Spark Pulse
Spark Pulse is a web control plane for deploying inference engines (vLLM, SGLang) on NVIDIA DGX Spark hardware. It drives Docker directly from Python through its own engine plugins. Recipes and mods stay format-compatible with spark-vllm-docker; nothing else is shared, and no checkout of it is read.
It brings recipe discovery, deployment management, live monitoring, cache cleanup, and configuration into one interface. Multi-node deployment goes through the same deploy path and has run on two DGX Sparks — vLLM tensor-parallel and llama.cpp over its RPC backend, both benchmarked there. Three or more nodes, the fabric's full bandwidth and SGLang across machines have not been measured; docs/upstream-cluster-parity.md lists what is still open.
License: MIT — Copyright © 2026 Kharkevich Engineering Lab
Disclaimer: This project is not sponsored by, endorsed by, or affiliated with NVIDIA Corporation or any of its subsidiaries. NVIDIA, DGX, and related trademarks are property of their respective owners.
Features
- Recipe browsing - Explore deployment recipes, model variants, and mod combinations in a clean catalog view.
- Deployment jobs - Launch deployments, watch live logs, inspect status, and stop running jobs from the UI.
- Real-time monitoring - Track GPU, CPU, RAM, and disk usage with streaming updates.
- Cache management - Review and clean Hugging Face, wheel, ccache, Triton, and related caches.
- Settings and auth - Configure the backend path, defaults, and OIDC authentication.
- MCP server - Expose the same operations to Model Context Protocol clients and automation.
Documentation
kharkevich-engineering-lab.github.io/spark-pulse — a tour of every page, how deploys and reconciliation work, the agent protocol, and the configuration, CLI, API and MCP reference.
Screenshots
Inference
What is running, with per-rank state, the engine's own metrics and the log.
Monitoring
Every node, asked through its own agent: GPUs, memory, disks, and which deployment holds each GPU process.
Recipes
A recipe is a model, an engine and its arguments in one file — bundled, your own, or installed from an OCI registry.
Installation
Install the published package from PyPI:
python3 -m pip install spark-pulse
This installs the spark-pulse command-line interface.
Usage
Start the web app after installation:
spark-pulse start
Then open the UI in your browser. The default port is 8100 unless you changed it in configuration.
Common runtime commands:
# Start the MCP server for assistants and automation
spark-pulse mcp
# Install and manage the app as a systemd service
spark-pulse install
spark-pulse status
spark-pulse start-service
spark-pulse stop-service
spark-pulse uninstall
Add --user to any of the service commands if you want a user-scoped systemd unit.
Authentication is optional. When enabled, Spark Pulse redirects users through your configured OIDC provider and protects the UI and API routes.
Configuration
Spark Pulse reads settings from config.yaml (bundled with the package) and merges user overrides from ~/.config/spark-pulse/settings.json. Environment variables take highest priority.
config.yaml Reference
The full table lives in the configuration reference. The keys most people touch:
| Key | Type | Default | Description |
|---|---|---|---|
webui_port |
int | 8100 |
TCP port the web UI listens on. |
runtime |
string | native |
Deployment runtime. native — Spark Pulse drives Docker itself through the engine registry — is the only one. |
default_port_range_start |
int | 9000 |
Start of the port range deployments are allocated from. |
default_port_range_end |
int | 9100 |
End of that range. |
job_retention_days |
int | 7 |
Days to retain finished deployment records. |
default_engine |
string | vllm |
Engine used when a recipe does not name one. |
cluster_enabled |
bool | false |
Forces recipes marked cluster_only on. They are offered anyway once a second node is enrolled. |
database_url |
string | (empty) | Empty means SQLite under ~/.config/spark-pulse; any SQLAlchemy URL otherwise. |
cors_allowed_origins |
list | [] |
Browser origins allowed to call this API. Never *. |
mcp_enabled |
bool | true |
Enable the MCP endpoint. |
mcp_path |
string | /mcp |
Where it is mounted. |
mcp_api_token |
string | (empty) | Optional token protecting MCP requests. |
auth_enabled |
bool | false |
Enable OIDC authentication. |
oidc_provider_url |
string | (empty) | OIDC provider URL. |
oidc_client_id |
string | (empty) | OIDC client ID. |
oidc_client_secret |
string | (empty) | Stored in ~/.config/spark-pulse/secrets.json, never returned by the API. |
Environment Variable Overrides
The following environment variables override their corresponding config keys:
| Environment Variable | Config Key | Description |
|---|---|---|
WEBUI_PORT |
webui_port |
Override the web UI port. |
SPARK_PULSE_DATABASE_URL |
database_url |
Override the database URL. |
SPARK_PULSE_AUTH_ENABLED |
auth_enabled |
Turn OIDC on or off. |
SPARK_PULSE_MCP_ENABLED |
mcp_enabled |
Turn the MCP endpoint on or off. |
SIMULATION_MODE |
— | Run everything against a simulated cluster: no Docker, no GPU. |
File Locations
| File | Purpose |
|---|---|
config.yaml |
Bundled defaults (read-only, overwritten on package update). |
~/.config/spark-pulse/settings.json |
Persistent user overrides (survives package updates). |
~/.config/spark-pulse/secrets.json |
Securely stored secrets (mode 0600). |
Example: Enabling OIDC Authentication
# config.yaml or settings.json
auth_enabled: true
oidc_provider_url: https://keycloak.example.com/realms/myrealm
oidc_client_id: spark-pulse
Then set the client secret via the UI Settings page or directly in secrets.json:
{
"oidc_client_secret": "your-secret-here"
}
Development Mode with Mock OIDC Provider
For local development, Spark Pulse ships with convenience scripts that start a mock OIDC provider alongside the dev server. This lets you test the full SSO login flow without a real identity provider.
Full stack (backend + frontend with hot-reload):
./scripts/run-dev-oidc-full.sh
This script:
- Starts the mock OIDC provider on
http://localhost:9400 - Creates
~/.config/spark-pulse/settings.jsonandsecrets.jsonwith dev credentials - Launches the backend with
--reload(serves both API and built frontend)
Backend only (for API testing):
./scripts/run-dev-oidc.sh
Dev credentials (both scripts):
- Provider URL:
http://localhost:9400 - Client ID:
spark-pulse-dev - Client Secret:
dev-secret
To stop, press Ctrl+C — the scripts clean up all background processes.
Development
Clone the repository and install local development dependencies:
python3 -m pip install -e ".[dev]"
Build the frontend and run the app locally:
cd web && npm install && npm run build && cd ..
./scripts/run-dev-server.sh
Useful development commands:
./scripts/run-backend.sh
./scripts/run-production.sh
./scripts/build-ui.sh
pytest
python -m build
Architecture, in one paragraph
One control plane coordinates; one agent per node executes — including on the machine the control plane itself runs on, which it reaches over loopback exactly as it reaches a peer. Nothing in the control plane touches a node's Docker daemon, GPU or model cache directly. See the architecture.
API
REST under /api/*, with interactive docs at /docs and /redoc on the running app, plus /auth/* for OIDC login and /mcp for Model Context Protocol clients. The API reference is the map.
License
MIT. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for spark-pulse 1.33.0
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| File | Size | Uploaded | |
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| spark_pulse-1.33.0.tar.gz | 5.7 MB | Details |
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|---|---|---|---|---|
| spark_pulse-1.33.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 11.1 MB
Release files / spark_pulse-1.33.0.tar.gz
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