metaflow-prefect 0.2.0

Metaflow extension: deploy and run flows as Prefect deployments

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

python my_flow.py prefect create my_flow_prefect.py
python my_flow_prefect.py

Usage

Generate and run a Prefect flow

# Write the generated file and run it directly
python my_flow.py prefect create my_flow_prefect.py
python my_flow_prefect.py

# Or compile and run in one step
python my_flow.py prefect run

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

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 (branch)
class BranchFlow(FlowSpec):
    @step
    def start(self):
        self.next(self.branch_a, self.branch_b)
    ...

# 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 create param_flow_prefect.py
python param_flow_prefect.py --message "hello" --count 5

Step decorator support

@retry, @timeout, and @environment 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"})
    ...

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 my_flow_prefect.py

Or via environment variables (applied to all flows):

export METAFLOW_DEFAULT_METADATA=service
export METAFLOW_DEFAULT_DATASTORE=s3
python my_flow.py prefect create my_flow_prefect.py

Flow-level timeout

python my_flow.py prefect create my_flow_prefect.py --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 create my_flow_prefect.py --with=sandbox

# Multiple decorators are supported
python my_flow.py prefect deploy --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 deploy --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
• passes --input-paths correctly for joins and foreach splits
• runs foreach body tasks concurrently via Prefect's task runner
• maps @retry, @timeout, and @environment decorators to Prefect task settings
• writes Metaflow artifacts to the Prefect UI as markdown artifacts with a ready-to-use retrieval snippet

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

Decorator	Behaviour
@retry(times=N, minutes_between_retries=M)	Maps to @task(retries=N, retry_delay_seconds=M*60)
@timeout(seconds=N) / @timeout(minutes=N)	Maps to @task(timeout_seconds=N)
@environment(vars={...})	Merges vars into the step subprocess environment
@schedule(cron=...)	Used as the deployment cron schedule
@project(name=...)	Prefixes the deployment name with the project name

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

Development

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

License

Apache 2.0