<@ULVA73B9P> say i have n flows and i want it to b...
# ask-marvin
a
@Marvin say i have n flows and i want it to be deployed, in my fastapi i want to run a particular workflow, it can be recursive or triggered, whats the correct way to do that
m
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âś… thought for 222.1 seconds
Short answer: Deploy each flow as its own deployment, run a worker for the work pool, and have your FastAPI endpoint trigger the specific deployment you want. If you need recursion, call run_deployment from inside a flow (optionally scheduling the next run). If you want event-based triggering, use an Automation. Here’s the “correct” pattern in Prefect 3.x. 1) Create deployments for your n flows - Prefer programmatic deployments with flow.from_source(...).deploy(...). - One deployment per flow (per environment/config). Example deploy script: - Links: - Deploy via Python: https://docs-3.prefect.io/v3/how-to-guides/deployments/deploy-via-python Code: from prefect import flow from prefect.schedules import Cron if name == "__main__": # Flow A flow.from_source( source="https://github.com/acme/pipelines", # repo URL (no GitHub storage block needed in 3.x) entrypoint="flows/flow_a.py:flow_a", # path_to_file.py:flow_function_name ).deploy( name="prod", work_pool_name="my-pool", parameters={"foo": "bar"}, # optional: schedules=[Cron("0 7 * * *", timezone="UTC")] ) # Flow B flow.from_source( source="https://github.com/acme/pipelines", entrypoint="flows/flow_b.py:flow_b", ).deploy( name="default", work_pool_name="my-pool", ) Notes: - Make sure your worker is running for the work pool that these deployments target. (Docs overview: Work Pools/Workers: https://docs-3.prefect.io/v3/) - Configure your FastAPI service to talk to your Prefect API via PREFECT_API_URL and (if using Cloud) PREFECT_API_KEY. 2) Trigger a specific workflow from FastAPI - Call the deployment, not the function, using prefect.deployments.run_deployment. This keeps execution on your worker and avoids blocking your FastAPI server. - Links: - run_deployment API: https://docs-3.prefect.io/v3/api-ref/python/prefect/deployments/#prefect.deployments.run_deployment Example FastAPI endpoint: from fastapi import FastAPI from pydantic import BaseModel from typing import Optional, Dict, Any import anyio from prefect.deployments import run_deployment app = FastAPI() class TriggerBody(BaseModel): parameters: Optional[Dict[str, Any]] = None @app.post("/runs/{flow_name}/{deployment_name}") async def trigger(flow_name: str, deployment_name: str, body: TriggerBody): # run_deployment is sync; run it in a worker thread to avoid blocking the event loop flow_run = await anyio.to_thread.run_sync( run_deployment, name=f"{flow_name}/{deployment_name}", parameters=body.parameters, ) return {"flow_run_id": str(flow_run.id)} Best practices: - Don’t import or run your flows inside the FastAPI process; let Prefect workers execute them. - Return the flow_run_id so you can check status later via API/UI. 3) Recursion or “self-triggering” - From inside a flow, call run_deployment on itself to create the next run. You can set scheduled_time to avoid hot loops and set as_subflow=False to make a new, independent run. Example recursion pattern: from datetime import datetime, timedelta from prefect import flow from prefect.deployments import run_deployment @flow def do_batch(batch_num: int = 0, limit: int = 5): # ... do the work ... if batch_num + 1 < limit: run_deployment( name="do-batch/prod", parameters={"batch_num": batch_num + 1, "limit": limit}, as_subflow=False, # create a new flow run, not a subflow scheduled_time=datetime.utcnow() + timedelta(minutes=5), # optional delay ) 4) Event-based triggering (decoupled) - If you want “trigger B when A finishes” without putting chaining logic in your flows, use an Automation with a RunDeployment action. - Links: - Chaining with Automations: https://docs-3.prefect.io/v3/how-to-guides/automations/chaining-deployments-with-events
- Schedules concept: https://docs-3.prefect.io/v3/concepts/schedules Putting it together - Deploy each flow with flow.from_source(...).deploy(...). - Run a worker for your work pool. - FastAPI calls run_deployment("flow/deployment", parameters=...). - For recursive behavior, call run_deployment from within the flow, optionally scheduling the next run. - For external or completion-based triggers, use Automations. If you share: - Where your code lives (Git repo vs local), - Your execution environment (K8s, Docker, VM), - Whether you want FastAPI to call Python or REST, I can tailor the deploy/trigger snippets to your exact setup. I can hardly wait.
a
@Marvin the use-case is i have a flow to be run every-time a new user onboards as-well as create a recursive workflow everyday (say), the parameter for the workflow will differ for each such user. can you give me a minimal implementation of the workflow and worker deployment and the fastapi endpoint to trigger it onboarding
m
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âś… thought for 156.5 seconds
Got it. Here’s a minimal, working pattern for Prefect 3.x that does: - Trigger-on-onboarding: your FastAPI endpoint kicks off a flow run with user-specific parameters. - Daily recursion: the flow schedules its own next run for the same user, once per day. I’m keeping this lean, but complete. 1) The flow: user_daily_flow.py - Does work for a user. - Schedules the next day’s run of the same deployment with the same user_id. - Stops scheduling if your own check says the user is inactive. Links: - run_deployment API: https://docs-3.prefect.io/v3/api-ref/python/prefect/deployments/#prefect.deployments.run_deployment from datetime import datetime, timedelta, timezone from prefect import flow, get_run_logger from prefect.deployments import run_deployment def user_is_active(user_id: str) -> bool: # TODO: replace with your actual check (DB/API/etc.) return True @flow def user_daily_flow(user_id: str, run_ts: datetime | None = None, schedule_next: bool = True): logger = get_run_logger() now = run_ts or datetime.now(timezone.utc) logger.info(f"Running daily workflow for user_id={user_id} at {now.isoformat()}") # TODO: your actual work here # e.g., fetch data, transform, write to storage, etc. # Recursively schedule tomorrow's run if schedule_next and user_is_active(user_id): next_time = now + timedelta(days=1) logger.info(f"Scheduling next run for user_id={user_id} at {next_time.isoformat()}") run_deployment( name="user-daily/prod", # flow_name/deployment_name parameters={"user_id": user_id, "run_ts": next_time}, scheduled_time=next_time, # enqueue for the future as_subflow=False, # independent flow run ) else: logger.info(f"Not scheduling next run (schedule_next={schedule_next}, active={user_is_active(user_id)})") 2) Deploy the flow (programmatically) - One deployment, executed by a worker in your work pool. - We’ll override parameters at runtime; defaults are just placeholders. Links: - Deploy via Python: https://docs-3.prefect.io/v3/how-to-guides/deployments/deploy-via-python # deploy.py from prefect import deploy from prefect.deployments import RunnerDeployment if name == "__main__": deploy( RunnerDeployment( name="prod", entrypoint="user_daily_flow.py:user_daily_flow", # path:func work_pool_name="users-pool", parameters={"user_id": "placeholder", "schedule_next": True}, # no schedules here; the flow self-schedules ) ) 3) FastAPI endpoint to kick off onboarding - This starts the chain: run once now for that user; the flow schedules the next one. # app.py from fastapi import FastAPI from pydantic import BaseModel from typing import Optional, Any, Dict import anyio from prefect.deployments import run_deployment app = FastAPI() class OnboardBody(BaseModel): user_id: str # optional: allow passing schedule_next=False to just run once schedule_next: Optional[bool] = True @app.post("/onboard") async def onboard(body: OnboardBody): flow_run = await anyio.to_thread.run_sync( run_deployment, name="user-daily/prod", parameters={"user_id": body.user_id, "schedule_next": body.schedule_next}, ) return {"flow_run_id": str(flow_run.id)} 4) Run a worker for your work pool - Create a work pool (e.g., “users-pool”) and start a worker that listens to it. - You can do this in the UI or via CLI. Then set PREFECT_API_URL (and PREFECT_API_KEY for Cloud) in the worker’s environment so it can reach your Prefect API. Typical CLI (if you prefer CLI over UI): - Create pool: prefect work-pool create users-pool --type process - Start worker: prefect worker start --pool users-pool Note: If flags differ in your environment, check: - prefect work-pool create --help - prefect worker start --help Operational notes
- Don’t import or run flows inside FastAPI; always trigger deployments so workers do the work. - The recursion is controlled in the flow; use user_is_active(user_id) or schedule_next to prevent infinite runs when a user offboards. - If you prefer schedules over recursion, you can create one deployment per user with a Cron schedule and bake their user_id into the deployment parameters. That’s fine for small/known user counts; the recursive approach scales better for large/unknown counts. If you share where you want the worker to run (VM, Docker, K8s) and whether you use Prefect Cloud or Server, I can give you the exact worker command and env settings. I live to serve. Begrudgingly.
a
@Marvin how do i create and start a work pool
m
âś… thought for 41.4 seconds
The thrilling two-step. 1) Create a work pool - Pick a type that matches your runtime. Common ones: process (local machine/VM), docker, kubernetes. - Example (local process worker): prefect work-pool create users-pool --type process - To see more options: prefect work-pool create --help 2) Start a worker that polls the pool - Run this on the machine (or container/node) that should execute your flows. - Ensure PREFECT_API_URL (and PREFECT_API_KEY for Cloud) are set in the environment. Example: prefect worker start --pool users-pool Optional niceties: - Name the worker: prefect worker start --pool users-pool --name users-worker-1 - Target specific queues (if you use queues under the pool): prefect worker start --pool users-pool --work-queue high-priority --work-queue low-priority - Check the pool exists and is reachable: prefect work-pool inspect users-pool If you’re on Docker or Kubernetes, create a docker/kubernetes pool instead and start a matching worker in that environment.
a
i get: prefect.exceptions.PrefectHTTPStatusError: Client error '403 Forbidden' for url 'https://api.prefect.cloud/api/accounts/4dfd5b81-352e-4ef1-86ff-d117a9c1a80c/workspaces/a7df1977-35a1-4fdb-a086-561beb4af6b4/work_pools/' 2025-08-08T110923.284710170Z Response: {'detail': 'Your plan does not support hybrid or push work pools.'} for prefect work-pool create local-pool --type process
@Marvin i get: prefect.exceptions.PrefectHTTPStatusError: Client error '403 Forbidden' for url 'https://api.prefect.cloud/api/accounts/4dfd5b81-352e-4ef1-86ff-d117a9c1a80c/workspaces/a7df1977-35a1-4fdb-a086-561beb4af6b4/work_pools/' 2025-08-08T110923.284710170Z Response: {'detail': 'Your plan does not support hybrid or push work pools.'} for prefect work-pool create local-pool --type process
m
âś… thought for 158.8 seconds
You’re hitting a Cloud plan restriction. “process” is a hybrid (pull) pool, and your plan doesn’t allow hybrid or push work pools. You’ve got three paths: A) Use a Prefect-managed (serverless) work pool - No worker to start; Prefect runs it for you. - Create the pool: - prefect work-pool create serverless-pool --type prefect:managed - Point your deployment at this pool (work_pool_name="serverless-pool") and run. That’s it. B) Upgrade your Prefect Cloud plan to allow hybrid/push - Then your original commands will work: - prefect work-pool create local-pool --type process - prefect worker start --pool local-pool C) Use self-hosted Prefect Server - No plan limits; you can use process/docker/k8s pools and start workers locally. Docs and references: - Work pools overview: https://docs-3.prefect.io/v3/concepts/work-pools - Managing work pools: https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/manage-work-pools - Prefect Serverless (managed pools) intro: https://www.prefect.io/blog/introducing-prefect-serverless If you want help switching your deployment to a prefect:managed pool (and what, if anything, you need to change about packaging), tell me how you’re deploying today (Python deploy script vs CLI, Docker image vs source) and I’ll give exact steps. I’ll try to contain my excitement.
a
how do i start the worker in this case
@Marvin how do i start the worker in this case
m
âś… thought for 33.7 seconds
Short answer: you don’t. With a Prefect-managed (serverless) work pool, there is no worker process to start—Prefect runs it for you. What to do instead: - Create a managed pool: - prefect work-pool create serverless-pool --type prefect:managed - Point your deployment at that pool (work_pool_name="serverless-pool"). - Trigger runs (from FastAPI or the UI/CLI) and Prefect’s managed compute executes them. Minimal deploy tweak - If you’re using the Python deploy snippet I gave earlier, just set the pool name: from prefect import deploy from prefect.deployments import RunnerDeployment if name == "__main__": deploy( RunnerDeployment( name="prod", entrypoint="user_daily_flow.py:user_daily_flow", work_pool_name="serverless-pool", # managed pool, no worker to start parameters={"user_id": "placeholder", "schedule_next": True}, ) ) If you actually want to run your own worker - That requires a hybrid (pull) pool like process/docker/kubernetes, which your current Cloud plan blocks. Your options: - Upgrade your plan to enable hybrid/push pools, then: - prefect work-pool create local-pool --type process - prefect worker start --pool local-pool - Or use self-hosted Prefect Server, which has no plan restrictions. Docs - Work pools overview: https://docs-3.prefect.io/v3/concepts/work-pools - Managing work pools: https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/manage-work-pools - Prefect Serverless (managed pools): https://www.prefect.io/blog/introducing-prefect-serverless If you want me to sanity-check your deployment config for serverless (image, Python version, env vars), paste it here and I’ll tighten the bolts. Begrudgingly thorough, as always.
a
Copy code
from pathlib import Path

import dotenv

dotenv.load_dotenv(Path(__file__).parent.parent / ".env")

from prefect import flow

from config import Settings

settings = Settings()

if __name__ == "__main__":
    flow.from_source(
        source="file://./flows",
        entrypoint="mscopilot_flow.py:mscopilot_flow"
    ).deploy(
        name="mscopilot_flow",
        work_pool_name="serverless-pool",
        tags=settings.build_tags,
        parameters={}
    )

    flow.from_source(
        source="file://./flows",
        entrypoint="analysis_report_flow.py:analysis_report_flow"
    ).deploy(
        name="analysis_report_flow",
        work_pool_name="serverless-pool",
        tags=settings.build_tags,
        parameters={}
    )
this is my deployment code: i get[Errno 2] No such file or directory: '/Users/amithmundur/PycharmProjects/armor-adk/app/prefect_workers/flows' in the cloud instance
@Marvin
Copy code
from pathlib import Path

import dotenv

dotenv.load_dotenv(Path(__file__).parent.parent / ".env")

from prefect import flow

from config import Settings

settings = Settings()

if __name__ == "__main__":
    flow.from_source(
        source="file://./flows",
        entrypoint="mscopilot_flow.py:mscopilot_flow"
    ).deploy(
        name="mscopilot_flow",
        work_pool_name="serverless-pool",
        tags=settings.build_tags,
        parameters={}
    )

    flow.from_source(
        source="file://./flows",
        entrypoint="analysis_report_flow.py:analysis_report_flow"
    ).deploy(
        name="analysis_report_flow",
        work_pool_name="serverless-pool",
        tags=settings.build_tags,
        parameters={}
    )
this is my deployment code: i get[Errno 2] No such file or directory: '/Users/amithmundur/PycharmProjects/armor-adk/app/prefect_workers/flows' in the cloud instance
m
âś… thought for 174.0 seconds
You’re pointing a managed (serverless) deployment at your local disk. The serverless runner can’t see file://./flows on your laptop, so it quite reasonably complains. Fix: put your code somewhere the serverless runner can fetch it (e.g., a Git repo) and reference that as the source. Two minimal ways: Option 1 — flow.from_source with a Git URL - Works for public or private repos (private requires credentials; see Option 2). - Ensure your entrypoint is relative to the repo root. Example: from prefect import flow if name == "__main__": flow.from_source( source="https://github.com/your-org/your-repo", entrypoint="flows/mscopilot_flow.py:mscopilot_flow", ).deploy( name="mscopilot_flow", work_pool_name="serverless-pool", tags=["whatever"], parameters={}, ) flow.from_source( source="https://github.com/your-org/your-repo", entrypoint="flows/analysis_report_flow.py:analysis_report_flow", ).deploy( name="analysis_report_flow", work_pool_name="serverless-pool", tags=["whatever"], parameters={}, ) Option 2 — Explicit runner storage with Git (good for private repos) - Supply credentials via a token; Prefect will clone your repo at run time. from prefect import deploy from prefect.deployments import RunnerDeployment from prefect.runner.storage import GitRepository, GitCredentials import os if name == "__main__": repo = GitRepository( url="https://github.com/your-org/your-repo", branch="main", # for private repos: # credentials=GitCredentials(token=os.environ["GITHUB_TOKEN"]), ) deploy( RunnerDeployment( name="mscopilot_flow", entrypoint="flows/mscopilot_flow.py:mscopilot_flow", storage=repo, work_pool_name="serverless-pool", tags=["whatever"], parameters={}, ), RunnerDeployment( name="analysis_report_flow", entrypoint="flows/analysis_report_flow.py:analysis_report_flow", storage=repo, work_pool_name="serverless-pool", tags=["whatever"], parameters={}, ), ) Important notes - Dependencies: Put requirements.txt or pyproject.toml in the repo root so Prefect can install them on the managed runner. - Environment variables: Your .env on your laptop won’t exist in serverless. Use Prefect Secrets or Variables instead, and read them inside your flow. - Example with a Secret block: from prefect.blocks.system import Secret api_key = Secret.load("my-api-key").get() - Docs: Store secrets: https://docs-3.prefect.io/v3/how-to-guides/configuration/store-secrets - file:// sources only work with pull workers that can see your filesystem (process/docker/k8s) — which your plan currently doesn’t allow. Managed pools require remote-accessible source. Docs - Deploy via Python: https://docs-3.prefect.io/v3/how-to-guides/deployments/deploy-via-python If your repo is private, tell me which host (GitHub/GitLab/Bitbucket) and I’ll show the exact credential setup. If you really must use local files, you’ll need a hybrid pool and a worker with the code checked out locally (which means upgrading your plan or using self-hosted Prefect Server). I know, joyless realities.
a
@Marvin flow.from_source vs serve
m
âś… thought for 67.6 seconds
They solve different problems: flow.from_source(...).deploy(...) - What it does: Registers a deployment that points at your code in a remote source (Git/remote storage) and associates it with a work pool. It doesn’t run anything locally. - Where it runs: Wherever the work pool executes (managed/serverless, or your own workers if you’re on hybrid/pull). - When to use: Production or CI/CD. Perfect for Prefect-managed (serverless) pools because they can fetch your code from Git. - Gotcha: Don’t use file:// paths with managed pools; use a Git URL (or explicit RunnerStorage). serve(...) - What it does: Starts a local runner process and “serves” one or more deployments from your current machine. It registers ephemeral deployments and immediately runs them locally. - Where it runs: Your machine/container. No managed compute involved. - When to use: Local dev and rapid iteration. Not what you want if you’re relying on Prefect-managed serverless to execute your flows. Minimal examples - Managed/serverless deploy (production-ish): from prefect import flow if name == "__main__": flow.from_source( source="https://github.com/your-org/your-repo", entrypoint="flows/mscopilot_flow.py:mscopilot_flow", ).deploy( name="mscopilot_flow", work_pool_name="serverless-pool", parameters={}, ) - Local dev with serve (runs on your box): from prefect import serve from prefect.deployments import RunnerDeployment if name == "__main__": serve( RunnerDeployment( name="dev", entrypoint="flows/mscopilot_flow.py:mscopilot_flow", # With serve, this runs locally; a local/hybrid pool is typical # but the execution is on this process, not managed serverless. ) ) Which should you pick for your case? - You’re on a Prefect-managed pool: use flow.from_source(...).deploy(...) with a Git repo for your code. Don’t use serve. - You want quick local iteration: use serve; your runs stay on your machine. Docs - Deploy via Python (flow.from_source + deploy): https://docs-3.prefect.io/v3/how-to-guides/deployments/deploy-via-python If you want, I can convert your current file:// deployments into a Git-based deploy script that works with your serverless pool.
a
can i use serve if am using gcp
@Marvin can i use serve if am using gcp
m
âś… thought for 40.9 seconds
Short answer: yes, but only if you run it on GCP infrastructure you control (e.g., a long‑lived GCE VM or a GKE pod). “Serve” runs your flows in the same process it starts; it’s great for dev or a single-box runner. It is not suited for Cloud Run or Cloud Functions. How to use serve on GCP - Where it works: - GCE VM: fine. - GKE Pod/Deployment: fine. - Cloud Run / Functions / Cloud Run Jobs: not appropriate (stateless, request-driven, will be idled/terminated). - Minimal serve script: from prefect import serve from prefect.deployments import RunnerDeployment if name == "__main__": serve( RunnerDeployment( name="mscopilot_flow-dev", entrypoint="flows/mscopilot_flow.py:mscopilot_flow", ), RunnerDeployment( name="analysis_report_flow-dev", entrypoint="flows/analysis_report_flow.py:analysis_report_flow", ), ) - Run it on a GCE VM: 1) Install your code + Prefect. 2) Set PREFECT_API_URL and (for Cloud) PREFECT_API_KEY in the environment. 3) python serve_app.py 4) Keep the process alive with systemd/supervisor or run it inside a container. - Run it on GKE: - Build a container image with your code and the serve script. - Deploy a Deployment with restartPolicy: Always. - Inject PREFECT_API_URL and PREFECT_API_KEY via Secrets/ConfigMaps. Caveats - No worker or work pool needed; serve sidesteps your plan’s hybrid/push restriction. That’s the point. - The deployments it registers are ephemeral; if the process stops, they vanish until the process starts again. - It’s a single runner process. For real production on GCP, prefer: - Push/hybrid work pools for Cloud Run/GKE (requires plan that allows them), or - Prefect-managed (serverless) pools with deploy(...) and a Git source (no serve). If you tell me where on GCP you want to run this (GCE vs GKE) I can give you a minimal systemd unit or Kubernetes manifest to keep serve running. I know, thrilling.