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# ask-marvin
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m
✅ thought for 106.6 seconds
In Prefect 3.x, the Prefect Client is the Python interface to the Prefect API. You use it when you want to programmatically inspect or control Prefect objects like flow runs, deployments, work pools, variables, blocks, etc. Docs: - Python client API - REST API overview Most common entry point: 
get_client()
Async client:
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python
from prefect import get_client

async def main():
    async with get_client() as client:
        response = await client.hello()
        print(response.json())
Sync client:
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python
from prefect import get_client

def main():
    with get_client(sync_client=True) as client:
        response = client.hello()
        print(response.json())
Important: use the client as a context manager: - 
async with get_client()
for async code - 
with get_client(sync_client=True)
for sync code A closed client should not be reused. Example: read flow runs
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python
from prefect import get_client
from prefect.client.schemas.filters import FlowRunFilter

async def show_completed_runs():
    async with get_client() as client:
        runs = await client.read_flow_runs(
            flow_run_filter=FlowRunFilter(
                state={"type": {"any_": ["COMPLETED"]}}
            ),
            limit=10,
        )

        for run in runs:
            print(run.name, run.state.type)
Example: read deployments with pagination
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python
from prefect import get_client

async def read_all_deployments():
    deployments = []
    offset = 0
    page_size = 200

    async with get_client() as client:
        while True:
            page = await client.read_deployments(limit=page_size, offset=offset)
            if not page:
                break

            deployments.extend(page)

            if len(page) < page_size:
                break

            offset += page_size

    return deployments
When should you use the client? - Querying flow/task runs - Creating or inspecting deployments - Managing work pools / queues - Reading or setting variables, artifacts, blocks - Performing lower-level orchestration actions not covered by decorators alone Common caveats: - Async and sync clients are different; don’t mix 
with
/ 
async with
- Results are often paginated; use `limit`/`offset` if you need everything - 
get_client()
may reuse a client inside the same active context - Auth is usually picked up from your Prefect settings/environment automatically If you want, I can also give you: 1. a cheatsheet of the most useful client methods, or 2. a real example for a specific use case like listing deployments, cancelling runs, or triggering a deployment.
✅ thought for 121.3 seconds
m
@Marvin is prefect command available in prefect-client module
m
✅ thought for 8.3 seconds
No — the 
prefect
CLI command is not provided by the 
prefect-client
package. In Prefect 3.x: - 
prefect
package → includes the SDK and the 
prefect
CLI - 
prefect-client
package → client library for interacting with the API, but not the full CLI So if you install only 
prefect-client
, you should not expect the 
prefect
shell command to be available. If you want the CLI, install `prefect`:
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bash
pip install prefect
If helpful: - Prefect docs: docs.prefect.io - Python client API: Prefect client API If you want, I can also explain the practical difference between 
prefect
vs 
prefect-client
packages.
Yes — 
prefect.settings
is available in 
prefect-client
 in Prefect 3.x. That means if you're using the lighter 
prefect-client
package, you can still do things like:
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python
from prefect.settings import PREFECT_API_URL, get_current_settings

print(PREFECT_API_URL.value())

settings = get_current_settings()
print(settings.api.url)
The client package itself uses 
prefect.settings
internally for things like: - API URL - API key - retry behavior - Cloud/server connection settings So settings support is not just present — it’s part of how the client works. Useful docs: - `prefect.settings` API - Prefect client API Example with temporary settings override:
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python
from prefect.settings import PREFECT_API_URL, temporary_settings
from prefect import get_client

async def main():
    with temporary_settings(updates={PREFECT_API_URL: "<http://localhost:4200/api>"}):
        async with get_client() as client:
            response = await client.hello()
            print(response.json())
A couple caveats: - Some settings namespaces may exist even if the related feature is not especially useful in 
prefect-client
alone - The CLI is not included in 
prefect-client
, even though settings APIs are - Settings sources still work normally: env vars, profiles, 
.env
, 
prefect.toml
, etc. So the short answer is: yes, 
prefect.settings
is available and supported in 
prefect-client
. If you want, I can also show: - the minimum imports needed for 
prefect-client
only, or - the difference between 
from prefect import get_client
vs 
from prefect.client.orchestration import get_client
in this setup.
m
@Marvin How do I execute a new deployment with prefect client
m
✅ thought for 76.0 seconds
To execute a new run from an existing deployment with the Prefect client in Prefect 3.x, use: 
create_flow_run_from_deployment()
 Usually the flow is: 1. Look up the deployment 2. Get its 
id
3. Create a flow run from that deployment Docs: - Prefect client API Async example
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python
from prefect.client.orchestration import get_client

async def trigger():
    async with get_client() as client:
        deployment = await client.read_deployment_by_name("my-flow/my-deployment")

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters={"x": 1, "y": 2},
            name="manual-trigger",
            tags=["ad-hoc", "api"],
            idempotency_key="manual-trigger-001",
        )

        print(flow_run.id)
Sync example
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python
from prefect.client.orchestration import get_client

def trigger():
    with get_client(sync_client=True) as client:
        deployment = client.read_deployment_by_name("my-flow/my-deployment")

        flow_run = client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters={"x": 1, "y": 2},
            name="manual-trigger",
            tags=["ad-hoc", "api"],
            idempotency_key="manual-trigger-001",
        )

        print(flow_run.id)
If you already know the deployment UUID
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python
from uuid import UUID
from prefect.client.orchestration import get_client

async def trigger():
    async with get_client() as client:
        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=UUID("12345678-1234-5678-1234-567812345678"),
            parameters={"x": 1},
        )
        print(flow_run.id)
Optional arguments you can pass - 
parameters
— override deployment parameters - 
name
— custom flow run name - 
tags
— extra tags for this run - 
idempotency_key
— avoid duplicate run creation on retries - 
state
— provide an initial state, e.g. scheduled later - 
work_queue_name
— override the queue - 
job_variables
— override infrastructure/job settings - 
labels
— attach labels Example scheduling it for later
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python
from datetime import datetime, timedelta
from prefect.client.orchestration import get_client
from prefect.states import Scheduled

async def trigger_later():
    async with get_client() as client:
        deployment = await client.read_deployment_by_name("my-flow/my-deployment")

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            state=Scheduled(scheduled_time=datetime.utcnow() + timedelta(minutes=30)),
            name="run-later",
        )

        print(flow_run.id)
A few caveats: - You need a deployment ID for 
create_flow_run_from_deployment()
- If you only know the name, use 
read_deployment_by_name("flow-name/deployment-name")
- This triggers a run of an existing deployment, so it uses the deployment’s configured infrastructure / worker setup - This is different from calling a flow function directly, which just runs Python code and does not create a deployment-backed run in the same way If you want, I can also show a minimal example using only 
prefect-client
imports specifically, without the broader 
prefect
package style imports.
m
@Marvin I am using microservices architecture for connecting my prefect client to the actual server, how come I configure the .env especially the API Key, and others
m
✅ thought for 102.3 seconds
In a microservices setup, the simplest pattern is: - each service gets its own environment - set Prefect connection/auth settings in that environment - let 
get_client()
pick them up automatically You usually want to configure at least: - 
PREFECT_API_URL
- 
PREFECT_API_KEY
for Prefect Cloud - or 
PREFECT_API_AUTH_STRING
for self-hosted server with basic auth Relevant docs: - Prefect settings API - Prefect client API - Prefect docs 1. If you use Prefect Cloud Set:
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PREFECT_API_URL=<https://api.prefect.cloud/api/accounts/<account-id>/workspaces/<workspace-id>>
PREFECT_API_KEY=pnu_xxxxx
Example `.env`:
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PREFECT_API_URL=<https://api.prefect.cloud/api/accounts/ACCOUNT_ID/workspaces/WORKSPACE_ID>
PREFECT_API_KEY=pnu_your_api_key_here
PREFECT_API_REQUEST_TIMEOUT=60
2. If you use self-hosted Prefect server Set:
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PREFECT_API_URL=<http://prefect-server:4200/api>
If your server is protected with basic auth:
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PREFECT_API_AUTH_STRING=username:password
Example `.env`:
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PREFECT_API_URL=<http://prefect-server:4200/api>
PREFECT_API_AUTH_STRING=admin:password
PREFECT_API_REQUEST_TIMEOUT=60
Important caveat: for self-hosted setups, do not also set 
PREFECT_API_KEY
unless you specifically mean to use token auth supported by your environment. If both are set, auth behavior can be confusing, and 
PREFECT_API_KEY
may take precedence. 3. How 
.env
works Prefect settings are loaded from multiple places, including: - environment variables - 
.env
- 
prefect.toml
- 
pyproject.toml
- profiles In practice for microservices, the cleanest approach is: - local development: 
.env
- containers/Kubernetes: real environment variables or secrets injection So your code can stay minimal:
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python
from prefect.client.orchestration import get_client

async def main():
    async with get_client() as client:
        response = await client.hello()
        print(response.json())
No need to manually pass URL/key if the environment is configured correctly. 4. Recommended microservices pattern Each service should receive its own config through: - Docker Compose 
environment:
- Kubernetes 
ConfigMap
+ 
Secret
- ECS task env vars / secrets - your secret manager Docker Compose example
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yaml
services:
  my-service:
    image: my-service:latest
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_API_AUTH_STRING: admin:password
Kubernetes-style pattern - 
PREFECT_API_URL
in a ConfigMap - 
PREFECT_API_KEY
or 
PREFECT_API_AUTH_STRING
in a Secret That is much better than baking credentials into the image or committing 
.env
to git. 5. Other useful settings Depending on your network/security setup, these can help: - 
PREFECT_API_REQUEST_TIMEOUT
- 
PREFECT_API_TLS_INSECURE_SKIP_VERIFY=true
(dev only) - 
PREFECT_API_SSL_CERT_FILE=/path/to/ca.pem
- 
PREFECT_CLIENT_CUSTOM_HEADERS='{"X-Service-Name":"orders"}'
Example:
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PREFECT_API_URL=<https://prefect.internal/api>
PREFECT_API_SSL_CERT_FILE=/etc/ssl/certs/internal-ca.pem
PREFECT_API_REQUEST_TIMEOUT=90
PREFECT_CLIENT_CUSTOM_HEADERS={"X-Service-Name":"billing"}
6. Suggested setup by environment Local development
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# .env
PREFECT_API_URL=<http://localhost:4200/api>
PREFECT_API_AUTH_STRING=admin:password
Production Cloud
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PREFECT_API_URL=<https://api.prefect.cloud/api/accounts/ACCOUNT/workspaces/WORKSPACE>
PREFECT_API_KEY=${INJECTED_SECRET}
Production self-hosted
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PREFECT_API_URL=<https://prefect.mycompany.internal/api>
PREFECT_API_AUTH_STRING=${INJECTED_SECRET}
PREFECT_API_SSL_CERT_FILE=/etc/ssl/certs/company-root.pem
7. Best practices - Use 
.env
only for local dev - Use secret injection for production
- Give every service the same 
PREFECT_API_URL
if they target the same Prefect API - Do not commit API keys to source control - Keep Cloud auth (
PREFECT_API_KEY
) and self-hosted basic auth (
PREFECT_API_AUTH_STRING
) separate If you want, I can give you a ready-to-use example for your exact stack: - Docker Compose - Kubernetes - FastAPI microservice - Node-to-Python sidecar - self-hosted Prefect server vs Prefect Cloud
m
@Marvin When is the case Prefect is compatible with python slim base image? because lately I can't use image other than standard python image
m
✅ thought for 146.9 seconds
Yes — Prefect is compatible with 
python:<version>-slim
images. In fact, for Prefect 3.x, slim images are generally a good choice. The big exception is usually not “slim vs standard”, but rather: - 
slim
works - 
alpine
often causes trouble There’s even a recent issue showing Prefect working with 
python:3.12-slim
while 
python:3.12-alpine
crashes with a segmentation fault: - Segmentation fault on Alpine, slim works Also relevant: - Prefect Docker guide - Serve flows with Docker So why might 
python:slim
fail for you? Usually it’s because the extra OS packages your app or dependencies need are missing, not because Prefect itself requires the full Python image. Common missing pieces in slim images: - 
git
— needed if you pull code from git repos - 
build-essential
— needed if any package must compile native extensions - 
curl
— sometimes used in startup scripts/healthchecks - DB/system libs like 
libpq-dev
— if using some Postgres drivers from source - 
graphviz
— only if you use flow visualization - certificates / SSL packages — if you connect to internal HTTPS endpoints with custom certs Important distinction: - 
python:3.12-slim
→ Debian-based, usually fine - 
python:3.12
→ larger Debian image, more tools preinstalled - 
python:3.12-alpine
→ musl-based, much more likely to break A good baseline Dockerfile for Prefect on slim
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dockerfile
FROM python:3.12-slim

ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    build-essential \
    curl \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "your_script.py"]
If you want a worker image, a common pattern is:
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dockerfile
FROM python:3.12-slim

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /opt/prefect

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["prefect", "worker", "start", "--pool", "my-pool"]
Even better: if you want a Prefect-first base image, start from Prefect’s published image:
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dockerfile
FROM prefecthq/prefect:3-latest
That can save you some dependency headaches. When slim is usually safe - client-only microservices using 
prefect-client
- flow containers with pure Python dependencies - workers that don’t need special system libraries - cloud SDK integrations that provide wheels When slim often needs extra work - ML / data science stacks - packages with native extensions - browser automation - database drivers with compiled dependencies - custom internal cert/network setups My guess for your case If “standard Python works, slim doesn’t”, the likely root cause is one of: 1. missing 
git
2. missing compiler/build tools 3. missing shared library for one of your dependencies 4. using Alpine somewhere, not slim 5. a dependency without a prebuilt wheel for your Python version / architecture Best way to debug Build the slim image and inspect the actual install error:
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docker build . --progress=plain
If the build succeeds but runtime fails:
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docker run --rm -it your-image bash
python -c "import prefect; print('ok')"
python -c "import your_other_dependency"
That will usually reveal the package causing the issue. If you want, paste your current Dockerfile and 
requirements.txt
/ 
pyproject.toml
, and I can tell you exactly what is missing for 
slim
.
m
@Marvin How I retrieve all task logs from subflows in Prefect
m
✅ thought for 140.9 seconds
In Prefect 3.x, subflow logs are not automatically bundled into the parent flow run logs. You need to: 1. get the parent flow run’s logs 2. find its subflow runs 3. query logs for each subflow run separately 4. combine them yourself Relevant docs / references: - Prefect client API - REST API overview ### Key idea A subflow is its own flow run. That means: - parent flow logs live under the parent 
flow_run_id
- subflow logs live under each child/subflow 
flow_run_id
So if you want all task logs from subflows, you need to query each subflow run’s logs. ### Basic pattern
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python
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import LogFilter

async def get_logs_for_flow_run(flow_run_id):
    async with get_client() as client:
        logs = await client.read_logs(
            log_filter=LogFilter(
                flow_run_id={"any_": [flow_run_id]}
            )
        )
        return logs
That gets logs for one flow run only. --- ## How to retrieve subflow logs The relationship is tracked through 
parent_task_run_id
on the subflow run. In practice, the easiest approach is: - read the parent flow run - find child flow runs whose 
parent_task_run_id
belongs to a task run from the parent flow - query logs for those child flow runs ### Example: parent + direct subflows
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python
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import (
    FlowRunFilter,
    TaskRunFilter,
    LogFilter,
)

async def get_parent_and_subflow_logs(parent_flow_run_id):
    async with get_client() as client:
        all_logs = []

        # 1. Parent flow logs
        parent_logs = await client.read_logs(
            log_filter=LogFilter(
                flow_run_id={"any_": [parent_flow_run_id]}
            )
        )
        all_logs.extend(parent_logs)

        # 2. Get task runs in the parent flow
        parent_task_runs = await client.read_task_runs(
            flow_run_filter=FlowRunFilter(
                id={"any_": [parent_flow_run_id]}
            )
        )
        parent_task_run_ids = [tr.id for tr in parent_task_runs]

        # 3. Find child flow runs (subflows) started by those task runs
        subflow_runs = await client.read_flow_runs(
            flow_run_filter=FlowRunFilter(
                parent_task_run_id={"any_": parent_task_run_ids}
            )
        )

        # 4. Read logs for each subflow
        for subflow_run in subflow_runs:
            subflow_logs = await client.read_logs(
                log_filter=LogFilter(
                    flow_run_id={"any_": [subflow_run.id]}
                )
            )
            all_logs.extend(subflow_logs)

        return all_logs
--- ## If you want all nested subflows recursively If subflows can themselves launch subflows, use recursion: ```python from prefect.client.orchestration import get_client from prefect.client.schemas.filters import FlowRunFilter, LogFilter async def get_all_logs_recursive(flow_run_id, client): logs = await client.read_logs( log_filter=LogFilter( flow_run_id={"any_": [flow_run_id]} ) ) task_runs = await client.read_task_runs( flow_run_filter=FlowRunFilter( id={"any_": [flow_run_id]} ) ) task_run_ids = [tr.id for tr in task_runs] if not task_run_ids: return logs subflow_runs = await client.read_flow_runs( flow_run_filter=FlowRunFilter( parent_task_run_id={"any_": task_run_ids} ) ) for subflow_run in subflow_runs: child_logs = await get_all_logs_recursive(subflow_run.id, client) logs.extend(child_logs) return logs async def fetch_everything(root_flow_run_id): async with get_client() as client:
return await get_all_logs_recursive(root_flow_run_id, client)
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---

## Important caveats

- *Logs are separate from flow run records* — `read_flow_runs()` does not include them
- `read_logs()` is usually *paginated / limited*, so if you have lots of logs, use `limit` + `offset`
- querying only the parent flow run *will not* return subflow task logs
- for large trees of nested subflows, recursive querying can be expensive

### Pagination example

```python
from prefect.client.schemas.sorting import LogSort

async def read_all_logs_for_run(flow_run_id, client, page_size=200):
    offset = 0
    all_logs = []

    while True:
        page = await client.read_logs(
            log_filter=LogFilter(
                flow_run_id={"any_": [flow_run_id]}
            ),
            limit=page_size,
            offset=offset,
            sort=LogSort.TIMESTAMP_ASC,
        )

        if not page:
            break

        all_logs.extend(page)

        if len(page) < page_size:
            break

        offset += page_size

    return all_logs
--- ## CLI note If you just need logs for one flow run, the CLI can help:
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prefect flow-run logs <FLOW_RUN_ID>
But for parent + all subflows, the Python client is the better option because you need multiple queries. If you want, I can give you a ready-to-run utility function that returns: - all logs grouped by subflow - all logs flattened and sorted by timestamp - or all task-only logs excluding flow-level logs
m
@Marvin how prefect client's create_flow_run_from_deployment know which deployment to be executed
m
✅ thought for 15.9 seconds
create_flow_run_from_deployment()
knows which deployment to execute because you explicitly tell it via the 
deployment_id
you pass in. So this call:
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python
flow_run = await client.create_flow_run_from_deployment(
    deployment_id=deployment.id
)
means: - find the deployment record with that exact UUID - create a new flow run using that deployment’s configuration - schedule it for execution It does not infer the deployment automatically from your code or current flow. ### What the deployment contains A deployment stores the information Prefect needs to launch a run, such as: - which flow it belongs to - parameters/default parameters - work pool / queue routing - infrastructure/job settings - code location / entrypoint - schedules, tags, and related metadata So when you call 
create_flow_run_from_deployment(deployment_id=...)
, Prefect creates a flow run based on that deployment definition. ### Typical flow Usually you do one of these first: Lookup by name
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python
from prefect.client.orchestration import get_client

async with get_client() as client:
    deployment = await client.read_deployment_by_name("my-flow/my-deployment")

    flow_run = await client.create_flow_run_from_deployment(
        deployment_id=deployment.id
    )
Or use a known UUID directly
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python
from uuid import UUID

flow_run = await client.create_flow_run_from_deployment(
    deployment_id=UUID("12345678-1234-5678-1234-567812345678")
)
### So how does execution actually happen? 
create_flow_run_from_deployment()
itself does not run the code directly. It: 1. creates a flow run associated with that deployment 2. stores it in the API 3. sets it up for workers to pick up Then a worker polls the appropriate work pool / queue and executes the run according to the deployment configuration. ### Short answer It knows which deployment to execute because of the 
deployment_id
argument — that ID points to one specific deployment record in the Prefect API. Reference: - Prefect client API If you want, I can also explain: - how Prefect maps 
flow/deployment-name
to 
deployment_id
, or - what happens internally after the flow run is created.
m
@Marvin how to get flow status
m
✅ thought for 11.8 seconds
To get a flow run’s status with the Prefect client, read the flow run and inspect its 
state
. Async example
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python
from prefect.client.orchestration import get_client
from uuid import UUID

async def get_flow_run_status(flow_run_id: str):
    async with get_client() as client:
        flow_run = await client.read_flow_run(UUID(flow_run_id))
        return flow_run.state.name, flow_run.state.type
Sync example
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python
from prefect.client.orchestration import get_client
from uuid import UUID

def get_flow_run_status(flow_run_id: str):
    with get_client(sync_client=True) as client:
        flow_run = client.read_flow_run(UUID(flow_run_id))
        return flow_run.state.name, flow_run.state.type
If you print it:
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state_name, state_type = await get_flow_run_status("YOUR-FLOW-RUN-ID")
print(state_name)   # e.g. "Completed"
print(state_type)   # e.g. "COMPLETED"
### Common values - 
Scheduled
- 
Pending
- 
Running
- 
Completed
- 
Failed
- 
Cancelled
- 
Crashed
### If you want to poll until completion
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import asyncio
from prefect.client.orchestration import get_client
from uuid import UUID

async def wait_for_flow_run(flow_run_id: str, interval: int = 5):
    async with get_client() as client:
        while True:
            flow_run = await client.read_flow_run(UUID(flow_run_id))
            state = flow_run.state
            print(f"{flow_run.id}: {state.name}")

            if state.is_final():
                return state

            await asyncio.sleep(interval)
Docs: - Prefect client API If you want, I can also show: - how to get the status of all subflows too - how to get status by deployment name - how to get status via raw REST API instead of the Python client
m
@Marvin how to make pydantic settings's PREFECT_API_URL to be known by the prefect client, and how the mechanism is the different compared merely using .env file
m
✅ thought for 100.6 seconds
Your own Pydantic settings class does not automatically configure the Prefect client. Prefect has its own settings system, and the client reads from Prefect settings, not from an arbitrary 
BaseSettings
object in your app. ## Short answer If you have something like:
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from pydantic_settings import BaseSettings

class AppSettings(BaseSettings):
    PREFECT_API_URL: str
that alone does not make Prefect use it. You must do one of these: 1. set a real environment variable 
PREFECT_API_URL
2. put 
PREFECT_API_URL
in a 
.env
file that Prefect loads 3. bridge your app setting into Prefect with 
temporary_settings(...)
--- ## How Prefect actually loads settings Prefect 3.x reads settings from its own settings stack, roughly in this order: 1. programmatic overrides via Prefect settings context 2. environment variables 3. 
.env
4. 
prefect.toml
5. 
pyproject.toml
6. Prefect profiles 7. defaults So the Prefect client does things like: - read 
PREFECT_API_URL
- read 
PREFECT_API_KEY
- build the API client from those values Docs: - Prefect settings API - Prefect client API --- ## Case 1: using your own Pydantic settings class Example:
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from pydantic_settings import BaseSettings

class AppSettings(BaseSettings):
    prefect_api_url: str
This is just your app config. Prefect will not inspect this object unless you explicitly pass its values into Prefect’s settings system. ### Correct bridge with 
temporary_settings
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from pydantic_settings import BaseSettings
from prefect.settings import PREFECT_API_URL, temporary_settings
from prefect.client.orchestration import get_client

class AppSettings(BaseSettings):
    prefect_api_url: str

settings = AppSettings()

async def main():
    with temporary_settings(
        updates={PREFECT_API_URL: settings.prefect_api_url}
    ):
        async with get_client() as client:
            response = await client.hello()
            print(response.json())
That works because now you are explicitly telling Prefect’s settings system what value to use. --- ## Case 2: using 
.env
If your 
.env
contains:
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PREFECT_API_URL=<http://localhost:4200/api>
and Prefect is running in a directory where that 
.env
is loaded, then:
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from prefect.client.orchestration import get_client

async def main():
    async with get_client() as client:
        response = await client.hello()
        print(response.json())
will just work. ### Mechanism difference vs your own Pydantic settings With `.env`: - Prefect reads the file as part of its own settings loading - no bridge code needed - the setting is globally available to Prefect in that process With your app’s `BaseSettings`: - only your code knows about that object - Prefect does not automatically consult it - you must copy the value into Prefect settings yourself --- ## Environment variable vs 
.env
These are related but not identical. ### Real environment variable
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export PREFECT_API_URL=<http://localhost:4200/api>
This is an OS/process environment variable. Prefect reads it directly. ### 
.env
file
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PREFECT_API_URL=<http://localhost:4200/api>
This is just a file. It matters only if something loads it. Prefect can load 
.env
as part of its settings system. ### Precedence If both exist: - the real environment variable usually wins over 
.env
So this:
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export PREFECT_API_URL=<http://prod-server/api>
with `.env`:
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PREFECT_API_URL=<http://dev-server/api>
usually means Prefect will use:
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<http://prod-server/api>
--- ## Best practice options ### Option A: let Prefect use env / 
.env
Best when you want standard deployment behavior.
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PREFECT_API_URL=<http://localhost:4200/api>
PREFECT_API_KEY=...
Then just: ```python
from prefect.client.orchestration import get_client
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### Option B: bridge from your app settings
Best when your app centrally manages config.

```python
from prefect.settings import PREFECT_API_URL, PREFECT_API_KEY, temporary_settings
Then inject those values into Prefect at runtime. --- ## Example with API URL and API key
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from pydantic_settings import BaseSettings
from prefect.settings import PREFECT_API_URL, PREFECT_API_KEY, temporary_settings
from prefect.client.orchestration import get_client

class AppSettings(BaseSettings):
    prefect_api_url: str
    prefect_api_key: str | None = None

settings = AppSettings()

async def main():
    updates = {
        PREFECT_API_URL: settings.prefect_api_url,
    }

    if settings.prefect_api_key:
        updates[PREFECT_API_KEY] = settings.prefect_api_key

    with temporary_settings(updates=updates):
        async with get_client() as client:
            response = await client.hello()
            print(response.json())
--- ## Practical difference in behavior ### 
.env
approach - declarative - process-wide - easy for containers/microservices - no Python bridge code needed ### 
temporary_settings
approach - explicit and local to a code block - useful when different requests/services need different Prefect targets - great if your app config is already managed with Pydantic --- ## Recommendation For microservices, I’d usually recommend: - production: real environment variables / secrets injection - local dev: 
.env
- only use 
temporary_settings
 if you specifically want to map app-level config into Prefect dynamically ## Minimal examples ### 
.env
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PREFECT_API_URL=<http://prefect-server:4200/api>
PREFECT_API_KEY=your_key_here
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from prefect.client.orchestration import get_client

async def main():
    async with get_client() as client:
        ...
### Pydantic settings bridge
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from pydantic_settings import BaseSettings
from prefect.settings import PREFECT_API_URL, temporary_settings

class Settings(BaseSettings):
    prefect_api_url: str
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settings = Settings()

with temporary_settings(updates={PREFECT_API_URL: settings.prefect_api_url}):
    ...
If you want, I can give you a complete FastAPI example showing: - 
BaseSettings
- 
.env
- Prefect client - dependency injection - Cloud vs self-hosted config
m
@Marvin what if the .env file is inside another directory, and can setting the env in docker compose make it works like the .env on the same directory
m
✅ thought for 27.4 seconds
Yes — setting env vars in Docker Compose works, and in practice it’s often better than relying on a 
.env
file in the app directory. ## Short answer - If the 
.env
file is in another directory, Prefect will not automatically treat it the same as a 
.env
in the current working directory unless that file is explicitly loaded. - If you set 
PREFECT_API_URL
, 
PREFECT_API_KEY
, etc. in Docker Compose under 
environment:
, Prefect will read them just fine. - From Prefect’s perspective, real environment variables from Docker Compose are even more direct than 
.env
file values. --- ## 1. What if 
.env
is in another directory? Prefect’s 
.env
loading depends on what file is available in the process working context that its settings loader checks. So if your project looks like:
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project/
  services/
    api/
      app.py
  config/
    .env
and you run your service from 
services/api
, Prefect will not automatically know to read 
config/.env
just because it exists elsewhere. ### That means this may not work automatically:
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config/.env
unless you do something explicit like: - load it yourself before creating the Prefect client - or expose those values as actual environment variables - or copy/mount it into the runtime working directory --- ## 2. Docker Compose 
environment:
absolutely works Example:
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services:
  my-service:
    image: my-service:latest
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_API_KEY: ${PREFECT_API_KEY}
Inside the container, those become real environment variables. So Prefect client code like:
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from prefect.client.orchestration import get_client

async def main():
    async with get_client() as client:
        response = await client.hello()
        print(response.json())
will pick them up automatically. --- ## 3. Is Docker Compose env the same as 
.env
in the app directory? Effectively for Prefect client usage: yes, and usually stronger. Why: - 
.env
is just a file that must be loaded - 
environment:
in Docker Compose creates actual process environment variables - Prefect reads environment variables directly - environment variables typically have higher precedence than 
.env
So if your question is “can Docker Compose make it work like a local 
.env
?” the answer is: Yes — and usually more reliably. --- ## 4. Docker Compose 
.env
vs 
env_file
vs 
environment
These are easy to mix up. ### A. 
environment:
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environment:
  PREFECT_API_URL: <http://prefect-server:4200/api>
This directly injects env vars into the container. Prefect sees these immediately. ### B. 
env_file:
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env_file:
  - ./config/prefect.env
This tells Docker Compose to read that file and inject those values into the container environment. Prefect also sees these fine. Example:
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services:
  my-service:
    image: my-service:latest
    env_file:
      - ./config/prefect.env
`./config/prefect.env`:
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PREFECT_API_URL=<http://prefect-server:4200/api>
PREFECT_API_KEY=pnu_xxx
This is a great solution if your env file lives elsewhere. ### C. Compose project 
.env
Docker Compose also has its own 
.env
file for variable substitution in the compose file itself. That is a different concept. Example:
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environment:
  PREFECT_API_URL: ${PREFECT_API_URL}
Here Compose reads 
${PREFECT_API_URL}
from its 
.env
or your shell, then injects it into the container. That still works — just remember this 
.env
is for Compose interpolation, not directly for Prefect. --- ## 5. Best options if your 
.env
is elsewhere ### Option 1: use 
env_file
in Docker Compose Best if you already have a separate env file.
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services:
  my-service:
    image: my-service:latest
    env_file:
      - ./config/prefect.env
### Option 2: use 
environment:
Best if values come from secrets or CI/CD. ```yaml services: my-service: image: my-service:latest
environment: PREFECT_API_URL: http://prefect-server:4200/api PREFECT_API_KEY: ${PREFECT_API_KEY}
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### Option 3: load the file in Python yourself
Only if you really need app-managed loading.

For example with `pydantic-settings` or `python-dotenv`, then bridge into Prefect if needed.

But if you’re already in containers, I’d recommend letting Docker inject the env instead.

---

## 6. Recommended pattern for containers

For Docker/microservices, I recommend:

- *don’t rely on Prefect discovering a `.env` in some nested path*
- use Docker Compose `environment:` or `env_file:`
- inject `PREFECT_API_URL`, `PREFECT_API_KEY`, `PREFECT_API_AUTH_STRING` directly into the container

Example:

```yaml
services:
  orders-service:
    build: .
    env_file:
      - ./config/prefect.env
./config/prefect.env
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PREFECT_API_URL=<http://prefect-server:4200/api>
PREFECT_API_AUTH_STRING=admin:password
Then Prefect client code needs no extra config. --- ## 7. Practical summary - 
.env
in another directory: not automatically guaranteed to be loaded by Prefect - Docker Compose `environment:`: yes, works - Docker Compose `env_file:`: yes, also works - For containers, 
environment:
/ 
env_file:
is usually the preferred approach Useful docs: - Prefect settings API - Prefect client API If you want, I can show you the exact Docker Compose setup for: - Prefect Cloud - self-hosted Prefect server - microservice + worker + server on the same Compose network
m
@Marvin does that mean the variable is already defined in the container so I can use it in my prefect client located inside the project subdirectory
m
✅ thought for 16.0 seconds
Yes — if Docker/Compose injects the variable into the container environment, then your Prefect client can use it regardless of your project subdirectory, as long as the Python process runs inside that container. So if your Compose has something like:
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services:
  app:
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_API_KEY: ${PREFECT_API_KEY}
then inside the container, those are normal environment variables. Prefect can read them from anywhere in your project:
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from prefect.client.orchestration import get_client

async def main():
    async with get_client() as client:
        ...
It does not matter that your Python file is in a subdirectory like:
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/app/src/services/prefect_client/client.py
because environment variables are attached to the process/container environment, not to a specific file path. ## In other words This works:
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container env
  PREFECT_API_URL=<http://prefect-server:4200/api>
even if your code is here:
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/app/project/subdir/my_prefect_code.py
because Prefect is not looking for “a variable near that file”; it is reading from the process environment. --- ## What matters What matters is: - the env var exists in the container - your Python process is running in that same container - Prefect client is created in that process What does not matter: - where in the project tree the file lives - whether that file sits next to 
.env
- whether the code is in a subdirectory --- ## Simple example ### Docker Compose
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services:
  my-service:
    build: .
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
### Project layout
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project/
  src/
    services/
      prefect_code.py
### 
prefect_code.py
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from prefect.client.orchestration import get_client

async def test():
    async with get_client() as client:
        response = await client.hello()
        print(response.json())
That works because 
PREFECT_API_URL
is in the container environment. --- ## Quick way to verify inside container You can confirm with:
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docker exec -it <container-name> env | grep PREFECT
or:
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docker exec -it <container-name> printenv PREFECT_API_URL
If you see the value there, Prefect should be able to use it. You can also verify in Python:
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import os
print(os.environ.get("PREFECT_API_URL"))
and with Prefect settings:
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from prefect.settings import PREFECT_API_URL
print(PREFECT_API_URL.value())
--- ## Short answer Yes: if the variable is injected into the container, then your Prefect client can use it from code in a project subdirectory, because env vars are process-wide, not directory-scoped. If you want, I can give you a tiny test script to verify your container is correctly seeing 
PREFECT_API_URL
and 
PREFECT_API_KEY
.
m
@Marvin if my docker container needs to run "uv run python main.py" to serve prefect deployment but it typically takes around 1 minute, then how do I execute "prefect server start --host 0.0.0.0" afterwards
m
✅ thought for 20.9 seconds
You generally don’t run both in the same container. 
prefect server start --host 0.0.0.0
starts the Prefect API/UI server. 
uv run python main.py
sounds like your application / flow-serving / worker process. In a containerized setup, these should usually be separate services: - one container for Prefect server - one container for your app / worker / deployment-serving code ## Recommended architecture ### Service 1: Prefect server
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
### Service 2: your app / worker
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services:
  my-app:
    build: .
    command: uv run python main.py
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    depends_on:
      - prefect-server
Your app connects to the Prefect server via 
PREFECT_API_URL
. --- ## Why not run both in one container? Because both commands are typically long-running foreground processes: - 
uv run python main.py
keeps the container busy - 
prefect server start
also keeps the container busy A container normally has one main process. If you start one, the second won’t run unless you use a process manager or shell tricks. Also, running server + app in one container is usually harder to manage, debug, and scale. --- ## If you really must run both in one container It’s possible, but not recommended. You’d need a shell script that starts one process in the background, then starts the other in the foreground. Example:
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#!/bin/sh
set -e

prefect server start --host 0.0.0.0 &
uv run python main.py

wait
or the reverse:
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#!/bin/sh
set -e

uv run python main.py &
prefect server start --host 0.0.0.0

wait
But this raises important questions: - Which one is the primary process? - What happens if one crashes? - How do signals/shutdown work? - Is 
main.py
supposed to exit, or run forever? This is why separate containers are better. --- ## If 
main.py
must wait for Prefect server If your 
main.py
depends on the server being available, use: - separate containers - 
depends_on
- ideally a healthcheck or startup wait loop Example:
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    healthcheck:
      test: ["CMD", "curl", "-f", "<http://localhost:4200/api/health>"]
      interval: 10s
      timeout: 5s
      retries: 12

  my-app:
    build: .
    command: uv run python main.py
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    depends_on:
      prefect-server:
        condition: service_healthy
--- ## Important distinction: server vs worker vs deployed flow Sometimes users mix these up: - 
prefect server start
→ runs Prefect backend/API/UI - 
prefect worker start
→ polls work pools and executes runs - 
python main.py
/ 
flow.serve()
→ may register/serve deployments depending on your code If your goal is just to execute deployments, you probably want: - Prefect server running in one container - a worker running in another container - your deployments created separately For example, instead of 
uv run python main.py
, maybe what you really want is:
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command: uv run prefect worker start --pool my-pool
or your 
main.py
may be creating deployments. --- ## Likely best solution for your case If you are self-hosting Prefect: ### Container A
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command: prefect server start --host 0.0.0.0
### Container B
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command: uv run python main.py
environment:
  PREFECT_API_URL: <http://prefect-server:4200/api>
Then 
main.py
can talk to the server after it comes up. --- ## Short answer If you want to run 
prefect server start --host 0.0.0.0
after 
uv run python main.py
, that suggests both are in one container — which is usually the wrong pattern. Best practice: run them in separate containers/services.
If you want, I can help you write the exact: - 
docker-compose.yml
- 
Dockerfile
- startup script for either: 1. self-hosted Prefect server + app container, or 2. Prefect server + worker + deployment container
m
@Marvin so both containerized fastapi application and prefect worker have PREFECT_API_URL? Prefect server container acts as the orchestrator for the worker and receiver from the FastAPI?
m
✅ thought for 20.2 seconds
Yes — that’s the right mental model. In a self-hosted Prefect setup: - Prefect server = the orchestrator / API / UI - Prefect worker = polls the server for work and executes flow runs - your FastAPI app = talks to the server via the API, for example to trigger deployments, query run status, fetch logs, etc. So both the FastAPI container and the worker container usually need 
PREFECT_API_URL
. ## Typical architecture ### 1. Prefect server container This runs:
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prefect server start --host 0.0.0.0
It provides: - the REST API - the orchestration logic - the UI - the state database layer This is the central coordinator. ### 2. FastAPI app container This uses 
PREFECT_API_URL
to connect to the Prefect server. Typical use cases: - trigger deployment runs - query flow run status - read logs - manage deployments programmatically Example:
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PREFECT_API_URL=<http://prefect-server:4200/api>
### 3. Prefect worker container This also uses 
PREFECT_API_URL
to connect to the same Prefect server. It: - polls work pools / queues - receives scheduled flow runs - executes them Example:
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PREFECT_API_URL=<http://prefect-server:4200/api>
--- ## So yes: both usually have 
PREFECT_API_URL
For example:
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"

  fastapi-app:
    build: .
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>

  prefect-worker:
    image: prefecthq/prefect:3-latest
    command: prefect worker start --pool my-pool
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
--- ## Role of each component ### FastAPI → Prefect server The FastAPI app is a client of Prefect server. It might do things like:
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await client.create_flow_run_from_deployment(...)
await client.read_flow_run(...)
await client.read_logs(...)
So FastAPI sends requests to the server. ### Worker → Prefect server The worker is also a client of Prefect server. It asks: - is there any work for my pool? - what run should I execute? - what state updates should I send back? So the worker also talks to the server. ### Prefect server The server sits in the middle and coordinates both sides: - receives requests from FastAPI - stores flow run records - schedules/orchestrates runs - gives work to workers - receives state/log updates from workers --- ## Important distinction The Prefect server does not directly execute flows. It orchestrates them. The worker does the actual execution. So if your FastAPI app triggers a deployment: 1. FastAPI sends request to Prefect server 2. Prefect server creates a flow run 3. Worker polls the server 4. Worker sees the run and executes it 5. Worker reports status/logs back to the server 6. FastAPI can later query the server for results/status --- ## Summary flow
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FastAPI app  --->  Prefect Server  <---  Prefect Worker
                     |
                     v
               orchestration state
More concretely:
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FastAPI app
  -> create deployment run
  -> get run status
  -> fetch logs

Prefect Server
  -> stores run metadata
  -> schedules/orchestrates
  -> exposes API/UI

Worker
  -> polls server
  -> runs deployments
  -> sends logs/state back
--- ## Short answer Yes: - FastAPI container should usually have 
PREFECT_API_URL
- worker container should usually have 
PREFECT_API_URL
- Prefect server container is the orchestrator/API that both connect to If you want, I can draw you a complete Docker Compose example with: - FastAPI - Prefect server - Prefect worker - optional Postgres wired correctly together.
m
@Marvin why using "prefect worker start --pool my-pool" instead of "python main.py"? How does it know the actual logic within the flow itself
m
✅ thought for 27.7 seconds
Because those two commands do different jobs. - 
prefect worker start --pool my-pool
starts a worker - 
python main.py
runs your Python application/code A Prefect worker does not contain your flow logic by magic. It only: - polls the Prefect API for scheduled flow runs - receives run instructions - launches execution in the environment defined by the deployment ## Short version Use: - 
python main.py
when you want to directly run your code - 
prefect worker start --pool my-pool
when you want a worker that executes deployment runs --- ## Why use 
prefect worker start --pool my-pool
? Because in Prefect’s deployment model, the worker is the component responsible for picking up and executing deployment-backed flow runs. If your FastAPI app does:
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await client.create_flow_run_from_deployment(deployment_id=...)
that only creates a run in Prefect. It does not execute the flow immediately by itself. A worker must be running and watching the correct work pool so it can pick up that run. --- ## Then how does the worker know the actual flow logic? It knows because the deployment tells it where the flow code is and how to run it. A deployment contains things like: - flow entrypoint - code location - parameters - work pool / queue - infrastructure configuration - image / environment information So the worker does not need your code baked into the worker process itself unless your deployment model expects that. It learns what to run from the deployment. --- ## Two common patterns ### Pattern A: worker + deployment + code in image The deployment points to code that exists in the execution environment/image. For example, your deployment may reference:
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flows/main.py:my_flow
and the worker executes a run in a container/image that already contains that file. In this pattern: - worker starts - worker polls the pool - Prefect tells it to run deployment X - the execution environment has your code - Prefect imports the flow from the configured entrypoint ### Pattern B: directly running Python If you do:
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python main.py
then you are just running the script directly. That bypasses the worker/pool orchestration model unless 
main.py
itself does something like: - define and serve deployments - start a long-lived process - register flows --- ## Why not replace worker with 
python main.py
? Because 
python main.py
usually does not poll the work pool. A worker is specifically built to: - ask Prefect server for available work - claim runs - manage execution lifecycle - report states/logs back Your 
main.py
usually does not do that unless you wrote custom orchestration logic. --- ## Example mental model ### 
prefect worker start --pool my-pool
Means:
“I am an execution agent. Give me runs from 
my-pool
and I will execute them.”
### 
python main.py
Means:
“Run this script now.”
These are not equivalent. --- ## Where does the flow code come from? That depends on deployment configuration. Common options: - code baked into the Docker image - code pulled from git / remote storage - local mounted volume - image specified by deployment/job variables The deployment tells Prefect what the entrypoint is. For example:
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entrypoint = "flows/orders.py:process_orders"
Then the worker execution environment imports: - module/file 
flows/orders.py
- flow function 
process_orders
--- ## Example Suppose you have:
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from prefect import flow

@flow
def my_flow(name: str = "world"):
    print(f"Hello {name}")
and deploy it. The deployment stores metadata like: - flow name - deployment name - entrypoint - target work pool Then:
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await client.create_flow_run_from_deployment(deployment_id=...)
creates a run. The worker watching that pool eventually picks it up and executes the entrypoint defined by the deployment. --- ## So when would you still use 
python main.py
?
You use 
python main.py
when that file is doing one of these: ### 1. local execution
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if __name__ == "__main__":
    my_flow()
This just runs the flow directly. ### 2. deployment registration / creation
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if __name__ == "__main__":
    my_flow.from_source(...).deploy(...)
This creates a deployment, but is not the worker. ### 3. custom service behavior Your app might use the Prefect client inside 
main.py
to trigger runs, check status, etc. Again, that is not the worker. --- ## Key distinction ### Worker Responsible for executing scheduled deployment runs ### Your code / app Responsible for defining flows, creating deployments, triggering runs, business logic --- ## In your architecture You likely want: ### FastAPI container Runs:
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uv run python main.py
if 
main.py
is your FastAPI app This app can trigger Prefect deployments via the client. ### Worker container Runs:
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prefect worker start --pool my-pool
This picks up the deployment runs and executes them. ### Prefect server container Runs:
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prefect server start --host 0.0.0.0
This orchestrates everything. --- ## Short answer You use 
prefect worker start --pool my-pool
because a worker is what actually listens for and executes deployment runs. It knows the flow logic because the deployment metadata tells it: - what flow to run - where the code lives - what environment/image to use - what entrypoint to import 
python main.py
just runs your app/script directly; it is not a worker unless you intentionally wrote it to behave like one. If you want, I can show you a concrete end-to-end example: 1. define a flow 2. create a deployment 3. start a worker 4. trigger it from FastAPI 5. explain exactly where the code is loaded from.
m
@Marvin my main.py already acts with main_pipeline.serve(name="pipeline-auto"), how can I alter this into server-worker paradigm pretending I have other prefect server container
m
✅ thought for 33.6 seconds
If your 
main.py
currently does:
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main_pipeline.serve(name="pipeline-auto")
then you are using the serve pattern, not the server + worker deployment pattern. Those are related, but not the same. ## What 
serve(...)
is doing 
flow.serve(...)
creates a long-running process that: - registers a deployment-like runner for that flow - stays alive - handles scheduled/ad-hoc execution for that served flow So your app process itself is acting as the thing that makes that flow runnable. That’s why it feels different from the worker model. --- # If you want to move to Prefect server + worker You generally want to change from:
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if __name__ == "__main__":
    main_pipeline.serve(name="pipeline-auto")
to a pattern where: 1. your flow is still defined in code 2. you create a deployment for it 3. a worker executes runs from a work pool 4. your FastAPI app or client triggers the deployment via Prefect API --- ## Conceptual change ### Current
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main.py with .serve()
    = long-running flow-serving process
### Target
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Prefect server container
    = orchestrator

Prefect worker container
    = execution agent

your code container / image
    = contains flow code

deployment
    = tells worker what flow to run and where
--- # How to alter it ## Step 1: keep the flow definition, remove 
.serve()
Instead of this:
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from prefect import flow

@flow
def main_pipeline():
    print("running pipeline")

if __name__ == "__main__":
    main_pipeline.serve(name="pipeline-auto")
change to:
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from prefect import flow

@flow
def main_pipeline():
    print("running pipeline")
Now this file just defines the flow. --- ## Step 2: create a deployment In Prefect 3.x, the preferred deployment flow is to deploy from source, or use 
prefect deploy
if you have a 
prefect.yaml
. If your flow lives in your repo, one common pattern is to create the deployment in a separate script. Example:
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from flows.main import main_pipeline

if __name__ == "__main__":
    main_pipeline.deploy(
        name="pipeline-auto",
        work_pool_name="my-pool",
    )
But depending on your code source strategy, the more modern / flexible 3.x approach is often:
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from prefect import flow

@flow
def main_pipeline():
    print("running pipeline")

if __name__ == "__main__":
    main_pipeline.from_source(
        source=".",
        entrypoint="main.py:main_pipeline",
    ).deploy(
        name="pipeline-auto",
        work_pool_name="my-pool",
    )
This creates a deployment that the worker can later execute. Important: don’t use removed 2.x methods like 
Deployment.build_from_flow()
. --- ## Step 3: run a worker in a separate container Your worker container runs:
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prefect worker start --pool my-pool
This worker polls Prefect server for runs assigned to 
my-pool
. Before suggesting CLI syntax, I should verify it. The correct command in Prefect 3.x is indeed 
prefect worker start --pool <name>
. A worker does not need 
.serve()
in your app. --- ## Step 4: your FastAPI app triggers the deployment Your FastAPI app can then do:
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from prefect.client.orchestration import get_client

async def trigger_pipeline():
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(
            "main-pipeline/pipeline-auto"
        )

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id
        )

        return str(flow_run.id)
Now: - FastAPI calls Prefect server - Prefect server creates a run - worker picks it up - worker executes the flow --- # The big question: where does the worker get the code? This is the most important migration detail. When you used 
.serve()
, the process already had the flow in memory. When using workers, the worker needs a way to access the flow code. There are a few common options:
## Option A: code baked into the worker image Best if your flows live in the same repo and you control the image. Example worker image:
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FROM prefecthq/prefect:3-latest

WORKDIR /app
COPY . /app

RUN pip install -r requirements.txt
Then the deployment entrypoint can point to:
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main.py:main_pipeline
and the worker can import it because the code is inside the container image. ## Option B: code pulled from git / remote source The deployment references a source repository and the worker pulls it at runtime. ## Option C: shared mounted volume Less common in production, but possible. --- # A concrete migration example ## Before
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from prefect import flow

@flow
def main_pipeline():
    print("running pipeline")

if __name__ == "__main__":
    main_pipeline.serve(name="pipeline-auto")
## After: flow module
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from prefect import flow

@flow
def main_pipeline():
    print("running pipeline")
## After: deployment script
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from main import main_pipeline

if __name__ == "__main__":
    main_pipeline.from_source(
        source=".",
        entrypoint="main.py:main_pipeline",
    ).deploy(
        name="pipeline-auto",
        work_pool_name="my-pool",
    )
Then run that once to register the deployment. --- # Container roles ## Prefect server container Runs:
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prefect server start --host 0.0.0.0
## Worker container Runs:
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prefect worker start --pool my-pool
and has:
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PREFECT_API_URL=<http://prefect-server:4200/api>
## FastAPI container Runs your API app and also has:
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PREFECT_API_URL=<http://prefect-server:4200/api>
The FastAPI app triggers runs via the client. --- # Docker Compose sketch
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"

  prefect-worker:
    build: .
    command: prefect worker start --pool my-pool
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    depends_on:
      - prefect-server

  fastapi-app:
    build: .
    command: uv run python api.py
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    depends_on:
      - prefect-server
If the worker needs your flow code, make sure the worker image includes it. --- # Important migration note 
serve()
is convenient for: - one service - one long-running process - simpler local/dev patterns The worker model is better for: - decoupled orchestration - scaling workers independently - FastAPI triggering deployments - cleaner self-hosted architecture So your migration is basically: - remove 
.serve()
- create a deployment instead - run a worker - trigger the deployment through the API/client --- # What to change in 
main.py
If 
main.py
is currently both: - defining the flow - serving it split responsibilities. ### 
flows/main.py
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from prefect import flow

@flow
def main_pipeline():
    print("running pipeline")
### 
deploy.py
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from flows.main import main_pipeline

if __name__ == "__main__":
    main_pipeline.from_source(
        source=".",
        entrypoint="flows/main.py:main_pipeline",
    ).deploy(
        name="pipeline-auto",
        work_pool_name="my-pool",
    )
### 
api.py
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from fastapi import FastAPI
from prefect.client.orchestration import get_client

app = FastAPI()

@app.post("/run")
async def run_pipeline():
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(
            "main-pipeline/pipeline-auto"
        )
        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id
        )
        return {"flow_run_id": str(flow_run.id)}
--- # One more subtle point If you deploy from source using 
source="."
, be sure the worker’s runtime filesystem matches what the deployment expects, or use a more explicit source strategy like a git repo. ---
# Recommended path for you Given your setup, I’d recommend: 1. keep flow definition only in code 2. stop using 
.serve()
3. create a deployment for 
main_pipeline
4. run a dedicated worker container on a work pool 5. let FastAPI trigger the deployment using the Prefect client 6. point both worker and FastAPI to the same 
PREFECT_API_URL
--- If you want, I can build you a complete working example for your case with: - 
main.py
- 
deploy.py
- 
api.py
- 
Dockerfile
- 
docker-compose.yml
for a self-hosted Prefect server + worker + FastAPI setup.
m
@Marvin The 
PREFECT_API_URL
setting for your current profile doesn't match the address of the server that's running. You need to set it to communicate with the server.? How would you like to proceed? [Use arrows to move; enter to select] How do I avoid this in my prefect-server container? once I prefect server start --host 0.0.0.0
m
✅ thought for 133.8 seconds
That prompt appears because 
prefect server start
is trying to help configure the CLI profile for talking to the server it just started. In a container, you usually want to disable that interactive behavior. ## Best way to avoid it Set:
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PREFECT_CLI_PROMPT=false
in the server container. So your server container should look like:
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prefect-server:
  image: prefecthq/prefect:3-latest
  command: prefect server start --host 0.0.0.0
  environment:
    PREFECT_CLI_PROMPT: "false"
That prevents the interactive prompt. ## Important point Your server container usually does not need 
PREFECT_API_URL
at all. Use this mental model: - server container → hosts the Prefect API/UI - worker container → needs 
PREFECT_API_URL=<http://prefect-server:4200/api>
- FastAPI/app container → also needs 
PREFECT_API_URL=<http://prefect-server:4200/api>
So typically: ### Server container
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environment:
  PREFECT_CLI_PROMPT: "false"
### Worker/app containers
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environment:
  PREFECT_API_URL: <http://prefect-server:4200/api>
  PREFECT_CLI_PROMPT: "false"
## Why the message happens When you run:
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prefect server start --host 0.0.0.0
Prefect computes the server address and compares it to the active CLI profile’s 
PREFECT_API_URL
. If they don’t match, it asks whether you want to update the profile. That is useful on a laptop, but annoying in containers. ## Recommended Docker Compose pattern
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_CLI_PROMPT: "false"
    ports:
      - "4200:4200"

  prefect-worker:
    image: prefecthq/prefect:3-latest
    command: prefect worker start --pool my-pool
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_CLI_PROMPT: "false"
    depends_on:
      - prefect-server

  fastapi-app:
    build: .
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_CLI_PROMPT: "false"
    depends_on:
      - prefect-server
## Important hostname caveat Inside Docker Compose: - 
localhost
inside the worker/app container means that same container - to reach the server container, use the service name, e.g. 
prefect-server
So this is correct for other containers:
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PREFECT_API_URL=<http://prefect-server:4200/api>
not:
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PREFECT_API_URL=<http://localhost:4200/api>
unless the code is running inside the server container itself. ## Optional note 
prefect server start --help
shows there is no dedicated 
--no-prompt
flag on this command, so 
PREFECT_CLI_PROMPT=false
is the correct way to suppress the prompt behavior. Reference: - Prefect settings API If you want, I can give you a complete working 
docker-compose.yml
 for: - Prefect server - Prefect worker - FastAPI app - Postgres backend
m
@Marvin How to use Prefect LogFilter if I want to retrieve task logs only instead of the flow
m
✅ thought for 80.3 seconds
Use 
LogFilter.task_run_id
if you want task logs only. That’s the key distinction: - flow logs → have 
flow_run_id
but no 
task_run_id
- task logs → have both 
flow_run_id
and 
task_run_id
So if you filter on 
task_run_id
, you’ll get task-associated logs instead of flow-level logs. ## Example: logs for one task run
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from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import LogFilter, LogFilterTaskRunId

async def get_task_logs(task_run_id):
    async with get_client() as client:
        logs = await client.read_logs(
            log_filter=LogFilter(
                task_run_id=LogFilterTaskRunId(any_=[task_run_id])
            )
        )
        return logs
--- ## If you want all task logs for a flow run You usually need to: 1. get the task runs for that flow run 2. collect their IDs 3. query logs with 
task_run_id=...
Example:
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from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import (
    LogFilter,
    LogFilterTaskRunId,
    TaskRunFilter,
    TaskRunFilterFlowRunId,
)

async def get_all_task_logs_for_flow(flow_run_id):
    async with get_client() as client:
        task_runs = await client.read_task_runs(
            task_run_filter=TaskRunFilter(
                flow_run_id=TaskRunFilterFlowRunId(any_=[flow_run_id])
            )
        )

        task_run_ids = [task_run.id for task_run in task_runs]
        if not task_run_ids:
            return []

        logs = await client.read_logs(
            log_filter=LogFilter(
                task_run_id=LogFilterTaskRunId(any_=task_run_ids)
            )
        )
        return logs
## Why not just filter by 
flow_run_id
? Because 
flow_run_id
includes: - flow-level logs - task logs inside that flow So 
flow_run_id
alone does not exclude the flow’s own logs. ## Useful combination: task logs + error level
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import logging
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import (
    LogFilter,
    LogFilterTaskRunId,
    LogFilterLevel,
)
from prefect.client.schemas.sorting import LogSort

async def get_error_task_logs(task_run_id):
    async with get_client() as client:
        logs = await client.read_logs(
            log_filter=LogFilter(
                task_run_id=LogFilterTaskRunId(any_=[task_run_id]),
                level=LogFilterLevel(ge_=logging.ERROR),
            ),
            sort=LogSort.TIMESTAMP_ASC,
        )
        return logs
## Caveat There isn’t a simple client-side “give me logs where 
task_run_id
is not null” helper. So if your goal is:
all logs for this flow, but exclude flow logs
the safest pattern is: - first query task runs - then query logs by those `task_run_id`s ## References - Prefect client API - REST API overview If you want, I can also give you: 1. a version that includes subflow task logs too, or 2. a version that returns logs grouped by 
task_run_id
.
m
@Marvin logs = await client.read_logs( log_filter=LogFilter( task_run_id=LogFilterTaskRunId(any_=[task_run_id]) ) ) explain the example of the output from this code
m
✅ thought for 20.7 seconds
client.read_logs(...)
returns a list of log objects. So this code:
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logs = await client.read_logs(
    log_filter=LogFilter(
        task_run_id=LogFilterTaskRunId(any_=[task_run_id])
    )
)
means:
“Give me all logs whose 
task_run_id
matches this specific task run.”
## What 
logs
looks like 
logs
is typically a list like:
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[
    Log(...),
    Log(...),
    Log(...),
]
Each item represents one log entry emitted during that task run. Conceptually, each log object contains fields like: - 
id
- 
name
- 
level
- 
message
- 
timestamp
- 
flow_run_id
- 
task_run_id
- 
worker_id
/ metadata fields depending on context ## Example output shape If you printed each log in a readable way:
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for log in logs:
    print({
        "timestamp": str(log.timestamp),
        "level": log.level,
        "message": log.message,
        "flow_run_id": str(log.flow_run_id),
        "task_run_id": str(log.task_run_id),
        "name": log.name,
    })
you might see something like:
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{
    "timestamp": "2026-05-21 10:15:01.123456+00:00",
    "level": 20,
    "message": "Beginning task run 'extract-data'",
    "flow_run_id": "11111111-1111-1111-1111-111111111111",
    "task_run_id": "22222222-2222-2222-2222-222222222222",
    "name": "prefect.task_runs"
}
{
    "timestamp": "2026-05-21 10:15:03.567890+00:00",
    "level": 20,
    "message": "Fetched 150 records from source API",
    "flow_run_id": "11111111-1111-1111-1111-111111111111",
    "task_run_id": "22222222-2222-2222-2222-222222222222",
    "name": "prefect.task_runs"
}
{
    "timestamp": "2026-05-21 10:15:05.999999+00:00",
    "level": 40,
    "message": "Request failed with timeout, retrying...",
    "flow_run_id": "11111111-1111-1111-1111-111111111111",
    "task_run_id": "22222222-2222-2222-2222-222222222222",
    "name": "prefect.task_runs"
}
## What the fields mean ### 
timestamp
When that log line was created. Example:
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"2026-05-21 10:15:03.567890+00:00"
### 
level
Python logging level as an integer: - 
10
= DEBUG - 
20
= INFO - 
30
= WARNING - 
40
= ERROR - 
50
= CRITICAL So in the example:
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20
means INFO. ### 
message
The actual log text. Example:
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"Fetched 150 records from source API"
### 
flow_run_id
The parent flow run this task belongs to. ### 
task_run_id
The exact task run that emitted the log. Because you filtered on 
task_run_id
, all returned logs should share the same task run ID. ### 
name
The logger name, often something Prefect-related or your app logger depending on how logs are emitted. --- ## Practical example Suppose you have a task:
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from prefect import task, flow
from prefect.logging import get_run_logger

@task
def my_task():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting task")
    <http://logger.info|logger.info>("Doing work")
    logger.error("Something went wrong")
If you retrieve logs for that task run, the output might correspond to those three log lines. For example:
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for log in logs:
    print(log.timestamp, log.level, log.message)
might print:
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2026-05-21 10:15:01.123456+00:00 20 Starting task
2026-05-21 10:15:02.456789+00:00 20 Doing work
2026-05-21 10:15:03.789012+00:00 40 Something went wrong
--- ## What if there are no logs? Then:
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logs == []
Possible reasons: - that task emitted no logs - you passed the wrong 
task_run_id
- logs are not available/retained in your environment - the task has not run yet --- ## A more useful formatting example
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for log in logs:
    print(f"[{log.timestamp}] {log.level} - {log.message}")
Example output:
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[2026-05-21 10:15:01.123456+00:00] 20 - Starting task
[2026-05-21 10:15:02.456789+00:00] 20 - Doing work
[2026-05-21 10:15:03.789012+00:00] 40 - Something went wrong
Or convert the numeric level: ```python import logging for log in logs:
print(f"[{log.timestamp}] {logging.getLevelName(log.level)} - {log.message}")
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Output:

```text
[2026-05-21 10:15:01.123456+00:00] INFO - Starting task
[2026-05-21 10:15:02.456789+00:00] INFO - Doing work
[2026-05-21 10:15:03.789012+00:00] ERROR - Something went wrong
## Summary That code returns: - a list of log records - all belonging to the given 
task_run_id
- each record includes timestamp, level, message, and related run IDs If you want, I can also show you: - the actual Prefect 
Log
object schema - how to serialize the logs to JSON for FastAPI response - how to retrieve only the latest log line
m
@Marvin how to set flow runs name right after submitting deployment, lets say I have additional field for the name in my pydantic model for the deployment
m
✅ thought for 16.8 seconds
Use the 
name
argument on 
create_flow_run_from_deployment()
. If your Pydantic model includes a custom run name, you can pass it directly when you submit the deployment run. ## Example
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from pydantic import BaseModel
from prefect.client.orchestration import get_client

class DeploymentRequest(BaseModel):
    deployment_name: str
    parameters: dict = {}
    flow_run_name: str | None = None

async def submit_deployment(req: DeploymentRequest):
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(req.deployment_name)

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters=req.parameters,
            name=req.flow_run_name,
        )

        return flow_run
So if the request is:
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DeploymentRequest(
    deployment_name="my-flow/my-deployment",
    parameters={"x": 1},
    flow_run_name="customer-sync-job-001",
)
then the created flow run will be named:
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customer-sync-job-001
--- ## If you are using FastAPI
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from fastapi import FastAPI
from pydantic import BaseModel
from prefect.client.orchestration import get_client

app = FastAPI()

class DeploymentRequest(BaseModel):
    deployment_name: str
    parameters: dict = {}
    flow_run_name: str | None = None

@app.post("/run")
async def run_deployment(req: DeploymentRequest):
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(req.deployment_name)

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters=req.parameters,
            name=req.flow_run_name,
        )

        return {
            "flow_run_id": str(flow_run.id),
            "flow_run_name": flow_run.name,
            "state": flow_run.state.name if flow_run.state else None,
        }
--- ## Example request body
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json
{
  "deployment_name": "main-pipeline/pipeline-auto",
  "parameters": {
    "customer_id": 123
  },
  "flow_run_name": "pipeline-customer-123"
}
--- ## Notes - 
name=
sets the flow run name, not the deployment name - if you omit 
name
, Prefect generates a random human-readable run name - this happens at creation time, so the flow run is created with that name immediately ## Relevant client method The method is: - 
create_flow_run_from_deployment(deployment_id=..., name=...)
Reference: - Prefect client API If you want, I can also show how to: - enforce a naming convention from your Pydantic model - append timestamps/UUIDs automatically - update a flow run name after creation if needed
m
@Marvin How do I make each task logs contained in a certain flow to be regex-ed, mathematically processed, and forwarded to be available into the Flow logs
m
✅ thought for 173.5 seconds
Task logs are not automatically promoted into flow logs in Prefect 3.x. If you want: 1. task logs collected 2. regex / numeric processing applied 3. summarized results written into the flow logs then you need to do that manually in your flow logic. ## Recommended pattern The most reliable pattern is: - let tasks log normally with 
get_run_logger()
- after task completion, query those task logs with the Prefect client - parse / aggregate them - emit a new flow-level log with 
get_run_logger()
inside the flow That way the summary appears in the flow logs. --- ## Example ### Task emits logs
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from prefect import flow, task
from prefect.logging import get_run_logger
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import LogFilter, LogFilterTaskRunId
from prefect.client.schemas.sorting import LogSort
import re

@task
def compute():
    logger = get_run_logger()
    <http://logger.info|logger.info>("value=10")
    <http://logger.info|logger.info>("value=20")
    <http://logger.info|logger.info>("value=30")
    return "done"
### Flow queries task logs, processes them, forwards summary to flow logs
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from prefect import flow
from prefect.logging import get_run_logger

@flow
async def my_flow():
    flow_logger = get_run_logger()

    task_state = await compute(return_state=True)
    task_run_id = task_state.state_details.task_run_id

    async with get_client() as client:
        task_logs = await client.read_logs(
            log_filter=LogFilter(
                task_run_id=LogFilterTaskRunId(any_=[task_run_id])
            ),
            sort=LogSort.TIMESTAMP_ASC,
        )

    values = []
    for log in task_logs:
        matches = re.findall(r"value=(\d+)", log.message)
        values.extend(int(m) for m in matches)

    if values:
        total = sum(values)
        avg = total / len(values)
        <http://flow_logger.info|flow_logger.info>(
            f"Task log summary: count={len(values)}, total={total}, avg={avg}"
        )
    else:
        <http://flow_logger.info|flow_logger.info>("Task log summary: no matching values found")
This creates a flow log entry like:
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Task log summary: count=3, total=60, avg=20.0
--- ## Why this works - logs written inside the task stay as task logs - logs written inside the flow stay as flow logs - so your “forwarding” step is really: - read task logs - transform them - write a new summary from the flow context --- ## If you want per-task summaries
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@flow
async def pipeline():
    flow_logger = get_run_logger()

    task_states = []
    for _ in range(3):
        state = await compute(return_state=True)
        task_states.append(state)

    async with get_client() as client:
        for i, task_state in enumerate(task_states, start=1):
            task_run_id = task_state.state_details.task_run_id

            logs = await client.read_logs(
                log_filter=LogFilter(
                    task_run_id=LogFilterTaskRunId(any_=[task_run_id])
                )
            )

            values = []
            for log in logs:
                matches = re.findall(r"value=(\d+)", log.message)
                values.extend(int(m) for m in matches)

            <http://flow_logger.info|flow_logger.info>(
                f"Task {i} summary: count={len(values)}, sum={sum(values) if values else 0}"
            )
--- ## Caveats ### 1. task logs are separate records Prefect does not merge them into flow logs for you. ### 2. you may need to query after task completion If you query too early, logs may not all be available yet. ### 3. for many tasks, this adds API calls If you have lots of tasks, batch carefully. ### 4. use the flow logger for the forwarded summary That is what makes the result show up in flow logs. --- ## Alternative: avoid log scraping entirely If your goal is mathematical processing, often the better pattern is: - return structured values from tasks - aggregate in the flow - log the summary in the flow Example: ```python @task def compute(): return [10, 20, 30] @flow
def my_flow(): logger = get_run_logger() values = compute() logger.info(f"Summary: count={len(values)}, total={sum(values)}") ``` This is usually more robust than regex-parsing logs. Use log parsing only if the information truly exists only in logs. --- ## References - Prefect client API - State change hooks - REST API overview If you want, I can give you: 1. a complete reusable helper like 
summarize_task_logs(task_run_id)
2. a version that works for subflows and all tasks inside them 3. a FastAPI-ready JSON version of the summarized logs
m
@Marvin prefect-worker-1 | Failed to initialize plugins: cannot import name 'BeforeValidator' from 'pydantic' (/app/.venv/lib/python3.13/site-packages/pydantic/__init__.py) prefect-worker-1 | Traceback (most recent call last): prefect-worker-1 | File "/app/.venv/bin/prefect", line 4, in <module> prefect-worker-1 | from prefect.cli import app prefect-worker-1 | File "/app/.venv/lib/python3.13/site-packages/prefect/cli/__init__.py", line 4, in <module> prefect-worker-1 | from prefect.cli._app import app prefect-worker-1 | File "/app/.venv/lib/python3.13/site-packages/prefect/cli/_app.py", line 8, in <module> prefect-worker-1 | from rich.console import Console prefect-worker-1 | ImportError: cannot import name 'Console' from 'rich.console' (/app/.venv/lib/python3.13/site-packages/rich/console.py). Did you mean: 'console'? prefect-worker-1 exited with code 1 from the traceback what could be wrong
m
✅ thought for 31.2 seconds
From that traceback, the most likely problem is your environment has incompatible or broken dependency versions, not the worker command itself. Two big red flags: 1. 
cannot import name 'BeforeValidator' from 'pydantic'
2. 
cannot import name 'Console' from 'rich.console'
Those imports should normally exist in compatible versions of 
pydantic
and 
rich
. ## What this usually means Your worker environment likely has one of these issues: - incompatible 
pydantic
version - incompatible 
rich
version - a broken install caused by dependency resolution - local files/modules shadowing installed packages - Python 3.13 compatibility issue with one of your pinned packages/plugins --- ## Most suspicious issue: dependency mismatch ### 
BeforeValidator
error 
BeforeValidator
is associated with Pydantic v2. So if some Prefect plugin or dependency expects Pydantic v2, but your environment ended up with Pydantic v1, you’ll get:
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cannot import name 'BeforeValidator' from 'pydantic'
That strongly suggests: - something expects 
pydantic>=2
- but your environment may have 
pydantic<2
### 
rich.Console
error This is also abnormal. 
Console
should be importable from 
rich.console
. So this may mean: - 
rich
is corrupted - wrong version installed - or you have a local module/file named 
rich
interfering --- ## The traceback suggests plugin initialization is also failing This line is important:
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Failed to initialize plugins
So a plugin may be importing something that drags in incompatible dependencies. That could mean: - a Prefect integration package - your own package - another installed library exposing entrypoints/plugins --- ## Most likely causes in order ### 1. Pydantic v1/v2 conflict Very likely. Check what is installed:
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uv run python -c "import pydantic; print(pydantic.__version__)"
If it is v1.x, that is probably the issue. ### 2. Broken 
rich
install Check:
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uv run python -c "from rich.console import Console; print(Console)"
If that fails, your 
rich
install is broken or shadowed. Also check version:
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uv run python -c "import rich; print(rich.__version__)"
### 3. Local module shadowing Make sure you do not have files like:
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rich.py
pydantic.py
prefect.py
or directories like:
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rich/
pydantic/
inside your project that could shadow real packages. ### 4. Python 3.13 edge compatibility You are using:
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/app/.venv/lib/python3.13/...
Python 3.13 is still new enough that some dependency stacks can be shaky. Even if Prefect itself supports it in many cases, some plugins/integrations may not. If you can, I would strongly recommend testing with Python 3.11 or 3.12 first. --- ## What I would check first Inside the worker container, run:
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uv run python -c "import sys; print(sys.version)"
uv run python -c "import pydantic; print(pydantic.__version__)"
uv run python -c "import rich; print(getattr(rich, '__version__', 'no version attr'))"
uv run python -c "from rich.console import Console; print(Console)"
uv run python -c "from pydantic import BeforeValidator; print(BeforeValidator)"
## What the results would mean ### If this fails:
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from pydantic import BeforeValidator
then your Pydantic version is wrong for the installed code. ### If this fails:
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from rich.console import Console
then either: - 
rich
is corrupted - wrong version - or module shadowing is happening --- ## Likely fix ### Prefer Python 3.12 If possible, switch your base image from 3.13 to 3.12. For example:
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FROM <http://ghcr.io/astral-sh/uv:python3.12-bookworm-slim|ghcr.io/astral-sh/uv:python3.12-bookworm-slim>
### Rebuild dependencies cleanly If using 
uv
, rebuild without stale cache:
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uv sync --reinstall
or rebuild the image with no cache. ### Pin compatible dependencies Make sure you are not forcing old versions of: - 
pydantic
- 
rich
If you explicitly pin them, that may be the source of the issue. ---
## My best reading of the traceback The root problem is probably: Your worker container has an inconsistent Python environment — likely Pydantic and/or Rich versions incompatible with Prefect or one of its plugins, possibly worsened by Python 3.13. --- ## Practical recommendation 1. switch to Python 3.12 2. inspect installed versions of 
prefect
, 
pydantic
, and 
rich
3. check for local files named 
pydantic.py
, 
rich.py
, or 
prefect.py
4. rebuild the venv cleanly If you want, paste your: - 
Dockerfile
- 
pyproject.toml
or 
requirements.txt
- Prefect version and I can pinpoint the exact mismatch.
m
@Marvin by executing prefect worker start --pool worker-pool command, how does the prefect-worker container knows the work pool name in microservices architecture
m
✅ thought for 18.6 seconds
It knows the work pool name because you explicitly pass it in the command. So if your worker container runs:
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prefect worker start --pool worker-pool
then that worker registers itself with Prefect as a worker for the work pool named 
worker-pool
. It is not discovered automatically from your microservices architecture. ## What happens internally The worker container needs two things: 1. where the Prefect API is via 
PREFECT_API_URL
2. which work pool to poll via 
--pool worker-pool
So the worker starts up and effectively says:
“Connect to this Prefect server, and poll for runs assigned to the 
worker-pool
work pool.”
--- ## In microservices architecture The microservices part doesn’t change the rule. Your worker container still needs: - network access to the Prefect server - 
PREFECT_API_URL
- a pool name passed in the command Example:
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prefect-worker:
  build: .
  command: prefect worker start --pool worker-pool
  environment:
    PREFECT_API_URL: <http://prefect-server:4200/api>
In this setup: - 
PREFECT_API_URL
tells it which Prefect server - 
--pool worker-pool
tells it which work pool on that server --- ## How does this connect to deployments? A deployment is configured with a work pool, for example:
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work_pool:
  name: worker-pool
or in Python deployment code:
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flow.from_source(
    source=".",
    entrypoint="flows/main.py:my_flow",
).deploy(
    name="my-deployment",
    work_pool_name="worker-pool",
)
When you create a run from that deployment, Prefect knows:
“This run belongs to 
worker-pool
.”
Then any worker polling 
worker-pool
can pick it up. --- ## So the matching works like this ### Deployment Says:
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I should run on work pool = worker-pool
### Worker Says:
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I poll work pool = worker-pool
### Prefect server Matches them together. --- ## Important point The worker container does not infer the pool name from: - Docker Compose service name - container name - FastAPI service - deployment code automatically - environment structure It only knows from the startup configuration you give it. --- ## Typical architecture example
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0

  prefect-worker:
    image: prefecthq/prefect:3-latest
    command: prefect worker start --pool worker-pool
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>

  fastapi-app:
    build: .
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
And your deployment is created with:
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my_flow.from_source(
    source=".",
    entrypoint="flows/main.py:my_flow",
).deploy(
    name="pipeline-auto",
    work_pool_name="worker-pool",
)
Now the worker knows what to poll because you told it 
--pool worker-pool
. --- ## If you want to make it configurable You can inject the pool name with an env var at the shell level:
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prefect-worker:
  command: sh -c "prefect worker start --pool $PREFECT_WORK_POOL"
  environment:
    PREFECT_API_URL: <http://prefect-server:4200/api>
    PREFECT_WORK_POOL: worker-pool
That can be useful in microservices setups where different environments use different pool names. --- ## Short answer The worker container knows the work pool name because you pass it explicitly in:
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prefect worker start --pool worker-pool
Microservices architecture does not change that; the worker still needs: - 
PREFECT_API_URL
to find the server - 
--pool ...
to know which work pool to poll If you want, I can also show you: - how to create the work pool automatically in Docker Compose - how FastAPI-triggered deployments get routed to that pool - or an end-to-end self-hosted example with server + worker + app
m
@Marvin How do I proceed with serverless architecture for my flow run execution instead? So my docker compose doesnt include prefect orchestrator at all and the communication between FastAPI and Prefect flow run code container will be direct
m
✅ thought for 28.1 seconds
If you want no Prefect server/orchestrator in Docker Compose, then you’re moving away from Prefect’s normal deployment/worker orchestration model. In Prefect, flow run execution is normally coordinated by an API/orchestrator: - Prefect Cloud, or - self-hosted Prefect server So if you remove the orchestrator entirely, there are really two possible meanings of “serverless”: ## Option 1: Use Prefect Cloud as the orchestrator This is the usual “serverless Prefect” architecture. You do not run 
prefect server
yourself. Instead: - FastAPI talks to Prefect Cloud - workers or cloud-executed infrastructure also talk to Prefect Cloud - your Docker Compose does not include a Prefect server container This is the recommended way if you still want: - deployments - flow run records - logs - retries - observability - work pools / workers - API-driven triggering ### Architecture
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FastAPI container  --->  Prefect Cloud  <---  Worker / execution environment
In this model, your Compose might only contain: - FastAPI app - optional worker - your app dependencies No self-hosted Prefect API container. ### What you configure For FastAPI and worker:
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PREFECT_API_URL=<https://api.prefect.cloud/api/accounts/<account>/workspaces/<workspace>>
PREFECT_API_KEY=pnu_xxx
Then FastAPI can still do:
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await client.create_flow_run_from_deployment(...)
and workers can still execute those runs. This is the cleanest “no orchestrator container in Compose” setup. --- ## Option 2: No Prefect orchestrator at all This means your FastAPI app directly runs Python code or calls another container/service directly. At that point, you are not really using Prefect deployments/workers/orchestration anymore. You may still use Prefect as a Python workflow library by doing something like:
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from my_flows import my_flow

result = my_flow(...)
or calling a subprocess/container yourself. But then: - no deployment orchestration - no 
create_flow_run_from_deployment
- no worker polling - no Prefect-managed scheduling - no central Prefect API state unless you use ephemeral/local behavior This is basically direct app-to-code execution, not normal Prefect orchestration. --- # Important clarification You said:
“communication between FastAPI and Prefect flow run code container will be direct”
If by that you mean: - FastAPI sends an HTTP request to another service/container - that service/container runs the flow code directly then that is possible, but that is your own microservice execution model, not the usual Prefect deployment model. Example:
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FastAPI ---> Flow Runner Service ---> executes Python flow function
That can work, but Prefect is then mostly being used inside that runner service as a library, not as an orchestrated platform. --- # If you still want Prefect features, use Prefect Cloud If you want “serverless architecture” and still want Prefect’s orchestration features, the best answer is: Use Prefect Cloud instead of self-hosted Prefect server. Then your Compose does not include: - 
prefect server
- 
postgres
for Prefect backend - Prefect API/UI container You just configure clients/workers to talk to Cloud. --- ## Example architecture with Prefect Cloud ### FastAPI container
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services:
  fastapi-app:
    build: .
    environment:
      PREFECT_API_URL: <https://api.prefect.cloud/api/accounts/ACCOUNT/workspaces/WORKSPACE>
      PREFECT_API_KEY: ${PREFECT_API_KEY}
### Worker container
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services:
  prefect-worker:
    image: prefecthq/prefect:3-latest
    command: prefect worker start --pool worker-pool
    environment:
      PREFECT_API_URL: <https://api.prefect.cloud/api/accounts/ACCOUNT/workspaces/WORKSPACE>
      PREFECT_API_KEY: ${PREFECT_API_KEY}
Then your FastAPI app can trigger deployments, and the worker executes them. --- # If you want direct FastAPI → flow container communication Then you should think of it as:
- FastAPI is your orchestrator - the flow container is your execution service For example, the flow runner container exposes an API:
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POST /run-flow
FastAPI sends:
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{
  "flow_name": "main_pipeline",
  "parameters": {...}
}
Then inside that service:
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from flows.main import main_pipeline
main_pipeline(**parameters)
This can use Prefect decorators, but it is not using deployment orchestration. ### Pros - simple topology - no Prefect server - direct service-to-service communication ### Cons - you lose most of Prefect’s orchestration advantages - you must build your own run tracking / queueing / retries / status APIs - deployment APIs like 
create_flow_run_from_deployment
no longer make sense --- # So which approach should you choose? ## Choose Prefect Cloud if you want: - no self-hosted orchestrator - still use Prefect properly - deployments, work pools, workers, logs, retries, UI ## Choose direct FastAPI → runner service if you want: - no orchestrator at all - simple internal execution - you are okay building orchestration behavior yourself --- # Practical recommendation Based on your earlier questions about: - Prefect client - deployments - workers - flow run names - logs - statuses it sounds like you do want orchestration features. So I would recommend: Use Prefect Cloud rather than removing the orchestrator entirely. That gives you “serverless” from your infrastructure point of view, while keeping the Prefect model intact. --- # Example: FastAPI + Prefect Cloud ## FastAPI trigger
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from prefect.client.orchestration import get_client

async def trigger():
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(
            "main-pipeline/pipeline-auto"
        )
        run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            name="api-triggered-run",
        )
        return str(run.id)
## Env
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PREFECT_API_URL=<https://api.prefect.cloud/api/accounts/<account>/workspaces/<workspace>>
PREFECT_API_KEY=pnu_xxx
No self-hosted server container needed. --- # Short answer If you want serverless Prefect, the correct pattern is usually: - remove self-hosted Prefect server from Compose - use Prefect Cloud as the orchestrator - keep FastAPI and workers talking to Cloud If you want direct FastAPI → flow container communication with no orchestrator at all, that is possible, but then you are mostly not using Prefect’s deployment/worker orchestration model anymore. If you want, I can give you one of these two concrete blueprints: 1. FastAPI + Prefect Cloud + worker 2. FastAPI directly calling a flow-runner container with no Prefect orchestrator
m
@Marvin the difference between prefect and prefect-client, can I use prefect-client in my flow source code project
m
✅ thought for 19.3 seconds
Yes — you can use 
prefect-client
in a project, but not if that project contains and runs Prefect flow source code in the normal way. ## Short version - 
prefect
= full Prefect SDK - 
prefect-client
= API client subset If your project defines flows like:
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from prefect import flow, task
then you need 
prefect
, not just 
prefect-client
. --- ## Main difference ### 
prefect
The full package. It includes: - flow/task decorators - runtime engine - workers/CLI - deployment functionality - settings - orchestration client Typical use cases: - defining flows - running flows - deploying flows - starting workers - using the CLI Install:
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pip install prefect
--- ### 
prefect-client
A lighter package focused on the API client side. It includes: - client/orchestration code - settings support - ability to call Prefect API programmatically But it does not include the full flow execution SDK/CLI surface you’d typically use to define and run flows. So it’s suited for things like: - FastAPI backend triggering deployments - querying flow runs - reading logs - status/reporting services Install:
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pip install prefect-client
--- ## Can I use 
prefect-client
in my flow source code project? ### Yes, if that project is only acting as a client For example: - a service that triggers deployments - a service that checks flow run states - a backend that reads Prefect logs Then 
prefect-client
is enough. Example:
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from prefect.client.orchestration import get_client
That is a valid 
prefect-client
-only style use case. --- ### No, if the project defines or runs flows If your project contains code like:
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from prefect import flow, task

@flow
def my_flow():
    ...
then you want the full 
prefect
package. That’s because the flow decorators and execution engine come from 
prefect
, not just 
prefect-client
. --- ## Good rule of thumb ### Use 
prefect-client
for: - API-only microservices - FastAPI apps that trigger deployments - monitoring/reporting apps - services that never define 
@flow
or 
@task
### Use 
prefect
for: - any repository that defines flows - any worker image - any deployment image that executes flows - any environment using 
prefect worker ...
- any environment using 
prefect deploy
- any environment importing 
from prefect import flow, task
--- ## In your architecture ### FastAPI service If it only triggers runs and checks status: - 
prefect-client
can be enough ### Flow source code project If it defines actual Prefect flows: - use 
prefect
### Worker container - use 
prefect
--- ## Examples ### Client-only service
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from prefect.client.orchestration import get_client
This can work with 
prefect-client
. ### Flow code project
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from prefect import flow, task

@task
def hello():
    print("hi")

@flow
def my_flow():
    hello()
This needs full 
prefect
. --- ## Also important The 
prefect
package already includes client functionality, so if you install 
prefect
, you do not need to separately prefer 
prefect-client
for the same environment. So in a flow project, there is usually no benefit to using 
prefect-client
instead of 
prefect
. --- ## Short answer - Yes, 
prefect-client
can be used in a project - but not as a replacement for 
prefect
in a flow source code project - if the project defines/runs flows, use 
prefect
 - if the project only talks to the Prefect API, 
prefect-client
is fine References: - Prefect docs - Prefect client API If you want, I can also give you a package split recommendation for your architecture: - FastAPI app dependencies - worker dependencies - flow code dependencies
m
@Marvin prefect-server: image: prefecthq/prefect:3-latest command: - prefect server start --host 0.0.0.0 - prefect work-pool create --type docker work=pool is this valid
m
✅ thought for 24.3 seconds
No — that Compose snippet is not valid the way it’s written. ## Why In Docker Compose, 
command:
is for the single command the container should run. This part:
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command:
  - prefect server start --host 0.0.0.0
  - prefect work-pool create --type docker work=pool
does not mean “run these two commands in sequence”. It becomes an invalid command form for what you want. Also, the work-pool command itself looks wrong:
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prefect work-pool create --type docker work=pool
The correct syntax is:
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prefect work-pool create <POOL_NAME> --type docker
So for example:
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prefect work-pool create worker-pool --type docker
Verified CLI help: - 
prefect server start
- 
prefect work-pool create NAME --type docker
--- ## If you want to do both You need a shell wrapper that: 1. starts the server 2. waits for it to be available 3. creates the work pool But there’s an important issue: - 
prefect server start
is a long-running foreground process - so if you run it first, the second command never executes unless you background it ## Example with shell
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yaml
prefect-server:
  image: prefecthq/prefect:3-latest
  command: >
    sh -c "
      prefect server start --host 0.0.0.0 &
      sleep 10 &&
      prefect work-pool create worker-pool --type docker &&
      wait
    "
  environment:
    PREFECT_CLI_PROMPT: "false"
  ports:
    - "4200:4200"
That can work, but it’s a bit fragile because 
sleep 10
is just a guess. --- ## Better approach Usually it’s cleaner to separate concerns: ### Option A: create the work pool from a one-off init container Example pattern: - 
prefect-server
starts - another short-lived container runs:
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  prefect work-pool create worker-pool --type docker
### Option B: create the pool manually once If the pool is persistent, you may only need to create it one time. ### Option C: startup script with a health check More reliable than fixed 
sleep
. --- ## Corrected command syntax If you only want the work-pool command syntax fixed:
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prefect work-pool create worker-pool --type docker
not:
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prefect work-pool create --type docker work=pool
--- ## Recommended Compose pattern
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_CLI_PROMPT: "false"
    ports:
      - "4200:4200"

  prefect-init:
    image: prefecthq/prefect:3-latest
    command: >
      sh -c "
        sleep 10 &&
        prefect work-pool create worker-pool --type docker
      "
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_CLI_PROMPT: "false"
    depends_on:
      - prefect-server
This is cleaner because: - server container runs only the server - init container creates the pool ## Important note The container creating the pool needs:
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PREFECT_API_URL=<http://prefect-server:4200/api>
because it is acting as a client to the server. The server container itself usually does not need 
PREFECT_API_URL
. --- ## Short answer No, that exact YAML is not valid. Use either: - one command only for the server container, or - a shell script if you really want sequential behavior, or - better, a separate init container for 
prefect work-pool create
If you want, I can write a full valid Docker Compose file for: - Prefect server - work-pool init - Prefect worker - FastAPI app
m
@Marvin prefect-worker-1 | Discovered type 'docker' for work pool 'work-pool'. prefect-worker-1 | Unable to start worker. Please ensure you have the necessary dependencies prefect-worker-1 | installed to run your desired worker type. and sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) database is locked
m
✅ thought for 17.4 seconds
You likely have two separate issues: 1. the worker cannot start a Docker worker type because required Docker extras/dependencies are missing 2. your Prefect server is using SQLite, and SQLite is hitting concurrency limits (
database is locked
) --- ## 1) 
Discovered type 'docker' ... necessary dependencies
This means the work pool is of type 
docker
, but the worker container does not have the Docker worker dependencies installed. For Docker work pools, don’t use plain 
prefect
only. Install/run Prefect with Docker extras. ### Recommended worker image Use a worker image with Docker integration available. If building your own image:
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FROM python:3.12-slim

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

COPY pyproject.toml uv.lock ./
RUN uv sync --extra docker
Or if using pip:
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RUN pip install "prefect[docker]"
The key is: the worker for a Docker pool needs 
prefect[docker]
. If you run commands with 
uv
, same idea applies in ephemeral command verification:
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uv run --with 'prefect[docker]' prefect worker start --pool work-pool
--- ## 2) 
sqlite3.OperationalError: database is locked
This is a classic sign that your self-hosted Prefect server is using SQLite under concurrent load. SQLite is okay for light/local/dev usage, but it is not a good backend for multi-container orchestration with: - server - worker(s) - API clients - concurrent state/log writes For anything beyond trivial local testing, switch Prefect server to PostgreSQL. ### Recommended fix: use Postgres Set 
PREFECT_API_DATABASE_CONNECTION_URL
on the server/services containers. Example:
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yaml
services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_USER: prefect
      POSTGRES_PASSWORD: prefect
      POSTGRES_DB: prefect
    volumes:
      - postgres_data:/var/lib/postgresql/data

  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_API_DATABASE_CONNECTION_URL: <postgresql+asyncpg://prefect:prefect@postgres:5432/prefect>
      PREFECT_CLI_PROMPT: "false"
    ports:
      - "4200:4200"
    depends_on:
      - postgres
This will eliminate the SQLite locking problem in normal setups. --- ## What’s probably happening in your setup ### Worker side Your work pool is 
docker
, so the worker tries to initialize Docker worker code, but your image only has base Prefect installed. ### Server side Your server likely has no 
PREFECT_API_DATABASE_CONNECTION_URL
set, so it falls back to SQLite. Under worker/server activity, writes contend and SQLite locks. --- ## Practical next steps ### Fix worker dependencies If your worker uses Docker work pool, install:
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prefect[docker]
### Fix backend database Move from SQLite to Postgres by setting:
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PREFECT_API_DATABASE_CONNECTION_URL=postgresql+asyncpg://...
--- ## Example Compose sketch
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yaml
services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_USER: prefect
      POSTGRES_PASSWORD: prefect
      POSTGRES_DB: prefect

  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_API_DATABASE_CONNECTION_URL: <postgresql+asyncpg://prefect:prefect@postgres:5432/prefect>
      PREFECT_CLI_PROMPT: "false"
    ports:
      - "4200:4200"
    depends_on:
      - postgres

  prefect-worker:
    build: .
    command: prefect worker start --pool work-pool
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_CLI_PROMPT: "false"
    depends_on:
      - prefect-server
And in the worker image, ensure:
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RUN pip install "prefect[docker]"
--- ## One more important Docker-pool caveat If you use a Docker work pool, the worker typically needs access to the Docker daemon too, often via: ```yaml volumes:
- /var/run/docker.sock:/var/run/docker.sock ``` Otherwise it may have the Python dependencies but still fail to launch Docker jobs. --- ## Short answer Your traceback suggests: - worker problem: missing Docker worker dependencies → install 
prefect[docker]
- server problem: SQLite backend locking → switch to PostgreSQL If you want, paste your current 
docker-compose.yml
and 
Dockerfile
, and I can rewrite them into a working Prefect server + Postgres + Docker worker setup.
m
@Marvin how to make official Prefect base image slim
m
✅ thought for 15.5 seconds
You generally don’t need to make it slim yourself — the official Prefect image is already based on a slim Python image. Prefect’s published image and Dockerfile use a slim-style base rather than the full 
python:<version>
image. So if you use:
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FROM prefecthq/prefect:3-latest
you are already getting a relatively slim Prefect base image. ## If you want the smallest practical approach You have two options: ### Option 1: use the official Prefect image directly
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FROM prefecthq/prefect:3-latest

WORKDIR /app
COPY . /app
RUN pip install -r requirements.txt
This is the easiest option. --- ### Option 2: build your own slimmer image from 
python:slim
If you want tighter control, start from a slim Python image and install only what you need. Example:
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FROM python:3.12-slim

ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1

RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
And in `requirements.txt`:
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prefect
or if you need Docker worker support:
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prefect[docker]
--- ## If your question is “can I make 
prefecthq/prefect:3-latest
even slimmer?” Not really in a simple official toggle sense. There isn’t a separate “super-slim” official tag you can switch to by just appending something obvious. If image size matters a lot, the common pattern is: - use 
python:<version>-slim
- install only the exact Prefect extras you need - avoid unnecessary OS packages That often gives you a leaner custom image than a general-purpose official one. --- ## Best practice by use case ### FastAPI client-only service Use custom slim image:
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FROM python:3.12-slim
RUN pip install --no-cache-dir prefect-client
### Flow/worker image Use custom slim image:
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FROM python:3.12-slim
RUN pip install --no-cache-dir "prefect[docker]"
### Quick start / less maintenance Use:
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FROM prefecthq/prefect:3-latest
--- ## Important note Use slim, not alpine. 
python:3.x-slim
is generally fine with Prefect. 
python:3.x-alpine
is much more likely to cause dependency/runtime issues. --- ## Short answer - the official Prefect base image is already effectively slim-based - if you want maximum control/minimal size, build from 
python:<version>-slim
and install 
prefect
or 
prefect-client
yourself Useful docs: - Prefect Docker guide - Serve flows with Docker If you want, I can give you: - a minimal Dockerfile for 
prefect-client
only - a minimal Dockerfile for a Docker worker - a size-optimized multi-stage Dockerfile for Prefect
m
@Marvin sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) database is locked how can I just disable so the traceback isn't triggered
m
✅ thought for 18.9 seconds
You generally should not try to disable that traceback. 
sqlite3.OperationalError: database is locked
is not just noisy logging — it means your Prefect server database is actually failing to handle concurrent writes. So the right fix is not “hide the traceback”, but: - reduce concurrency temporarily, or preferably - move off SQLite to PostgreSQL ## Short answer If you’re running self-hosted Prefect with workers / multiple services, SQLite is the wrong backend. Use Postgres instead. Why this happens: SQLite locks the whole DB much more aggressively, and Prefect orchestration generates concurrent reads/writes for: - state updates - logs - scheduling - worker polling That’s why you see:
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sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) database is locked
--- ## The real solution: use PostgreSQL Set:
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PREFECT_API_DATABASE_CONNECTION_URL=<postgresql+asyncpg://user:password@postgres:5432/prefect>
Example in Docker Compose:
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yaml
services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_USER: prefect
      POSTGRES_PASSWORD: prefect
      POSTGRES_DB: prefect

  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_API_DATABASE_CONNECTION_URL: <postgresql+asyncpg://prefect:prefect@postgres:5432/prefect>
      PREFECT_CLI_PROMPT: "false"
    ports:
      - "4200:4200"
    depends_on:
      - postgres
This is the proper fix. --- ## If you only want to suppress the traceback There isn’t a good supported “disable SQLite lock traceback” setting in Prefect that makes the problem harmless. You could try to reduce log verbosity, but that only hides symptoms and the failures still happen. For example, lowering SQLAlchemy/logging output won’t prevent: - failed writes - stuck orchestration - worker instability So I would not recommend it. --- ## If you absolutely need a temporary workaround You can sometimes reduce how often it happens by reducing concurrency: - fewer workers - fewer simultaneous runs - fewer background services - lighter logging volume But this still doesn’t solve the root issue. ### Temporary mitigation ideas - run only one worker - avoid bursty task/log writes - don’t run many services against the same SQLite DB - avoid production use of self-hosted SQLite --- ## Why “just disabling it” is dangerous Because then you’d stop seeing evidence of a real backend failure while Prefect may still: - fail to update states - fail to persist logs - fail to schedule runs correctly So even if hidden, your orchestrator would still be unhealthy. --- ## Recommended conclusion If you are using: - Prefect server - worker(s) - FastAPI client - Docker-based orchestration then switch to PostgreSQL now. That is the intended path for this kind of setup. Relevant docs: - Run a Prefect server - Docker Compose self-hosted setup If you want, I can rewrite your current 
docker-compose.yml
to replace SQLite with Postgres cleanly.
m
@Marvin prefect-worker-1 | Work pool 'work-pool' does not exist and no worker type was provided. Starting a prefect-worker-1 | process worker... prefect-worker-1 | 024621.195 | INFO | prefect.workers.process.processworker 873abc48-4edf-4cd0-b241-2b368c404f24 - Work pool 'work-pool' created. am I capable of making work pool on the worker side as suggested by the above logs
m
✅ thought for 18.1 seconds
Yes — a worker can create the work pool if it does not already exist. That log means the worker started, did not find 
work-pool
, and because no explicit worker type was resolved from an existing pool, it defaulted to a process worker and created the pool itself. So in your case, this happened: 1. worker started with something like:
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   prefect worker start --pool work-pool
2. Prefect checked whether 
work-pool
exists 3. it did not exist 4. worker defaulted to a process worker 5. worker created 
work-pool
That is why you see:
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Work pool 'work-pool' created.
## Important caveat This only means it created a pool for the worker type it is running as. From your log:
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Starting a process worker...
So the created pool is effectively a process work pool, not Docker/Kubernetes/etc. That matters a lot. --- ## What this means practically ### Yes, you can rely on worker-side creation If you are okay with: - the worker deciding/using its own type - the pool being created as that worker type then yes, the worker can bootstrap the pool. ### But be careful If you intended to use: - 
docker
pool - 
kubernetes
pool - another specific worker type then letting the worker auto-create it as 
process
may be not what you want. --- ## In your log, the worker created a process pool So if your deployment expects a Docker pool, this is a mismatch. For example: - deployment configured for Docker execution - worker auto-created a process pool - runs may not behave the way you expect --- ## Best practice ### If you want a specific pool type, create it explicitly For example:
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prefect work-pool create work-pool --type docker
or:
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prefect work-pool create work-pool --type process
Then start the matching worker. ### If you are fine with process execution, worker auto-creation is acceptable In that case:
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prefect worker start --pool work-pool
is enough, and the worker can create the pool if missing. --- ## Recommended rule - For dev/simple setups: worker-side auto-creation is okay - For production/intentional infra: explicitly create the pool with the desired type --- ## Why your earlier Docker worker issue matters Previously, you had logs about Docker worker type/dependencies. If now the worker is auto-creating a process pool, then it may be because: - the Docker pool did not exist - or the worker could not initialize Docker type - so it fell back to process That could explain inconsistent behavior. --- ## Short answer Yes, the worker is capable of creating the work pool from its side. But in your log it created a process pool, because it says:
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Starting a process worker...
So this is only safe if you actually want a process work pool. If you want, I can help you determine whether your setup should use: - process pool, or - docker pool based on how your containers are arranged.
m
@Marvin Make comprehensive explanation of the type of work pool
m
✅ thought for 34.5 seconds
A work pool in Prefect is the routing/configuration layer that tells Prefect what kind of execution infrastructure should run a deployment’s flow runs. Think of it like this: - deployment says: “I want to run on pool X” - worker says: “I poll pool X” - work pool type says: “runs from this pool should be executed using infrastructure Y” So the type of work pool is very important because it determines how runs are launched. ## Core idea A work pool is not just a queue name. It also defines the execution model. Examples: - 
process
- 
docker
- 
kubernetes
- other worker/integration-specific types when available --- # 1. What a work pool does A work pool is responsible for: - grouping runs for a certain execution backend - matching those runs to compatible workers - storing infrastructure/job configuration defaults - telling Prefect how jobs should be launched So when a deployment is created with:
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work_pool_name="my-pool"
Prefect expects that a worker polling 
my-pool
knows how to execute that deployment according to the pool type. --- # 2. Why the type matters The type determines the launch mechanism. ## 
process
work pool Runs the flow as a local subprocess in the worker environment. ### How it works - the worker polls the Prefect API - when it gets a run, it starts a local process - the code executes directly in that worker container/VM environment ### Good for - local development - simple self-hosted setups - cases where the worker already has the flow code and dependencies ### Requirements - worker must have your flow code available - worker must have all required Python dependencies installed ### Mental model
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worker container
  -> runs flow directly inside itself
### Pros - simple - less moving parts - easy to debug ### Cons - weaker isolation - dependencies for all flows often need to exist in the worker image - scaling/isolation is limited compared to container-per-run approaches --- ## 
docker
work pool Runs each flow run as a Docker container. ### How it works - worker polls Prefect API - when it gets a run, it launches a Docker container for that run - that container executes the flow ### Good for - stronger isolation per run - different images per deployment - container-native infra ### Requirements - worker must have Docker worker dependencies (
prefect[docker]
) - worker must be able to access Docker daemon - deployment/job config must specify a suitable image or environment ### Mental model
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worker container
  -> talks to Docker daemon
  -> launches another container for each flow run
### Pros - better isolation - easier per-deployment image control - nice for containerized workloads ### Cons - more setup complexity - Docker socket/daemon access required - nested container patterns can be tricky --- ## 
kubernetes
work pool Runs each flow run as a Kubernetes job/pod. ### How it works - worker polls Prefect API - worker submits Kubernetes jobs/pods for flow runs ### Good for - cloud-native / cluster-based infra - scalable production workloads - teams already on Kubernetes ### Requirements - Kubernetes worker dependencies - cluster credentials / RBAC / namespace setup - proper job templates/images ### Pros - strong isolation - scalable - aligned with K8s operations ### Cons - most operational complexity - requires Kubernetes platform knowledge --- # 3. Process vs Docker vs Kubernetes: the practical difference ## Process Run inside the worker itself. If your worker container has: - app code - Python deps - correct env vars then the run can execute there directly. ## Docker Run in a new container launched by the worker. The worker is more like a launcher/controller. ## Kubernetes Run in a K8s job/pod launched by the worker. The worker is even more remote from the actual runtime. --- # 4. Why your pool type must match your worker A worker must support the type of the pool it polls. For example:
### Valid - process worker ↔️ process pool - docker worker ↔️ docker pool - kubernetes worker ↔️ kubernetes pool ### Invalid / problematic - process worker trying to handle docker pool - docker worker without Docker dependencies - kubernetes pool without k8s integration/config That is why pool type is not just metadata — it affects execution compatibility. --- # 5. What happens if the pool does not exist If you start:
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prefect worker start --pool work-pool
and the pool does not exist, Prefect may create it automatically based on the worker’s own type. Example:
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Work pool 'work-pool' does not exist ... Starting a process worker...
Work pool 'work-pool' created.
That means the worker created a process pool. This is convenient, but can surprise you if you intended Docker or Kubernetes. --- # 6. Which work pool type should you choose? ## Choose 
process
if: - you want the simplest setup - flows can run in the same environment as the worker - you are doing local/dev/small self-hosted deployments - you don’t need one-container-per-run isolation This is often the easiest starting point. ## Choose 
docker
if: - you want each run isolated in its own container - you already manage images for your flows - your environment is Docker-centric - you want different deployments to use different images This is a common production-friendly self-hosted pattern. ## Choose 
kubernetes
if: - your platform already runs on Kubernetes - you want scalable, isolated job execution - you need cluster-native scheduling and control This is the most infrastructure-heavy option. --- # 7. How deployments interact with work pools A deployment is associated with one work pool. Example:
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my_flow.from_source(
    source=".",
    entrypoint="flows/main.py:my_flow",
).deploy(
    name="daily-job",
    work_pool_name="work-pool",
)
When a run is created from that deployment: - Prefect records it under 
work-pool
- a worker polling 
work-pool
picks it up - the pool type determines how the run is launched --- # 8. What job variables/base job templates do Work pools can also hold infrastructure defaults. For example, in Docker/Kubernetes pools, the pool may define defaults like: - image - environment variables - network settings - memory/cpu settings - job template details Then deployments can override some of these with 
job_variables
. This matters much more in 
docker
and 
kubernetes
pools than in 
process
pools. --- # 9. How this maps to your architecture Given your questions, here’s the practical mapping: ## FastAPI app Usually does not care about pool type directly. It just triggers deployments. ## Prefect server / Prefect Cloud Stores the pool and routes runs logically. ## Worker Must support the pool type. ## Flow code Must be available according to the pool type: - `process`: in worker environment - `docker`: in run image/container - `kubernetes`: in pod/job image or mounted/pulled source --- # 10. Common mistakes ## Mistake 1: creating a Docker pool but starting a plain process worker This leads to worker startup or execution mismatch. ## Mistake 2: using Docker pool without Docker dependencies You need 
prefect[docker]
. ## Mistake 3: using process pool when worker image does not contain the flow code Then the worker can’t import/run the flow. ## Mistake 4: letting the worker auto-create a process pool when you intended Docker This happens if you start the worker without explicitly creating the desired pool type first. --- # 11. Recommended decision tree ## Start with 
process
if: - you are still learning the architecture - your worker and code live together - you want fewer moving parts ## Move to 
docker
if: - you need per-run isolation - you want deployment-specific images - you are comfortable with Docker daemon setup ## Use 
kubernetes
if: - you already have a K8s platform - you want cluster scheduling/isolation at scale --- # 12. Example scenarios
## Scenario A: one worker container with your code baked in Use process pool. Why: - easiest - worker directly executes flows ## Scenario B: worker launches specialized runtime containers Use docker pool. Why: - each flow run can use its own image ## Scenario C: EKS/GKE/AKS cluster Use kubernetes pool. Why: - best alignment with infra --- # 13. CLI examples Create a process pool:
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prefect work-pool create my-process-pool --type process
Create a docker pool:
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prefect work-pool create my-docker-pool --type docker
Create a kubernetes pool:
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prefect work-pool create my-k8s-pool --type kubernetes
Then start a matching worker:
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prefect worker start --pool my-process-pool
or:
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prefect worker start --pool my-docker-pool
--- # 14. Best advice for your current setup Given your Docker Compose / self-hosted / FastAPI questions: - if you want simple and stable first: use a process pool - if you want container-per-run isolation: use a docker pool and install Docker worker extras properly - don’t let auto-created pool type surprise you — create the pool explicitly if type matters --- # Final summary A work pool type tells Prefect how to execute flow runs: - process → run inside the worker process environment - docker → launch a Docker container per run - kubernetes → launch a Kubernetes job/pod per run The pool type must match: - your worker capabilities - your infrastructure - where your flow code and dependencies live If you want, I can next give you a comparison table specifically for 
process
vs 
docker
for your microservices setup, and tell you which one fits your architecture best.
m
@Marvin explain this bad gateway error
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{
  "detail": "Invalid deployment name format: desktop-study-auto. Expected format: <FLOW_NAME>/<DEPLOYMENT_NAME>"
}
m
✅ thought for 14.5 seconds
That error means your code tried to look up a deployment by name, but the name you passed is in the wrong format. You passed:
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desktop-study-auto
but Prefect expects:
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<FLOW_NAME>/<DEPLOYMENT_NAME>
So it wants something like:
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my-flow/desktop-study-auto
## Why this happens When using methods like:
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client.read_deployment_by_name(...)
Prefect does not accept just the deployment name by itself. It expects the fully qualified deployment name: - flow name - slash 
/
- deployment name ### Example If your flow is named:
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main-pipeline
and your deployment is named:
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desktop-study-auto
then the correct input is:
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main-pipeline/desktop-study-auto
--- ## So what is wrong with your input? You used only:
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desktop-study-auto
That is only the deployment name. Prefect needs:
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<flow-name>/desktop-study-auto
--- ## Why it may show as a bad gateway in your app The real underlying application error is the invalid deployment name format. Your FastAPI / reverse proxy / gateway may be wrapping it as a “bad gateway” style response, but the meaningful part is this:
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{
  "detail": "Invalid deployment name format: desktop-study-auto. Expected format: <FLOW_NAME>/<DEPLOYMENT_NAME>"
}
So the core problem is not networking — it is incorrect input. --- ## Fix If your code currently does:
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deployment = await client.read_deployment_by_name("desktop-study-auto")
change it to something like:
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deployment = await client.read_deployment_by_name("my-flow/desktop-study-auto")
--- ## Example
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from prefect.client.orchestration import get_client

async def get_deployment():
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(
            "main-pipeline/desktop-study-auto"
        )
        return deployment
--- ## How to know the flow name The flow name is the name Prefect registered for the flow, often: - the function name by default, or - an explicitly set flow name Example:
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from prefect import flow

@flow(name="main-pipeline")
def my_flow():
    pass
Then the deployment name might be:
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desktop-study-auto
So full deployment name becomes:
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main-pipeline/desktop-study-auto
--- ## Summary Your input:
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desktop-study-auto
Expected:
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<FLOW_NAME>/<DEPLOYMENT_NAME>
Example:
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main-pipeline/desktop-study-auto
Reference: - Prefect client API If you want, I can help you figure out the exact full deployment name from your flow code or deployment definition.
m
@Marvin async def trigger_flow(body:TriggerRequest): async with get_client() as client: deployment = await client.read_deployment_by_name(f"{body.deployment_name}/{os.environ["DEPLOYMENT_NAME"]}") flow_run = await client.create_flow_run_from_deployment( deployment_id=deployment.id, parameters=body.parameters.model_dump(), name=body.deployment_name, ) print(flow_run.id) is there anything wrong
m
✅ thought for 19.3 seconds
Yes — there are a couple things to fix. ## 1. The f-string quoting is broken This line is invalid Python:
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f"{body.deployment_name}/{os.environ["DEPLOYMENT_NAME"]}"
because the inner 
"
closes the outer string. Use single quotes inside:
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f"{body.deployment_name}/{os.environ['DEPLOYMENT_NAME']}"
--- ## 2. You may have flow name vs deployment name reversed 
read_deployment_by_name(...)
expects:
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<FLOW_NAME>/<DEPLOYMENT_NAME>
But your code currently builds:
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f"{body.deployment_name}/{os.environ['DEPLOYMENT_NAME']}"
That suggests: - 
body.deployment_name
is being used as the flow name - 
DEPLOYMENT_NAME
env var is being used as the deployment name That may be correct only if 
body.deployment_name
actually contains the flow name. But from the name 
body.deployment_name
, it sounds like it probably contains the deployment name already. If so, this is wrong conceptually. --- ## 3. You are naming the flow run with 
body.deployment_name
This part is valid syntactically:
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name=body.deployment_name
but semantically it means the flow run name will be the same as whatever 
body.deployment_name
contains. That may be okay, but usually you probably want a dedicated run name field, like:
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name=body.flow_run_name
rather than reusing the deployment name. --- # Likely intended structure You probably want something like: - 
body.flow_name
- 
body.deployment_name
- 
body.parameters
- optionally 
body.flow_run_name
## Better example
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import os
from prefect.client.orchestration import get_client

async def trigger_flow(body: TriggerRequest):
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(
            f"{body.flow_name}/{body.deployment_name}"
        )

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters=body.parameters.model_dump(),
            name=body.flow_run_name if body.flow_run_name else None,
        )

        print(flow_run.id)
        return flow_run
--- # If you really want env var + request body If your environment variable stores the deployment name, and the body stores the flow name, then:
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deployment = await client.read_deployment_by_name(
    f"{body.flow_name}/{os.environ['DEPLOYMENT_NAME']}"
)
That is valid. But I would rename fields to make the meaning clearer. --- # 4. Potential issue with 
model_dump()
This is okay only if:
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body.parameters
is a Pydantic model instance. If 
body.parameters
is already a plain dict, then:
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body.parameters.model_dump()
will fail. So make sure the type supports 
model_dump()
. If it might already be a dict, use it directly:
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parameters=body.parameters
--- # 5. Missing return value You print the flow run id:
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print(flow_run.id)
but don’t return anything. If this is a FastAPI handler, you probably want:
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return {"flow_run_id": str(flow_run.id)}
--- # Corrected version Assuming: - 
body.flow_name
= flow name - 
body.deployment_name
= deployment name - 
body.flow_run_name
optional - 
body.parameters
is a Pydantic model
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python
from prefect.client.orchestration import get_client

async def trigger_flow(body: TriggerRequest):
    async with get_client() as client:
        deployment = await client.read_deployment_by_name(
            f"{body.flow_name}/{body.deployment_name}"
        )

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=deployment.id,
            parameters=body.parameters.model_dump(),
            name=body.flow_run_name if body.flow_run_name else None,
        )

        return {"flow_run_id": str(flow_run.id), "flow_run_name": flow_run.name}
--- # If you want to keep env var deployment name ```python import os from prefect.client.orchestration import get_client
async def trigger_flow(body: TriggerRequest): async with get_client() as client: deployment = await client.read_deployment_by_name( f"{body.flow_name}/{os.environ['DEPLOYMENT_NAME']}" ) flow_run = await client.create_flow_run_from_deployment( deployment_id=deployment.id, parameters=body.parameters.model_dump(), name=body.flow_run_name if body.flow_run_name else None, ) return {"flow_run_id": str(flow_run.id)}
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---

# Biggest likely mistake

The *biggest likely problem* is that you are using `body.deployment_name` in the first half of:

```python
<flow-name>/<deployment-name>
which is probably wrong if that field truly contains a deployment name. If you want, I can help you redesign the 
TriggerRequest
model so the field names correctly match Prefect’s expectations.
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