Nextflow Turret

A self-hosted replacement for Seqera Platform (formerly Nextflow Tower). Run any Nextflow pipeline with -with-tower, track progress in real time, launch new runs from a web UI, and inspect logs — all without a cloud subscription.

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Features

Feature	Details
Tower trace receiver	Accepts the full Nextflow -with-tower HTTP protocol
SQLite persistence	Every run is immediately written to disk; survives restarts
Pipeline launcher	Submit pipelines via web form or REST API; cancel running jobs
Web dashboard	Live run list, per-run progress detail, failed-task drill-down
REST API	JSON API for runs and launches (OpenAPI docs at /docs)
Embeddable library	Use WorkflowRegistry + TowerRouter inside your own Python app

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Installation

# Into an existing project
uv add "nextflow-turret @ git+https://github.com/msk-mind/nextflow-turret.git"

# Or with pip
pip install "nextflow-turret @ git+https://github.com/msk-mind/nextflow-turret.git"

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Quick start — standalone server

# Start the server (default: http://0.0.0.0:8000, DB: turret.db)
turret

# Or with explicit options (always override config file)
turret --host 127.0.0.1 --port 9000 --db /data/turret.db --log-dir /data/logs

# Or via Python
python -m nextflow_turret.server --port 9000

Config file

Create a turret.toml in the working directory (or ~/.config/turret/config.toml for user-level defaults). CLI flags always win over config values.

[server]
host    = "0.0.0.0"
port    = 8000
db      = "/data/turret.db"
log_dir = "/data/turret-logs"

[launcher]
nextflow        = "/opt/nextflow/nextflow"
work_dir        = "/scratch/nf-work"
default_profile = "slurm"

A fully-commented template is at turret.toml.example.

Then point Nextflow at it:

nextflow run nf-core/rnaseq -with-tower http://localhost:8000 -name dispatcher_mybatch \
  -profile docker -r 3.14.0

Open http://localhost:8000 to see the live dashboard.

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REST API

Interactive docs are available at /docs when the server is running.

Tower trace endpoints (called by Nextflow)

Method	Path	Purpose
GET	/user-info	Auth check on Nextflow startup
POST	/trace/create	Workflow registered → returns {workflowId}
PUT	/trace/{id}/begin	Workflow running
PUT	/trace/{id}/progress	Periodic task counts + per-task list
PUT	/trace/{id}/heartbeat	Keepalive (same payload as progress)
PUT	/trace/{id}/complete	Workflow finished

Runs API

Method	Path	Description
GET	/api/runs	List all runs (newest first)
GET	/api/runs/{workflow_id}	Single run detail with task counts, failures, resources

Launches API

Method	Path	Description
GET	/api/launches	List all launches (newest first)
POST	/api/launches	Submit a new pipeline launch
GET	/api/launches/{id}	Launch status and metadata
GET	/api/launches/{id}/log?tail=N	stdout/stderr log (optionally tailed)
DELETE	/api/launches/{id}	Cancel a running launch (SIGTERM)

Submit a launch via REST

curl -X POST http://localhost:8000/api/launches \
  -H "Content-Type: application/json" \
  -d '{
    "pipeline":  "nf-core/rnaseq",
    "revision":  "3.14.0",
    "profile":   "docker",
    "params":    {"input": "samplesheet.csv", "genome": "GRCh38"},
    "work_dir":  "/scratch/work"
  }'

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run_name → batch_id mapping

Nextflow is launched with -name dispatcher_{batch_id}. The dispatcher_ prefix is stripped automatically to produce the batch_id that identifies the run.

To use a different prefix, create your own TowerRouter:

from nextflow_turret import TowerRouter, WorkflowRegistry

registry = WorkflowRegistry()
router   = TowerRouter(
    registry=registry,
    run_name_to_batch_id=lambda name: name.removeprefix("mypipeline_"),
)

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Embedding in your own Python app

If you prefer to host the Tower receiver inside an existing service rather than running the standalone server, use the core library directly:

from nextflow_turret import TowerRouter, WorkflowRegistry

registry = WorkflowRegistry()
router   = TowerRouter(registry=registry)

# In your HTTP handler:
result = router.handle_get(path)          # GET /user-info
result = router.handle_post(path, body)   # POST /trace/create
result = router.handle_put(path, body)    # PUT  /trace/{id}/progress

# Read progress at any time:
state = registry.get_by_batch("mybatch")
print(state["pct"], "% complete")
print(state["task_counts"])   # {"succeeded": 42, "running": 3, ...}
print(state["failures"])      # [{"taskId": 7, "process": "ALIGN", "exit": 1}, ...]

Module-level singleton

For quick scripts, a global registry is available:

import nextflow_turret as turret

turret.register_workflow(workflow_id, batch_id, run_name)
turret.update_progress(workflow_id, progress_dict, tasks_list)
turret.mark_complete(workflow_id)

state = turret.get_progress(batch_id)   # → dict or None

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Utilities

from nextflow_turret import tower_process_to_slurm_name

# Convert a Tower process name to a SLURM job name prefix
tower_process_to_slurm_name("MUSSEL:EXTRACT_FEATURES:TESSELLATE_FEATURIZE_BATCH")
# → "MUSSEL_EXTRACT_FEATURES_TESSELLATE_FEATURIZE_BATCH"

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Architecture

nextflow_turret/
├── state.py            # WorkflowRegistry, WorkflowState — core in-memory model
├── handlers.py         # TowerRouter — parses Tower HTTP payloads
├── db/
│   └── store.py        # RunStore — SQLite persistence (runs + launches)
├── launcher/
│   └── launcher.py     # Launcher — subprocess management for pipeline runs
└── server/
    ├── app.py          # FastAPI application factory (create_app)
    ├── registry.py     # PersistentWorkflowRegistry — DB-backed registry
    ├── __main__.py     # CLI entry point (turret / python -m nextflow_turret.server)
    └── templates/      # Jinja2 HTML templates (dashboard, detail, launch form)

Key design decisions:

• Write-through persistence — every Tower trace event is committed to SQLite before the HTTP response is sent.
• Restart recovery — on startup, PersistentWorkflowRegistry re-hydrates all rows from the DB so in-progress runs can continue receiving trace events.
• No external dependencies for the core library — only fastapi, uvicorn, and jinja2 are needed for the server extras.

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Development

git clone https://github.com/msk-mind/nextflow-turret.git
cd nextflow-turret

# Run tests (uv installs deps automatically)
uv run pytest

# Or sync into a venv and work interactively
uv sync --extra server
source .venv/bin/activate
pytest --tb=short -q

Test layout

File	Tests	Coverage
tests/test_turret.py	39	Core library (WorkflowRegistry, WorkflowState, TowerRouter)
tests/test_server.py	44	Server layer (RunStore, PersistentWorkflowRegistry, all endpoints)
tests/test_integration.py	32	Cross-layer: Tower→SQLite, restart recovery, concurrency, API/DB/UI consistency
tests/test_e2e.py	34	Full scenarios: NF trace lifecycle, failed tasks, multi-run dashboard, launch UI journey, error paths

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License

Nextflow Turret is released under the GNU Affero General Public License v3.0 (AGPL-3.0-or-later).

This means:

• Free to use, modify, and self-host for any purpose.
• If you modify Nextflow Turret and run it as a network service, you must make your modified source code available to users of that service.

See LICENSE for the full terms.