
 
    This command should list all GPUs across both nodes.
 
 ## Step 7: Submit the Slurm job script
 
 Run the following command **on the primary node** (`node-0`) to submit the test job script and confirm that your cluster is working properly:
 
 ```bash
 sbatch test_batch.sh
 ```
 
 Check the output file created by the test (`test_simple_[JOBID].out`) and look for the hostnames of both nodes. This confirms that the job ran successfully across the cluster.
 
 ## Step 8: Clean up
 
 If you no longer need your cluster, make sure you return to the [Instant Clusters page](https://www.console.runpod.io/cluster) and delete your cluster to avoid incurring extra charges.
 

pods/troubleshooting/jupyterlab-checkpoints-folder.mdx

@@ -1,13 +1,14 @@
 ---
 title: "JupyterLab checkpoints folder access"
 sidebarTitle: "JupyterLab checkpoints folder"
+description: "Access folders named checkpoints in JupyterLab by renaming them, moving files through the interface, or using terminal commands."
 ---
 
 If you're unable to open a folder named "checkpoints" in JupyterLab, this is a known issue where JupyterLab treats "checkpoints" as a reserved keyword.
 
 ## The issue
 
 JupyterLab cannot open any directory named exactly "checkpoints". When you try to click on it, nothing happens because the system triggers its internal `listCheckpoints` function instead of opening the directory. This commonly affects ML model directories and ComfyUI installations.
 
 ## Solutions
 
@@ -27,7 +28,7 @@
 
 ### Option 2: Drag and drop method
 
 Download your files to a different directory, then use JupyterLab's interface to drag and drop files into the checkpoints folder. The folder won't open, but it will accept dropped files.
 
 ### Option 3: Use the terminal for file operations
 
@@ -46,4 +47,4 @@
 
 ## Related information
 
-This issue has been reported in multiple GitHub issues and affects both JupyterLab and classic Jupyter Notebook. The folder name "checkpoints" conflicts with Jupyter's internal checkpoint system for notebook files.
+This issue has been reported in multiple GitHub issues and affects both JupyterLab and classic Jupyter Notebook. The folder name "checkpoints" conflicts with Jupyter's internal checkpoint system for notebook files.

pods/troubleshooting/storage-full.mdx

@@ -1,5 +1,6 @@
 ---
 title: "Storage full"
+description: "Diagnose and resolve storage-full errors on Runpod Pods by checking disk usage, locating large files, and managing container or volume storage."
 ---
 
 Storage full errors can occur when users generate many files, transfer files, or perform other storage-intensive tasks. This document provides guidance to help you troubleshoot this.
@@ -34,7 +35,7 @@
 
 ## Key areas to check
 
 **Container Disk Usage**: The primary storage area for your container is mounted on the `overlay` filesystem. This indicates the container's root directory.
 
 ```sh
 Filesystem                    Size  Used Avail Use% Mounted on
@@ -43,7 +44,7 @@
 
 You can use the command `du -sh .` to check the space usage of the current directory. 
 
 By default, the volume disk or network volume is mounted at `/workspace`, You can check the usage with the following example::
 
 ```sh
 root@9b8e325167b2:/# cd workspace/
@@ -51,7 +52,7 @@
 194M    .
 ```
 
 **Identifying Large Files**: To identify the top 10 largest files in your `/workspace`, you can run the following command:
 
 ```sh
 root@9b8e325167b2:/# find /workspace -type f -exec du -h {} + | sort -rh | head -n 10
@@ -80,7 +81,7 @@
 rm -r /path/to/directory
 ```
 
 ## JupyterLab hidden trash bin
 
 If you deleted files through JupyterLab's UI but storage isn't freed, they may be in a hidden trash directory.
 
@@ -92,7 +93,7 @@
 { ls -lah $HOME/.local/share/Trash/ 2>/dev/null && echo "✓ Found: $HOME/.local/share/Trash/"; ls -lah /workspace/.Trash* 2>/dev/null && echo "✓ Found: /workspace/.Trash*"; } || echo "✗ No trash directories found"
 ```
 
 If trash was found above, clear it:
 
 ```bash Clear trash directories
 [ -d "$HOME/.local/share/Trash" ] && rm -rf $HOME/.local/share/Trash/* && echo "✓ Cleared home trash" || echo "✗ No home trash to clear"
@@ -101,4 +102,4 @@
 
 ## Additional storage
 
-If your Pod needs more than 20GB of storage, consider using a network volume. For more information, see [Network volumes](/storage/network-volumes), or refer to [this blog post](https://www.runpod.io/blog/network-volumes-on-runpod-secure-cloud).
+If your Pod needs more than 20GB of storage, consider using a network volume. For more information, see [Network volumes](/storage/network-volumes), or refer to [this blog post](https://www.runpod.io/blog/network-volumes-on-runpod-secure-cloud).

pods/troubleshooting/token-authentication-enabled.mdx

@@ -1,8 +1,9 @@
 ---
 title: "JupyterLab server token authentication"
+description: "Recover the JupyterLab server token from a Runpod Pod terminal, then use it to authenticate when the JupyterLab login screen appears."
 ---
 
 If you see a "Token authentication is enabled" screen when trying to access your Pod's JupyterLab server, follow the steps below to log in.
 
 <Frame caption="Jupyter server token authentication screen">
   <img src="/images/jupyter-server-token.png" alt="Jupyter server token authentication" />
@@ -26,6 +27,6 @@
 root@2779b5db68b8:/#
 ```
 
 The token you need is the string of characters that appears after the `=` sign, such as `ua5nw5fwkdzseqpp5apj` in the example above.
 
 Copy this token, return to your JupyterLab login page, and paste it into the **Token** field to sign in.

pods/troubleshooting/troubleshooting-502-errors.mdx

@@ -1,24 +1,25 @@
 ---
 title: "502 errors"
+description: "Troubleshoot 502 errors on Runpod Pods by checking GPU attachment, application status, port configuration, logs, and template requirements."
 ---
 
 502 errors can occur when users attempt to access a program running on a specific port of a deployed Pod and the program isn't running or has encountered an error. This document provides guidance to help you troubleshoot this error.
 
 ### Check your Pod's GPU
 
 The first step to troubleshooting a 502 error is to check whether your Pod has a GPU attached.
 
 1. **Access your Pod's settings**: Click on your Pod's settings in the user interface to access detailed information about your Pod.
 
 2. **Verify GPU attachment**: Here, you should be able to see if your Pod has a GPU attached. If it does not, you will need to attach a GPU.
 
 If a GPU is attached, you will see it under the Pods screen (e.g. 1 x A6000). If a GPU is not attached, this number will be 0. Runpod does allow you to spin up a Pod with 0 GPUs so that you can connect to it via a Terminal or CloudSync to access data. However, the options to connect to Runpod via the web interface will be nonfunctional, even if they are lit up.
 
 <Frame caption="">
   <img src="/images/1950025a-fb4c0dd-image-a00848a71ff50d8ce10e5e7f35cc33ff.png" />
 </Frame>
 
 ### Check your Pod's logs
 
 After confirming that your Pod has a GPU attached, the next step is to check your Pod's logs for any errors.
 
@@ -27,16 +28,16 @@
 2. <Frame caption="">
      <img src="/images/ef293f09-3500eba-image-dc2d7107a41028eb6ed2c97919c4e61c.png" />
    </Frame>
    **Look for errors**: Browse through the logs to find any error messages that may provide clues about why you're experiencing a 502 error.
 
 ### Verify additional steps for official templates
 
 In some cases, for our official templates, the user interface does not work right away and may require additional steps to be performed by the user.
 
 1. **Access the template's ReadMe**: Navigate to the template's page and open the ReadMe file.
 
 2. **Follow additional steps**: The ReadMe file should provide instructions on any additional steps you need to perform to get the UI functioning properly. Make sure to follow these instructions closely.
 
 Remember, each template may have unique requirements or steps for setup. It is always recommended to thoroughly review the documentation associated with each template.
 
 If you continue to experience 502 errors after following these steps, please contact our support team. We're here to help ensure that your experience on our platform is as seamless as possible.

references/cpu-types.mdx

@@ -1,5 +1,6 @@
 ---
 title: CPU types
+description: "Review the CPU models available on Runpod, including core counts and threads per core, to compare compute options for your workload."
 ---
 
 The following list contains all CPU types available on Runpod.
@@ -8,306 +9,306 @@
 
 | Display Name                                    |   Cores |   Threads Per Core |
 |-------------------------------------------------|---------|--------------------|
 | 11th Gen Intel(R) Core(TM) i5-11400 @ 2.60GHz   |       6 |                  2 |
 | 11th Gen Intel(R) Core(TM) i5-11400F @ 2.60GHz  |       6 |                  2 |
 | 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz  |       2 |                  1 |
 | 11th Gen Intel(R) Core(TM) i7-11700 @ 2.50GHz   |       8 |                  2 |
 | 11th Gen Intel(R) Core(TM) i7-11700F @ 2.50GHz  |       8 |                  2 |
 | 11th Gen Intel(R) Core(TM) i7-11700K @ 3.60GHz  |       8 |                  2 |
 | 11th Gen Intel(R) Core(TM) i7-11700KF @ 3.60GHz |       8 |                  2 |
 | 11th Gen Intel(R) Core(TM) i9-11900K @ 3.50GHz  |       8 |                  2 |
 | 11th Gen Intel(R) Core(TM) i9-11900KF @ 3.50GHz |       8 |                  2 |
 | 12th Gen Intel(R) Core(TM) i3-12100             |       4 |                  2 |
 | 12th Gen Intel(R) Core(TM) i7-12700F            |      12 |                  1 |
 | 12th Gen Intel(R) Core(TM) i7-12700K            |      12 |                  1 |
 | 13th Gen Intel(R) Core(TM) i3-13100F            |       4 |                  2 |
 | 13th Gen Intel(R) Core(TM) i5-13600K            |      14 |                  1 |
 | 13th Gen Intel(R) Core(TM) i7-13700K            |      16 |                  1 |
 | 13th Gen Intel(R) Core(TM) i7-13700KF           |      16 |                  1 |
 | 13th Gen Intel(R) Core(TM) i9-13900F            |      24 |                  1 |
 | 13th Gen Intel(R) Core(TM) i9-13900K            |      24 |                  1 |
 | 13th Gen Intel(R) Core(TM) i9-13900KF           |      24 |                  1 |
 | AMD Eng Sample: 100-000000053-04_32/20_N        |      48 |                  1 |
 | AMD Eng Sample: 100-000000314-04_30/16_N        |      64 |                  2 |
 | AMD Eng Sample: 100-000000897-03                |      32 |                  2 |
 | AMD Eng Sample: 100-000001245-08                |     128 |                  2 |
 | AMD EPYC 4564P 16-Core Processor                |      16 |                  2 |
 | AMD EPYC 7251 8-Core Processor                  |       8 |                  2 |
 | AMD EPYC 7252 8-Core Processor                  |       8 |                  2 |
 | AMD EPYC 7272 12-Core Processor                 |      12 |                  2 |
 | AMD EPYC 7281 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7282 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7302 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7302P 16-Core Processor                |      16 |                  2 |
 | AMD EPYC 7313 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7313P 16-Core Processor                |      16 |                  2 |
 | AMD EPYC 7343 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7351P 16-Core Processor                |      16 |                  2 |
 | AMD EPYC 7352 24-Core Processor                 |      24 |                  2 |
 | AMD EPYC 7371 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7402 24-Core Processor                 |      24 |                  2 |
 | AMD EPYC 7402P 24-Core Processor                |      24 |                  2 |
 | AMD EPYC 7413 24-Core Processor                 |      24 |                  2 |
 | AMD EPYC 7443 24-Core Processor                 |      48 |                  1 |
 | AMD EPYC 7443P 24-Core Processor                |      24 |                  2 |
 | AMD EPYC 7452 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7453 28-Core Processor                 |      28 |                  1 |
 | AMD EPYC 74F3 24-Core Processor                 |      24 |                  2 |
 | AMD EPYC 7502 32-Core Processor                 |      32 |                  1 |
 | AMD EPYC 7502P 32-Core Processor                |      32 |                  1 |
 | AMD EPYC 7513 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7532 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7542 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7543 32-Core Processor                 |      28 |                  1 |
 | AMD EPYC 7543P 32-Core Processor                |      32 |                  2 |
 | AMD EPYC 7551 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7551P 32-Core Processor                |      32 |                  2 |
 | AMD EPYC 7552 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 7573X 32-Core Processor                |      32 |                  2 |
 | AMD EPYC 75F3 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7601 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 7642 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 7643 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 7663 56-Core Processor                 |      56 |                  2 |
 | AMD EPYC 7702 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7702P 64-Core Processor                |      64 |                  2 |
 | AMD EPYC 7713 64-Core Processor                 |      64 |                  1 |
 | AMD EPYC 7713P 64-Core Processor                |      64 |                  2 |
 | AMD EPYC 7742 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7763 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7773X 64-Core Processor                |      64 |                  2 |
 | AMD EPYC 7B12 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7B13 64-Core Processor                 |      64 |                  1 |
 | AMD EPYC 7C13 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7F32 8-Core Processor                  |       8 |                  2 |
 | AMD EPYC 7F52 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 7F72 24-Core Processor                 |      24 |                  2 |
 | AMD EPYC 7H12 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7J13 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 7K62 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 7R13 Processor                         |      48 |                  2 |
 | AMD EPYC 7R32 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 7T83 64-Core Processor                 |     127 |                  1 |
 | AMD EPYC 7V13 64-Core Processor                 |      24 |                  1 |
 | AMD EPYC 9124 16-Core Processor                 |      16 |                  2 |
 | AMD EPYC 9254 24-Core Processor                 |      24 |                  2 |
 | AMD EPYC 9274F 24-Core Processor                |      24 |                  2 |
 | AMD EPYC 9334 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 9335 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 9354 32-Core Emb Processor             |      32 |                  2 |
 | AMD EPYC 9354 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 9354P                                  |      64 |                  1 |
 | AMD EPYC 9354P 32-Core Processor                |      32 |                  2 |
 | AMD EPYC 9355 32-Core Processor                 |      32 |                  2 |
 | AMD EPYC 9355P 32-Core Processor                |      32 |                  2 |
 | AMD EPYC 9374F 32-Core Processor                |      32 |                  1 |
 | AMD EPYC 9454 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 9454P 48-Core Emb Processor            |      48 |                  2 |
 | AMD EPYC 9455 48-Core Processor                 |      48 |                  2 |
 | AMD EPYC 9455P 48-Core Processor                |      48 |                  2 |
 | AMD EPYC 9474F 48-Core Processor                |      48 |                  2 |
 | AMD EPYC 9534 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 9535 64-Core Processor                 |      64 |                  2 |
 | AMD EPYC 9554 64-Core Emb Processor             |      64 |                  1 |
 | AMD EPYC 9554 64-Core Processor                 |     126 |                  1 |
 | AMD EPYC 9555 64-Core Processor                 |      56 |                  2 |
 | AMD EPYC 9575F 64-Core Processor                |      64 |                  2 |
 | AMD EPYC 9654 96-Core Emb Processor             |      96 |                  1 |
 | AMD EPYC 9654 96-Core Processor                 |      96 |                  2 |
 | AMD EPYC 9655 96-Core Processor                 |      56 |                  2 |
 | AMD EPYC 9655P 96-Core Processor                |      96 |                  2 |
 | AMD EPYC 9754 128-Core Processor                |     128 |                  2 |
 | AMD EPYC 9755 128-Core Processor                |     128 |                  2 |
 | AMD EPYC 9B14 96-Core Processor                 |      96 |                  1 |
 | AMD EPYC 9J14 96-Core Processor                 |      96 |                  2 |
 | AMD EPYC Processor                              |       1 |                  1 |
 | AMD EPYC Processor (with IBPB)                  |      16 |                  1 |
 | AMD EPYC-Rome Processor                         |      16 |                  1 |
 | AMD Ryzen 3 2200G with Radeon Vega Graphics     |       4 |                  1 |
 | AMD Ryzen 3 3200G with Radeon Vega Graphics     |       4 |                  1 |
 | AMD Ryzen 3 4100 4-Core Processor               |       4 |                  2 |
 | AMD Ryzen 5 1600 Six-Core Processor             |       6 |                  2 |
 | AMD Ryzen 5 2600 Six-Core Processor             |       6 |                  2 |
 | AMD Ryzen 5 2600X Six-Core Processor            |       6 |                  2 |
 | AMD Ryzen 5 3600 6-Core Processor               |       6 |                  2 |
 | AMD Ryzen 5 3600X 6-Core Processor              |       6 |                  2 |
 | AMD Ryzen 5 5500                                |       6 |                  2 |
 | AMD Ryzen 5 5600G with Radeon Graphics          |       6 |                  2 |
 | Ryzen 5 5600X                                   |       6 |                  2 |
 | AMD Ryzen 5 7600 6-Core Processor               |       6 |                  2 |
 | AMD Ryzen 5 8600G w/ Radeon 760M Graphics       |       6 |                  2 |
 | AMD Ryzen 5 PRO 2600 Six-Core Processor         |       6 |                  2 |
 | AMD Ryzen 7 1700 Eight-Core Processor           |       8 |                  2 |
 | AMD Ryzen 7 1700X Eight-Core Processor          |       8 |                  2 |
 | AMD Ryzen 7 3700X 8-Core Processor              |      16 |                  1 |
 | AMD Ryzen 7 5700G with Radeon Graphics          |       8 |                  2 |
 | AMD Ryzen 7 5700X 8-Core Processor              |       8 |                  2 |
 | AMD Ryzen 7 5800X 8-Core Processor              |       8 |                  2 |
 | AMD Ryzen 7 7700 8-Core Processor               |       8 |                  2 |
 | AMD Ryzen 7 PRO 3700 8-Core Processor           |       8 |                  2 |
 | AMD Ryzen 9 3900X 12-Core Processor             |      12 |                  2 |
 | Ryzen 9 5900X                                   |      12 |                  2 |
 | AMD Ryzen 9 5950X 16-Core Processor             |      16 |                  2 |
 | AMD Ryzen 9 7900 12-Core Processor              |      12 |                  2 |
 | AMD Ryzen 9 7950X 16-Core Processor             |      16 |                  2 |
 | AMD Ryzen 9 7950X3D 16-Core Processor           |      16 |                  2 |
 | AMD Ryzen 9 9900X 12-Core Processor             |      12 |                  2 |
 | AMD Ryzen 9 9950X 16-Core Processor             |      16 |                  2 |
 | AMD Ryzen Threadripper 1900X 8-Core Processor   |       8 |                  2 |
 | AMD Ryzen Threadripper 1920X 12-Core Processor  |      12 |                  2 |
 | AMD Ryzen Threadripper 1950X 16-Core Processor  |      16 |                  2 |
 | AMD Ryzen Threadripper 2920X 12-Core Processor  |      12 |                  2 |
 | AMD Ryzen Threadripper 2950X 16-Core Processor  |      16 |                  2 |
 | AMD Ryzen Threadripper 2970WX 24-Core Processor |      24 |                  1 |
 | AMD Ryzen Threadripper 2990WX 32-Core Processor |      32 |                  2 |
 | AMD Ryzen Threadripper 3960X 24-Core Processor  |      24 |                  2 |
 | AMD Ryzen Threadripper 7960X 24-Cores           |      24 |                  2 |
 | AMD Ryzen Threadripper 9960X 24-Cores           |      24 |                  2 |
 | Ryzen Threadripper PRO 3955WX                   |      16 |                  2 |
 | AMD Ryzen Threadripper PRO 3975WX 32-Cores      |      32 |                  2 |
 | AMD Ryzen Threadripper PRO 3995WX 64-Cores      |      64 |                  2 |
 | AMD Ryzen Threadripper PRO 5945WX 12-Cores      |      12 |                  2 |
 | AMD Ryzen Threadripper PRO 5955WX 16-Cores      |      16 |                  2 |
 | AMD Ryzen Threadripper PRO 5965WX 24-Cores      |      24 |                  2 |
 | AMD Ryzen Threadripper PRO 5975WX 32-Cores      |      32 |                  2 |
 | AMD Ryzen Threadripper PRO 5995WX 64-Cores      |      18 |                  1 |
 | AMD Ryzen Threadripper PRO 7955WX 16-Cores      |      16 |                  2 |
 | AMD Ryzen Threadripper PRO 7965WX 24-Cores      |      24 |                  2 |
 | AMD Ryzen Threadripper PRO 7975WX 32-Cores      |      32 |                  2 |
 | AMD Ryzen Threadripper PRO 7985WX 64-Cores      |     112 |                  1 |
 | Common KVM processor                            |      28 |                  1 |
 | Genuine Intel(R) CPU @ 2.20GHz                  |      14 |                  2 |
 | Genuine Intel(R) CPU $0000%@                    |      24 |                  2 |
 | Intel Xeon E3-12xx v2 (Ivy Bridge)              |       1 |                  1 |
 | Intel Xeon Processor (Icelake)                  |      40 |                  2 |
 | Intel(R) Celeron(R) CPU G3900 @ 2.80GHz         |       2 |                  1 |
 | Intel(R) Celeron(R) G5905 CPU @ 3.50GHz         |       2 |                  1 |
 | Intel(R) Core(TM) i3-10100F CPU @ 3.60GHz       |       4 |                  2 |
 | Intel(R) Core(TM) i3-10105F CPU @ 3.70GHz       |       4 |                  2 |
 | Intel(R) Core(TM) i3-6100 CPU @ 3.70GHz         |       2 |                  2 |
 | Intel(R) Core(TM) i3-9100F CPU @ 3.60GHz        |       4 |                  1 |
 | Intel(R) Core(TM) i5-10400 CPU @ 2.90GHz        |       6 |                  2 |
 | Intel(R) Core(TM) i5-10400F CPU @ 2.90GHz       |       6 |                  2 |
 | Intel(R) Core(TM) i5-10600 CPU @ 3.30GHz        |       6 |                  2 |
 | Intel(R) Core(TM) i5-14500                      |      14 |                  2 |
 | Intel(R) Core(TM) i5-14600K                     |      14 |                  2 |
 | Intel(R) Core(TM) i5-14600KF                    |      14 |                  2 |
 | Intel(R) Core(TM) i5-4570 CPU @ 3.20GHz         |       4 |                  1 |
 | Intel(R) Core(TM) i5-6400 CPU @ 2.70GHz         |       4 |                  1 |
 | Intel(R) Core(TM) i5-6500 CPU @ 3.20GHz         |       4 |                  1 |
 | Intel(R) Core(TM) i5-7400 CPU @ 3.00GHz         |       4 |                  1 |
 | Intel(R) Core(TM) i5-9400F CPU @ 2.90GHz        |       6 |                  1 |
 | Intel(R) Core(TM) i7-10700F CPU @ 2.90GHz       |       8 |                  2 |
 | Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz       |       8 |                  2 |
 | Intel(R) Core(TM) i7-3770 CPU @ 3.40GHz         |       4 |                  2 |
 | Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz         |       4 |                  2 |
 | Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz         |       4 |                  2 |
 | Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz        |       4 |                  2 |
 | Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz        |       6 |                  2 |
 | Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz        |       4 |                  2 |
 | Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz         |       6 |                  2 |
 | Intel(R) Core(TM) i7-9700 CPU @ 3.00GHz         |       8 |                  1 |
 | Intel(R) Core(TM) i9-10940X CPU @ 3.30GHz       |      14 |                  2 |
 | Intel(R) Core(TM) i9-14900K                     |      24 |                  1 |
 | Intel(R) Core(TM) Ultra 5 245K                  |       1 |                  1 |
 | Intel(R) Pentium(R) CPU G3260 @ 3.30GHz         |       2 |                  1 |
 | Intel(R) Pentium(R) CPU G4560 @ 3.50GHz         |       2 |                  2 |
 | Intel(R) Xeon(R) 6732P                          |      32 |                  2 |
 | Intel(R) Xeon(R) 6747P                          |      48 |                  2 |
 | Intel(R) Xeon(R) 6767P                          |      64 |                  2 |
 | Intel(R) Xeon(R) 6787P                          |      86 |                  1 |
 | Intel(R) Xeon(R) 6952P                          |      96 |                  2 |
 | Intel(R) Xeon(R) 6960P                          |      72 |                  2 |
 | Intel(R) Xeon(R) Bronze 3204 CPU @ 1.90GHz      |       6 |                  1 |
 | Intel(R) Xeon(R) CPU           X5660  @ 2.80GHz |       6 |                  2 |
 | Intel(R) Xeon(R) CPU E3-1220 v3 @ 3.10GHz       |       4 |                  1 |
 | Intel(R) Xeon(R) CPU E3-1225 V2 @ 3.20GHz       |       4 |                  1 |
 | Intel(R) Xeon(R) CPU E5-1650 v4 @ 3.60GHz       |       6 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2603 v3 @ 1.60GHz       |       6 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2609 0 @ 2.40GHz        |       4 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2609 v3 @ 1.90GHz       |       1 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz       |       8 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2630 0 @ 2.30GHz        |       6 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2630 v2 @ 2.60GHz       |       6 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz       |       8 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz       |      10 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2637 v2 @ 3.50GHz       |       4 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2643 0 @ 3.30GHz        |       4 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2648L v3 @ 1.80GHz      |      12 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz       |      16 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2650 v3 @ 2.30GHz       |      10 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz       |      12 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2660 v2 @ 2.20GHz       |      10 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2667 v2 @ 3.30GHz       |       1 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2667 v3 @ 3.20GHz       |       8 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2667 v4 @ 3.20GHz       |       1 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz        |       8 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz       |      10 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2673 v4 @ 2.30GHz       |      20 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50GHz       |      12 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz       |      12 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz       |      14 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2683 v4 @ 2.10GHz       |      16 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2690 0 @ 2.90GHz        |       8 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz       |      14 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2695 v4 @ 2.10GHz       |      18 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2696 v3 @ 2.30GHz       |      18 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2696 v4 @ 2.20GHz       |      22 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2698 v3 @ 2.30GHz       |      16 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz       |      20 |                  2 |
 | Intel(R) Xeon(R) CPU E5-2699 v3 @ 2.30GHz       |       1 |                  1 |
 | Intel(R) Xeon(R) CPU E5-2699 v4 @ 2.20GHz       |      22 |                  2 |
 | Intel(R) Xeon(R) CPU E5-4667 v3 @ 2.00GHz       |      16 |                  2 |
 | Intel(R) Xeon(R) Gold 5118 CPU @ 2.30GHz        |      12 |                  2 |
 | Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz       |      20 |                  2 |
 | Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz       |      32 |                  1 |
 | Intel(R) Xeon(R) Gold 5318N CPU @ 2.10GHz       |      24 |                  2 |
 | Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz        |      26 |                  2 |
 | Intel(R) Xeon(R) Gold 5420+                     |      28 |                  2 |
 | Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz        |      16 |                  2 |
 | Intel(R) Xeon(R) Gold 6133 CPU @ 2.50GHz        |      40 |                  1 |
 | Intel(R) Xeon(R) Gold 6136 CPU @ 3.00GHz        |      12 |                  2 |
 | Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz        |      20 |                  2 |
 | Intel(R) Xeon(R) Gold 6150 CPU @ 2.70GHz        |      18 |                  2 |
 | Intel(R) Xeon(R) Gold 6226 CPU @ 2.70GHz        |      12 |                  2 |
 | Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz       |       8 |                  2 |
 | Intel(R) Xeon(R) Gold 6238R CPU @ 2.20GHz       |      28 |                  2 |
 | Intel(R) Xeon(R) Gold 6240R CPU @ 2.40GHz       |      24 |                  2 |
 | Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz       |      16 |                  1 |
 | Intel(R) Xeon(R) Gold 6252 CPU @ 2.10GHz        |      24 |                  1 |
 | Intel(R) Xeon(R) Gold 6266C CPU @ 3.00GHz       |      22 |                  2 |
 | Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz        |      24 |                  2 |
 | Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz        |      28 |                  2 |
 | Intel(R) Xeon(R) Gold 6448Y                     |      32 |                  2 |
 | INTEL(R) XEON(R) GOLD 6530                      |      32 |                  2 |
 | INTEL(R) XEON(R) GOLD 6548Y+                    |      32 |                  2 |
 | Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz    |      24 |                  2 |
 | Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz    |      24 |                  2 |
 | Intel(R) Xeon(R) Platinum 8171M CPU @ 2.60GHz   |      26 |                  2 |
 | Intel(R) Xeon(R) Platinum 8173M CPU @ 2.00GHz   |      28 |                  2 |
 | Intel(R) Xeon(R) Platinum 8176M CPU @ 2.10GHz   |      28 |                  2 |
 | Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz    |      28 |                  2 |
 | Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10GHz   |      36 |                  2 |
 | Intel(R) Xeon(R) Platinum 8352Y CPU @ 2.20GHz   |      32 |                  2 |
 | Intel(R) Xeon(R) Platinum 8452Y                 |      36 |                  2 |
 | Intel(R) Xeon(R) Platinum 8460Y+                |      40 |                  2 |
 | Intel(R) Xeon(R) Platinum 8462Y+                |      32 |                  2 |
 | Intel(R) Xeon(R) Platinum 8468                  |      48 |                  2 |
 | Intel(R) Xeon(R) Platinum 8468V                 |      44 |                  2 |
 | Intel(R) Xeon(R) Platinum 8470                  |      52 |                  2 |
 | Intel(R) Xeon(R) Platinum 8480+                 |      56 |                  2 |
 | Intel(R) Xeon(R) Platinum 8480C                 |      56 |                  2 |
 | Intel(R) Xeon(R) Platinum 8480CL                |      56 |                  2 |
 | INTEL(R) XEON(R) PLATINUM 8558                  |      48 |                  2 |
 | INTEL(R) XEON(R) PLATINUM 8568Y+                |      48 |                  2 |
 | INTEL(R) XEON(R) PLATINUM 8570                  |      56 |                  2 |
 | INTEL(R) XEON(R) PLATINUM 8592V                 |      64 |                  2 |
 | Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz      |      10 |                  2 |
 | Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz      |      10 |                  2 |
 | Intel(R) Xeon(R) Silver 4214 CPU @ 2.20GHz      |      24 |                  1 |
 | Intel(R) Xeon(R) Silver 4310T CPU @ 2.30GHz     |      10 |                  2 |
 | Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz      |      16 |                  2 |
 | Intel(R) Xeon(R) W-2223 CPU @ 3.60GHz           |       4 |                  2 |
 | Intel(R) Xeon(R) w5-2455X                       |      12 |                  2 |
 | Intel(R) Xeon(R) w7-3465X                       |      28 |                  2 |
 | QEMU Virtual CPU version 2.5+                   |      16 |                  1 |

references/video-resources.mdx

@@ -1,8 +1,9 @@
 ---
 title: "Video resources"
+description: "Browse community video tutorials covering Runpod setup, Stable Diffusion, text generation, file transfer, SSH, and Linux tools."
 ---
 
 ## Runpod Usage
 
 | Video Link                                                                                                | Topics Covered |
 | --------------------------------------------------------------------------------------------------------- | -------------- |
@@ -15,16 +16,16 @@
 
 | Video Link                                                                                                                      | Topics Covered                |
 | ------------------------------------------------------------------------------------------------------------------------------- | ----------------------------- |
 | [Generate Stable Diffusion Images FAST with Runpod](https://www.youtube.com/watch?v=susnjHSWFq0\&t=32s\&ab_channel=BillMeeks)   | StableDiffusion, Automatic111 |
 | [Generate Text On Images with DeepFloyd IF](https://www.youtube.com/watch?v=Px7Vv9WYl88\&t=2s\&ab_channel=BillMeeks)            | DeepFloyd                     |
 | [Remix Your Pics With Stable Diffusion and ControlNet](https://www.youtube.com/watch?v=BqdIdk9LU4w\&t=1s\&ab_channel=BillMeeks) | StableDiffusion, ControlNet   |
 | [Using Automatic1111 WebUI on Runpod](https://www.youtube.com/watch?v=R6HUQOtsVic\&ab_channel=OpenCVCourses)                    | WebUI, Automatic1111          |
 | [RUN TextGen AI WebUI LLM On Runpod & Colab!](https://www.youtube.com/watch?v=TP2yID7Ubr4\&ab_channel=Aitrepreneur)             | GoogleColab, TextGeneration   |
 | [Make Your Renders 10x Faster With Runpod](https://www.youtube.com/watch?v=sJ-Diy93TAg\&ab_channel=RahulAhire)                  | Rendering, Blender            |
 
 ***
 
 ## General/Generic Linux
 
 | Video Link                                                                                                                           | Topics Covered     |
 | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------ |

runpodctl/reference/runpodctl-billing.mdx

@@ -1,6 +1,7 @@
 ---
 title: "billing"
 sidebarTitle: "billing"
+description: "Use runpodctl to retrieve billing history for Pods, Serverless endpoints, and network volumes across configurable time ranges."
 ---
 
 View billing history for Pods, Serverless endpoints, and network volumes.
@@ -13,7 +14,7 @@
 
 ## Subcommands
 
 ### View Pod billing
 
 View billing history for Pods:
 
@@ -47,7 +48,7 @@
 Filter by specific GPU type.
 </ResponseField>
 
 ### View Serverless billing
 
 View billing history for Serverless endpoints:
 

runpodctl/reference/runpodctl-config.mdx

@@ -1,9 +1,10 @@
 ---
 title: "config"
 sidebarTitle: "config"
+description: "Configure runpodctl with your Runpod API key and API endpoint URL so the CLI can authenticate and manage account resources."
 ---
 
 Configure the Runpod CLI with your API credentials and API URL to enable programmatic access to your Runpod resources.
 
 <RequestExample>
 ```bash Command
@@ -13,7 +14,7 @@
 
 ## Example
 
 Configure the CLI with your API key:
 
 ```bash
 runpodctl config --apiKey "your-api-key-here"

runpodctl/reference/runpodctl-datacenter.mdx

@@ -1,6 +1,7 @@
 ---
 title: "datacenter"
 sidebarTitle: "datacenter"
+description: "Use runpodctl to list Runpod data centers, review their locations, and inspect current GPU availability from the command line."
 ---
 
 List available datacenters and their locations.

runpodctl/reference/runpodctl-doctor.mdx

@@ -1,9 +1,10 @@
 ---
 title: "doctor"
 sidebarTitle: "doctor"
+description: "Run runpodctl doctor to configure API and SSH credentials, verify the CLI setup, and diagnose common connection or configuration problems."
 ---
 
 Diagnose and fix CLI issues, including first-time setup for API keys and SSH configuration.
 
 <RequestExample>
 ```bash Command
@@ -19,17 +20,17 @@
 - Setting up SSH keys for Pod access
 - Verifying your configuration
 
 If you encounter issues with the CLI, running `doctor` can help diagnose and fix common problems.
 
 ## Example
 
 Run the doctor command to set up or troubleshoot your CLI:
 
 ```bash
 runpodctl doctor
 ```
 
 The command will interactively guide you through the setup process and report any issues it finds.
 
 ## Related commands
 

runpodctl/reference/runpodctl-gpu.mdx

@@ -1,6 +1,7 @@
 ---
 title: "gpu"
 sidebarTitle: "gpu"
+description: "Use runpodctl to list available GPU types, include unavailable options, and review hardware specifications from the command line."
 ---
 
 List available GPU types and their specifications.
@@ -13,9 +14,9 @@
 
 ## Subcommands
 
 ### List available GPUs
 
 List GPUs that are currently available:
 
 ```bash
 runpodctl gpu list
@@ -58,7 +59,7 @@
 ]
 ```
 
 ## Using GPU IDs
 
 When creating Pods or Serverless endpoints, use the GPU ID from the list with the `--gpu-id` flag:
 

runpodctl/reference/runpodctl-hub.mdx

@@ -1,6 +1,7 @@
 ---
 title: "hub"
 sidebarTitle: "hub"
+description: "Browse and search Runpod Hub repos with runpodctl, inspect repository details, and use Hub IDs to deploy Serverless endpoints."
 ---
 
 Browse and search the Runpod Hub marketplace to discover deployable repos. You can list popular repos, search by name, and get details for specific repos. Use Hub repo IDs with [`runpodctl serverless create --hub-id`](/runpodctl/reference/runpodctl-serverless) to deploy endpoints directly from the Hub.
@@ -122,7 +123,7 @@
 runpodctl hub get runpod-workers/worker-vllm
 ```
 
 ## Deploy from the Hub
 
 After finding a repo you want to deploy, use its listing ID with `serverless create`:
 

runpodctl/reference/runpodctl-network-volume.mdx

@@ -1,6 +1,7 @@
 ---
 title: "network-volume"
 sidebarTitle: "network-volume"
+description: "Use runpodctl to create, inspect, list, update, and remove persistent network volumes shared by Pods and Serverless endpoints."
 ---
 
 Manage network volumes for persistent shared storage across Pods and Serverless endpoints.

runpodctl/reference/runpodctl-pod.mdx

@@ -1,6 +1,7 @@
 ---
 title: "pod"
 sidebarTitle: "pod"
+description: "Use runpodctl to create, list, start, stop, inspect, and delete Pods from the command line with status and resource filters."
 ---
 
 Manage Pods, including creating, listing, starting, stopping, and deleting Pods.
@@ -11,9 +12,9 @@
 
 ## Subcommands
 
 ### List Pods
 
 List your Pods. By default, this command shows only running Pods (similar to `docker ps`):
 
 ```bash
 runpodctl pod list
@@ -70,7 +71,7 @@
 Filter by Pod name.
 </ResponseField>
 
 ### Get Pod details
 
 Get detailed information about a specific Pod, including SSH connection info:
 
@@ -78,7 +79,7 @@
 runpodctl pod get <pod-id>
 ```
 
 ### Create a Pod
 
 Create a new Pod from a template:
 
@@ -196,7 +197,7 @@
 Compliance settings for the Pod (e.g., regulatory requirements for data handling).
 </ResponseField>
 
 ### Start a Pod
 
 Start a stopped Pod:
 
@@ -204,7 +205,7 @@
 runpodctl pod start <pod-id>
 ```
 
 ### Stop a Pod
 
 Stop a running Pod:
 
@@ -212,7 +213,7 @@
 runpodctl pod stop <pod-id>
 ```
 
 ### Restart a Pod
 
 Restart a Pod:
 
@@ -220,7 +221,7 @@
 runpodctl pod restart <pod-id>
 ```
 
 ### Reset a Pod
 
 Reset a Pod to its initial state:
 
@@ -228,7 +229,7 @@
 runpodctl pod reset <pod-id>
 ```
 
 ### Update a Pod
 
 Update Pod configuration:
 
@@ -266,7 +267,7 @@
 New environment variables as a JSON object.
 </ResponseField>
 
 ### Delete a Pod
 
 Delete a Pod:
 

runpodctl/reference/runpodctl-receive.mdx

@@ -1,6 +1,7 @@
 ---
 title: "receive"
 sidebarTitle: "receive"
+description: "Receive files or folders through runpodctl using a secure peer-to-peer connection code shared by the sending machine."
 ---
 
 Receive files or folders sent from another machine using a secure peer-to-peer connection established with a connection code.

runpodctl/reference/runpodctl-registry.mdx

@@ -1,6 +1,7 @@
 ---
 title: "registry"
 sidebarTitle: "registry"
+description: "Use runpodctl to list, inspect, create, update, and remove credentials for private container registries used by Runpod workloads."
 ---
 
 Manage container registry authentications for private Docker images.

runpodctl/reference/runpodctl-send.mdx

@@ -1,6 +1,7 @@
 ---
 title: "send"
 sidebarTitle: "send"
+description: "Send files or folders from your machine to a Pod or another computer through runpodctl using a secure peer-to-peer connection."
 ---
 
 Transfer files or folders from your local machine to a Pod or another computer using a secure peer-to-peer connection.

runpodctl/reference/runpodctl-serverless.mdx

@@ -1,6 +1,7 @@
 ---
 title: "serverless"
 sidebarTitle: "serverless"
+description: "Use runpodctl to create, list, inspect, update, and delete Serverless endpoints, including Hub-based deployments and templates."
 ---
 
 import { VolumeDiskTooltip } from "/snippets/tooltips.jsx";
@@ -86,7 +87,7 @@
   --env MAX_TOKENS=4096
 ```
 
 When using `--hub-id`, GPU IDs and container disk size are automatically pulled from the Hub release config. You can override the GPU type with `--gpu-id`. Environment variables from the Hub release are included automatically, and you can override or add to them with `--env`.
 
 <Note>
 

runpodctl/reference/runpodctl-ssh.mdx

@@ -1,6 +1,7 @@
 ---
 title: "ssh"
 sidebarTitle: "ssh"
+description: "Use runpodctl to manage SSH keys and retrieve the connection command and key details required to access a Runpod Pod."
 ---
 
 Manage SSH keys and get SSH connection information for Pods.
@@ -13,9 +14,9 @@
 
 ## Subcommands
 
 ### Get SSH connection info
 
 Get SSH connection details for a Pod. This returns the SSH command and key information, but does not initiate an interactive session:
 
 ```bash
 runpodctl ssh info <pod-id>
@@ -38,7 +39,7 @@
 
 </Note>
 
 ### List SSH keys
 
 List all SSH keys associated with your account:
 
@@ -46,7 +47,7 @@
 runpodctl ssh list-keys
 ```
 
 ### Add an SSH key
 
 Add a new SSH key to your account:
 
@@ -68,7 +69,7 @@
 Path to a file containing the public key.
 </ResponseField>
 
 ### Remove an SSH key
 
 Remove an SSH key from your account by name or fingerprint:
 

runpodctl/reference/runpodctl-template.mdx

@@ -1,6 +1,7 @@
 ---
 title: "template"
 sidebarTitle: "template"
+description: "Use runpodctl to list, search, inspect, create, update, and delete reusable templates for Pods and Serverless endpoints."
 ---
 
 import { VolumeDiskTooltip } from "/snippets/tooltips.jsx";
@@ -94,7 +95,7 @@
 
 ### Get template details
 
 Get detailed information about a specific template, including README, environment variables, and exposed ports (with their labels):
 
 ```bash
 runpodctl template get <template-id>

runpodctl/reference/runpodctl-update.mdx

@@ -1,9 +1,10 @@
 ---
 title: "update"
 sidebarTitle: "update"
+description: "Update runpodctl to the latest release, verify the downloaded binary with its SHA-256 checksum, and confirm the installed version."
 ---
 
 Update `runpodctl` to the latest version to access new features and bug fixes.
 
 <RequestExample>
 ```bash Command
@@ -13,7 +14,7 @@
 
 ## Description
 
 The `update` command downloads the latest version of `runpodctl` and verifies it against its SHA-256 checksum before replacing the installed binary. Verification always runs. If it fails, the update aborts and leaves your existing binary in place. After updating, verify the installation with [`runpodctl version`](/runpodctl/reference/runpodctl-version).
 
 ## Related commands
 

runpodctl/reference/runpodctl-user.mdx

@@ -1,6 +1,7 @@
 ---
 title: "user"
 sidebarTitle: "user"
+description: "Use runpodctl to view your Runpod account email, credit balance, hourly spend, spending limit, and notification settings."
 ---
 
 View your account information and current balance.

runpodctl/reference/runpodctl-version.mdx

@@ -1,6 +1,7 @@
 ---
 title: "version"
 sidebarTitle: "version"
+description: "Use runpodctl to display the currently installed CLI version and confirm that an installation or update completed successfully."
 ---
 
 Display the current version of `runpodctl` installed on your system.

tips-and-tricks/tmux.mdx

@@ -1,7 +1,8 @@
 ---
 title: "Using TMUX for persistent sessions"
 sidebarTitle: "Persistent sessions with TMUX"
 icon: "square-terminal"
+description: "Use TMUX on a Runpod Pod to keep terminal sessions and long-running workloads active through disconnects or JupyterLab interruptions."
 ---
 
 TMUX is a terminal multiplexer that creates persistent terminal sessions on your Pod.