{
  "version": "3",
  "templates": [
    {
      "id": 1,
      "type": 3,
      "title": "Ollama",
      "description": "Local LLM inference engine supporting Llama, Mistral, Qwen, Gemma, Phi and 100+ models with GPU acceleration",
      "note": "Requires NVIDIA GPU with Docker GPU runtime configured. Pull models after deployment with: <code>docker exec ollama ollama pull llama3.1</code>",
      "categories": ["ai", "llm", "inference"],
      "platform": "linux",
      "logo": "https://ollama.com/public/ollama.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/ollama/docker-compose.yml"
      },
      "env": [
        {
          "name": "OLLAMA_PORT",
          "label": "Ollama API port",
          "default": "11434"
        },
        {
          "name": "OLLAMA_NUM_PARALLEL",
          "label": "Max parallel requests",
          "default": "4"
        },
        {
          "name": "OLLAMA_MAX_LOADED_MODELS",
          "label": "Max models loaded in VRAM",
          "default": "2"
        }
      ]
    },
    {
      "id": 2,
      "type": 3,
      "title": "Open WebUI + Ollama",
      "description": "Full-featured ChatGPT-like web interface bundled with Ollama backend for local LLM inference",
      "note": "Access the web UI at the configured port. First user to register becomes admin. Requires NVIDIA GPU.",
      "categories": ["ai", "llm", "chat-ui"],
      "platform": "linux",
      "logo": "https://docs.openwebui.com/img/logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/open-webui/docker-compose.yml"
      },
      "env": [
        {
          "name": "OPEN_WEBUI_PORT",
          "label": "Web UI port",
          "default": "3000"
        },
        {
          "name": "OLLAMA_PORT",
          "label": "Ollama API port",
          "default": "11434"
        },
        {
          "name": "WEBUI_SECRET_KEY",
          "label": "Secret key for sessions",
          "default": "changeme"
        },
        {
          "name": "ENABLE_SIGNUP",
          "label": "Allow user registration",
          "default": "true"
        }
      ]
    },
    {
      "id": 3,
      "type": 3,
      "title": "LocalAI",
      "description": "Drop-in OpenAI API compatible replacement. Run LLMs, generate images, audio locally with GPU acceleration",
      "note": "Exposes an OpenAI-compatible API at /v1/. Models can be loaded via the API or placed in the models volume.",
      "categories": ["ai", "llm", "openai-api"],
      "platform": "linux",
      "logo": "https://localai.io/logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/localai/docker-compose.yml"
      },
      "env": [
        {
          "name": "LOCALAI_PORT",
          "label": "API port",
          "default": "8080"
        },
        {
          "name": "THREADS",
          "label": "CPU threads for inference",
          "default": "4"
        },
        {
          "name": "CONTEXT_SIZE",
          "label": "Default context window size",
          "default": "4096"
        }
      ]
    },
    {
      "id": 4,
      "type": 3,
      "title": "vLLM",
      "description": "High-throughput LLM serving engine with PagedAttention, continuous batching, and OpenAI-compatible API",
      "note": "Requires NVIDIA GPU with sufficient VRAM for the chosen model. HuggingFace token needed for gated models.",
      "categories": ["ai", "llm", "inference", "high-performance"],
      "platform": "linux",
      "logo": "https://docs.vllm.ai/en/latest/_static/vllm-logo-text-light.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/vllm/docker-compose.yml"
      },
      "env": [
        {
          "name": "VLLM_PORT",
          "label": "API port",
          "default": "8000"
        },
        {
          "name": "MODEL_NAME",
          "label": "HuggingFace model ID",
          "default": "meta-llama/Llama-3.1-8B-Instruct"
        },
        {
          "name": "HF_TOKEN",
          "label": "HuggingFace access token"
        },
        {
          "name": "MAX_MODEL_LEN",
          "label": "Max sequence length",
          "default": "4096"
        },
        {
          "name": "GPU_MEM_UTIL",
          "label": "GPU memory utilization (0-1)",
          "default": "0.90"
        },
        {
          "name": "TENSOR_PARALLEL",
          "label": "Tensor parallel GPU count",
          "default": "1"
        }
      ]
    },
    {
      "id": 5,
      "type": 3,
      "title": "Text Generation WebUI",
      "description": "Comprehensive web UI for running LLMs locally (oobabooga). Supports GGUF, GPTQ, AWQ, EXL2, and HF formats",
      "note": "Requires NVIDIA GPU. Models should be placed in the models volume. Supports extensions for RAG, TTS, and more.",
      "categories": ["ai", "llm", "chat-ui"],
      "platform": "linux",
      "logo": "https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/docs/logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/text-generation-webui/docker-compose.yml"
      },
      "env": [
        {
          "name": "WEBUI_PORT",
          "label": "Web UI port",
          "default": "7860"
        },
        {
          "name": "API_PORT",
          "label": "API port",
          "default": "5000"
        },
        {
          "name": "STREAM_PORT",
          "label": "Streaming API port",
          "default": "5005"
        },
        {
          "name": "EXTRA_LAUNCH_ARGS",
          "label": "Extra launch arguments",
          "default": "--listen --api"
        }
      ]
    },
    {
      "id": 6,
      "type": 3,
      "title": "LiteLLM Proxy",
      "description": "Unified LLM API gateway supporting 100+ providers (OpenAI, Anthropic, Ollama, vLLM, etc.) with spend tracking and load balancing",
      "note": "Configure models in /app/config/litellm_config.yaml after deployment. Includes PostgreSQL for usage tracking.",
      "categories": ["ai", "llm", "api-gateway", "proxy"],
      "platform": "linux",
      "logo": "https://litellm.ai/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/litellm/docker-compose.yml"
      },
      "env": [
        {
          "name": "LITELLM_PORT",
          "label": "Proxy API port",
          "default": "4000"
        },
        {
          "name": "LITELLM_MASTER_KEY",
          "label": "Master API key",
          "default": "sk-master-key"
        },
        {
          "name": "PG_USER",
          "label": "PostgreSQL user",
          "default": "litellm"
        },
        {
          "name": "PG_PASSWORD",
          "label": "PostgreSQL password",
          "default": "litellm"
        }
      ]
    },
    {
      "id": 7,
      "type": 3,
      "title": "ComfyUI",
      "description": "Node-based Stable Diffusion workflow engine for image and video generation with GPU acceleration",
      "note": "Requires NVIDIA GPU. Access the node editor at the configured port. Models go in the models volume.",
      "categories": ["ai", "image-generation", "stable-diffusion"],
      "platform": "linux",
      "logo": "https://raw.githubusercontent.com/comfyanonymous/ComfyUI/master/web/assets/comfyui-logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/comfyui/docker-compose.yml"
      },
      "env": [
        {
          "name": "COMFYUI_PORT",
          "label": "Web UI port",
          "default": "8188"
        },
        {
          "name": "CLI_ARGS",
          "label": "Launch arguments",
          "default": "--listen 0.0.0.0 --port 8188"
        }
      ]
    },
    {
      "id": 8,
      "type": 3,
      "title": "Stable Diffusion WebUI",
      "description": "AUTOMATIC1111 web interface for Stable Diffusion image generation with extensive extension ecosystem",
      "note": "Requires NVIDIA GPU with 8GB+ VRAM. First startup downloads the base model and may take several minutes.",
      "categories": ["ai", "image-generation", "stable-diffusion"],
      "platform": "linux",
      "logo": "https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/html/logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/stable-diffusion-webui/docker-compose.yml"
      },
      "env": [
        {
          "name": "SD_PORT",
          "label": "Web UI port",
          "default": "7860"
        },
        {
          "name": "CLI_ARGS",
          "label": "Launch arguments",
          "default": "--listen --api --xformers"
        }
      ]
    },
    {
      "id": 9,
      "type": 3,
      "title": "Langflow",
      "description": "Visual framework for building multi-agent and RAG applications. Drag-and-drop LLM pipeline builder",
      "note": "Access the visual editor at the configured port. Connect to Ollama, OpenAI, or any LLM backend.",
      "categories": ["ai", "agents", "rag", "workflows"],
      "platform": "linux",
      "logo": "https://avatars.githubusercontent.com/u/128686189",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/langflow/docker-compose.yml"
      },
      "env": [
        {
          "name": "LANGFLOW_PORT",
          "label": "Web UI port",
          "default": "7860"
        },
        {
          "name": "AUTO_LOGIN",
          "label": "Skip login screen",
          "default": "true"
        }
      ]
    },
    {
      "id": 10,
      "type": 3,
      "title": "Flowise",
      "description": "Drag-and-drop LLM orchestration tool. Build chatbots, agents, and RAG pipelines without coding",
      "note": "Default credentials are admin/changeme. Connect to any OpenAI-compatible API backend.",
      "categories": ["ai", "agents", "rag", "chatbots"],
      "platform": "linux",
      "logo": "https://flowiseai.com/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/flowise/docker-compose.yml"
      },
      "env": [
        {
          "name": "FLOWISE_PORT",
          "label": "Web UI port",
          "default": "3000"
        },
        {
          "name": "FLOWISE_USERNAME",
          "label": "Admin username",
          "default": "admin"
        },
        {
          "name": "FLOWISE_PASSWORD",
          "label": "Admin password",
          "default": "changeme"
        }
      ]
    },
    {
      "id": 11,
      "type": 3,
      "title": "n8n (AI-Enabled)",
      "description": "Workflow automation platform with built-in AI agent nodes, LLM chains, and vector store integrations",
      "note": "AI features include: AI Agent nodes, LLM Chain, Document Loaders, Vector Stores, Text Splitters, and Memory nodes.",
      "categories": ["ai", "automation", "workflows", "agents"],
      "platform": "linux",
      "logo": "https://n8n.io/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/n8n-ai/docker-compose.yml"
      },
      "env": [
        {
          "name": "N8N_PORT",
          "label": "Web UI port",
          "default": "5678"
        },
        {
          "name": "N8N_USER",
          "label": "Admin username",
          "default": "admin"
        },
        {
          "name": "N8N_PASSWORD",
          "label": "Admin password",
          "default": "changeme"
        },
        {
          "name": "WEBHOOK_URL",
          "label": "External webhook URL",
          "default": "http://localhost:5678/"
        }
      ]
    },
    {
      "id": 12,
      "type": 3,
      "title": "Qdrant",
      "description": "High-performance vector similarity search engine for RAG, semantic search, and AI applications",
      "note": "REST API on port 6333, gRPC on 6334. Supports filtering, payload indexing, and distributed mode.",
      "categories": ["ai", "vector-database", "rag", "embeddings"],
      "platform": "linux",
      "logo": "https://qdrant.tech/images/logo_with_text.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/qdrant/docker-compose.yml"
      },
      "env": [
        {
          "name": "QDRANT_HTTP_PORT",
          "label": "REST API port",
          "default": "6333"
        },
        {
          "name": "QDRANT_GRPC_PORT",
          "label": "gRPC port",
          "default": "6334"
        },
        {
          "name": "QDRANT_API_KEY",
          "label": "API key (optional)"
        }
      ]
    },
    {
      "id": 13,
      "type": 3,
      "title": "ChromaDB",
      "description": "AI-native open-source embedding database. The easiest vector store to get started with for RAG applications",
      "note": "Persistent storage enabled by default. Compatible with LangChain, LlamaIndex, and all major AI frameworks.",
      "categories": ["ai", "vector-database", "rag", "embeddings"],
      "platform": "linux",
      "logo": "https://www.trychroma.com/chroma-logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/chromadb/docker-compose.yml"
      },
      "env": [
        {
          "name": "CHROMA_PORT",
          "label": "API port",
          "default": "8000"
        },
        {
          "name": "CHROMA_TOKEN",
          "label": "Auth token (optional)"
        },
        {
          "name": "TELEMETRY",
          "label": "Anonymous telemetry",
          "default": "FALSE"
        }
      ]
    },
    {
      "id": 14,
      "type": 3,
      "title": "Weaviate",
      "description": "AI-native vector database with built-in vectorization modules and hybrid search capabilities",
      "note": "Supports text2vec-transformers, generative-openai, and many other modules. Configure modules via environment variables.",
      "categories": ["ai", "vector-database", "rag", "search"],
      "platform": "linux",
      "logo": "https://weaviate.io/img/site/weaviate-logo-light.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/weaviate/docker-compose.yml"
      },
      "env": [
        {
          "name": "WEAVIATE_HTTP_PORT",
          "label": "HTTP API port",
          "default": "8080"
        },
        {
          "name": "WEAVIATE_GRPC_PORT",
          "label": "gRPC port",
          "default": "50051"
        },
        {
          "name": "VECTORIZER",
          "label": "Default vectorizer module",
          "default": "none"
        },
        {
          "name": "MODULES",
          "label": "Enabled modules",
          "default": "text2vec-transformers,generative-openai"
        },
        {
          "name": "ANON_ACCESS",
          "label": "Anonymous access enabled",
          "default": "true"
        }
      ]
    },
    {
      "id": 15,
      "type": 3,
      "title": "MLflow",
      "description": "Open-source ML lifecycle platform — experiment tracking, model registry, and model serving",
      "note": "Access the tracking UI at the configured port. Uses SQLite backend by default — switch to PostgreSQL for production.",
      "categories": ["ai", "mlops", "experiment-tracking", "model-registry"],
      "platform": "linux",
      "logo": "https://mlflow.org/img/mlflow-black.svg",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/mlflow/docker-compose.yml"
      },
      "env": [
        {
          "name": "MLFLOW_PORT",
          "label": "Tracking UI port",
          "default": "5000"
        }
      ]
    },
    {
      "id": 16,
      "type": 3,
      "title": "Label Studio",
      "description": "Multi-type data labeling and annotation platform for training ML and AI models",
      "note": "Supports image, text, audio, video, and time-series annotation. Export to all major ML formats.",
      "categories": ["ai", "mlops", "data-labeling", "annotation"],
      "platform": "linux",
      "logo": "https://labelstud.io/images/ls-logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/label-studio/docker-compose.yml"
      },
      "env": [
        {
          "name": "LS_PORT",
          "label": "Web UI port",
          "default": "8080"
        },
        {
          "name": "LS_USER",
          "label": "Admin email",
          "default": "admin@example.com"
        },
        {
          "name": "LS_PASSWORD",
          "label": "Admin password",
          "default": "changeme"
        }
      ]
    },
    {
      "id": 17,
      "type": 3,
      "title": "Jupyter (GPU / PyTorch)",
      "description": "GPU-accelerated Jupyter Lab with PyTorch, CUDA, and data science libraries pre-installed",
      "note": "Requires NVIDIA GPU. Access with the configured token. Workspace persists in the work volume.",
      "categories": ["ai", "ml-development", "notebooks", "pytorch"],
      "platform": "linux",
      "logo": "https://jupyter.org/assets/homepage/main-logo.svg",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/jupyter-gpu/docker-compose.yml"
      },
      "env": [
        {
          "name": "JUPYTER_PORT",
          "label": "Jupyter Lab port",
          "default": "8888"
        },
        {
          "name": "JUPYTER_TOKEN",
          "label": "Access token",
          "default": "changeme"
        },
        {
          "name": "GRANT_SUDO",
          "label": "Allow sudo in notebooks",
          "default": "yes"
        }
      ]
    },
    {
      "id": 18,
      "type": 3,
      "title": "Whisper ASR",
      "description": "OpenAI Whisper speech-to-text API server with GPU acceleration. Supports transcription and translation",
      "note": "Requires NVIDIA GPU. API documentation available at /docs. Supports models: tiny, base, small, medium, large-v3.",
      "categories": ["ai", "speech-to-text", "transcription", "audio"],
      "platform": "linux",
      "logo": "https://upload.wikimedia.org/wikipedia/commons/0/04/ChatGPT_logo.svg",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/whisper/docker-compose.yml"
      },
      "env": [
        {
          "name": "WHISPER_PORT",
          "label": "API port",
          "default": "9000"
        },
        {
          "name": "ASR_MODEL",
          "label": "Whisper model size",
          "description": "Options: tiny, base, small, medium, large-v3",
          "default": "base"
        },
        {
          "name": "ASR_ENGINE",
          "label": "ASR engine",
          "default": "openai_whisper"
        }
      ]
    },
    {
      "id": 19,
      "type": 3,
      "title": "NVIDIA Triton Inference Server",
      "description": "Production-grade inference serving for any AI model — supports TensorRT, ONNX, PyTorch, TensorFlow, vLLM, and Python backends with dynamic batching, model ensembles, and multi-GPU scheduling",
      "note": "Requires NVIDIA GPU. Place model repositories in the models volume following Triton's <a href=\"https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_repository.html\" target=\"_blank\">model repository layout</a>. Health check at /v2/health/ready.",
      "categories": ["ai", "inference", "edge", "production", "nvidia"],
      "platform": "linux",
      "logo": "https://developer.nvidia.com/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/triton/docker-compose.yml"
      },
      "env": [
        {
          "name": "HTTP_PORT",
          "label": "HTTP inference port",
          "default": "8000"
        },
        {
          "name": "GRPC_PORT",
          "label": "gRPC inference port",
          "default": "8001"
        },
        {
          "name": "METRICS_PORT",
          "label": "Prometheus metrics port",
          "default": "8002"
        },
        {
          "name": "TRITON_VERSION",
          "label": "Triton version tag",
          "default": "24.08"
        },
        {
          "name": "MODEL_CONTROL",
          "label": "Model control mode (none, poll, explicit)",
          "default": "poll"
        },
        {
          "name": "POLL_INTERVAL",
          "label": "Model repository poll interval (seconds)",
          "default": "30"
        },
        {
          "name": "SHM_SIZE",
          "label": "Shared memory size",
          "default": "1g"
        }
      ]
    },
    {
      "id": 20,
      "type": 3,
      "title": "ONNX Runtime Server",
      "description": "Lightweight cross-platform inference server for ONNX models. Supports GPU and CPU-only profiles for edge deployment on resource-constrained nodes",
      "note": "Use <code>docker compose --profile gpu up</code> for GPU nodes or <code>--profile edge up</code> for CPU-only edge nodes. Place your .onnx model file in the models volume.",
      "categories": ["ai", "inference", "edge", "lightweight", "onnx"],
      "platform": "linux",
      "logo": "https://onnxruntime.ai/images/icons/ONNX-Runtime-logo.svg",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/onnx-runtime/docker-compose.yml"
      },
      "env": [
        {
          "name": "HTTP_PORT",
          "label": "HTTP port",
          "default": "8001"
        },
        {
          "name": "GRPC_PORT",
          "label": "gRPC port",
          "default": "50051"
        },
        {
          "name": "MODEL_FILE",
          "label": "Model filename in /models",
          "default": "model.onnx"
        },
        {
          "name": "NUM_THREADS",
          "label": "Inference threads",
          "default": "4"
        },
        {
          "name": "CPU_LIMIT",
          "label": "CPU core limit (edge profile)",
          "default": "2.0"
        },
        {
          "name": "MEM_LIMIT",
          "label": "Memory limit (edge profile)",
          "default": "2G"
        }
      ]
    },
    {
      "id": 21,
      "type": 3,
      "title": "NVIDIA DeepStream",
      "description": "GPU-accelerated video analytics and computer vision pipeline for industrial inspection, anomaly detection, and smart factory applications with Triton backend",
      "note": "Requires NVIDIA GPU with video decode capabilities. For camera access on edge devices, set PRIVILEGED=true. Supports RTSP output on port 8554.",
      "categories": ["ai", "computer-vision", "industrial", "edge", "video-analytics"],
      "platform": "linux",
      "logo": "https://developer.nvidia.com/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/deepstream/docker-compose.yml"
      },
      "env": [
        {
          "name": "RTSP_PORT",
          "label": "RTSP output port",
          "default": "8554"
        },
        {
          "name": "REST_PORT",
          "label": "REST API port",
          "default": "9000"
        },
        {
          "name": "DS_VERSION",
          "label": "DeepStream version",
          "default": "7.1"
        },
        {
          "name": "SHM_SIZE",
          "label": "Shared memory size",
          "default": "2g"
        },
        {
          "name": "PRIVILEGED",
          "label": "Privileged mode (for device access)",
          "default": "false"
        }
      ]
    },
    {
      "id": 22,
      "type": 3,
      "title": "Ray Cluster (GPU)",
      "description": "Distributed compute cluster for LLM fine-tuning, distributed training, hyperparameter tuning, and scalable inference with Ray Serve. Head + configurable worker nodes",
      "note": "Requires NVIDIA GPU on all nodes. Scale workers with NUM_WORKERS. Dashboard accessible at the configured port. Includes Ray Train, Tune, Serve, and Data.",
      "categories": ["ai", "distributed-training", "fine-tuning", "inference", "cluster"],
      "platform": "linux",
      "logo": "https://docs.ray.io/en/latest/_static/ray_logo.png",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/ray-cluster/docker-compose.yml"
      },
      "env": [
        {
          "name": "DASHBOARD_PORT",
          "label": "Ray Dashboard port",
          "default": "8265"
        },
        {
          "name": "SERVE_PORT",
          "label": "Ray Serve port",
          "default": "8000"
        },
        {
          "name": "RAY_VERSION",
          "label": "Ray version",
          "default": "2.40.0"
        },
        {
          "name": "NUM_WORKERS",
          "label": "Number of worker nodes",
          "default": "1"
        },
        {
          "name": "HEAD_GPUS",
          "label": "GPUs on head node",
          "default": "1"
        },
        {
          "name": "WORKER_GPUS",
          "label": "GPUs per worker",
          "default": "1"
        },
        {
          "name": "WORKER_CPUS",
          "label": "CPUs per worker",
          "default": "4"
        },
        {
          "name": "SHM_SIZE",
          "label": "Shared memory per node",
          "default": "8g"
        }
      ]
    },
    {
      "id": 23,
      "type": 3,
      "title": "Prefect (ML Pipeline Orchestration)",
      "description": "Governed ML pipeline orchestration platform with scheduling, retries, audit logging, and role-based access. Includes server, worker, and PostgreSQL backend",
      "note": "Access the Prefect UI at the configured port. Create flows in Python and register them against this server. Worker uses Docker execution for isolation.",
      "categories": ["ai", "mlops", "pipelines", "governance", "orchestration"],
      "platform": "linux",
      "logo": "https://www.prefect.io/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/prefect/docker-compose.yml"
      },
      "env": [
        {
          "name": "PREFECT_PORT",
          "label": "Prefect UI port",
          "default": "4200"
        },
        {
          "name": "PREFECT_VERSION",
          "label": "Prefect version",
          "default": "3-latest"
        },
        {
          "name": "PG_USER",
          "label": "PostgreSQL user",
          "default": "prefect"
        },
        {
          "name": "PG_PASSWORD",
          "label": "PostgreSQL password",
          "default": "prefect"
        },
        {
          "name": "ANALYTICS",
          "label": "Enable analytics",
          "default": "false"
        }
      ]
    },
    {
      "id": 24,
      "type": 3,
      "title": "BentoML",
      "description": "Unified model serving framework for packaging, deploying, and managing ML models as production-ready API endpoints with GPU support",
      "note": "Requires NVIDIA GPU. Build Bentos (model packages) and serve them through this runtime. Prometheus metrics on port 3001.",
      "categories": ["ai", "model-serving", "inference", "mlops"],
      "platform": "linux",
      "logo": "https://docs.bentoml.com/en/latest/_static/img/logo.svg",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/bentoml/docker-compose.yml"
      },
      "env": [
        {
          "name": "BENTO_PORT",
          "label": "Serving API port",
          "default": "3000"
        },
        {
          "name": "METRICS_PORT",
          "label": "Prometheus metrics port",
          "default": "3001"
        },
        {
          "name": "BENTO_VERSION",
          "label": "BentoML version",
          "default": "latest"
        },
        {
          "name": "LOG_LEVEL",
          "label": "Log level",
          "default": "INFO"
        }
      ]
    },
    {
      "id": 25,
      "type": 3,
      "title": "MLflow + MinIO (Production MLOps)",
      "description": "Production-grade MLOps stack: MLflow tracking server with PostgreSQL backend and MinIO S3-compatible artifact store for governed model registry, experiment tracking, and versioned artifact storage",
      "note": "MinIO console available at port 9001. MLflow auto-creates the artifact bucket on startup. For production, change all default credentials.",
      "categories": ["ai", "mlops", "model-registry", "governance", "experiment-tracking"],
      "platform": "linux",
      "logo": "https://mlflow.org/img/mlflow-black.svg",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/minio-mlops/docker-compose.yml"
      },
      "env": [
        {
          "name": "MLFLOW_PORT",
          "label": "MLflow UI port",
          "default": "5000"
        },
        {
          "name": "MINIO_API_PORT",
          "label": "MinIO S3 API port",
          "default": "9000"
        },
        {
          "name": "MINIO_CONSOLE_PORT",
          "label": "MinIO console port",
          "default": "9001"
        },
        {
          "name": "PG_USER",
          "label": "PostgreSQL user",
          "default": "mlflow"
        },
        {
          "name": "PG_PASSWORD",
          "label": "PostgreSQL password",
          "default": "mlflow"
        },
        {
          "name": "MINIO_ROOT_USER",
          "label": "MinIO root user",
          "default": "mlflow"
        },
        {
          "name": "MINIO_ROOT_PASSWORD",
          "label": "MinIO root password",
          "default": "mlflow123"
        },
        {
          "name": "ARTIFACT_BUCKET",
          "label": "S3 artifact bucket name",
          "default": "mlflow-artifacts"
        }
      ]
    },
    {
      "id": 26,
      "type": 3,
      "title": "NVIDIA NIM",
      "description": "Enterprise-grade optimized LLM inference microservice from NVIDIA. Pre-optimized with TensorRT-LLM for maximum throughput with OpenAI-compatible API",
      "note": "Requires NVIDIA GPU and an NGC API key from <a href=\"https://build.nvidia.com/\" target=\"_blank\">NVIDIA Build</a>. Model downloads are cached in the nim_cache volume. First startup may take several minutes.",
      "categories": ["ai", "llm", "inference", "enterprise", "nvidia"],
      "platform": "linux",
      "logo": "https://developer.nvidia.com/favicon.ico",
      "repository": {
        "url": "https://git.oe74.net/adelorenzo/portainer_scripts",
        "stackfile": "ai-templates/stacks/nvidia-nim/docker-compose.yml"
      },
      "env": [
        {
          "name": "NIM_PORT",
          "label": "API port",
          "default": "8000"
        },
        {
          "name": "NGC_API_KEY",
          "label": "NVIDIA NGC API key (required)"
        },
        {
          "name": "NIM_MODEL",
          "label": "NIM model container",
          "description": "Model from NVIDIA NGC catalog",
          "default": "meta/llama-3.1-8b-instruct"
        },
        {
          "name": "NIM_VERSION",
          "label": "NIM version",
          "default": "latest"
        },
        {
          "name": "MAX_MODEL_LEN",
          "label": "Max sequence length",
          "default": "4096"
        },
        {
          "name": "GPU_MEM_UTIL",
          "label": "GPU memory utilization (0-1)",
          "default": "0.9"
        },
        {
          "name": "SHM_SIZE",
          "label": "Shared memory size",
          "default": "16g"
        }
      ]
    }
  ]
}