huggingface/dell-ai

Dernière activité source enregistrée: 17 septembre 2026 à 11:15

Catalogue SkillsMP mis à jour: 12 novembre 2026 à 12:52

skills collectés: 1

Étoiles GitHub: 14
Forks GitHub: 10

Installer avec un assistant IA

Copiez ce prompt dans l’assistant IA que vous utilisez.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/huggingface/dell-ai.

README du dépôt

Fichier source du README : README.md

Lire le README source

Afficher le README complet

# Dell AI SDK and CLI [![Version](https://img.shields.io/badge/version-1.1.0-orange)](https://github.com/huggingface/dell-ai) [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Python Versions](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/downloads/) A Python SDK and CLI for interacting with the Dell Enterprise Hub (DEH), allowing users to programmatically browse available AI models, view platform configurations, generate deployment snippets or run DEH models and apps on Dell systems. ## Features - Browse available AI models and apps - View platform configurations - Generate deployment snippets for running AI models on Dell hardware - Size deployments for a goodput scenario (e.g. `balanced`) and let the server pick the optimal configuration, instead of choosing a GPU count by hand - Inspect goodput scenario definitions and per-SKU SLO targets - Deploy models and applications directly onto the local node, with automatic host port and GPU management - Track, discover, and tear down local deployments through a deployment registry - Manage local and global environment variables - Check the status of deployed endpoints, checkpoints, and active deployments - Simple and easy-to-use API - Consistent CLI commands ## Installation We recommend installing the package using `uv`, a fast Rust-based Python package and project manager, after setting up a Python virtual environment: ```bash # Install uv if you don't have it curl -LsSf https://astral.sh/uv/install.sh | sh # Create and activate virtual environment uv venv .venv source .venv/bin/activate # Install dell-ai uv pip install dell-ai ``` ### Alternative: `pip` You can also use `pip` to install the package: ```bash # Create and activate virtual environment python -m venv .venv source .venv/bin/activate # Install dell-ai pip install dell-ai ``` ### Installing from Source If you want to contribute to development or need the latest changes, you can install from source: ```bash # Clone the repository git clone https://github.com/huggingface/dell-ai.git cd dell-ai # Create and activate virtual environment (using either method above) # Then install in development mode pip install -e . # or uv pip install -e . ``` ## Quick Start For detailed, guided examples of using the Dell AI SDK and CLI, check out the example documents in the `examples` directory: - `examples/sdk-getting-started.ipynb`: A comprehensive walkthrough of the SDK features - `examples/cli-getting-started.md`: A guide to using the CLI commands ### Using the CLI ```bash # Authenticate with Hugging Face dell-ai login # List available models dell-ai models list # Get details about a specific model dell-ai models show meta-llama/Llama-4-Maverick-17B-128E-Instruct # List available platform SKUs dell-ai platforms list # Generate a Docker deployment snippet dell-ai models get-snippet --model-id meta-llama/Llama-4-Maverick-17B-128E-Instruct --platform-id xe9680-nvidia-h200 --engine docker --gpus 8 --replicas 1 # List the container image tags available for a model on a platform dell-ai models list-tags --model-id meta-llama/Llama-4-Maverick-17B-128E-Instruct --platform-id xe9680-nvidia-h200 # Pin one of those tags in the snippet dell-ai models get-snippet --model-id meta-llama/Llama-4-Maverick-17B-128E-Instruct --platform-id xe9680-nvidia-h200 --engine docker --gpus 8 --image-tag vllm-v0.11.2 ``` ### Using the SDK ```python from dell_ai.client import DellAIClient # Initialize the client (authentication happens automatically if you've logged in via CLI) client = DellAIClient() # List available models models = client.list_models() print(models) # Get model details model_details = client.get_model(model_id="meta-llama/Llama-4-Maverick-17B-128E-Instruct") print(model_details.model_dump()) # List available platforms platforms = client.list_platforms() print(platforms) # Get platform details platform_details = client.get_platform(platform_id="xe9680-nvidia-h200") print(platform_details.model_dump()) # Get deployment snippet snippet = client.get_deployment_snippet( model_id="meta-llama/Llama-4-Maverick-17B-128E-Instruct", platform_id="xe9680-nvidia-h200", engine="docker", num_gpus=8, num_replicas=1, image_tag="vllm-v0.11.2", # optional; pin a specific container image tag ) print(snippet) # Inspect the container image tags available for a model on a platform tags = client.get_container_tags( model_id="meta-llama/Llama-4-Maverick-17B-128E-Instruct", platform_id="xe9680-nvidia-h200", ) print([tag.id for tag in tags]) ``` ## Deploying models and applications In addition to generating snippets, `dell-ai` can execute them directly on the local node, so the code you get from the Dell Enterprise Hub is deployed for you. Deployment uses the locally available engine: `docker` (Docker CLI), `kubernetes` (`kubectl apply`), or Helm for applications. By default deployments run in detached/background mode. For Docker, the interactive flags (`-it`) are automatically converted to detached mode (`-d`), the container ID is captured, and the inferred endpoint URL is recorded. For Docker deployments, `dell-ai` also manages host resources automatically: if the snippet's host port is already in use it is remapped to a free port, and free GPU indices are allocated and pinned to the container. On a successful deployment the metadata (endpoint, engine, container ID or Kubernetes deployment name, and any assigned GPUs) is recorded in the **deployment registry** so it can later be inspected with `dell-ai status` and torn down with `dell-ai models undeploy`. See [Deployment registry](#deployment-registry). ### Using the CLI ```bash # Deploy a model with Docker (runs in the background by default) dell-ai models deploy --model-id meta-llama/Llama-4-Maverick-17B-128E-Instruct --platform-id xe9680-nvidia-h200 --engine docker --gpus 8 --replicas 1 # Deploy a model with Kubernetes dell-ai models deploy -m meta-llama/Llama-4-Maverick-17B-128E-Instruct -p xe9680-nvidia-h200 -e kubernetes -g 8 -r 1 # Run in the foreground instead of detached mode dell-ai models deploy -m meta-llama/Llama-4-Maverick-17B-128E-Instruct -p xe9680-nvidia-h200 -e docker --no-detach # Deploy optimized for a goodput scenario instead of a fixed GPU count (mutually exclusive with --gpus) dell-ai models deploy -m meta-llama/Llama-4-Maverick-17B-128E-Instruct -p xe9680-nvidia-h200 -e docker --goodput balanced # Mount local model weights instead of downloading from the Hub (Docker only) dell-ai models deploy -m meta-llama/Llama-4-Maverick-17B-128E-Instruct -p xe9680-nvidia-h200 -e docker --gpus 8 --local-dir /data/my-model # Or reuse an existing HuggingFace cache directory (mutually exclusive with --local-dir) dell-ai models deploy -m meta-llama/Llama-4-Maverick-17B-128E-Instruct -p xe9680-nvidia-h200 -e docker --gpus 8 --hf-cache-dir ~/.cache/huggingface # Deploy an application (Helm) dell-ai apps deploy openwebui --config '{"config":[{"helmPath":"main.config.storageClassName","type":"string","value":"custom-storage-class"}]}' # Stop and remove a deployment (Docker container / K8s deployment) and its registry entry. # The ID is the "Deployment ID" shown by `dell-ai status` (see note on duplicates below). dell-ai models undeploy -d meta-llama/Llama-4-Maverick-17B-128E-Instruct ``` ### Using the SDK ```python from dell_ai.client import DellAIClient client = DellAIClient() # Deploy a model on the local node result = client.deploy_model( model_id="meta-llama/Llama-4-Maverick-17B-128E-Instruct", platform_id="xe9680-nvidia-h200", engine="docker", num_gpus=8, # or use goodput="balanced" instead of num_gpus num_replicas=1, detach=True, # local_dir="/data/my-model", # mount local weights (Docker only) # hf_cache_dir="~/.cache/huggingface", # or reuse an existing HF cache ) print(result["success"], result.get("container_id"), result.get("endpoint")) # Deploy an application on the local node result = client.deploy_app(app_id="openwebui", config=[], detach=True) print(result["success"]) ``` > [!NOTE] > Deployment executes the snippet returned by the Dell Enterprise Hub on the > local machine, so it requires the relevant tooling (`docker`, `kubectl`, or > `helm`) to be installed and configured. ## Optimized Configurations Instead of picking GPU count and deployment args by hand, `dell-ai` and **Dell Enterprise Hub** offer optimized configurations for a snippet or deployment for a set of **goodput scenarios**. Goodput scenarios are named workload profiles (e.g. `balanced`) with an associated set of Service Level Objectives (SLOs). When you pass `--goodput` (CLI) or `goodput=` (SDK), DEH offers the optimal configuration for that scenario on the target platform. > [!NOTE] > Check out the [Goodput Scenarios documentation](https://dell.huggingface.co/docs/optimized-deployments/goodput-scenarios) on DEH to learn more. The available scenarios, the SLO field descriptions, and the SLO *targets* per SKU come from global reference data (`dell-ai models goodput-scenarios`). ### Using the CLI ```bash # List the available goodput scenarios and SLO field descriptions dell-ai models goodput-scenarios --format table # Drill into the SLO targets for a single SKU (scenario x SLO-field grid) dell-ai models goodput-scenarios --platform-id xe9680-nvidia-h100 --format table # Generate a snippet optimized for a scenario instead of a fixed GPU count dell-ai models get-snippet -m google/gemma-3-27b-it -p xe9680-nvidia-h100 --engine docker --goodput balanced # Deploy optimized for a scenario (mutually exclusive with --gpus) dell-ai models deploy -m google/gemma-3-27b-it -p xe9680-nvidia-h100 --engine docker --goodput balanced ``` > [!NOTE] > `--goodput`/`goodput` and `--gpus`/`num_gpus` > are **mutually exclusive** — provide exactly one. ### Using the SDK ```python from dell_ai.client import DellAIClient client = DellAIClient() # Get the global goodput reference data (scenarios, SLO docs, SLO targets per SKU) reference = client.get_goodput_scenarios() for scenario in reference.scenarios: print(scenario.id, "-", scenario.label) # Inspect the SLO targets for a specific SKU slos = reference.slos_by_platform_id.get("xe9680-nvidia-h100", {}) print({scenario: slo.model_dump() for scenario, slo in slos.items()}) # Generate a snippet optimized for a scenario (omit num_gpus) snippet = client.get_deployment_snippet( model_id="google/gemma-3-27b-it", platform_id="xe9680-nvidia-h100", engine="docker", goodput="balanced", # mutually exclusive with num_gpus num_replicas=1, ) print(snippet) # Deploy optimized for a scenario result = client.deploy_model( model_id="google/gemma-3-27b-it", platform_id="xe9680-nvidia-h100", engine="docker", goodput="balanced", # mutually exclusive with num_gpus num_replicas=1, detach=True, ) print(result["success"], result.get("endpoint")) ``` ## Deployment registry Successful deployments are recorded in a **deployment registry** so they can be listed, inspected, and torn down later. The registry has two scopes: - **Local** — `.dell-ai-deployments.json` in the current working directory - **Global** — `~/.config/dell-ai/deployments.json` (user-wide) Each entry is keyed by a **deployment ID** and stores the endpoint, engine, container ID or Kubernetes deployment name, assigned GPUs, and a timestamp. When listing deployments (e.g. via `dell-ai status`), running Dell Enterprise Hub Docker containers that are not yet tracked are **auto-discovered** and added, and registry entries whose Docker containers are no longer running are pruned automatically. The deployment ID defaults to the model ID. If the **same model is deployed more than once**, each additional instance gets a numeric suffix (e.g. `meta-llama/Llama-4`, `meta-llama/Llama-4_1`, …) so they can coexist — each instance is automatically given its own host port and GPU indices. Use `dell-ai models undeploy -d <deployment_id>` to stop the underlying container or Kubernetes deployment and remove its registry entry. Undeploy acts on **one deployment at a time**: with duplicate instances, run `dell-ai status` first to see the suffixed IDs, then undeploy each one individually. ## Environment variables `dell-ai` can store configuration as environment variables in two scopes: - **Local** — stored in `.dell-ai-env.json` in the current working directory - **Global** — stored in `~/.config/dell-ai/env.json` (user-wide) Variables are loaded automatically into the process environment on CLI startup and when a `DellAIClient` is created. When resolving a variable, precedence is: the active shell environment, then local, then global. This is useful for recording checkpoint paths (`DELL_AI_CHECKPOINT`, which `dell-ai status` reports on) and any other settings your deployments rely on. ### Using the CLI ```bash # Set a variable locally (current directory) or globally (-g/--global) dell-ai env set DELL_AI_ENDPOINT http://localhost:80 dell-ai env set DELL_AI_ENDPOINT http://localhost:80 --global # Get a variable's value dell-ai env get DELL_AI_ENDPOINT # List variables (combined by default; --local or --global to scope) dell-ai env list dell-ai env list --local dell-ai env list --global # Delete a variable (from local, or --global) dell-ai env delete DELL_AI_ENDPOINT ``` ### Using the SDK ```python from dell_ai import env # Set / get / delete env.set_env_var("DELL_AI_ENDPOINT", "http://localhost:80", is_global=False) print(env.get_env_var("DELL_AI_ENDPOINT")) env.delete_env_var("DELL_AI_ENDPOINT", is_global=False) # List (is_global=None -> combined, True -> global only, False -> local only) print(env.list_env_vars()) ``` ## Checking deployment status `dell-ai status` inspects your environment and local node and reports on: - **Active deployments** — reads the [deployment registry](#deployment-registry), probes each recorded endpoint, and reports whether it is online and its response time (running Dell Enterprise Hub containers are auto-discovered) - **Checkpoints** — checks whether paths in `DELL_AI_CHECKPOINT` (or any `*_CHECKPOINT` variable) exist, and reports their type and size - **Active Docker/K8s** — scans the local Docker daemon and Kubernetes cluster for running Dell Enterprise Hub deployments ```bash dell-ai status # Also remove exited Docker containers and stopped Kubernetes deployments dell-ai status --clean ``` > [!NOTE] > Docker and Kubernetes scanning are skipped gracefully if `docker` or `kubectl` > are not available on the node. ## Testing The project uses pytest for testing. To run the tests: ```bash # Check code formatting ruff format --check . # Check linting and import sorting ruff check . # Format code ruff format . # Run all tests pytest # Run tests with coverage report pytest --cov=dell_ai # Run specific test file pytest tests/unit/test_exceptions.py ``` ## Contributing Contributions are welcome! Please see [RELEASE_PROCESS.md](RELEASE_PROCESS.md) for information on how the release process works when contributing code changes. When submitting a PR: 1. Ensure all tests pass 2. Add tests for new functionality 3. Follow the existing code style ## License Licensed under the Apache License, Version 2.0.

Chargement du README du dépôt…

Skills dans ce dépôt

classification en attente

skill

métier

description

mis à jour

dell-ai

non classé

Skill for interacting with Dell Enterprise Hub (DEH) - discovering models, exploring hardware platforms, generating deployment snippets, etc.

Langue du texte source : anglais

2026-09-17

Affichage de 1 skills collectés sur 1.