# Registry > Manage recipe registries and create inference recipes Use when this capability is needed. - Skill: `tomevault-io/registry-2` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/registry-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/registry-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/registry-2 --- Provides complete reference for managing sparkrun recipe registries, browsing and searching recipes, managing benchmark profiles, validating recipes, and understanding the recipe file format for NVIDIA DGX Spark inference workloads. - User wants to add, remove, enable, disable, or update recipe registries - User wants to browse or search for available recipes - User wants to create or edit a recipe YAML file - User wants to validate a recipe or check VRAM requirements - User wants to browse or inspect benchmark profiles - User asks about recipe format or fields - User wants to run, stop, or monitor workloads -- use the run skill instead - User wants to install sparkrun or set up clusters -- use the setup skill instead ## Registry Commands ```bash # List configured registries (enabled only by default) sparkrun registry list sparkrun registry list --show-disabled sparkrun registry list --only-show-visible # Add registries from a git repo's .sparkrun/registry.yaml manifest sparkrun registry add # Remove a registry sparkrun registry remove # Enable a disabled registry sparkrun registry enable # Disable a registry (recipes will not appear in searches) sparkrun registry disable # Update all enabled registries from git (fetches latest recipes) sparkrun registry update # Update a specific registry sparkrun registry update # Update sparkrun + registries in one command sparkrun update ``` ## Browsing Recipes ```bash # List all recipes across all registries (no filter) sparkrun list # List with filters sparkrun list --all # include hidden registry recipes sparkrun list --registry # filter by registry sparkrun list --runtime vllm # filter by runtime (vllm, sglang, llama-cpp) sparkrun list # filter by name # Search for recipes by name, model, runtime, or description (contains-match) sparkrun recipe search sparkrun recipe search --registry --runtime sglang # Inspect a specific known recipe (by exact name or file path) sparkrun recipe show [--tp N] # Export a normalized recipe sparkrun recipe export sparkrun recipe export --json sparkrun recipe export --save out.yaml ``` Use `sparkrun recipe search` as the first attempt when looking for a particular recipe. Use `sparkrun recipe show` when given a specific recipe name or file -- it may not appear in search results. Recipe names support `@registry/name` syntax for explicit registry selection (e.g. `@spark-arena/qwen3-1.7b-vllm`). ## Benchmark Profiles ```bash # List available benchmark profiles across registries sparkrun registry list-benchmark-profiles sparkrun registry list-benchmark-profiles --registry sparkrun registry list-benchmark-profiles --all # include hidden registries # Show detailed benchmark profile information sparkrun registry show-benchmark-profile ``` ## Validating Recipes ```bash # Check a recipe for issues sparkrun recipe validate # Estimate VRAM usage with overrides sparkrun recipe vram [--tp N] [--max-model-len 32768] [--gpu-mem 0.9] ``` ## Recipe File Format Recipes are YAML files defining an inference workload: ```yaml model: org/model-name # HuggingFace model ID (required) runtime: vllm | sglang | llama-cpp # Inference runtime (required) container: registry/image:tag # Docker image (required) min_nodes: 1 # Minimum hosts needed max_nodes: 4 # Maximum hosts (optional) model_revision: abc123 # Pin to specific HF revision (optional) metadata: description: Human-readable description maintainer: name model_params: 7B model_dtype: fp16 category: general # Recipe category defaults: port: 8000 host: 0.0.0.0 tensor_parallel: 2 pipeline_parallel: 1 gpu_memory_utilization: 0.9 max_model_len: 32768 served_model_name: my-model tokenizer_path: org/base-model # Required for GGUF models on SGLang # Optional: explicit command template (overrides auto-generation) command: | python3 -m vllm.entrypoints.openai.api_server \ --model {model} \ --tensor-parallel-size {tensor_parallel} \ --port {port} # Optional: environment variables passed to the container env: NCCL_DEBUG: INFO # Optional: post-launch hooks post_exec: # Commands to run inside the head container - "echo 'Model loaded'" post_commands: # Commands to run on the control machine - "curl http://{head_ip}:{port}/v1/models" stop_after_post: false # Stop workload after post hooks (default: false) ``` **Key fields:** - `{placeholder}` in `command:` templates are substituted from defaults + CLI overrides - `model_revision` pins downloads to a specific HuggingFace commit/tag - `tokenizer_path` is required for GGUF models on SGLang (points to base non-GGUF model) - `min_nodes` / `max_nodes` control cluster size validation - Shell variable references like `${HF_TOKEN}` in `env:` are expanded from the control machine's environment - `post_exec` and `post_commands` run after the server is healthy (port listening + /v1/models check) - `pipeline_parallel` enables pipeline parallelism (total nodes = TP * PP) Use the `sparkrun_exec` tool for all sparkrun commands. - Run `sparkrun registry update` or `sparkrun update` periodically to get the latest community recipes - Use `sparkrun recipe validate` before publishing custom recipes - Use `sparkrun recipe vram` to check if a model fits on DGX Spark before trying to run it - When creating GGUF + SGLang recipes, always set `tokenizer_path` in defaults - Custom command templates should include all relevant `{placeholder}` references to pick up defaults and CLI overrides - Registries are cached at `~/.cache/sparkrun/registries/` and updated with `sparkrun registry update` - sparkrun ships with built-in recipes; additional registries point to any git repo containing `.yaml` recipe files - Use `sparkrun registry list-benchmark-profiles` to discover available benchmark profiles from registries - Recipe names support `@registry/name` syntax for explicit registry selection - Use `sparkrun recipe export` to get a normalized view of a recipe (useful for debugging) Task: {{ARGUMENTS}} --- > Source: [spark-arena/sparkrun](https://github.com/spark-arena/sparkrun) — distributed by [TomeVault](https://tomevault.io).