metaflow-prefect

Deploy and run Metaflow flows as Prefect deployments.

metaflow-prefect generates a self-contained Prefect flow file from any Metaflow flow, letting you schedule, deploy, and monitor your pipelines through Prefect while keeping all your existing Metaflow code unchanged.

Install

pip install metaflow-prefect

Or from source:

git clone https://github.com/npow/metaflow-prefect.git
cd metaflow-prefect
pip install -e ".[dev]"

Quick start

# Compile and run locally (no Prefect server needed)
python my_flow.py prefect run

# Register as a named deployment on a Prefect server
python my_flow.py prefect create --name prod --work-pool my-pool

Usage

Commands

# Run locally
python my_flow.py prefect run

# Resume a failed run (reuses already-completed step outputs)
python my_flow.py prefect resume --clone-run-id prefect-<uuid>

# Deploy to a Prefect server
python my_flow.py prefect create --name prod --work-pool my-pool

# Trigger a run of the deployed flow
python my_flow.py prefect trigger --name prod

All graph shapes are supported

# Linear
class SimpleFlow(FlowSpec):
    @step
    def start(self):
        self.value = 42
        self.next(self.end)
    @step
    def end(self): pass

# Split/join (static branch)
class BranchFlow(FlowSpec):
    @step
    def start(self):
        self.next(self.branch_a, self.branch_b)
    ...

# Conditional (dynamic branch — only one path runs at runtime)
class ConditionalFlow(FlowSpec):
    value = Parameter("value", default=42, type=int)
    @step
    def start(self):
        self.route = "high" if self.value >= 50 else "low"
        self.next({"high": self.high_branch, "low": self.low_branch}, condition="route")
    @step
    def high_branch(self): ...
    @step
    def low_branch(self): ...
    @step
    def join(self): ...

# Foreach fan-out (body tasks run concurrently)
class ForeachFlow(FlowSpec):
    @step
    def start(self):
        self.items = [1, 2, 3]
        self.next(self.process, foreach="items")
    ...

Parametrised flows

Parameters defined with metaflow.Parameter are forwarded automatically:

python param_flow.py prefect run
# Or pass parameters at the CLI:
python param_flow.py prefect compile param_flow_prefect.py
python param_flow_prefect.py --message "hello" --count 5

Step decorator support

@retry, @timeout, @environment, and @resources decorators are read from your flow and applied to the generated Prefect tasks automatically — no changes to your flow code required.

class MyFlow(FlowSpec):
    @retry(times=3, minutes_between_retries=2)
    @timeout(seconds=600)
    @environment(vars={"API_KEY": "secret"})
    @step
    def train(self):
        ...

The generated Prefect task becomes:

@task(name="train", retries=3, timeout_seconds=600, retry_delay_seconds=120)
def _step_train(run_id, prev_task_id):
    _extra_env.update({"API_KEY": "secret"})
    ...

Event-based triggers

@trigger and @trigger_on_finish are wired as Prefect automations when the deployment is registered. Re-running prefect create updates the automations in place.

@trigger_on_finish(flow="UpstreamFlow")
class MyFlow(FlowSpec):
    ...

python my_flow.py prefect create --name prod --work-pool my-pool
# → Registers deployment AND creates a Prefect automation:
#   "on prefect.flow-run.Completed for flow 'UpstreamFlow' → run deployment 'prod'"

@trigger(event="data.ready")
class MyFlow(FlowSpec):
    ...

python my_flow.py prefect create --name prod --work-pool my-pool
# → Registers deployment AND creates a Prefect automation:
#   "on event 'data.ready' → run deployment 'prod'"

Resume failed runs

Pass --clone-run-id to reuse outputs from steps that already succeeded in a previous run:

python my_flow.py prefect resume --clone-run-id prefect-<uuid-of-failed-run>

Configuration

Metadata service and datastore

By default, metaflow-prefect uses whatever metadata and datastore backends are active in your Metaflow environment. The generated Prefect file bakes in METADATA_TYPE and DATASTORE_TYPE at creation time so every step subprocess uses the same backend.

To use a remote metadata service or object store, configure them before running prefect create:

# Remote metadata service + S3 datastore
python my_flow.py \
  --metadata=service \
  --datastore=s3 \
  prefect create --name prod --work-pool my-pool

Or via environment variables (applied to all flows):

export METAFLOW_DEFAULT_METADATA=service
export METAFLOW_DEFAULT_DATASTORE=s3
python my_flow.py prefect create --name prod --work-pool my-pool

Flow-level timeout

python my_flow.py prefect create --name prod --workflow-timeout=3600

Step decorators (--with)

Inject Metaflow step decorators at deploy time without modifying the flow source:

# Run each step inside a sandbox (e.g. metaflow-sandbox extension)
python my_flow.py prefect run --with=sandbox

# Multiple decorators supported at deployment time
python my_flow.py prefect create --name prod \
  --with=sandbox \
  --with="resources:cpu=4,memory=8000"

@project support

Flows decorated with @project use a project-qualified name for the deployment:

@project(name="my-team")
class MyFlow(FlowSpec):
    ...

# Deployment will be registered as "my-team.MyFlow"
python my_flow.py prefect create --name prod

How it works

metaflow-prefect generates a self-contained Prefect flow file from your Metaflow flow's DAG. Each Metaflow step becomes a @task. The generated file:

• runs each step as a subprocess via the standard metaflow step CLI
• streams stdout and stderr from each step subprocess to the Prefect logger in real time
• passes --input-paths correctly for joins and foreach splits
• runs foreach body tasks concurrently via Prefect's task runner
• maps @retry, @timeout, @environment, and @resources decorators to Prefect task settings
• writes Metaflow artifacts to the Prefect UI as markdown artifacts with a ready-to-use retrieval snippet
• creates Prefect automations for @trigger and @trigger_on_finish when deploying

Prefect UI: flow run timeline

The generated flow preserves the Metaflow DAG structure — foreach fan-outs appear as parallel task runs in the Prefect timeline:

Prefect UI: artifact retrieval snippets

After each step completes, a Prefect artifact is posted showing the Metaflow self.* artifact names and a one-liner to fetch each value:

Supported decorators

Unsupported decorators (@batch, @slurm, @exit_hook, @parallel) raise a clear error at compile time.

Limitations

Development

git clone https://github.com/npow/metaflow-prefect.git
cd metaflow-prefect
pip install -e ".[dev]"
pytest -v

License

Apache 2.0