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<@ULVA73B9P> give me a docker-compose.yaml which h...
# ask-marvin
p
@Marvin give me a docker-compose.yaml which have docker type’s workpool
🤯 1
m
✅ thought for 345.3 seconds
Here’s a ready-to-use docker-compose.yaml that runs a Prefect Docker worker (Docker-type work pool). It also includes a self-hosted Prefect Server so everything is local. The worker is configured to launch flow runs as Docker containers by mounting the Docker socket.
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version: "3.9"

services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    container_name: prefect-server
    environment:
      PREFECT_SERVER_API_HOST: 0.0.0.0
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('<http://localhost:4200/api/health>', timeout=1)"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 60s

  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    container_name: prefect-docker-worker
    depends_on:
      prefect-server:
        condition: service_healthy
    environment:
      # Point the worker to the local Prefect API
      PREFECT_API_URL: <http://prefect-server:4200/api>
      # Optional tuning:
      # PREFECT_WORKER_PREFETCH_SECONDS: "10"
      # PREFECT_WORKER_QUERY_SECONDS: "15"
    volumes:
      # Critical: allow the worker to create Docker containers
      - /var/run/docker.sock:/var/run/docker.sock
    # Use 'uv' to ensure the docker integration is available
    command: >
      uv run --with 'prefect[docker]'
      prefect worker start
      --pool docker-pool
    restart: on-failure
Important notes: - Create the Docker-type work pool once before starting the worker: - 
prefect work-pool create --type docker docker-pool
- Or create it in the UI (Work Pools → Create → Type: Docker → Name: docker-pool) - The worker needs Docker access to start flow-run containers; that’s why we mount 
/var/run/docker.sock
. - The 
uv run --with 'prefect[docker]'
ensures the Docker integration is available in the worker container. - For Prefect Cloud, remove the 
prefect-server
service and set 
PREFECT_API_URL
to your Cloud workspace API URL (found in your workspace settings), then keep the worker service as-is. Handy commands: - Bring it up: 
docker compose up -d
- Tail worker logs: 
docker compose logs -f prefect-docker-worker
- Verify workers: 
prefect worker ls
Docs: - Docker deployments/workers: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Workers + work pools concepts: https://docs.prefect.io/v3/concepts/workers - Managing work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/manage-work-pools If you want this tailored for Prefect Cloud or with custom base job templates (e.g., private registry auth, volumes, env), tell me your details and I’ll adjust the compose for you.
p
@Marvin I use wsl2 to commend docker compose up ,but prefect ui was not functional properly. Docker ps showing all up.
m
✅ thought for 138.4 seconds
Got it — if containers are “up” but the UI isn’t working on WSL2, it’s usually one of these: - The API isn’t listening on 0.0.0.0 (so the browser can’t reach it) - The UI can’t find the API (wrong/implicit API URL) - Windows/WSL2 networking oddities (localhost vs 127.0.0.1, firewall/VPN) Quick fixes to try: 1) Ensure the server listens on all interfaces and explicitly point the UI at the API Update your compose to include these on the server, then recreate:
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services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    container_name: prefect-server
    environment:
      PREFECT_SERVER_API_HOST: 0.0.0.0
      PREFECT_UI_ENABLED: "true"
      # Explicitly tell the UI where the API lives from your browser’s perspective
      PREFECT_UI_API_URL: <http://localhost:4200/api>
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
2) Verify the API from your host - From Windows PowerShell or WSL shell:
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curl -s <http://localhost:4200/api/health>
Expected: 
true
- If that fails, try 
<http://127.0.0.1:4200/api/health>
3) Check server logs
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docker compose logs -f prefect-server
You should see it serving on 0.0.0.0:4200 and no errors. 4) UI symptom: “Couldn’t connect to API” If you see that banner in the browser, it means the UI couldn’t reach 
/api
. Setting 
PREFECT_UI_API_URL
as above fixes the common case (browser → http://localhost:4200 → UI → http://localhost:4200/api). 5) WSL2/Windows tips - Use http://127.0.0.1:4200 if http://localhost:4200 doesn’t work. - Temporarily disable VPN/firewall to rule out interference. - Make sure Docker Desktop uses the WSL2 backend (Settings → General). - Ensure nothing else is bound to port 4200 on Windows. If you’d like, paste: - The compose file you’re using - Output of:
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docker compose ps
  docker compose logs --tail=100 prefect-server
  curl -v <http://localhost:4200/api/health>
and I’ll pinpoint the issue. Docs for reference: - Workers and work pools: https://docs.prefect.io/v3/concepts/workers - Docker work pool setup: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Managing work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/manage-work-pools
p
@Marvin base_job_template = jsonencode({ job_configuration = { image = "prefecthq/prefect:3-python3.11-uv" env = { PREFECT_APPLY_MARK = "true" PREFECT_JOB_EXTRA_COMMANDS = "uv sync --system-site-packages" } } is this format correct or not?
m
✅ thought for 236.5 seconds
Short answer: the structure is valid, but those specific env vars aren’t interpreted by Prefect. - The base job template for a Docker work pool expects a top-level 
job_configuration
object. Your 
image
and 
env
keys fit the schema. - 
env
must be a map of string → string/null. Using 
"true"
is fine. - 
prefecthq/prefect:3-python3.11-uv
is a valid image tag. - However, 
PREFECT_APPLY_MARK
and 
PREFECT_JOB_EXTRA_COMMANDS
are not recognized by Prefect/Docker worker out of the box; they’ll just be passed into your flow-run container as environment variables. They’ll only do something if your code or entrypoint reads them. If your goal is to run 
uv sync --system-site-packages
before executing the flow, set a custom 
command
that runs both the sync and the flow-run execute. For example (Terraform/HCL with 
jsonencode
):
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base_job_template = jsonencode({
  job_configuration = {
    image   = "prefecthq/prefect:3-python3.11-uv"
    command = "bash -lc 'uv sync --system-site-packages && prefect flow-run execute'"
    env = {
      # Any env you really need at runtime; remove if not used by your code
      PREFECT_APPLY_MARK         = "true"
      PREFECT_JOB_EXTRA_COMMANDS = "uv sync --system-site-packages"
    }
  }
})
If you don’t need those env vars, you can omit them entirely. Tip: apply the template when creating/updating the pool:
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prefect work-pool create my-docker-pool --type docker --base-job-template ./base-job-template.json
Docs: - Docker work pools and job templates: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Workers overview: https://docs.prefect.io/v3/concepts/workers
p
@Marvin Nov 29th, 2025 Worker 'ProcessWorker edee5c3d-18db-49e1-903b-71e99af33e5f' submitting flow run '7e50fbf0-c16b-415f-844a-dcc342e6f950' 121101 AM prefect.flow_runs.worker Opening process... 121101 AM prefect.flow_runs.runner Completed submission of flow run '7e50fbf0-c16b-415f-844a-dcc342e6f950' 121101 AM prefect.flow_runs.worker Process for flow run 'ludicrous-squid' exited with status code: 2 121101 AM prefect.flow_runs.runner Reported flow run '7e50fbf0-c16b-415f-844a-dcc342e6f950' as crashed: Flow run process exited with non-zero status code 2. 121101 AM prefect.flow_runs.runner Loading flow to check for on_crashed hooks 121101 AM prefect.flow_runs.runner > Running git_clone step... how to fix this
m
✅ thought for 177.5 seconds
Thanks for the log snippet — the key lines are: - ProcessWorker exited with status code 2 - Right before that: “> Running git_clone step...” That means the deployment’s pull step failed while cloning your repo. Exit code 2 during git clone typically indicates one of: - git is not installed/available in PATH in the runtime - bad/insufficient credentials for a private repo - wrong URL/branch/commit - network/DNS/SSL issue - permissions or disk issues in the working dir Quick checks to pinpoint and fix: 1) Verify git is available in the worker runtime - If you’re using the Process worker locally, run:
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git --version
If using a Docker worker image, make sure the image includes git (the official prefect images do). - If not installed, install git (apt-get, apk, etc., depending on your worker environment). 2) Confirm your deployment’s source settings - If you used 
.from_source('<https://github.com/owner/repo.git>', entrypoint='path.py:flow')
, ensure: - URL is correct - Branch/commit exists - If it’s a private repo, you must provide credentials (token or SSH): - Recommended: use a credentials block and pass it via GitRepository:
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from prefect.runner.storage import GitRepository
    from prefect_github import GitHubCredentials

    my_flow = flow.from_source(
      source=GitRepository(
        url="<https://github.com/owner/repo.git>",
        branch="main",
        credentials=GitHubCredentials.load("my-github-creds"),
        # optionally: directories=["flows"]   # for sparse checkout
      ),
      entrypoint="flows/my_flow.py:my_flow",
    )
- Or embed a Secret block into 
credentials={"access_token": Secret.load("github-token")}
. - For SSH URLs (
git@github.com:...
), ensure the worker has an SSH key and host known. 3) Try the exact git clone from the worker host - On the same machine/container where the worker runs, try:
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git clone <https://github.com/owner/repo.git>
Or the exact URL (with branch/commit) your deployment uses. This helps reveal auth or network errors directly. 4) Enable debug logs to see the precise failure - Before starting the worker:
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export PREFECT_DEBUG_MODE=true
  export PREFECT_LOGGING_LEVEL=DEBUG
  export PREFECT_RUNNER_LOG_LEVEL=DEBUG
  prefect worker start --pool <your-pool> --type process
- Then re-run; you should see a message like:
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Failed to clone repository '<https://github.com/owner/repo.git>' with exit code 2.
and more context around the git command. 5) If you’re using sparse checkout or a specific commit - Double-check 
directories=[...]
paths exist in the repo - Verify 
branch
or 
commit_sha
is valid and accessible 6) Windows/WSL specifics (if applicable) - If the Process worker is running under WSL, ensure git is installed inside WSL and the repo URL is reachable from WSL’s network environment. If you paste the deployment definition you used (the 
.from_source(...)
snippet or 
prefect deploy
details) and whether the repo is private, I can give you an exact correction. Also useful: - Output of 
git --version
where the worker runs - The repo URL form (HTTPS vs SSH) - Any custom branch/commit/directory settings you set in 
GitRepository
Docs: - Deploy from source (Docker/Git/general): https://docs.prefect.io/v3/how-to-guides/deployments/ - Workers overview: https://docs.prefect.io/v3/concepts/workers
p
@Marvin still get error, check my terraform format if is correct resource "prefect_work_pool" "docker_pool" { name = var.work_pool_name type = "docker" description = "" base_job_template = jsonencode({ job_configuration = { image = "prefecthq/prefect:3-python3.11-uv" command = "sh -c 'uv sync --system-site-packages && prefect flow-run execute'" } variables = { properties = { image = { type = "string" default = "prefecthq/prefect:3-python3.11-uv" title = "Docker Image" description = "" } } required = ["image"] } }) } error: Process for flow run 'denim-cicada' exited with status code: 2
m
✅ thought for 41.7 seconds
Thanks — a few important things jump out: 1) Your run is being handled by a Process worker, not a Docker worker The log says: - Worker 'ProcessWorker ...' submitting flow run If you intended to use your Docker work pool, make sure: - Your deployment is targeted at that pool (work_pool_name = your docker pool) - You actually have a Docker worker running and polling that pool: - Start it: 
prefect worker start --pool <your-docker-pool> --type docker
- Or use the docker-compose I shared earlier If a Process worker picks up the run, any Docker-only template fields are ignored and the command runs in the worker’s local environment, where 
uv
may not exist, causing exit 2. 2) Your command string has HTML-escaped &&, which will break the shell In your Terraform, I see 
&&
instead of 
&&
. That will make 
sh -c ...
fail with a shell syntax error and typically exit code 2. Please change it to actual 
&&
. 3) Variables schema: add "type": "object" or drop 
variables
entirely If you include a 
variables
section, it should be a JSON Schema object. If you don’t need templating, you can omit 
variables
completely. Here are two corrected options. Option A: Minimal (no variables)
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resource "prefect_work_pool" "docker_pool" {
  name        = var.work_pool_name
  type        = "docker"
  description = ""

  base_job_template = jsonencode({
    job_configuration = {
      image   = "prefecthq/prefect:3-python3.11-uv"
      # Prefer -lc so PATH and shell expansions are consistent
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
    }
  })
}
Option B: With variables schema (if you want to make image configurable)
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resource "prefect_work_pool" "docker_pool" {
  name        = var.work_pool_name
  type        = "docker"
  description = ""

  base_job_template = jsonencode({
    job_configuration = {
      image   = "{{ image }}"
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
    }
    variables = {
      type = "object"
      properties = {
        image = {
          type        = "string"
          default     = "prefecthq/prefect:3-python3.11-uv"
          title       = "Docker Image"
          description = ""
        }
      }
      required = ["image"]
    }
  })
}
4) Double-check that uv sync is appropriate for your repo - 
uv sync
expects a pyproject.toml in the working directory of the flow run container. - If your pyproject.toml lives in a subdirectory, cd before syncing:
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command = "sh -lc 'cd subdir && uv sync --system-site-packages && prefect flow-run execute'"
- If you don’t need runtime sync, you can remove the 
uv sync
step entirely. 5) Verify your deployment targets the Docker pool - In the UI, open the deployment and confirm Work Pool = your Docker pool - Or redeploy with the correct pool - Ensure a Docker worker is running for that pool (not a Process worker) If you still see exit code 2 after these changes, please share: - The current deployment’s Work Pool name (from the UI) - Whether a Docker worker is running and polling that pool - The latest flow-run logs around the command start - Whether your repo has a pyproject.toml at the root or a subdir Docs for reference: - Docker work pools and job templates: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Workers overview: https://docs.prefect.io/v3/concepts/workers - Deployments from source: https://docs.prefect.io/v3/how-to-guides/deployments/
p
@Marvin "Could you please provide the best practices for developing a Terraform template for Prefect? My server environment is set up using Docker Compose, featuring a Docker Work Pool that executes code via Git Pull rather than baking the flow code directly into a Docker image."
m
✅ thought for 248.1 seconds
Absolutely — here’s a concise set of best practices for building a Terraform-first Prefect 3 setup when: - You run Prefect Server via Docker Compose - You use a Docker Work Pool - Your deployments pull code from Git (not baked in the image) I’ll include concrete Terraform patterns and call out gotchas specifically for Git-based deployments and Docker workers. High-level principles - Pin versions: pin Prefect image tags (e.g., prefecthq/prefect:3.9.x-python3.11-uv) and the Terraform provider (~> exact minor). - Idempotence: prefer variables/templating in base_job_template; avoid mutable state in deployments; use lifecycle where helpful. - Separate concerns: manage infra with Terraform (pools, blocks, automations), but let CI run deploys. - Secure secrets: never store raw tokens in Terraform state; use Prefect blocks or CI secrets. - Keep workers dumb: keep worker base images small; do runtime installs only if needed; prefer prebuilt images for heavy deps. Provider and authentication - Use a service account API key for Terraform. - For Prefect Server (self-hosted), set provider.host to your API; for Cloud use api_key/workspace. - Lock state (remote backend) and enable state encryption. Example:
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terraform {
  required_providers {
    prefect = {
      source  = "PrefectHQ/prefect"
      version = "~> 1.0"
    }
  }
}

provider "prefect" {
  # Self-hosted Server
  host   = var.prefect_api_url   # e.g., <http://localhost:4200/api> (dev) or your ingress URL
  api_key = var.prefect_api_key  # create a service account; for server, leave unset if auth disabled
}
Docker work pool (Git-pull friendly) - Ensure your Docker worker container can create sibling containers: mount /var/run/docker.sock. - In base_job_template, set a clear command that bootstraps your code env (if needed) and runs the flow. - If you use uv/pip at runtime, ensure pyproject.toml/requirements are where the container expects; cd first if needed. - Keep variables in the template so you can override image/env per-deployment later. Good starting template:
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resource "prefect_work_pool" "docker_pool" {
  name = var.work_pool_name
  type = "docker"

  base_job_template = jsonencode({
    job_configuration = {
      image   = "{{ image }}"
      # Prefer sh -lc so login shells and PATH behave as expected
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
      env = {
        ENVIRONMENT = var.environment
      }
      # Optional: map volumes or networks if your flows need them
      # volumes = ["/host/path:/container/path"]
      # network_mode = "bridge"
      # mem_limit = "1g"
    }
    variables = {
      type = "object"
      properties = {
        image = {
          type        = "string"
          default     = "prefecthq/prefect:3-python3.11-uv"
          title       = "Docker Image"
        }
        env = {
          type  = "object"
          title = "Environment Variables"
          additionalProperties = { type = ["string", "null"] }
          default = {}
        }
      }
      required = ["image"]
    }
  })

  tags = ["environment:${var.environment}", "runtime:docker", "code-source:git"]
}
Important notes - Use && (not HTML-escaped &&) in command strings. - If your repo root differs, cd to subdir in the command: - command = "sh -lc 'cd flows && uv sync --system-site-packages && prefect flow-run execute'" - If you don’t need runtime installs, simplify to: - command = "prefect flow-run execute" Git source and credentials (Blocks) - Use blocks for Git credentials to avoid leaking secrets into Terraform state. - For GitHub: create a GitHubCredentials block with a token and reference it in deployments. - If you must provision blocks via Terraform, store only references/indirect secrets. Example block (safe pattern) - Prefer creating the block at runtime (CI step) using environment secrets or 
prefect block create
with masked input, rather than putting the token inline in Terraform.
- If you still need Terraform to create it, ensure your TF state is encrypted and access-controlled.
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resource "prefect_block" "github_credentials" {
  name      = "github-creds"
  type_slug = "github-credentials"
  data = jsonencode({
    token = var.github_token  # WARNING: ends up in TF state; prefer CI-time creation
  })
}
Deployments: managed by CI, referenced by Terraform - Best practice: let CI call 
.deploy()
or 
prefect deploy
to create/update deployments from your repo. Terraform can manage metadata around deployments if needed, but drift is common if both TF and CI manage deployments. - If you must use Terraform’s prefect_deployment, keep it minimal and reference the Git repo + credentials block. Deployment pattern with Git pull (preferred via code/CI) - In your repo: - flow = flow.from_source(source=GitRepository(...), entrypoint="path/to.py:flow_fn") - flow.deploy(work_pool_name="your-docker-pool", name="my-deployment", parameters=..., tags=[...]) - Ensure the Docker worker is running and targets the correct pool. Concurrency, work queues, and tagging - Tags: apply consistent tags at pool, deployment, and automation levels (env, team, data-domain). - Work queues: optionally create named queues per SLA or team and attach concurrency limits. - Concurrency limits: - Global: prefect_global_concurrency_limit - Task-run: prefect_task_run_concurrency_limit - Queue: set on the queue in UI or API
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resource "prefect_global_concurrency_limit" "io_bound" {
  name      = "io-bound"
  limit     = 20
  active    = true
  slot_decay_per_second = 0
}
Automations and notifications - Set up Slack/email on failures or state changes. - Use env tags/labels to scope automations.
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resource "prefect_automation" "notify_failures" {
  name = "notify-failures-${var.environment}"

  trigger = {
    type  = "flow-run-state-change"
    match = {
      any_ = [
        { "flow_run": { "tags": ["environment:${var.environment}"] } }
      ]
    }
    after = "2m"  # debounce
    state = "Failed"
  }

  actions = [
    {
      type = "send-notification"
      block_document_name = "slack-notifier"
      payload = jsonencode({
        message = "Flow run {{ flow_run.name }} failed in ${var.environment}"
      })
    }
  ]
}
Multi-environment layout - Use Terraform workspaces or folders for dev/staging/prod. - Parameterize: - work pool names - image tags - API URL - tags - concurrency - Keep naming conventions consistent: prefix/suffix with environment. Workers and runtime environment - Don’t manage worker containers with Terraform; manage them with your Docker Compose or infra orchestrator. - In your docker-compose for the worker: - Mount /var/run/docker.sock - Set PREFECT_API_URL to your Server - Optionally run via uv to include docker integration: - command: uv run --with 'prefect[docker]' prefect worker start --pool your-docker-pool - Ensure git is installed in the final runtime image used for flow runs if you run uv sync or other tooling. Troubleshooting “exit code 2” - If you see shell exit 2, common causes: - Bad shell command (e.g., HTML-escaped &&) - Missing tools (git, uv) in the runtime image - Wrong working directory (pyproject.toml missing for uv) - Validate by exec’ing into a one-off container with the same image and running your command manually: - docker run --rm -it prefecthq/prefect:3-python3.11-uv sh -lc "git --version && uv --version && prefect --version" Useful links - Terraform provider registry: https://registry.terraform.io/providers/PrefectHQ/prefect/latest - Work pools: https://docs.prefect.io/v3/concepts/work-pools - Docker work pool how-to: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Deployments (from source): https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Blocks: https://docs.prefect.io/v3/develop/blocks/ - Automations: https://docs.prefect.io/v3/concepts/automations
If you share your current Terraform module (provider config, pool, any blocks, and whether you plan to manage deployments via TF or CI), I can refine this into a vetted template tailored to your setup.
p
@Marvin why need git in the runtime?
m
✅ thought for 45.3 seconds
Great question. Short answer: because you’re deploying “from source” (Git pull), Prefect has to clone your repo at run time, inside the same environment that executes your flow. That environment is the flow-run container for a Docker work pool, so the git CLI must be available there. Why this matters with a Docker work pool - There are two environments: - Worker environment: polls the pool and submits jobs to Docker. - Flow-run environment: the Docker container that actually runs your flow. - With deployments created via 
flow.from_source(..., source=GitRepository(...))
, Prefect runs a git_clone “pull step” right before executing the flow. For Docker pools, that pull step runs inside the job container. If git isn’t installed in that image, the clone fails and your run crashes. How to satisfy the requirement - Use an image that includes git, or extend the Prefect image to add it. Debian/Ubuntu-based:
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FROM prefecthq/prefect:3-python3.11-uv
USER root
RUN apt-get update && apt-get install -y --no-install-recommends git && rm -rf /var/lib/apt/lists/*
USER prefect
Alpine-based:
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FROM prefecthq/prefect:3-python3.11-uv
USER root
RUN apk add --no-cache git
USER prefect
Then point your work pool template to this image:
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resource "prefect_work_pool" "docker_pool" {
  name = var.work_pool_name
  type = "docker"

  base_job_template = jsonencode({
    job_configuration = {
      image   = "your-registry/prefect:3.11-uv-with-git"
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
    }
  })
}
Alternatives (no git needed at runtime) - Bake your flow code into the image and deploy from that image (don’t use from_source/GitRepository). - Mount your code into the container at runtime (e.g., volume) if your infra allows it. - Use CI to build and push an image whenever code changes, then update deployments to use that image. Extras to keep in mind - If your repo is private, the job container also needs credentials (e.g., a GitHubCredentials block referenced by the deployment). SSH-based auth requires an SSH key and known_hosts in the container. - If you run 
uv sync
, ensure your pyproject.toml is present in the working directory you expect (cd to a subdir first if needed). If you paste your current image name and how you create deployments (
from_source
snippet), I can suggest the minimal Dockerfile and Terraform changes tailored to your setup.
p
@Marvin mounting code into container at runtime is my type.
m
✅ thought for 132.1 seconds
Great — mounting your code into the flow-run container is a solid pattern, and it removes the need for git inside the runtime. Here’s how to do it cleanly with a Docker work pool. What changes when you mount code instead of Git pull - Do not use flow.from_source(..., GitRepository(...)) for these deployments. - The flow-run container must have your code at the entrypoint path already (because you mount it). - Your base_job_template needs a volume mount to inject your code, and your deployment entrypoint must point to that path. Best-practice base_job_template (Terraform) - Define a variables schema so you can reuse across deployments and override mount points if needed. - Use sh -lc for reliable PATH and shell expansions. Example:
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resource "prefect_work_pool" "docker_pool" {
  name = var.work_pool_name
  type = "docker"

  base_job_template = jsonencode({
    job_configuration = {
      image   = "{{ image }}"
      # Run flow directly (no git, no uv). If you do need installs, mount a venv/cache too.
      command = "sh -lc 'prefect flow-run execute'"
      # Mount your host/source code into the container
      volumes = ["{{ code_mount }}"]
      # Optional defaults
      env = "{{ env }}"
      stream_output = true
    }
    variables = {
      type = "object"
      properties = {
        image = {
          type    = "string"
          default = "prefecthq/prefect:3-python3.11-uv"
          title   = "Docker image"
        }
        code_mount = {
          type        = "string"
          title       = "Code volume mount"
          description = "Host:Container mount for your code and entrypoint"
          # Example assumes your code lives at /opt/flows inside container
          default     = "/absolute/host/code:/opt/flows:ro"
        }
        env = {
          type  = "object"
          title = "Environment variables"
          additionalProperties = { type = ["string", "null"] }
          default = {}
        }
      }
      required = ["image", "code_mount"]
    }
  })
}
Key points - The mount must be absolute on the Docker host (e.g., /home/you/project:/opt/flows). - Use :ro for read-only if you don’t need writes. - Ensure your deployment entrypoint matches the container path: - If you mount /home/you/project to /opt/flows, and your flow file is flows/my_flow.py with a function my_flow, set entrypoint to /opt/flows/flows/my_flow.py:my_flow. Creating the deployment (no from_source) - In Prefect 3.x, you can deploy from code without Git source: Python pattern:
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from prefect import flow

@flow
def my_flow():
    ...

if __name__ == "__main__":
    my_flow.deploy(
        name="mount-code-deployment",
        work_pool_name="YOUR_DOCKER_POOL",
        # No image build here; rely on the pool image and the mounted code
        job_variables={
            "image": "prefecthq/prefect:3-python3.11-uv",
            "code_mount": "/home/you/project:/opt/flows:ro",
            "env": {"ENVIRONMENT": "dev"}
        },
        # Ensure Prefect knows where to find the entrypoint at runtime
        entrypoint_type="absolute",
        # Or set via prefect.yaml and `prefect deploy`
    )
CLI with prefect deploy - You can also define entrypoint in a prefect.yaml and run 
prefect deploy
. - Ensure the entrypoint points to the absolute path inside the container (or use a relative path with a working directory set by your command). Worker/runtime alignment - Your Docker worker container needs only the Docker socket; it does not need your code. The code is mounted into the flow-run container via the pool’s job template. - In your docker-compose for the worker, keep: - /var/run/docker.sock mounted - PREFECT_API_URL set correctly - Worker started with 
prefect worker start --pool <your-pool>
Troubleshooting tips - Exit code 2 often comes from shell syntax or missing files. When mounting: - Verify the host path exists and is absolute.
- Verify the target path in the container exists or can be created; most images allow Docker to create it. - Confirm the entrypoint path is correct and loadable by Prefect. - Exec into a test container to validate:
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docker run --rm -it -v /home/you/project:/opt/flows:ro prefecthq/prefect:3-python3.11-uv \
      sh -lc "ls -la /opt/flows && python -c 'import runpy; runpy.run_path(\"/opt/flows/flows/my_flow.py\")'"
When you still want uv/pip - If you need runtime installs, mount a writable location for a venv or cache: - volumes: - /home/you/project/opt/flowsro - /home/you/.cache/pip:/home/prefect/.cache/pip - command: - "sh -lc 'cd /opt/flows && uv sync --system-site-packages && prefect flow-run execute'" Summary - Don’t use Git pull steps; don’t require git in runtime. - Mount code via volumes in the Docker work pool’s base_job_template. - Ensure deployment entrypoint matches the mounted path. - Keep the worker simple; logic lives in the job container. If you share your host path and the in-container path you prefer, I can tailor the exact Terraform and deployment snippets for your setup.
p
@Marvin git-based deployment is my way. Where/how to let my runtime container having pyproject.toml set up.
m
✅ thought for 187.5 seconds
Great — with Git-based deployments, Prefect clones your repo into the flow-run container before execution. To ensure pyproject.toml is present and uv/pip can install dependencies, here’s exactly where/how to control the location and run installs. Where the repo is cloned - Prefect clones into a “destination” directory inside the runtime container. By default it’s a directory under the container’s working directory named after your repo (and branch), e.g. /workspace/my-repo or /workspace/my-repo-main. - The runner sets the working directory (cwd) to that destination before executing your flow, so relative paths in your entrypoint and install commands will work. How to ensure pyproject.toml is in place Option A: Let the default destination be the working directory - If pyproject.toml is at the repo root, you can just run uv from cwd: - command: sh -lc 'uv sync --system-site-packages && prefect flow-run execute' - Entrypoint can be relative to the repo root, e.g., flows/my_flow.py:my_flow. Option B: Explicitly control the clone destination - When constructing your deployment (via Python), you can set the base path and/or name to control the final clone path if needed: - base path: where Prefect will place the repo - name: the subdirectory name - Example: - storage = GitRepository(url="...", branch="main", name="current-release") - storage.set_base_path(Path("/opt/flows")) - Result: repo cloned to /opt/flows/current-release, and cwd is set there at runtime. Running dependency installs against the cloned repo You can do this via either the container command or a pull step. 1) Put it in the container command (simple) - In your Docker work pool’s base_job_template: - command: "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'" - Because Prefect sets cwd to the cloned repo, uv will find pyproject.toml in the current directory. - If pyproject.toml is in a subdir, cd first: - command: "sh -lc 'cd app && uv sync --system-site-packages && cd - && prefect flow-run execute'" 2) Use a dedicated pull step (more explicit) - Define a run_shell_script step after git_clone: - directory: "{{ clone-step.directory }}" ensures the script runs at the repo root - Then run uv there Example pull steps snippet (YAML-style for clarity):
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pull:
  - prefect.deployments.steps.git_clone:
      id: clone-step
      repository: <https://github.com/owner/repo.git>
      # credentials: {{ your_github_block }}    # for private repos
      # branch: main
  - prefect.deployments.steps.run_shell_script:
      directory: "{{ clone-step.directory }}"
      script: |
        uv --version
        uv sync --system-site-packages
Entrypoint resolution - Prefect resolves the entrypoint relative to the current working directory (the cloned repo). So: - entrypoint="flows/my_flow.py:my_flow" works if flows/ is under repo root - If your flow is deeper, point relative to root or use an absolute path (not usually necessary). Putting it together with Terraform - Your work pool’s base_job_template command can reference uv directly, no need to set a custom working directory — Prefect sets cwd to the clone directory. - Ensure your image contains git and uv if you use uv (git required for private deps and submodules). Example base_job_template (Terraform) for Git-based deployments: ``` resource "prefect_work_pool" "docker_pool" { name = var.work_pool_name type = "docker" base_job_template = jsonencode({ job_configuration = { image = "{{ image }}" command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'" env = "{{ env }}" # Optional: networks/volumes/limits as needed } variables = { type = "object" properties = { image = { type = "string" default = "prefecthq/prefect:3-python3.11-uv" } env = { type = "object" additionalProperties = { type = ["string", "null"] }
default = {} } } } }) } ``` Checklist - Your deployment must be created with from_source(..., source=GitRepository(...)) and target this Docker pool. - The image you run in the job container must include: - git (for cloning) - uv (if you call uv) - anything needed to build/install your dependencies (build-essential, etc., if compiled deps) - If pyproject.toml is not at repo root, prepend a cd in the command or set the run_shell_script directory accordingly. If you share: - Your from_source(...) snippet (URL, branch, entrypoint) - Where pyproject.toml lives in your repo - The image name you use I can provide the exact command and/or pull steps so uv runs in the right folder every time.
p
@Marvin as you mention that git is necessary, does prefecthq/prefect:3-python3.11-uv have git pre-install? Why I did not see any git installation step in your terraform example?
m
✅ thought for 37.7 seconds
Short answer: don’t assume git is in the stock image. The Prefect images (including prefecthq/prefect:3-python3.11-uv) are not guaranteed to include git. For Git-based deployments, the flow-run container must have git installed because the repo is cloned inside that container at runtime. Why my Terraform example didn’t “install” git - Terraform only tells Prefect which image to run and how to start it (base_job_template). It does not build the image. - You need to use an image that already has git (and any other tools like uv) baked in, or you must install them at container start (not recommended). How to verify your current image - Quick check:
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docker run --rm prefecthq/prefect:3-python3.11-uv sh -lc "git --version || echo 'git not found'"
If you see “git not found”, you need a custom image. Best practice: build a thin custom image with git - Debian/Ubuntu-based:
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FROM prefecthq/prefect:3-python3.11-uv
  USER root
  RUN apt-get update \
      && apt-get install -y --no-install-recommends git ca-certificates openssh-client \
      && rm -rf /var/lib/apt/lists/*
  USER prefect
- If you use SSH for Git, also add known_hosts:
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RUN mkdir -p /home/prefect/.ssh \
      && ssh-keyscan -t rsa <http://github.com|github.com> >> /home/prefect/.ssh/known_hosts \
      && chown -R prefect:prefect /home/prefect/.ssh
Point your work pool at this image in Terraform
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base_job_template = jsonencode({
  job_configuration = {
    image   = "your-registry/prefect:3.11-uv-with-git"
    command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
  }
  variables = {
    type = "object"
    properties = {
      image = { type = "string", default = "your-registry/prefect:3.11-uv-with-git" }
    }
    required = ["image"]
  }
})
Can I install git at runtime instead? - You could, but it’s slow and often won’t work because the container runs as a non-root user (
prefect
). e.g., 
apt-get
would fail without root. Building once into your image is the robust approach. Why git is required here - You’re using Git-based deployments (
flow.from_source(..., source=GitRepository(...))
). Prefect runs a git_clone pull step inside the flow-run container before executing your flow. If git isn’t available there, the run crashes (often exit code 2). If you want, share your current image name and I’ll suggest the exact Dockerfile plus the minimal Terraform change to reference it. Docs: - Docker work pool: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Create deployments (from source): https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments
p
@Marvin So I use docker-compose self-host server, I need to make sure prefect-worker have git installed which means I need to write Dockerfile to customize it?
m
✅ thought for 18.8 seconds
Short answer: if you’re using a Docker work pool, you do NOT need git in the docker-compose “prefect-worker” container. You need git in the image that runs your flow runs (the job image configured on the Docker work pool). The worker just submits jobs to Docker; the flow code is cloned inside the job container. What to do 1) Build a custom job image that includes git (and openssh-client if you use SSH): - Debian/Ubuntu base
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# runtime image used by the Docker work pool for flow runs
FROM prefecthq/prefect:3-python3.11-uv
USER root
RUN apt-get update \
 && apt-get install -y --no-install-recommends git ca-certificates openssh-client \
 && rm -rf /var/lib/apt/lists/*
# Optional: add GitHub host key for SSH-based clones
# RUN mkdir -p /home/prefect/.ssh \
#  && ssh-keyscan -t rsa <http://github.com|github.com> >> /home/prefect/.ssh/known_hosts \
#  && chown -R prefect:prefect /home/prefect/.ssh
USER prefect
- Alpine base (if you prefer)
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FROM prefecthq/prefect:3-python3.11-uv
USER root
RUN apk add --no-cache git openssh-client ca-certificates
USER prefect
2) Push the image to your registry and point your Docker work pool to it (Terraform)
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resource "prefect_work_pool" "docker_pool" {
  name = var.work_pool_name
  type = "docker"

  base_job_template = jsonencode({
    job_configuration = {
      image   = "your-registry/prefect:3.11-uv-with-git"
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
      # add env/volumes/etc as needed
    }
  })
}
3) Make sure your deployment targets this Docker work pool (not Process), and that a Docker worker is running for it: - Start worker:
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uv run --with 'prefect[docker]' prefect worker start --pool <your-docker-pool> --type docker
- In the UI, verify the deployment’s Work Pool = your Docker pool. Your flow-run logs should show “DockerWorker …” not “ProcessWorker …”. 4) Quick verification
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docker run --rm your-registry/prefect:3.11-uv-with-git sh -lc "git --version && uv --version && prefect --version"
Only install git in the worker container if you’re using a Process work pool (since the worker is the runtime in that case). For a Docker work pool, keep the worker minimal; just ensure it has Docker access (/var/run/docker.sock) and can reach the API. Docs for reference: - Docker work pool how-to: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Deployments from source (Git): https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments
p
@Marvin if there any docker prefect base image contain both uv & git?
m
✅ thought for 135.9 seconds
Short answer: there isn’t an official Prefect image variant that explicitly guarantees both uv and git. Don’t assume they’re present. For Git-based deployments, the safest approach is to use a small custom runtime image that includes both. How to check your current image
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docker run --rm -it <your-image> sh -lc "git --version || echo 'git not found'; uv --version || echo 'uv not found'; prefect --version"
Recommended custom runtime image (Debian/Ubuntu-based)
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FROM prefecthq/prefect:3-latest
USER root
RUN apt-get update \
 && apt-get install -y --no-install-recommends git ca-certificates openssh-client \
 && rm -rf /var/lib/apt/lists/*
# Optional: if you want uv in the image
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh -s -- -y \
 && ln -s /root/.local/bin/uv /usr/local/bin/uv
# Optional: add known_hosts for SSH-based clones
# RUN mkdir -p /home/prefect/.ssh \
#  && ssh-keyscan -t rsa <http://github.com|github.com> >> /home/prefect/.ssh/known_hosts \
#  && chown -R prefect:prefect /home/prefect/.ssh
USER prefect
Alpine-based alternative
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FROM prefecthq/prefect:3-latest
USER root
RUN apk add --no-cache git openssh-client ca-certificates curl
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh -s -- -y \
 && ln -s /root/.local/bin/uv /usr/local/bin/uv
USER prefect
Point your Docker work pool to this image (Terraform)
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resource "prefect_work_pool" "docker_pool" {
  name = var.work_pool_name
  type = "docker"

  base_job_template = jsonencode({
    job_configuration = {
      image   = "your-registry/prefect:3-with-uv-git"
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
      env     = {}
    }
  })
}
Notes - Git is required in the job image (the flow-run container) for Git-based deployments because Prefect clones your repo inside that container at run time. - The worker container (in your docker-compose) does not need git; it just submits jobs to Docker. - If you use SSH for private repos, include openssh-client and known_hosts (or a credentials block for HTTPS tokens). - Pin the Prefect base tag (e.g., 3.x.y) for repeatability. Docs - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Create deployments from source (Git): https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments If you share the exact tag you’re using now, I can confirm what’s inside and tailor the Dockerfile accordingly.
p
@Marvin l working at hospital if my team only have 3dataengineer and use dbt prefect uv(pyproject.toml) docker(future might use k8s). Trying to build data warehouse. What is your recommend prefect design?(work pool, git-based pull or docker image)
m
✅ thought for 44.1 seconds
Great question. Given your context (hospital, small team, dbt + Prefect + uv, Docker now and likely Kubernetes later), here’s a practical, low-ops design that balances reliability, compliance, and speed. Summary recommendation - Start with a Docker work pool and prebuilt runtime images for production flows; optionally keep a Git-pull path for fast iteration. - Keep Prefect Server self-hosted via docker-compose. - Use Terraform to manage work pools, automations, concurrency limits, and notification blocks. Let CI handle deployments. - Standardize a dbt-first runtime image (dbt-core + your adapter + git + uv) and pin versions. Why this design - Baked images → reproducible, fast, no internet required at runtime (important in hospital networks). - Docker work pool → simple to operate now; easy path to a Kubernetes work pool later with the same images. - Git-based option remains available for “preview/experimentation,” but not your production default. Concrete components 1) Work pools and workers - Work pool: Docker (name per env, e.g., docker-prod) - Worker: run 1–2 Docker workers per env (HA), with /var/run/docker.sock mounted - Concurrency: set a conservative limit to avoid overloading the warehouse (e.g., 2–4 concurrent dbt runs) - Tags: environment:prod, domain:analytics, runtime:docker 2) Runtime images - Build and maintain a small image that includes: - Prefect (pinned 3.x) - git - uv - dbt-core + your adapter (e.g., dbt-postgres/dbt-snowflake/dbt-bigquery) - any native build deps your adapter needs - Example Dockerfile (production)
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FROM prefecthq/prefect:3.9.x-python3.11-slim  # pin an exact 3.x version
USER root
RUN apt-get update \
 && apt-get install -y --no-install-recommends git ca-certificates openssh-client build-essential \
 && rm -rf /var/lib/apt/lists/*
# Install uv once (pin if you prefer)
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh -s -- -y \
 && ln -s /root/.local/bin/uv /usr/local/bin/uv

# Copy dependency manifests first to cache installs
WORKDIR /opt/app
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --system-site-packages

# If your dbt project lives in repo, you may install adapters here instead of pyproject
# RUN uv pip install dbt-core dbt-postgres  # if not in pyproject

# Drop to non-root
USER prefect
- Build per repo/project (one image per dbt project or per team). Tag images with app version or git SHA. 3) Production deployments (preferred: image-based) - Don’t do runtime installs. Your base_job_template command can be: - 
prefect flow-run execute
- Entrypoint points at code inside the image (COPY code in the Docker build) or at a mounted common code dir if you standardize that. - In Terraform, your docker pool template’s 
image
defaults to the prebuilt tag. Keep 
env
for secrets/flags (non-sensitive). 4) Fast-iteration deployments (optional: Git-based) - Keep a “dev” or “preview” work pool that uses an image with git + uv and does: - 
sh -lc 'uv sync --system-site-packages && prefect flow-run execute'
- Deployment created with 
flow.from_source(source=GitRepository(...), entrypoint=...)
- Useful for POCs or quick tests; slower cold starts; requires outbound access to your Git host and package index. 5) dbt execution pattern - Use the Prefect dbt integration (DbtCoreOperation) or a simple task/command wrapper. - Keep dbt profiles in a Prefect block or mounted secret file; never store credentials in code or TF state. - Example task:
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from prefect import flow
from prefect_dbt import DbtCoreOperation

@flow
def run_dbt(models: list[str] = None):
    DbtCoreOperation(
        commands=["dbt deps", "dbt build --select " + " ".join(models or ["state:modified+"])],
        env={"DBT_PROFILES_DIR": "/opt/app/profiles"},  # mount or baked path
    ).run()
6) Secrets, credentials, and compliance - Never commit secrets; do not put raw secrets in Terraform state. - Use Prefect Blocks for: - Warehouse creds (e.g., connection URL/creds)
- Slack/webhook for notifications - Git credentials if you must do Git-based deploys - Store block values via CI or at runtime (prefect block create) so TF state stays clean. - Consider environment-level masking and log redaction to avoid PHI in logs. 7) Scheduling, concurrency, and notifications - Use deployments with schedules or event-driven triggers. - Set global and queue-level concurrency to protect the warehouse (e.g., 2–4 flows concurrently). - Automations: notify on Fail/Cancelled/Crash to Slack; escalate after retries. 8) Terraform scope - Manage with Terraform: - Work pools (base_job_template with image/env defaults) - Concurrency limits (global/queue) - Automations (Slack notifications) - Variables (non-secret configuration) - Optionally: blocks that do NOT expose sensitive values in state - Let CI run deployments (flow.from_source(...).deploy or 
prefect deploy
) to avoid drift between TF and code. 9) Migration to Kubernetes (later) - Swap the Docker work pool for a Kubernetes work pool using a job template. - Reuse the same runtime images and most deployment settings. - Mount ConfigMaps/Secrets for dbt profiles; use k8s service account and network policies per hospital rules. Choosing between Git-based vs image-based (rules of thumb) - Prefer image-based for production dbt pipelines: - Reproducible, fast startup, no internet at runtime, easier change control and scanning - Use Git-based for quick iteration/preview: - Faster to change, but slower cold start and needs git + internet in the job container - If your hospital network restricts outbound internet from runtime nodes, image-based is almost required. Operational checklist - One docker-compose stack for Prefect Server per env. - 1–2 Docker workers per env; watch their logs and set restart policies. - Private registry for your runtime images; CI builds on merge to main; tag immutably. - Pin Prefect image and Python versions; pin dbt adapters and dependencies in uv.lock. - Health checks and Slack alerts wired from Automations. Useful links - Work pools: https://docs.prefect.io/v3/concepts/work-pools - Docker work pool how-to: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Deployments (create, from source): https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - dbt integration: https://docs.prefect.io/v3/integrations/collection/prefect-dbt/ - Automations: https://docs.prefect.io/v3/concepts/automations - Terraform provider: https://registry.terraform.io/providers/PrefectHQ/prefect/latest If you share: - Your target warehouse (and dbt adapter) - Whether runtime has outbound internet - Your preferred repo layout I can sketch a minimal Dockerfile and the exact Terraform base_job_template for both prod (image-based) and dev (git-based) that your team can adopt immediately.
p
@Marvin I wanna demo half baked mode. give me a example template which can actually running on my laptop(wsl2). it require pyproject.toml docker-compose.yaml(for self-host) Dockerfile(for create base-image) flow.py prefect.yaml( for deployment) right?
m
✅ thought for 96.0 seconds
Awesome — here’s a minimal, “half-baked” Git-pull demo you can run locally on WSL2. This setup: - Self-hosts Prefect Server via docker-compose - Uses a Docker work pool - Builds a small runtime image (with git + uv) for flow runs - Clones your repo at runtime and runs uv sync before executing the flow You’ll need to push this repo to GitHub/GitLab (or any Git host) so the container can clone it. Replace the placeholder repo URL below. Files to include in your repo 1) Dockerfile (runtime image used by the Docker work pool)
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FROM prefecthq/prefect:3-latest
USER root

# Install git + SSH client (for private repos) + certs + curl
RUN apt-get update \
 && apt-get install -y --no-install-recommends git ca-certificates openssh-client curl \
 && rm -rf /var/lib/apt/lists/*

# Install uv
RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh -s -- -y \
 && ln -s /root/.local/bin/uv /usr/local/bin/uv

# Optional: add GitHub known_hosts if you use SSH URLs
# RUN mkdir -p /home/prefect/.ssh \
#  && ssh-keyscan -t rsa <http://github.com|github.com> >> /home/prefect/.ssh/known_hosts \
#  && chown -R prefect:prefect /home/prefect/.ssh

USER prefect
2) pyproject.toml (minimal; uv will read this in the cloned repo)
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[project]
name = "half-baked-demo"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = []
3) flows/flow.py (your demo flow)
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from prefect import flow, get_run_logger

@flow
def hello(name: str = "world"):
    logger = get_run_logger()
    <http://logger.info|logger.info>("Hello, %s!", name)

if __name__ == "__main__":
    hello()
4) prefect.yaml (deployment spec that clones the repo and syncs deps with uv) - Replace https://github.com/your-org/your-repo.git with your repo URL (public or private). - If private, add a credentials block later (I can help wire that up).
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name: half-baked-demo

deployments:
  - name: hello
    entrypoint: flows/flow.py:hello
    work_pool:
      name: docker-pool
    # Tell the job to use the image you build locally below
    job_variables:
      image: half-baked:latest

    # Pull steps: clone repo then run uv
    pull:
      - prefect.deployments.steps.git_clone:
          id: clone
          repository: <https://github.com/your-org/your-repo.git>
          branch: main
          # For private repos, add a credentials block here; ask me for a snippet.

      - prefect.deployments.steps.run_shell_script:
          directory: "{{ clone.directory }}"
          script: |
            uv --version
            uv sync --system-site-packages
5) docker-compose.yaml (self-hosted server + Docker worker)
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version: "3.9"

services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    container_name: prefect-server
    environment:
      PREFECT_SERVER_API_HOST: 0.0.0.0
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('<http://localhost:4200/api/health>', timeout=1)"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 60s

  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    container_name: prefect-docker-worker
    depends_on:
      prefect-server:
        condition: service_healthy
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    # Ensure docker integration is available inside worker
    command: >
      uv run --with 'prefect[docker]'
      prefect worker start
      --pool docker-pool
    restart: on-failure
How to run it 1) Build your runtime image (contains git + uv)
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docker build -t half-baked:latest .
2) Start Prefect Server and the Docker worker
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docker compose up -d
UI will be at http://localhost:4200 in your browser (WSL2 tip: use 127.0.0.1 if localhost is finicky). 3) Create the Docker work pool (once)
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prefect work-pool create docker-pool --type docker
4) Deploy from your repo
- Push this repo to your Git host (update prefect.yaml’s repository URL). - From your repo root:
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prefect deploy -n hello
5) Kick off a run - In the UI, find the “hello” deployment and click Run. - Or CLI:
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prefect deployment run 'half-baked-demo/hello' --param name=WSL2
You should see the run logs showing: - git clone step - uv sync - flow execution (“Hello, WSL2!”) Private repo (optional) - Create a GitHubCredentials block and reference it in the git_clone step. If you want, I’ll add that block wiring and a secure pattern to avoid storing tokens in Terraform state. Common WSL2 tips - If UI doesn’t load, verify: curl http://localhost:4200/api/health returns true - Ensure nothing else is bound to port 4200 - Use 127.0.0.1:4200 if localhost is weird with VPN/firewall - Worker logs: docker compose logs -f prefect-docker-worker If you share your repo URL (or whether it’s private), I can add the credentials block snippet and adjust the prefect.yaml accordingly.
p
@Marvin is best practice using prefect.yaml and terraform simutanouesly? how are they working together?
m
✅ thought for 85.3 seconds
Short answer: yes—using Terraform and prefect.yaml together is a best practice. They solve different layers: - Terraform = platform layer (once-per-environment) - Creates and configures work pools (base_job_template), automations/notifications, concurrency limits, non-secret blocks, variables - Source of truth for names, tags, and infra defaults - prefect.yaml = application layer (per-repo/project) - Defines deployments: entrypoint, schedules, parameters, tags, pull/build steps, and job_variables that plug into the work pool template - Owned by the repo; deployed by CI with 
prefect deploy
The key rule: avoid double-managing the same resource. Let Terraform own infra; let prefect.yaml/CI own deployments. How they work together (recommended workflow) 1) Provision infra with Terraform - Create a Docker work pool with a variable-driven base_job_template - Set global/queue concurrency limits - Add automations (Slack/email on failure) - Create non-secret blocks (e.g., Slack webhook); handle secrets outside TF if possible 2) Define deployments in your repo’s prefect.yaml - Reference the Terraform-created work pool by name - Provide job_variables that match the pool’s base_job_template variables (e.g., image/env) - Add pull steps for Git-based runs (git_clone, uv sync) if you use the “half-baked” approach 3) CI/CD runs 
prefect deploy
- On merge to main, CI reads prefect.yaml and upserts deployments - No Terraform changes needed for app-level iteration - Workers pick up the new/updated deployments automatically 4) Run workers separately - Keep your Docker worker(s) running via docker-compose (not Terraform) - Workers are “stateless processes” that poll work pools; treat them like runtime services, not drift-managed infra Contract between Terraform work pool and prefect.yaml deployments - Terraform defines the base_job_template with a variables schema—this is the “contract” your deployments must satisfy via job_variables. - prefect.yaml passes values for those variables per deployment (e.g., which image to run, extra env). Example: Terraform work pool (Docker) with variables
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resource "prefect_work_pool" "docker_pool" {
  name = "docker-dev"
  type = "docker"

  base_job_template = jsonencode({
    job_configuration = {
      image   = "{{ image }}"
      command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
      env     = "{{ env }}"
      # optional: volumes, networks, resources
    }
    variables = {
      type = "object"
      properties = {
        image = { type = "string", default = "your-registry/prefect:3.11-uv-git" }
        env = {
          type  = "object"
          additionalProperties = { type = ["string", "null"] }
          default = {}
        }
      }
      required = ["image"]
    }
  })

  tags = ["environment:dev", "runtime:docker", "code-source:git"]
}
Example: prefect.yaml that plugs into the pool
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name: my-project

deployments:
  - name: hello
    entrypoint: flows/flow.py:hello
    work_pool:
      name: docker-dev
    job_variables:
      image: your-registry/prefect:3.11-uv-git
      env:
        ENVIRONMENT: dev
    pull:
      - prefect.deployments.steps.git_clone:
          id: clone
          repository: <https://github.com/your-org/your-repo.git>
          branch: main
          # credentials: {{ your_github_block }}   # for private repos
      - prefect.deployments.steps.run_shell_script:
          directory: "{{ clone.directory }}"
          script: |
            uv --version
            uv sync --system-site-packages
Deployment flow - Terraform apply (once) → creates work pool docker-dev - CI job (on merge) runs: - 
prefect deploy -n hello
- The deployment references docker-dev and supplies job_variables fitting the template - Docker worker is already running and polls docker-dev Secrets handling - Do not place raw secrets in Terraform state if you can avoid it. - Preferred:
- Use Prefect Blocks created via CI/CLI with masked inputs, or - Reference CI-injected environment variables in job_variables/env - If you must use Terraform for blocks, ensure state encryption and tight access controls. Avoiding drift - Don’t manage the same deployment in both Terraform and prefect.yaml. Choose one: - Best practice: prefect.yaml + CI own deployments; Terraform does not create 
prefect_deployment
for those - If you choose Terraform to manage deployments, do not run 
prefect deploy
for those names Multi-environment pattern - Terraform workspaces or separate stacks per env (dev/stage/prod) to create pools, automations, limits with environment tags/names - In repo, either: - Have multiple deployments in prefect.yaml pointing to different pools, or - Parameterize pool names via CI env and pass flags to 
prefect deploy
as needed Migration path to Kubernetes - Swap the Terraform-managed pool from Docker to Kubernetes later - Keep the same application-level prefect.yaml; only adjust job_variables to match the new pool’s template Useful links - Terraform provider: https://registry.terraform.io/providers/PrefectHQ/prefect/latest - Work pools: https://docs.prefect.io/v3/concepts/work-pools - Docker work pool how-to: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Deployments with prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments If you share your current TF work pool template and a sample prefect.yaml, I can sanity-check the contract between variables and job_variables so they work seamlessly.
p
@Marvin wsl2,Worker 'DockerWorker 8f5230d1-c880-49a5-b71b-62636970c94d' submitting flow run '019ad0cc-d6fd-7288-8630-028e4581b538' 020951 AM prefect.flow_runs.worker Completed submission of flow run '019ad0cc-d6fd-7288-8630-028e4581b538' 020952 AM prefect.flow_runs.worker Reported flow run '019ad0cc-d6fd-7288-8630-028e4581b538' as crashed: Flow run infrastructure exited with non-zero status code 1. socket.gaierror: [Errno -2] Name or service not known An exception occurred.
m
✅ thought for 50.7 seconds
That error means the flow-run container couldn’t resolve a hostname (DNS lookup failed). On WSL2 with a Docker work pool + self-hosted server, the most common culprit is the API URL or repo/package hosts not being reachable from the job container. Two reliable fixes (pick one): Option A: Point flows at host.docker.internal - Works without custom networks; simplest on laptops - Set PREFECT_API_URL so job containers can reach your server via the host port mapping Update your Docker work pool base_job_template (Terraform):
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base_job_template = jsonencode({
  job_configuration = {
    image   = "your-registry/prefect:3.11-uv-git"
    command = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
    env = {
      PREFECT_API_URL = "<http://host.docker.internal:4200/api>"
    }
  }
})
Notes: - Docker Desktop supports host.docker.internal inside Linux containers. - If that doesn’t work in your setup, hardcode your Windows host IP instead. Option B: Put job containers on the same Docker network as the server - Then you can keep PREFECT_API_URL = http://prefect-server:4200/api and rely on service discovery Compose with a named network:
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version: "3.9"

networks:
  prefect:
    name: prefect-network
    driver: bridge

services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_SERVER_API_HOST: 0.0.0.0
    ports:
      - "4200:4200"
    networks: ["prefect"]

  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    command: >
      uv run --with 'prefect[docker]'
      prefect worker start
      --pool docker-pool
    networks: ["prefect"]
Then tell the Docker work pool to attach job containers to that same network:
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base_job_template = jsonencode({
  job_configuration = {
    image    = "your-registry/prefect:3.11-uv-git"
    command  = "sh -lc 'uv sync --system-site-packages && prefect flow-run execute'"
    networks = ["prefect-network"]
  }
})
Find which hostname failed - In Prefect UI open the flow run → Details → Infrastructure logs. You’ll usually see which host failed to resolve (e.g., prefect-server, github.com, pypi.org). - Or grab the job container logs: - 
docker ps -a
to find the container - 
docker logs <container_id>
If the failure was github.com or pypi.org: - Your job container needs outbound DNS/internet. In hospital networks, you may need a proxy: - Add env to the job template:
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env = {
      HTTP_PROXY  = "<http://proxy:port>"
      HTTPS_PROXY = "<http://proxy:port>"
      NO_PROXY    = "localhost,127.0.0.1,.yourdomain"
    }
- Also ensure git and uv are in your job image (you’re using Git-based deploys). Quick tests - API reachable via network name:
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docker run --rm --network prefect-network your-registry/prefect:3.11-uv-git \
    sh -lc "curl -sSf <http://prefect-server:4200/api/health> && echo OK"
- API reachable via host.docker.internal:
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docker run --rm your-registry/prefect:3.11-uv-git \
    sh -lc "curl -sSf <http://host.docker.internal:4200/api/health> && echo OK"
Most teams on WSL2 choose Option A (host.docker.internal) because it avoids managing Docker networks across compose and worker-launched containers. If you want, share your current base_job_template and compose, and I’ll patch them for your exact setup.
p
@Marvin I use prefect.yaml
m
✅ thought for 40.7 seconds
Got it — here’s how to fix the DNS issue using prefect.yaml (no Terraform needed). You have two solid options. Pick one: Option A: Use host.docker.internal (simplest on WSL2) - Point the job container directly at your host-mapped API port. - Add this to your prefect.yaml deployment under job_variables:
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deployments:
  - name: your-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: docker-pool
    job_variables:
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
Notes: - Docker Desktop supports host.docker.internal inside Linux containers. - If it doesn’t resolve in your setup, replace with your host IP (or 127.0.0.1 if it works). Option B: Put job containers on the same Docker network as the server - Define a named network in your docker-compose and attach both services. - Then tell the job to join that same network so PREFECT_API_URL can stay http://prefect-server:4200/api. Compose example (add a named network):
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networks:
  prefect:
    name: prefect-network
    driver: bridge

services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    environment:
      PREFECT_SERVER_API_HOST: 0.0.0.0
    ports:
      - "4200:4200"
    networks: ["prefect"]

  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    command: >
      uv run --with 'prefect[docker]'
      prefect worker start
      --pool docker-pool
    networks: ["prefect"]
Then in prefect.yaml, add the network to job_variables so the flow-run container lands on that same network:
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deployments:
  - name: your-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: docker-pool
    job_variables:
      networks:
        - prefect-network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
Which should you choose? - Most WSL2 laptops: Option A (host.docker.internal) is quickest. - If you prefer service discovery by name and a closed network: Option B. Quick validation - After updating prefect.yaml, redeploy:
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prefect deploy -n your-deployment
- Trigger a run and check logs. If you still see socket.gaierror, the failing hostname is in the logs (e.g., prefect-server, github.com, pypi.org). If it’s Git/PyPI, you may need proxy env vars in job_variables.env:
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HTTP_PROXY, HTTPS_PROXY, NO_PROXY
Docs - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ If you paste your current prefect.yaml (redact secrets), I can patch it to the exact working version for your WSL2 setup.
p
@Marvin Worker 'DockerWorker 8f5230d1-c880-49a5-b71b-62636970c94d' submitting flow run '019ad0e6-8fea-7119-9877-6691f5e0f662' 023953 AM prefect.flow_runs.worker Completed submission of flow run '019ad0e6-8fea-7119-9877-6691f5e0f662' 023954 AM prefect.flow_runs.worker Opening process... 023955 AM prefect.flow_runs.runner > Running git_clone step... 023957 AM prefect.flow_runs Unexpected exception encountered when trying to load flow Traceback (most recent call last): File "<frozen importlib._bootstrap_external>", line 999, in exec_module File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed File "/opt/prefect/prefect_demo-main/flows/flow.py", line 3, in <module> import pandas as pd ModuleNotFoundError: No module named 'pandas' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.12/site-packages/prefect/engine.py", line 112, in <module> flow: "Flow[..., Any]" = load_flow(flow_run) ^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/flow_engine.py", line 152, in load_flow flow = run_coro_as_sync( ^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 207, in run_coro_as_sync return call.result() ^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 365, in result return self.future.result(timeout=timeout) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 192, in result return self.__get_result() ^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result raise self._exception File "/usr/local/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 441, in _run_async result = await coro ^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 188, in coroutine_wrapper return await task ^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/client/utilities.py", line 69, in wrapper return await func(client, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/flows.py", line 2836, in load_flow_from_flow_run flow = await run_sync_in_worker_thread( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 233, in run_sync_in_worker_thread result = await anyio.to_thread.run_sync( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2485, in run_sync_in_worker_thread return await future ^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 976, in run result = context.run(func, *args) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 243, in call_with_mark return call() ^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/flows.py", line 2509, in load_flow_from_entrypoint flow: Flow[P, Any] = import_object(entrypoint) # pyright: ignore[reportRedeclaration] ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/importtools.py", line 153, in import_object module = load_script_as_module(script_path) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/importtools.py", line 118, in load_script_as_module raise ScriptError(user_exc=exc, path=path) from exc prefect.exceptions.ScriptError: Script at 'flows/flow.py' encountered an exception: ModuleNotFoundError("No module named 'pandas'") 023958 AM prefect.flow_runs Process for flow run 'mindful-nyala' exited with status code: 1 023959 AM prefect.flow_runs.runner Reported flow run '019ad0e6-8fea-7119-9877-6691f5e0f662' as crashed: Flow run process exited with non-zero status code 1. 023959 AM prefect.flow_runs.runner Loading flow to check for on_crashed hooks 023959 AM prefect.flow_runs.runner > Running git_clone step...
m
✅ thought for 41.8 seconds
You’re hitting “ModuleNotFoundError: No module named 'pandas'” because the flow gets imported after the git_clone step, but your runtime container doesn’t have your Python deps installed yet. Two reliable ways to fix this for Git-based deployments: Option A: Install deps in the job command (simple, robust) - Prefect sets the working directory to the cloned repo before executing the flow, so you can run uv (or pip) first, then execute the flow. Add this to your prefect.yaml deployment’s job_variables:
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deployments:
  - name: your-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect:3.11-with-uv-git   # ensure uv + git are in this image
      # Optional: if you’re on WSL2 and using host.docker.internal for API
      # env:
      #   PREFECT_API_URL: <http://host.docker.internal:4200/api>
      command: "sh -lc 'uv --version || python -m pip install -U uv; uv sync --system-site-packages && prefect flow-run execute'"
Notes: - Ensure pandas is listed in your repo’s pyproject.toml (and ideally uv.lock). - If pyproject.toml lives in a subdir, prepend a cd: - 
command: "sh -lc 'cd app && uv sync --system-site-packages && cd - && prefect flow-run execute'"
Option B: Install deps via a pull step (more explicit) - Keep the default command and add a run_shell_script step after git_clone:
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deployments:
  - name: your-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect:3.11-with-uv-git
    pull:
      - prefect.deployments.steps.git_clone:
          id: clone
          repository: <https://github.com/your-org/your-repo.git>
          branch: main
          # credentials: {{ your_github_block }}   # if private
      - prefect.deployments.steps.run_shell_script:
          directory: "{{ clone.directory }}"
          script: |
            uv --version || python -m pip install -U uv
            uv sync --system-site-packages
Prereqs checklist - Your job image must include: - git (for cloning) - uv (since you’re calling it), or install uv on the fly as shown - pandas must be declared in pyproject.toml (or requirements.txt if you use pip) - Align Python versions between your local dev and the job image (your logs show Python 3.12; if you built for 3.11, use a 3.11 image/tag to match your lockfile) Quick verification - Test the job image locally:
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docker run --rm -it your-registry/prefect:3.11-with-uv-git \
    sh -lc "git --version; uv --version; python -c 'import sys; print(sys.version)'"
- After updating prefect.yaml, redeploy:
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prefect deploy -n your-deployment
If you paste your current prefect.yaml and where pyproject.toml lives (repo root or subdir), I can tailor the exact command/pull-step for your layout. Helpful docs: - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/
p
@Marvin i use baking into docker image way
m
✅ thought for 57.0 seconds
Perfect — baking your code and deps into the image is the right move to avoid “ModuleNotFoundError” (like pandas). Here’s a minimal, working template for baked-image deployments with a Docker work pool on WSL2. What you’ll have - Dockerfile: builds a runtime image with your code and dependencies - pyproject.toml: declares Python deps (e.g., pandas, dbt) - flows/flow.py: example flow - prefect.yaml: deployment definition that uses your baked image - Note: remove any git_clone/pull steps since code is in the image 1) Dockerfile (bakes code + deps) - Uses pip for simplicity and speed. If you insist on uv, I can adapt this for uv/uv.lock.
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FROM prefecthq/prefect:3-latest

# Optional: pin exact Prefect and Python variants for reproducibility
# FROM prefecthq/prefect:3.9.x-python3.11

WORKDIR /opt/prefect

# Install system packages you might need for dbt/adapters (example)
# USER root
# RUN apt-get update && apt-get install -y --no-install-recommends \
#     build-essential ca-certificates git openssh-client \
#   && rm -rf /var/lib/apt/lists/*
# USER prefect

# Copy dependency manifests first for better Docker layer caching
COPY pyproject.toml ./
# If you use a lock file, copy it too (poetry.lock/uv.lock/etc.)
# COPY uv.lock ./

# Install your Python deps (pip reads PEP 621/pyproject)
# If using poetry/uv, I can switch this to `uv sync` for you
RUN python -m pip install --upgrade pip \
 && pip install .

# Now copy your application code
COPY flows ./flows

# Default command: Prefect will load/execute the flow via entrypoint
# (no git or runtime installs needed)
2) pyproject.toml (declare deps like pandas)
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[project]
name = "baked-demo"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
  "pandas>=2.2.0",
  # "dbt-core==<pin>", "dbt-<your-adapter>==<pin>"  # if you use dbt
]

[build-system]
requires = ["setuptools", "wheel"]
build-backend = "setuptools.build_meta"
3) flows/flow.py (simple flow)
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from prefect import flow, get_run_logger
import pandas as pd  # baked into the image

@flow
def hello(name: str = "world"):
    logger = get_run_logger()
    <http://logger.info|logger.info>("Pandas version: %s", pd.__version__)
    <http://logger.info|logger.info>("Hello, %s!", name)

if __name__ == "__main__":
    hello()
4) prefect.yaml (deployment that uses your baked image) - No pull steps — code is already in the image - For WSL2 + docker-compose server, set PREFECT_API_URL so jobs can reach the API - Option A: host.docker.internal (simplest) - Option B: put jobs on same Docker network as your server and use http://prefect-server:4200/api
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name: baked-demo

deployments:
  - name: hello
    # Use an absolute path inside the container for reliability
    entrypoint: /opt/prefect/flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      image: your-dockerhub-or-registry/baked-demo:latest
      # WSL2 Option A: talk to the host-mapped Prefect API port
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
      # If you prefer Option B (same Docker network), instead include:
      # networks:
      #   - prefect-network
      # env:
      #   PREFECT_API_URL: <http://prefect-server:4200/api>
5) Build and run - Build image locally
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docker build -t your-dockerhub-or-registry/baked-demo:latest .
- Start/verify Prefect Server + Docker worker (your existing docker-compose). For WSL2, ensure: - Server publishes 4200:4200 and listens on 0.0.0.0 - Worker is running and polling your docker-pool - Create the Docker work pool once (if not already)
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prefect work-pool create docker-pool --type docker
- Deploy using prefect.yaml
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prefect deploy -n hello
- Trigger a run
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prefect deployment run "baked-demo/hello" --param name=WSL2
WSL2 networking note - If you see socket.gaierror or “Cannot connect to API,” switch to the named network approach: - In docker-compose, add a network (prefect-network) and attach both prefect-server and worker to it
- In prefect.yaml, add job_variables.networks: ["prefect-network"] and set PREFECT_API_URL: http://prefect-server:4200/api Why this fixes ModuleNotFoundError - pandas (and other deps) are installed at image build time - The flow is imported from /opt/prefect/flows in the container, not pulled at runtime - No git or uv is required at runtime (fewer moving parts, faster cold starts) If you want the Dockerfile to use uv/uv.lock instead of pip, reply with how you manage your lock files and I’ll provide a uv-based Dockerfile variant. Also happy to adjust for dbt (core + adapter) and your warehouse driver. Links: - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pool: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/
p
@Marvin Worker 'DockerWorker 8f5230d1-c880-49a5-b71b-62636970c94d' submitting flow run '019ad16c-2beb-7e40-91ce-9944b92165ad' 050200 AM prefect.flow_runs.worker Completed submission of flow run '019ad16c-2beb-7e40-91ce-9944b92165ad' 050201 AM prefect.flow_runs.worker Opening process... 050202 AM prefect.flow_runs.runner > Running git_clone step... 050204 AM prefect.flow_runs Unexpected exception encountered when trying to load flow Traceback (most recent call last): File "frozen importlib._bootstrap_external", line 999, in exec_module File "frozen importlib._bootstrap", line 488, in _call_with_frames_removed File "/opt/prefect/prefect_demo-main/flows/flow.py", line 3, in module import pandas as pd ModuleNotFoundError: No module named 'pandas' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.12/site-packages/prefect/engine.py", line 112, in module flow: "Flow[..., Any]" = load_flow(flow_run) ^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/flow_engine.py", line 152, in load_flow flow = run_coro_as_sync( ^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 207, in run_coro_as_sync return call.result() ^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 365, in result return self.future.result(timeout=timeout) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 192, in result return self.__get_result() ^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result raise self._exception File "/usr/local/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 441, in _run_async result = await coro ^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 188, in coroutine_wrapper return await task ^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/client/utilities.py", line 69, in wrapper return await func(client, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/flows.py", line 2836, in load_flow_from_flow_run flow = await run_sync_in_worker_thread( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 233, in run_sync_in_worker_thread result = await anyio.to_thread.run_sync( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2485, in run_sync_in_worker_thread return await future ^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 976, in run result = context.run(func, *args) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 243, in call_with_mark return call() ^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/flows.py", line 2509, in load_flow_from_entrypoint flow: Flow[P, Any] = import_object(entrypoint) # pyright: ignore[reportRedeclaration] ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/importtools.py", line 153, in import_object module = load_script_as_module(script_path) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/prefect/utilities/importtools.py", line 118, in load_script_as_module raise ScriptError(user_exc=exc, path=path) from exc prefect.exceptions.ScriptError: Script at 'flows/flow.py' encountered an exception: ModuleNotFoundError("No module named 'pandas'") 050205 AM prefect.flow_runs Process for flow run 'uncovered-axolotl' exited with status code: 1 050206 AM prefect.flow_runs.runner Reported flow run '019ad16c-2beb-7e40-91ce-9944b92165ad' as crashed: Flow run process exited with non-zero status code 1. 050206 AM prefect.flow_runs.runner Loading flow to check for on_crashed hooks 050206 AM prefect.flow_runs.runner > Running git_clone step... # Dockerfile FROM prefecthq/prefect:3-latest WORKDIR /opt/prefect COPY pyproject.toml uv.lock ./ RUN uv sync i use docker-git pyproject.toml: [project] name = "prefect-demo" version = "0.1.0" description = "Add your description here" readme = "README.md" requires-python = ">=3.11" dependencies = [ "pandas>=2.3.3", "prefect>=3.6.4", "pyarrow>=22.0.0", "requests>=2.32.5", ] [dependency-groups] dev = [ "prefect-docker>=0.6.6", "prefect-github>=0.3.2", ] there is pandas, why it have this error?
m
✅ thought for 51.0 seconds
You’re using Git-based runs, and Prefect clones your repo to a new directory inside the job container (e.g., /opt/prefect/prefect_demo-main) then imports flows/flow.py. Your Dockerfile ran 
uv sync
at build time in /opt/prefect, which by default creates a virtualenv (.venv) there. Prefect does not activate that venv when it imports your flow, so pandas isn’t on sys.path → ModuleNotFoundError. Fix it with one of these approaches: Option A (recommended for Git-based runs): install deps at runtime after git_clone - Either set the job command to run uv sync before executing the flow:
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# prefect.yaml
deployments:
  - name: your-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect:with-uv-git
      command: "sh -lc 'uv --version || python -m pip install -U uv; uv sync --system-site-packages && prefect flow-run execute'"
- Or add a pull step after git_clone:
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pull:
  - prefect.deployments.steps.git_clone:
      id: clone
      repository: <https://github.com/your/repo.git>
      branch: main
  - prefect.deployments.steps.run_shell_script:
      directory: "{{ clone.directory }}"
      script: |
        uv --version || python -m pip install -U uv
        uv sync --system-site-packages
Why this works: Prefect sets cwd to the cloned repo. Running 
uv sync --system-site-packages
there installs pandas (and all deps from that repo’s pyproject.toml) into the container’s system site-packages, so the subsequent import succeeds. Option B (bake deps into system site-packages at build time; then drop git_clone) - If you want to keep baking, don’t rely on a venv created at build. Install into system site-packages:
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# Dockerfile
FROM prefecthq/prefect:3-latest
WORKDIR /opt/prefect
# ensure uv is present; skip if your base image already has uv
# RUN curl -LsSf <https://astral.sh/uv/install.sh> | sh -s -- -y && ln -s /root/.local/bin/uv /usr/local/bin/uv

COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --system-site-packages
COPY flows ./flows
- Then remove the git_clone pull step from your deployment and just use the baked image. This is true “baked” mode. Other important notes - Keep Python versions aligned: your logs show Python 3.12. If your uv.lock was generated for 3.11, resolve with the same Python (use a 3.11 image tag) or regenerate the lock for 3.12. - If pyproject.toml lives in a subdirectory, cd before syncing: - command: 
sh -lc 'cd app && uv sync --system-site-packages && cd - && prefect flow-run execute'
- Ensure your job image has uv and git if you’re doing Git-based runs and uv sync at runtime. Quick sanity check - After you update, redeploy:
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prefect deploy -n your-deployment
- Trigger a run; you should see uv sync in logs before the flow import, and pandas will import. If you paste your current prefect.yaml (redact secrets), I’ll patch it to the exact working version for your layout (repo path of pyproject, Python version, and whether you want Option A or B).
p
@Marvin # Dockerfile FROM prefecthq/prefect:3-latest WORKDIR /opt/prefect COPY pyproject.toml uv.lock ./ RUN uv sync --system-site-packages Running deployment build steps... > Running build_docker_image step... Step 1/5 : FROM prefecthq/prefect:3-latest ---> e1c452f7af50 Step 2/5 : WORKDIR /opt/prefect ---> Using cache ---> 53f5f7c0d813 Step 3/5 : COPY pyproject.toml uv.lock ./ ---> Using cache ---> 1aa5d7c9acea Step 4/5 : RUN uv sync --system-site-packages ---> Running in 877b9fb68d06 error: unexpected argument '--system-site-packages' found tip: a similar argument exists: '--refresh-package' Usage: uv sync --refresh-package <REFRESH_PACKAGE> For more information, try '--help'. Traceback (most recent call last): File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 186, in run_steps step_output = await run_step(step, upstream_outputs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 140, in run_step result = await from_async.call_soon_in_new_thread( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 374, in aresult return await asyncio.wrap_future(self.future) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/_internal/concurrency/calls.py", line 399, in _run_sync result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect_docker/deployments/steps.py", line 115, in wrapper STEP_OUTPUT_CACHE[key] = func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect_docker/deployments/steps.py", line 274, in build_docker_image raise BuildError(event["error"]) prefect.utilities.dockerutils.BuildError: The command '/bin/sh -c uv sync --system-site-packages' returned a non-zero code: 2 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/cli/_utilities.py", line 44, in wrapper return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/cli/_types.py", line 159, in sync_fn return asyncio.run(async_fn(*args, **kwargs)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/home/pohengchou/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/pohengchou/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/cli/deploy/_commands.py", line 351, in deploy await _run_single_deploy( File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/cli/deploy/_core.py", line 268, in _run_single_deploy await run_steps(build_steps, step_outputs, print_function=app.console.print) File "/home/pohengchou/prefect_demo/.venv/lib/python3.11/site-packages/prefect/deployments/steps/core.py", line 231, in run_steps raise StepExecutionError(f"Encountered error while running {fqn}") from exc prefect.deployments.steps.core.StepExecutionError: Encountered error while running prefect_docker.deployments.steps.build_docker_image An exception occurred.
m
✅ thought for 48.3 seconds
You’re seeing that because your Dockerfile runs:
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uv sync --system-site-packages
but your uv version doesn’t support that flag. That option belongs to venv creation, not sync. Fix it by either installing into system site-packages with uv pip (recommended) or by using a venv and activating it at runtime. Pick one: Option 1 (recommended): install into system site-packages at build time - Works for both baked-image and git-pull runs (pandas available globally)
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# Dockerfile
FROM prefecthq/prefect:3.6.5-python3.11  # pin to match your lock; or regenerate lock for 3.12 if you prefer
WORKDIR /opt/prefect

# Ensure uv is available
RUN python -m pip install -U pip uv

# Copy manifests first for layer caching
COPY pyproject.toml uv.lock ./
# Install project + deps into system site-packages (uses lock)
RUN uv pip install --system --frozen .

# If you’re doing baked-image (no git pull), copy code:
# COPY flows ./flows
Notes: - If you stay on prefecthq/prefect:3-latest (often Python 3.12), regenerate uv.lock for 3.12 or switch to a 3.11-tagged image to match your existing lock. - After this, pandas is importable without activating a venv. Option 2: use a venv and activate it at runtime (for git-pull) - If you prefer uv’s venv, keep sync (no system install), but activate the venv when the container runs the flow. Dockerfile:
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FROM prefecthq/prefect:3.6.5-python3.11
WORKDIR /opt/prefect
RUN python -m pip install -U pip uv
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen  # creates .venv here
In prefect.yaml deployment, set the job command so the venv is used:
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deployments:
  - name: your-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect:with-uv
      command: "sh -lc '. .venv/bin/activate && prefect flow-run execute'"
Option 3: just use pip at build time (simple and robust)
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FROM prefecthq/prefect:3.6.5-python3.11
WORKDIR /opt/prefect
COPY pyproject.toml ./
RUN python -m pip install -U pip && pip install .
# COPY flows ./flows   # only for baked-image mode
Important alignment - Python version vs lock file: if your lock was created for 3.11, use a Python 3.11-based Prefect image (e.g., 3.x-python3.11) or regenerate the lock for 3.12. - Baked-image vs git-pull: - Baked-image: remove git_clone pull steps and set entrypoint to the path inside your image (e.g., /opt/prefect/flows/flow.py:hello). - Git-pull: keep git_clone; Option 1 or 2 ensures pandas is available even when Prefect imports the cloned file. After updating - Rebuild: 
docker build -t your-registry/prefect:with-deps .
- Redeploy: 
prefect deploy -n <your-deployment>
- Run a test: trigger a run and confirm no ModuleNotFoundError Docs - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ If you share which route you’re choosing (system install vs venv) and your current prefect.yaml, I’ll tailor the exact snippets for your setup.
p
@Marvin prefect init &choose docker-git. I need to delete some block depend on whcih way I want to run my flow. Right?
m
✅ thought for 48.4 seconds
Short answer: you don’t have to delete blocks when switching approaches. Blocks are only used if something references them (your prefect.yaml or your code). You can keep both “docker-git” and “baked-image” scaffolding in the repo and just enable the parts you use. How the “docker-git” template works - It usually includes: - build steps (optional) for image build/push that might reference a Docker registry credentials block - pull steps for git_clone that may reference a Git credentials block (only needed for private repos) - If you switch to baked-image: - Remove the pull steps (git_clone/run_shell_script) from prefect.yaml - Remove any build steps you’re not using (if you build images outside Prefect/CI) - Keep or delete the blocks — they’re harmless if unused. If you want to clean up, delete them. When to keep vs delete blocks - Keep a block if: - It’s referenced in prefect.yaml (e.g., git_clone credentials) or your flow code - You’ll reuse it later (e.g., registry creds, Slack webhooks) - Safe to delete a block if: - Nothing references it anymore (you removed the related step from prefect.yaml) - You want to reduce clutter Useful block commands - List blocks:
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prefect block ls
- Inspect a block:
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prefect block inspect <block-type-slug>/<block-name>
- Delete a block:
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prefect block delete <block-type-slug>/<block-name>
Two minimal prefect.yaml variants for clarity 1) Git-based (keep git_clone + install deps at runtime)
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name: my-project

deployments:
  - name: hello
    entrypoint: flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect:with-uv-git
      # For WSL2 to reach local server:
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
      # Optional: networks: ["prefect-network"] if you use a named network
    pull:
      - prefect.deployments.steps.git_clone:
          id: clone
          repository: <https://github.com/your-org/your-repo.git>
          branch: main
          # credentials: github-credentials/my-creds   # only if private
      - prefect.deployments.steps.run_shell_script:
          directory: "{{ clone.directory }}"
          script: |
            uv --version || python -m pip install -U uv
            uv sync && echo "deps ready"
2) Baked-image (no git, no runtime installs)
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name: my-project

deployments:
  - name: hello
    entrypoint: /opt/prefect/flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect:baked-with-deps
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
    # No pull/build steps
Key points - Don’t double-manage: if you use the baked image path, remove git_clone and any credentials references from prefect.yaml. If you use docker-git, ensure the job image has git + uv and install deps in the cloned repo. - Deleting blocks is optional. Just make sure you’re not deleting a block that a deployment still references; otherwise runs will fail when the step tries to load it. Docs - Blocks overview: https://docs.prefect.io/v3/develop/blocks/ - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ If you paste your current prefect.yaml, I can mark exactly which lines to keep/remove for the mode you want.
p
@Marvin give me prefect.yaml example,which use command prefect init and choose docker-git. I want to compare the diffs of git-baesd code pull method and bake into dockerimage method.
m
✅ thought for 28.9 seconds
Great idea. Here are two minimal, working prefect.yaml examples you can drop into your repo to compare: A) Git-based code pull (docker-git style) - Prefect clones your repo into the job container, then installs deps with uv before running the flow - Good for quick iteration; requires git and internet access in the job container
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name: my-project

deployments:
  - name: hello-git
    entrypoint: flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      # Base image the Docker work pool will use for the job container
      image: prefecthq/prefect:3-latest
      # WSL2 tip: point the job at your local server's API
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>

    # Pull steps: clone the repo then install deps in the cloned directory
    pull:
      - prefect.deployments.steps.git_clone:
          id: clone
          repository: <https://github.com/your-org/your-repo.git>
          branch: main
          # For private repos, add:
          # credentials: github-credentials/your-block-name

      - prefect.deployments.steps.run_shell_script:
          directory: "{{ clone.directory }}"
          script: |
            python -m pip install -U uv
            uv --version
            uv sync  # installs deps from pyproject.toml (and uv.lock if present)
B) Baked-into-image (no git at runtime) - Your image already contains your code and dependencies - Fast, reproducible, and works in restricted networks
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name: my-project

deployments:
  - name: hello-baked
    # Point to your flow inside the image (absolute path is most reliable)
    entrypoint: /opt/prefect/flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      # Your prebuilt image that already has code + deps baked in
      image: your-registry/my-project:latest
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>

    # No pull/build steps needed for baked image
What’s different between the two - Git-based: - Has pull steps (git_clone + run_shell_script) - Uses a generic base image; installs deps at runtime in the cloned repo - entrypoint is relative to the repo root (flows/flow.py:hello) - Baked image: - No pull steps - Uses your prebuilt image with code + deps included - entrypoint points to the path inside your image (e.g., /opt/prefect/flows/flow.py:hello) Tips - If you want the Git-based job to use a custom image that already includes git and uv, set job_variables.image to that image; the pull steps still run, but uv will be available without pip installing it. - If you prefer networking by Docker service name instead of host.docker.internal, put your server and worker on a named network in docker-compose and set: - env.PREFECT_API_URL: http://prefect-server:4200/api - job_variables.networks: ["prefect-network"] How to deploy - From your repo root: - Git-based: 
prefect deploy -n hello-git
- Baked image: 
prefect deploy -n hello-baked
If you share your repo URL structure (where pyproject.toml and flows live) or your image tag, I can tailor these exactly (e.g., add a cd subdir for uv sync or set an absolute entrypoint path).
p
@Marvin # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: prefect_demo prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.git_clone: repository: https://github.com/pohengchou/prefect_demo.git branch: main access_token: "{{ prefect.blocks.github-credentials.github-pat-token.token }}" # the deployments section allows you to provide configuration for deploying flows deployments: - name: my_first_deployment version: "1.0" tags: [demo, docker] description: "我的第一個deployment" schedule: cron: "*/2 * * * *" timezone: "Asia/Taipei" flow_name: main entrypoint: flows/flow.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' command: "sh -lc '. .venv/bin/activate && prefect flow-run execute'" env: PREFECT_API_URL: "http://host.docker.internal:4200/api" Explain to me ,build push pull section. If I decide use git pull method,I only need to keep pull section? If I choose baked into docker image ,I keep build and push section. Am I correct?
m
✅ thought for 34.2 seconds
Great question — here’s how those sections work and how to switch between “git pull” and “baked image” cleanly. What the sections do - build: runs locally during prefect deploy. Typically builds a Docker image from your Dockerfile. Outputs values (e.g., image_name, tag) you can reference later. - push: runs locally after build. Pushes the built image to your registry. - pull: runs at flow-run time inside the job container (the container your Docker work pool starts). Commonly used to git clone your repo and optionally run setup commands before Prefect imports your flow. So am I correct about keeping/removing sections? - Git-pull method - Minimum: keep pull. You can drop build/push if you point job_variables.image to an existing image that already has the tools you need (git, uv, system deps). - Optional: keep build/push if you want to provide a custom base image for the pull method (e.g., include git + uv + OS libs). Pull is orthogonal to build/push — you can use both. - Baked-into-image method - Keep build/push (if you want Prefect to build/push for you). Remove pull (code is already in the image, no clone needed). - If your CI builds/pushes images, you can drop build/push as well and just reference the final image tag in job_variables.image. Notes on your current file - Your job command uses 
. .venv/bin/activate && ...
— that only works if your image contains a .venv at runtime. If you didn’t create one during build, this will fail. For git-pull, a safer command is to run uv (or pip) in the cloned repo before execute. - In Slack the 
&&
appears as `&&`; be sure your file has real 
&&
. Two ready-to-use variants based on your file A) Git-based code pull (docker-git style) - Keep pull; optionally keep build/push if you want a custom base image - Install deps in the cloned repo before import
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name: prefect_demo
prefect-version: 3.6.4

# Optional: keep these if you want a custom base image for the git-pull run
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# Required for git-pull
pull:
- prefect.deployments.steps.git_clone:
    repository: <https://github.com/pohengchou/prefect_demo.git>
    branch: main
    # For private repos, prefer a block:
    # credentials: github-credentials/github-pat-token

# Deployments
deployments:
- name: my_first_deployment
  version: "1.0"
  tags: [demo, docker]
  description: "我的第一個deployment"
  schedule:
    cron: "*/2 * * * *"
    timezone: "Asia/Taipei"
  flow_name: main
  entrypoint: flows/flow.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      # If you kept build/push, use the built image; otherwise point at any image that has git + uv
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      env:
        # WSL2: point jobs at your local server
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
      # Install deps in the cloned repo, then execute
      command: "sh -lc 'python -m pip install -U uv && uv sync && prefect flow-run execute'"
B) Baked-into-image (no git at runtime) - Keep build/push; remove pull - Ensure your Dockerfile installs your deps into the image (pip or uv system install) ``` name: prefect_demo prefect-version: 3.6.4 build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo tag: test-v1 dockerfile: Dockerfile push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # No pull section for baked image deployments:
- name: my_first_deployment version: "1.0" tags: [demo, docker] description: "我的第一個deployment" schedule: cron: "*/2 * * * *" timezone: "Asia/Taipei" flow_name: main # Entrypoint is inside your image; absolute path is most reliable entrypoint: /opt/prefect/flows/flow.py:main work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' env: PREFECT_API_URL: http://host.docker.internal:4200/api # Command can be the default; omit unless you need to customize # command: "prefect flow-run execute" ``` Quick checklists - Git-based: - Job image must have git and a way to install deps (uv or pip) - Keep pull git_clone, and run uv/pip before execute - Baked image: - Install deps at build time (pip install . or uv pip install --system .) - No pull steps; entrypoint points to path inside image - Either approach: - Ensure PREFECT_API_URL is reachable from the job container (WSL2: host.docker.internal or a shared Docker network) Docs for deeper dives - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pool guide: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ If you paste your Dockerfile and confirm whether you want git-pull or baked for this deployment, I can tune the command/entrypoint to avoid the venv pitfall and ensure pandas is importable.
p
@Marvin my question is docker-git version of prefect.yaml have pull,push,build section. Explain it to me.
m
✅ thought for 30.8 seconds
Great question. In the docker-git template, the build, push, and pull sections each serve a different purpose and run at different times/places. Here’s the mental model: What each section does - build - Where/when: runs locally during prefect deploy (on your laptop or CI). - What it does: builds a Docker image from your Dockerfile. This image is your “runtime” base (OS libs, Python, git/uv, dbt, etc.). It usually does not contain your flow code in the docker-git pattern. - Outputs: values like image_name and tag, referenced later via templating (e.g., {{ build_image.image_name }}:{{ build_image.tag }}). - Why keep it: to standardize the runtime environment for your jobs. Optional if you use an existing image and don’t need to build one. - push - Where/when: runs locally right after build during prefect deploy. - What it does: pushes the built image to your Docker registry so the Docker worker can pull it at runtime. - Why keep it: required if the worker is on a different machine/engine or you want reproducibility. Optional for single-machine local dev where the worker shares the same Docker engine and can use your local image cache. - pull - Where/when: runs inside the flow-run container at runtime (after the Docker worker launches the job container). - What it does: clones your repo (git_clone) and optionally runs setup commands (e.g., run_shell_script to uv/pip install deps) before Prefect imports your flow entrypoint. - Why keep it: required for git-based code execution. Remove it if you “bake” code into the image. How they work together in docker-git - During deploy: build → push create/publish the base runtime image you want all jobs to use. - During run: Docker worker starts a container with that image → pull steps run inside the container to git clone your code (and install deps if you choose) → Prefect imports and runs your flow. Common Q&A - Do I need build/push for docker-git? - Not strictly. You can point job_variables.image at an existing image (e.g., prefecthq/prefect:3-latest) and only keep pull. However, the template includes build/push so you can control the runtime (e.g., ensure git/uv and system libraries are present). - Do I need pull when I bake code into the image? - No. In baked-image mode, remove pull. Your entrypoint should point to the flow path inside the image, and your dependencies are installed at build time. - What does requires: prefect-docker>=... mean? - It tells prefect deploy that it must have the prefect-docker collection installed locally to run those build/push steps. - Is “push” the same as “docker pull”? - No. “push” here uploads your image to a registry during deploy. At runtime, the Docker worker “pulls” the image from that registry (separate from Prefect’s pull section which is a code clone). Typical layouts - Git-based (keep pull; optionally keep build/push): - build/push: produce an image that contains git + uv + OS deps. - pull: git_clone your repo and run uv sync (or pip install) before execute. - Baked-image (keep build/push; remove pull): - build/push: bake your code + deps into the image. - run: Prefect imports the flow directly from the image path; no code clone at runtime. Tip for WSL2 - Ensure the job container can reach your API: - Easiest: set env.PREFECT_API_URL to http://host.docker.internal:4200/api in job_variables. - Or put jobs and server on a named Docker network and use http://prefect-server:4200/api plus job_variables.networks: ["that-network"]. Docs - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Prefect Docker collection (build/push steps): https://docs.prefect.io/v3/integrations/collection/prefect-docker/
If you share which mode you’re standardizing on (git-based or baked), I can mark exactly which lines to keep/remove in your current prefect.yaml and suggest the right command/env to avoid import/dependency issues.
p
@Marvin baked docker image way,you mention that it do not need pull section. But how I get that image from dockerhub if you don't pull it.
m
✅ thought for 32.0 seconds
Great question — “pull” in prefect.yaml is easy to confuse with Docker’s image pull. What each section means in the docker-git template - build - Runs during prefect deploy (on your laptop/CI). - Builds a Docker image from your Dockerfile. - Outputs values like image_name/tag you can reference later. - push - Runs during prefect deploy after build. - Pushes the built image to your registry (so other machines can fetch it). - pull - Runs at flow run time inside the job container. - These are Prefect “pull steps” to fetch your CODE (e.g., git_clone) and optionally run setup commands in that cloned repo. - Not related to Docker “docker pull”. So if you choose baked-image: - You do not need the prefect.yaml pull section (no git_clone at runtime because your code is already in the image). - The job image is still pulled by Docker automatically when the worker creates the container. That’s a Docker behavior, not the prefect.yaml pull section. - You can keep build/push if you want Prefect to build and publish the image for you during deploy. Or skip build/push if your CI already builds/pushes — just point job_variables.image at a tag that exists in your registry. How the job image is pulled without prefect.yaml pull - When your deployment starts, the Docker worker calls the Docker Engine to start a container with job_variables.image. - If the image is not present locally, Docker Engine pulls it from the registry automatically (subject to pull policy). - You can control this with image_pull_policy: - "IfNotPresent" (default): only pulls if missing - "Always": always pull latest from registry - "Never": never pull (use local only) Example: baked-image prefect.yaml (no code pull) with explicit pull policy
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name: my-project

deployments:
  - name: hello-baked
    entrypoint: /opt/prefect/flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/my-project:1.2.3
      image_pull_policy: "IfNotPresent"   # or "Always"
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
If you want Prefect to build/push during deploy (optional):
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build:
  - prefect_docker.deployments.steps.build_docker_image:
      id: build_image
      requires: prefect-docker>=0.3.1
      image_name: your-registry/my-project
      tag: 1.2.3
      dockerfile: Dockerfile

push:
  - prefect_docker.deployments.steps.push_docker_image:
      requires: prefect-docker>=0.3.1
      image_name: '{{ build_image.image_name }}'
      tag: '{{ build_image.tag }}'

deployments:
  - name: hello-baked
    entrypoint: /opt/prefect/flows/flow.py:hello
    work_pool:
      name: docker-pool
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: "IfNotPresent"
      env:
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
Private registries - Ensure the Docker host running the worker is authenticated: - docker login your-registry - Or provide registry credentials to Prefect via a DockerRegistryCredentials block and pass it in job_variables (or set it on the work pool’s base_job_template). Key takeaway - prefect.yaml pull = code retrieval steps at runtime, not Docker image pulls. - Docker pulls the image automatically when the worker starts the job container; control this with image_pull_policy and by ensuring your image is built/pushed to a registry your worker can access.
p
@Marvin what is best use case of baked into docker image? And what is best use case of git-base pull method?
m
✅ thought for 26.4 seconds
Great question. Both patterns are valid in Prefect 3 with a Docker work pool—they just optimize for different needs. Here’s a concise guide to when to use each. Best use cases: baked-into-image - Reproducible, stable production runs - You want deterministic environments, faster cold starts, and less “works on my machine.” - Restricted or regulated networks (e.g., hospital) - No outbound internet at runtime; no need for git/pypi access during execution. - Heavy/native dependencies - dbt + adapters, pandas/pyarrow, system libs, CUDA, ODBC drivers, etc. Build once, run everywhere. - Security/compliance - Images can be signed/scanned and promoted through environments; fewer moving parts at runtime. - Scale and latency - Startup time matters for frequent short flows; cold starts are much faster with prebuilt images. When to avoid - If you don’t have a CI pipeline to build/push images - If your team needs to iterate code minute-by-minute without building images Best use cases: git-based pull - Rapid iteration and prototyping - Push to main, runs pick up latest code with no image rebuild. - Lightweight dependencies - Small Python-only stacks where a quick uv/pip install is acceptable at runtime. - Centralized code reuse across repos - Pull latest shared code (e.g., a utils repo) without re-cutting images each change. When to avoid - Restricted networks or no outbound internet from job nodes - Large or native deps (slow installs, brittle builds at runtime) - Strict change control/compliance that favors immutable artifacts Common hybrid patterns (often “best of both”) - Prebake heavy deps, pull only the latest flow code - Image includes dbt/pandas/pyarrow; git pull your repo and run a small uv sync that only updates your app package (fast). - Prod = baked image; Dev/Preview = git pull - Developers iterate using git-based deployments; CI cuts immutable images for promotion to staging/prod. Operational differences to remember - Baked image - prefect.yaml: no pull section; entrypoint points to code inside image (e.g., /opt/prefect/flows/flow.py:flow) - job_variables.image references your pushed tag; set image_pull_policy (“IfNotPresent” or “Always”) - CI builds/pushes; Docker worker just runs - Git-based pull - prefect.yaml: keep pull (git_clone + optional run_shell_script to uv/pip install) - Job image must include git and your installer (uv/pip); ensure pyproject.toml location matches where you run the installer - Expect longer startup and need outbound access to git/package index (or a proxy) Quick decision checklist - Need internet at runtime? Baked: no; Git-pull: yes - Startup speed important? Baked: fast; Git-pull: slower (installs) - Heavy deps/native libs? Baked preferred - Regulated/compliance environment? Baked preferred - Moving fast in dev? Git-pull convenient - CI available to build/push images? If yes, baked is easy to standardize Docs - Deployments via prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pool: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ If you share your constraints (internet at runtime, CI availability, dbt adapter, and where pyproject.toml lives), I can suggest a concrete “prod baked + dev pull” setup with matching prefect.yaml snippets for each.
p
@Marvin what is the difference between docker-git, git ,docker option? (my_prefect_demo) (base) choupoheng@AIWS-126182:~/repos/my_prefect_demo$ prefect init ? Would you like to initialize your deployment configuration with a recipe? [Use arrows to move; enter to select; n to select none] ┏━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ Name ┃ Description ┃ ┡━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ │ docker-s3 │ Store code within S3 and build a custom docker image for runtime │ │ │ local │ Store code on a local filesystem │ │ │ docker-git │ Store code within a git repository and build a custom docker image for runtime │ │ │ docker-azure │ Store code within an Azure Blob Storage container and build a custom docker image for runtime │ │ │ s3 │ Store code within an S3 bucket │ │ │ azure │ Store code within an Azure Blob Storage container │ │ │ git │ Store code within git repository │ │ > │ docker │ Store code within a custom docker image alongside its runtime environment
m
✅ thought for 47.0 seconds
Great question — those recipes describe two axes: where your code lives (storage) and what runtime you use. Here’s the difference between docker-git, git, and docker in Prefect 3.x prefect init: Concepts - Storage: where Prefect fetches your flow code from at run time (git, object storage, or baked into an image) - Runtime: the environment that executes the flow (a Docker image you specify, or the local/process environment) docker-git - Storage: Git repository (Prefect clones your repo inside the job container at run time) - Runtime: Custom Docker image you build (controls OS/Python/system deps) - prefect.yaml typically includes: - build: build your base/runtime image (no code baked in) - push: push that image to a registry - pull: git_clone (and often a run_shell_script to install Python deps, e.g., uv/pip) before the flow is imported - Use when: you want a controlled runtime image but still pull the latest code from Git each run (fast iteration, but needs git + internet access in the job container) git - Storage: Git repository (same git_clone at run time) - Runtime: whatever your worker provides; the recipe does not build/push an image for you - If you use a Docker work pool, you must set job_variables.image yourself and ensure it has git and a way to install deps at run time (or deps already baked) - prefect.yaml typically includes: - pull: git_clone (and you can add a run_shell_script to install deps) - No build/push sections by default - Use when: you want the simplest Git-based setup and are okay not building a custom image during deploy (common for dev/POC) docker - Storage: Inside the Docker image (code and deps are baked during build) - Runtime: The same Docker image; no code fetch at run time - prefect.yaml typically includes: - build + push to produce and publish the image - No pull section (no git_clone) - Entrypoint points to a file path inside the image (e.g., /opt/prefect/flows/flow.py:flow_fn) - Use when: you want reproducible, fast, internet-free production runs with immutable artifacts Quick decision rules - Prefer docker for production (baked image): fast cold starts, no runtime internet, easy to sign/scan/promote - Choose docker-git for controlled runtime + fast code iteration: you build a small base image once, then pull code each run - Choose git for the lightest setup: no image build in deploy; make sure your chosen job image has git and can install deps at run time Important clarifications - prefect.yaml pull refers to code pull steps (e.g., git_clone) that run inside the job container, not Docker’s image pulling. - The Docker worker will docker pull your job image automatically if it’s not local; you can control this with job_variables.image_pull_policy ("IfNotPresent", "Always", "Never"). Docs - Deployments with prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ If you share your constraints (e.g., internet at runtime, CI availability) I can suggest the exact prefect.yaml lines to keep/remove for your chosen path.
p
@Marvin ┃ Field Name ┃ Description ┃ ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ image_name │ The image name, including repository, to give the built Docker image │ │ tag │ The tag to give the built Docker image what is naming format?
m
✅ thought for 37.2 seconds
Short answer: - image_name = the repository path (no tag) - tag = the tag string Docker will combine them as image_name:tag. Valid formats image_name - General: [REGISTRY[:PORT]/][NAMESPACE/]REPOSITORY - All repository path segments must be lowercase and may contain: a–z, 0–9, -, _, . - Segments are separated by / and cannot start/end with a separator - Examples: - docker hub (implicit registry): pohengchou/demo - docker hub (org): myorg/prefect-flows - GHCR: ghcr.io/myorg/prefect-flows - ECR: 123456789012.dkr.ecr.us-east-1.amazonaws.com/prefect-flows - Private: registry.example.com:5000/team/prefect-flows tag - Up to 128 chars; allowed: letters, digits, ., _, - - Must start with a letter, digit, or underscore - Examples: - latest - v1.0.0 - 2025.12.01-abc123 - main-4f2c1d8 - test-v1 Putting it together - In your prefect.yaml build step: - image_name: pohengchou/demo - tag: test-v1 - Later you reference it as: - job_variables.image: '{{ build_image.image_name }}:{{ build_image.tag }}' → pohengchou/demo:test-v1 Common pitfalls - Don’t include a tag in image_name; keep it separate (image_name:tag). - Use lowercase for repository/name segments; Docker rejects uppercase repo names. - Ensure the registry is reachable and your worker is logged in if it’s private. Refs - Docker naming and tagging: https://docs.docker.com/build/building/basics/#naming-and-tagging - Prefect Docker integration (build/push steps): https://docs.prefect.io/v3/integrations/collection/prefect-docker/
p
@Marvin give me baked docker image method's dockfile and prefect yaml example
m
✅ thought for 59.6 seconds
Here’s a minimal, working “baked image” setup (no git_clone at runtime). It bakes your deps into the image, copies your flow code into the image, and deploys with that image. Dockerfile
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FROM prefecthq/prefect:3-latest
# For stricter reproducibility, pin an exact tag, e.g.:
# FROM prefecthq/prefect:3.6.4-python3.11

WORKDIR /opt/prefect

# Install project dependencies defined in pyproject.toml into system site-packages
# (pip will read [project.dependencies] from pyproject.toml)
COPY pyproject.toml ./
RUN python -m pip install --upgrade pip && pip install .

# Copy your flow code into the image
# Ensure your flow entrypoint path matches this location
COPY flows ./flows
prefect.yaml (baked image; no pull section)
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name: prefect_demo
prefect-version: 3.6.4

# Build and push the image during `prefect deploy` (optional if your CI builds/pushes)
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: your-dockerhub-user/prefect-demo
    tag: v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# No 'pull' section for baked-image method

deployments:
- name: my_baked_deployment
  entrypoint: /opt/prefect/flows/flow.py:main
  work_pool:
    name: docker-pool
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: IfNotPresent
      env:
        # WSL2: point jobs at your local Prefect Server
        PREFECT_API_URL: <http://host.docker.internal:4200/api>
  parameters: {}
  tags: [baked, docker]
  # Optional schedule
  # schedule:
  #   cron: "*/10 * * * *"
  #   timezone: Asia/Taipei
Notes - Remove any git-related pull steps; code and deps are already in the image. - Ensure your Docker worker is running and polling the docker-pool. - If your Server and jobs share a named Docker network instead of host.docker.internal, set: - job_variables.networks: ["prefect-network"] - PREFECT_API_URL: http://prefect-server:4200/api Deploy - Build/push via 
prefect deploy -n my_baked_deployment
- Run from UI or:
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prefect deployment run "prefect_demo/my_baked_deployment"
If you prefer uv, swap the install line in the Dockerfile:
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RUN python -m pip install -U pip uv && uv pip install --system --frozen .
(Keep pyproject.toml in the image root; add uv.lock if you use one.)
p
@Marvin I not use wsl2 now. I use linux so how to change http://host.docker.internal:4200/api
m
✅ thought for 29.6 seconds
On Linux, don’t use host.docker.internal unless you’ve explicitly mapped it. Use one of these patterns: Option A (recommended): same Docker network, use service name - Put your Prefect Server and Worker on a named Docker network (e.g., prefect-network) - Have job containers join that network - Set PREFECT_API_URL to the server’s service name docker-compose.yml (excerpt)
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networks:
  prefect:
    name: prefect-network
    driver: bridge

services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    ports: ["4200:4200"]
    networks: ["prefect"]

  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    command: >
      uv run --with 'prefect[docker]' prefect worker start --pool docker-pool
    networks: ["prefect"]
prefect.yaml (baked image)
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deployments:
  - name: my_baked_deployment
    entrypoint: /opt/prefect/flows/flow.py:main
    work_pool:
      name: docker-pool
    job_variables:
      image: your-registry/prefect-demo:latest
      networks:
        - prefect-network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
Option B: talk to the host via the bridge gateway - Publish the server port (4200) and use the Docker bridge gateway IP from inside containers, often 172.17.0.1:
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PREFECT_API_URL=<http://172.17.0.1:4200/api>
- To be sure, find the gateway for your network: - docker network inspect bridge | grep -i gateway - docker network inspect prefect-network | grep -i gateway Option C: make host.docker.internal work on Linux - In docker-compose, add an extra host mapping:
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extra_hosts:
  - "host.docker.internal:host-gateway"
- For job containers (launched by the worker), add the same mapping via job_variables.container_create_kwargs:
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job_variables:
  image: your-registry/prefect-demo:latest
  container_create_kwargs:
    host_config:
      ExtraHosts:
        - "host.docker.internal:host-gateway"
  env:
    PREFECT_API_URL: <http://host.docker.internal:4200/api>
Pick one approach and make sure: - The server listens on 0.0.0.0 and publishes port 4200 - The worker can reach the URL you set - Your job containers inherit the correct network or extra_hosts so they can resolve and reach the API Docs: - Docker work pools: https://docs.prefect.io/v3/how-to-guides/deployment_infra/docker/ - Deployments with prefect.yaml: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments
p
@Marvin # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: my_prefect_demo prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/ # the deployments section allows you to provide configuration for deploying flows deployments: - name: my_first_deployment version: null tags: [] description: null schedule: cron: "*/2 * * * *" timezone: "Asia/Taipei" flow_name: main entrypoint: flows/main.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' image_pull_policy: IfNotPresent env: PREFECT_API_URL: http://prefect-server:4200/api # 基於官方 Prefect 3 最新版本鏡像 FROM prefecthq/prefect:3-latest WORKDIR /opt/prefect COPY pyproject.toml uv.lock ./ RUN uv sync COPY flows ./flows services: postgres: image: postgres:14 environment: POSTGRES_USER: ${DB_USER} POSTGRES_PASSWORD: ${DB_PASSWORD} POSTGRES_DB: ${DB_NAME} volumes: - postgres_data:/var/lib/postgresql/data healthcheck: test: ["CMD-SHELL", "pg_isready -U ${DB_USER}"] interval: 5s timeout: 5s retries: 5 redis: image: redis:7 volumes: - redis_data:/data healthcheck: test: ["CMD-SHELL", "redis-cli ping"] interval: 5s timeout: 5s retries: 5 prefect-server: image: prefecthq/prefect:3-latest depends_on: postgres: condition: service_healthy redis: condition: service_healthy environment: PREFECT_API_DATABASE_CONNECTION_URL: postgresql+asyncpg://${DB_USER}:${DB_PASSWORD}@postgres:5432/${DB_NAME} PREFECT_SERVER_API_HOST: 0.0.0.0 PREFECT_MESSAGING_BROKER: prefect_redis.messaging PREFECT_MESSAGING_CACHE: prefect_redis.messaging PREFECT_REDIS_MESSAGING_HOST: redis PREFECT_REDIS_MESSAGING_PORT: 6379 PREFECT_REDIS_MESSAGING_DB: 0 command: prefect server start --no-services ports: - "4200:4200" healthcheck: test: ["CMD", "python", "-c", "import urllib.request as u; u.urlopen('http://localhost:4200/api/health', timeout=1)"] interval: 30s timeout: 10s retries: 3 start_period: 60s prefect-services: image: prefecthq/prefect:3-latest depends_on: prefect-server: condition: service_healthy environment: PREFECT_API_DATABASE_CONNECTION_URL: postgresql+asyncpg://${DB_USER}:${DB_PASSWORD}@postgres:5432/${DB_NAME} PREFECT_MESSAGING_BROKER: prefect_redis.messaging PREFECT_MESSAGING_CACHE: prefect_redis.messaging PREFECT_REDIS_MESSAGING_HOST: redis PREFECT_REDIS_MESSAGING_PORT: 6379 PREFECT_REDIS_MESSAGING_DB: 0 command: prefect server services start # prefect-worker: # image: prefecthq/prefect:3-latest # depends_on: # prefect-server: # condition: service_healthy # environment: # PREFECT_API_URL: http://prefect-server:4200/api # command: prefect worker start --pool local-pool # restart: on-failure prefect-docker-worker: image: prefecthq/prefect:3-latest container_name: prefect-docker-worker depends_on: prefect-server: condition: service_healthy environment: # Point the worker to the local Prefect API PREFECT_API_URL: http://prefect-server:4200/api # Optional tuning: # PREFECT_WORKER_PREFETCH_SECONDS: "10" # PREFECT_WORKER_QUERY_SECONDS: "15" volumes: # Critical: allow the worker to create Docker containers - /var/run/docker.sock:/var/run/docker.sock # Use 'uv' to ensure the docker integration is available command: > uv run --with 'prefect[docker]' prefect worker start --pool docker-pool restart: on-failure volumes: postgres_data: redis_data:
m
✅ thought for 52.7 seconds
Thanks for sharing your files — you’re very close. For a baked-image deployment on Linux, here are the key fixes and a clean example. Fixes at a glance - Remove the pull section from prefect.yaml (no git_clone when code is baked). - Install dependencies into system site-packages at build time (don’t rely on a .venv unless you also activate it at runtime). - Use a stable Docker network so job containers can reach your server by name; add that network to job_variables.networks. - Remove HTML escapes in YAML (>=, &&, and raw URLs without < >). Corrected Dockerfile (baked image)
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FROM prefecthq/prefect:3.6.4-python3.11  # pin to match your Python/lock; adjust as needed

WORKDIR /opt/prefect

# Copy dependency manifest(s) first for better caching
COPY pyproject.toml uv.lock ./

# Install into system site-packages (no venv needed at runtime)
RUN python -m pip install -U pip uv \
 && uv pip install --system --frozen .

# Copy your flow code into the image
COPY flows ./flows
docker-compose.yml (define a named network so jobs can reach the server)
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version: "3.9"

networks:
  prefect:
    name: prefect-network
    driver: bridge

services:
  prefect-server:
    image: prefecthq/prefect:3-latest
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    networks: ["prefect"]

  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    depends_on:
      - prefect-server
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    command: >
      uv run --with 'prefect[docker]'
      prefect worker start
      --pool docker-pool
    networks: ["prefect"]
prefect.yaml (baked image; no pull)
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name: my_prefect_demo
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# No pull section for baked-image method

deployments:
- name: my_first_deployment
  schedule:
    cron: "*/2 * * * *"
    timezone: "Asia/Taipei"
  flow_name: main
  # Absolute path inside the image is the most robust
  entrypoint: /opt/prefect/flows/main.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: IfNotPresent
      # Ensure the job container joins the same network as your server
      networks:
        - prefect-network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
Notes and alternatives - If you prefer not to add a named network, you can keep the job off that network and set PREFECT_API_URL to the bridge gateway, e.g., http://172.17.0.1:4200/api (check with docker network inspect bridge). - If you really want to use a venv at build time (uv sync), then keep your original Dockerfile but set a job command that activates it: - command: "sh -lc '. .venv/bin/activate && prefect flow-run execute'" However, the system-site-packages approach above avoids this complexity. Run order - docker compose up -d - prefect work-pool create docker-pool --type docker (once) - prefect deploy -n my_first_deployment - Trigger a run from the UI or: - prefect deployment run "my_prefect_demo/my_first_deployment" Quick verification - From a throwaway container on the same network:
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docker run --rm --network prefect-network alpine sh -lc "apk add --no-cache curl >/dev/null && curl -sSf <http://prefect-server:4200/api/health> && echo OK"
If you paste the exact folder name of your compose project, I can confirm the network name, or we can keep the explicit “prefect-network” as shown for stability.
p
@Marvin (my_prefect_demo) (base) choupoheng@AIWS-126182:~/repos/my_prefect_demo$ prefect deploy ? Would you like to use an existing deployment configuration? [Use arrows to move; enter to select; n to select none] ┏━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ Name ┃ Entrypoint ┃ Description ┃ ┡━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ > │ my_first_deployment │ flows/main.py:main │ None │ │ │ │ │ No, configure a new deployment │ └────┴─────────────────────┴────────────────────┴────────────────────────────────┘ 102722.689 | INFO | prefect - Starting temporary server on http://127.0.0.1:8217 See https://docs.prefect.io/v3/concepts/server#how-to-guides for more information on running a dedicated Prefect server. This deployment configuration references work pool 'docker-pool' which does not exist. This means no worker will be able to pick up its runs. You can create a work pool in the Prefect UI. 102725.456 | INFO | prefect - Stopping temporary server on http://127.0.0.1:8217
m
✅ thought for 92.6 seconds
Your CLI isn’t pointed at your self-hosted server, so 
prefect deploy
spun up a temporary local server and couldn’t find the work pool. Fix it by 1) pointing the CLI at your running server, 2) creating the work pool, then 3) redeploying. Do this from your Linux host (not inside a container): 1) Point the CLI at your self-hosted API - If your docker-compose exposes the server on port 4200, use localhost from the host:
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export PREFECT_API_URL=<http://localhost:4200/api>
# or persist it to your Prefect profile:
prefect config set PREFECT_API_URL=<http://localhost:4200/api>
- Verify:
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prefect config view | grep PREFECT_API_URL
2) Create the Docker work pool (once)
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prefect work-pool create docker-pool --type docker
prefect work-pool ls
3) Redeploy using your existing prefect.yaml
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prefect deploy -n my_first_deployment
4) Ensure your worker is running and polling docker-pool - Your docker-compose worker should have 
PREFECT_API_URL=<http://prefect-server:4200/api>
(inside Docker network) and start with 
prefect worker start --pool docker-pool
. - From the host, you can confirm:
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prefect worker ls
Notes - The “temporary server on http://127.0.0.1:8217” message appears when 
PREFECT_API_URL
is not set; deployments created there will not appear in your real server. - From the host, use 
<http://localhost:4200/api>
. From job containers on the same Docker network, use 
<http://prefect-server:4200/api>
and add 
job_variables.networks: ["prefect-network"]
if you defined one. Docs - Work pools: https://docs.prefect.io/v3/concepts/work-pools - Deployments: https://docs.prefect.io/v3/how-to-guides/deployments/create-deployments
p
@Marvin # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: my_prefect_demo prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/ # the deployments section allows you to provide configuration for deploying flows deployments: - name: my_first_deployment version: null tags: [] description: null schedule: cron: "*/2 * * * *" timezone: "Asia/Taipei" flow_name: main entrypoint: flows/main.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' image_pull_policy: IfNotPresent env: PREFECT_API_URL: http://prefect-server:4200/api # 基於官方 Prefect 3 最新版本鏡像 FROM prefecthq/prefect:3-latest WORKDIR /opt/prefect COPY pyproject.toml uv.lock ./ RUN uv sync COPY flows ./flows Worker 'DockerWorker afcbd256-bf59-48b7-817c-6993e82a983e' submitting flow run '019ad7cb-48bb-74d9-91bd-c80f8b4a3793' 104351 AM prefect.flow_runs.worker Completed submission of flow run '019ad7cb-48bb-74d9-91bd-c80f8b4a3793' 104351 AM prefect.flow_runs.worker Reported flow run '019ad7cb-48bb-74d9-91bd-c80f8b4a3793' as crashed: Flow run infrastructure exited with non-zero status code 1.
m
✅ thought for 73.2 seconds
You’re almost there. The crash is most likely because your Dockerfile runs “uv sync” which creates a virtualenv at /opt/prefect/.venv, but your job does NOT activate that venv at runtime. Since pandas is only in the venv, importing it fails and the flow exits with code 1. For a baked-image deployment, do one of the following: Option 1 (recommended): install into system site-packages at build time - No venv activation needed; imports just work. Dockerfile
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FROM prefecthq/prefect:3.6.4-python3.11  # pin to a known Python; adjust as needed

WORKDIR /opt/prefect

# Copy manifests first for better caching
COPY pyproject.toml uv.lock ./

# Install deps into system site-packages using uv (honors uv.lock)
RUN python -m pip install -U pip uv \
 && uv pip install --system --frozen .

# Copy your flow code
COPY flows ./flows
prefect.yaml (no pull section needed for baked image; use absolute entrypoint; remove HTML escapes)
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name: my_prefect_demo
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

deployments:
- name: my_first_deployment
  schedule:
    cron: "*/2 * * * *"
    timezone: "Asia/Taipei"
  flow_name: main
  entrypoint: /opt/prefect/flows/main.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: IfNotPresent
      # If your server and jobs share a Docker network, include it here; otherwise omit
      # networks:
      #   - prefect-network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
Option 2: keep uv’s venv, but activate it at runtime - If you want to keep “uv sync” (creates .venv), set the job command to activate it. Dockerfile
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FROM prefecthq/prefect:3.6.4-python3.11
WORKDIR /opt/prefect
COPY pyproject.toml uv.lock ./
RUN python -m pip install -U pip uv && uv sync --frozen
COPY flows ./flows
prefect.yaml (set command; also remove HTML escapes and unnecessary pull)
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deployments:
- name: my_first_deployment
  entrypoint: /opt/prefect/flows/main.py:main
  work_pool:
    name: docker-pool
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      command: "sh -lc '. .venv/bin/activate && prefect flow-run execute'"
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
Additional cleanups - Remove the pull: set_working_directory step; it’s not needed for baked images (and your absolute entrypoint ignores CWD anyway). - Make sure your YAML doesn’t contain HTML-escaped characters: - Use >= not >= in requires - Use plain URLs, not <http://...> - Ensure the job container can reach your server: - If using a named Docker network, add job_variables.networks: ["prefect-network"] and keep PREFECT_API_URL=http://prefect-server:4200/api - Otherwise, set PREFECT_API_URL to your host/ingress URL that the job can reach If it still crashes, grab the job container logs to confirm the exact error: - From the flow run page → Infrastructure tab → copy container ID, then: - docker logs <container_id> Post that snippet and I’ll pinpoint any remaining issue.
p
@Marvin from prefect import flow, task import requests import pandas as pd import os from io import BytesIO from datetime import datetime URL="https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2025-01.parquet" @task(log_prints=True,retries=3,retry_delay_seconds=15) def check_url_avaliable(url: str): """ 檢查URL是否可用（狀態碼200） """ print(f"檢查{url}狀態碼") response=requests.head(url,timeout=15) response.raise_for_status() @task(log_prints=True) def read_parquet_from_url(url: str)-> pd.DataFrame: """ 下載指定 URL的 Parquet內容 """ response = requests.get(url) response.raise_for_status() object=BytesIO(response.content) df=pd.read_parquet(object) print(f"成功讀取{len(df)}筆資料") return df @task(log_prints=True) def write_data(df: pd.DataFrame,filepath: str): """ 儲存下載 的內容 """ os.makedirs(os.path.dirname(filepath),exist_ok=True) with open(filepath,'w', encoding='utf-8')as f: df.to_csv(f,index=False) print(f"數據寫入{filepath},包含{len(df)} 筆資料") return filepath @flow(log_prints=True, name="main") def main(url: str = URL, output_dir: str = "data", filename_prefix: str = "yellow_tripdata" ): today_str = datetime.now().strftime("%Y-%m-%d_%H%M%S") filepath = os.path.join( output_dir, f"{filename_prefix}_{today_str}.csv" ) check_url_avaliable(url) df=read_parquet_from_url(url) output_file=write_data(df,filepath) if name == "__main__": main().deploy(name="main") this code is correct right?
m
✅ thought for 50.9 seconds
You’ve got the right idea, but your snippet has a few issues caused by Slack/HTML escaping and one pandas call. Here’s a corrected version you can drop in:
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from prefect import flow, task
import requests
import pandas as pd
import os
from io import BytesIO
from datetime import datetime

URL = "<https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2025-01.parquet>"

@task(log_prints=True, retries=3, retry_delay_seconds=15)
def check_url_available(url: str) -> None:
    print(f"Checking {url} status code")
    resp = requests.head(url, timeout=15, allow_redirects=True)
    resp.raise_for_status()

@task(log_prints=True)
def read_parquet_from_url(url: str) -> pd.DataFrame:
    print(f"Downloading parquet from {url}")
    resp = requests.get(url, timeout=60)
    resp.raise_for_status()
    buf = BytesIO(resp.content)
    df = pd.read_parquet(buf)  # requires pyarrow (or fastparquet) to be installed in the image
    print(f"Loaded {len(df)} rows")
    return df

@task(log_prints=True)
def write_data(df: pd.DataFrame, filepath: str) -> str:
    os.makedirs(os.path.dirname(filepath), exist_ok=True)
    df.to_csv(filepath, index=False)  # fixed: use df.to_csv
    print(f"Wrote {len(df)} rows to {filepath}")
    return filepath

@flow(log_prints=True, name="main")
def main(
    url: str = URL,
    output_dir: str = "data",
    filename_prefix: str = "yellow_tripdata",
) -> str:
    ts = datetime.now().strftime("%Y-%m-%d_%H%M%S")
    filepath = os.path.join(output_dir, f"{filename_prefix}_{ts}.csv")

    check_url_available(url)
    df = read_parquet_from_url(url)
    return write_data(df, filepath)

if __name__ == "__main__":
    # For baked-image deployments, prefer using `prefect deploy` with prefect.yaml.
    # If you really want to deploy from code, provide pool/image explicitly:
    # main().deploy(
    #     name="main",
    #     work_pool_name="docker-pool",
    #     job_variables={"image": "pohengchou/demo:test-v1"},
    # )
    main()
Key fixes and notes: - Removed angle brackets around the URL and fixed the HTML escape for 
->
. - Replaced the broken 
<http://df.to|df.to>_csv
with 
df.to_csv
. - Added timeouts to requests.get and requests.head. - pandas.read_parquet requires pyarrow (or fastparquet) to be in your baked image. Make sure your Dockerfile installs dependencies from pyproject.toml (e.g., via pip or uv) so pandas + pyarrow are present at runtime. - For baked-image mode, do not use git pull steps. Use an absolute entrypoint in prefect.yaml that matches where you COPY your code (e.g., 
/opt/prefect/flows/main.py:main
) and set job_variables.image to your baked image. Quick sanity test locally before deploying: - Run the script directly: 
python flows/main.py
(or 
python -m flows.main
), confirm it writes a CSV. - Rebuild image with deps baked in, redeploy via 
prefect deploy -n my_first_deployment
. If you share your current Dockerfile and pyproject.toml, I can confirm the exact install line to ensure pandas/pyarrow/requests are baked correctly.
p
@Marvin Worker 'DockerWorker afcbd256-bf59-48b7-817c-6993e82a983e' submitting flow run '019ad7d7-f393-7668-93be-32016d99ca1b' 110656 AM prefect.flow_runs.worker Completed submission of flow run '019ad7d7-f393-7668-93be-32016d99ca1b' 110656 AM prefect.flow_runs.worker Reported flow run '019ad7d7-f393-7668-93be-32016d99ca1b' as crashed: Flow run infrastructure exited with non-zero status code 1.
m
✅ thought for 40.8 seconds
Exit code 1 is generic—we need the job container’s logs to see the actual exception. Meanwhile, here’s a tight checklist to get your baked-image run working and to debug quickly. Must-haves for baked-image - No pull section in prefect.yaml (no git at runtime) - Absolute entrypoint that matches where you COPY code in the image - Deps installed into system site-packages at build time (avoid a venv unless you activate it) - Job container can reach your API (network or host URL) - Ensure you’re using the fresh image (not a stale tag) 1) Dockerfile (system install with uv or pip)
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FROM prefecthq/prefect:3.6.4-python3.11   # pin to match your deps/lock

WORKDIR /opt/prefect
COPY pyproject.toml uv.lock ./

# Install deps into system site-packages (no venv needed at runtime)
RUN python -m pip install -U pip uv \
 && uv pip install --system --frozen .

# Copy your flows
COPY flows ./flows
2) prefect.yaml (no pull, absolute entrypoint, fresh image, API reachable)
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name: my_prefect_demo
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

deployments:
- name: my_first_deployment
  entrypoint: /opt/prefect/flows/main.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: Always          # avoids stale image during testing
      networks:
        - prefect-network               # if your server & worker share this network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
  schedule:
    cron: "*/2 * * * *"
    timezone: "Asia/Taipei"
3) Rebuild, push, redeploy
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docker build -t pohengchou/demo:test-v1 .
docker push pohengchou/demo:test-v1
prefect deploy -n my_first_deployment
4) If it still crashes, grab the job container logs - From the flow run in the UI → Infrastructure tab → copy container ID - Or via labels:
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docker ps -a --filter "label=<http://prefect.io/flow-run-id=019ad7d7-f393-7668-93be-32016d99ca1b|prefect.io/flow-run-id=019ad7d7-f393-7668-93be-32016d99ca1b>" --no-trunc
  docker logs <container_id>
- You can also enable debug logging in the job:
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job_variables:
    env:
      PREFECT_API_URL: <http://prefect-server:4200/api>
      PREFECT_LOGGING_LEVEL: DEBUG
Most common causes of exit 1 in baked-image - Entrypoint path mismatch (e.g., using flows/main.py but you copied to a different location) - Deps not actually installed into system site-packages (used uv sync without activating .venv) - API not reachable from the job container (wrong URL or network not attached) - Python/lock mismatch (e.g., lock for 3.11 but base image is 3.12) causing missing deps like pyarrow Quick sanity test of the image
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docker run --rm --network prefect-network pohengchou/demo:test-v1 \
  sh -lc "python -c 'import pandas, pyarrow; import runpy; runpy.run_path(\"/opt/prefect/flows/main.py\")' && echo OK"
Post the container logs from the failed run and I’ll pinpoint the exact fix.
Your message was too long, here's your message at the allowed limit: from prefect import flow task import requests import pandas as pd import os from io import BytesIO from datetime import datetime URL= <https //d37ci6vzurychx cloudfront net/trip-data/yellow_tripdata_2025-01 parquet> @task(log_prints=True retries=3 retry_delay_seconds=15) def check_url_avaliable(url str) 檢查URL是否可用（狀態碼200） print(f 檢查{url}狀態碼 ) response=requests head(url timeout=15) response raise_for_status() @task(log_prints=True) def read_parquet_from_url(url str)-> pd DataFrame 下載指定 URL的 Parquet內容 response = requests get(url) response raise_for_status() object=BytesIO(response content) df=pd read_parquet(object) print(f 成功讀取{len(df)}筆資料 ) return df @task(log_prints=True) def write_data(df pd DataFrame filepath str) 儲存下載 的內容 os makedirs(os path dirname(filepath) exist_ok=True) with open(filepath 'w' encoding='utf-8')as f <http //df to|df to>_csv(f index=False) print(f 數據寫入{filepath} 包含{len(df)} 筆資料 ) return filepath @flow(log_prints=True name= main ) def main(url str = URL output_dir str = data filename_prefix str = yellow_tripdata ) today_str = datetime now() strftime( %Y-%m-%d_%H%M%S ) filepath = os path join( output_dir f {filename_prefix}_{today_str} csv ) check_url_avaliable(url) df=read_parquet_from_url(url) output_file=write_data(df filepath) return output_file if name == main main() deploy(name= main ) # 基於官方 Prefect 3 最新版本鏡像 FROM prefecthq/prefect 3-latest WORKDIR /opt/prefect COPY pyproject toml uv lock / RUN uv sync ENV PATH= /opt/prefect/ venv/bin $PATH COPY flows /flows services postgres image postgres 14 environment POSTGRES_USER ${DB_USER} POSTGRES_PASSWORD ${DB_PASSWORD} POSTGRES_DB ${DB_NAME} volumes - postgres_data /var/lib/postgresql/data healthcheck test [ CMD-SHELL pg_isready -U ${DB_USER} ] interval 5s timeout 5s retries 5 redis image redis 7 volumes - redis_data /data healthcheck test [ CMD-SHELL redis-cli ping ] interval 5s timeout 5s retries 5 prefect-server image prefecthq/prefect 3-latest depends_on postgres condition service_healthy redis condition service_healthy environment PREFECT_API_DATABASE_CONNECTION_URL postgresql+asyncpg //${DB_USER} ${DB_PASSWORD}@postgres 5432/${DB_NAME} PREFECT_SERVER_API_HOST 0 0 0 0 PREFECT_MESSAGING_BROKER prefect_redis messaging PREFECT_MESSAGING_CACHE prefect_redis messaging PREFECT_REDIS_MESSAGING_HOST redis PREFECT_REDIS_MESSAGING_PORT 6379 PREFECT_REDIS_MESSAGING_DB 0 command prefect server start --no-services ports - 4200 4200 healthcheck test [ CMD python -c import urllib request as u; u urlopen('<http //localhost 4200/api/health>' timeout=1) ] interval 30s timeout 10s retries 3 start_period 60s prefect-services image prefecthq/prefect 3-latest depends_on prefect-server condition service_healthy environment PREFECT_API_DATABASE_CONNECTION_URL postgresql+asyncpg //${DB_USER} ${DB_PASSWORD}@postgres 5432/${DB_NAME} PREFECT_MESSAGING_BROKER prefect_redis messaging PREFECT_MESSAGING_CACHE prefect_redis messaging PREFECT_REDIS_MESSAGING_HOST redis PREFECT_REDIS_MESSAGING_PORT 6379 PREFECT_REDIS_MESSAGING_DB 0 command prefect server services start # prefect-worker # image prefecthq/prefect 3-latest # depends_on # prefect-server # condition service_healthy # environment # PREFECT_API_URL <http //prefect-server 4200/api> # command prefect worker start --pool local-pool # restart on-failure prefect-docker-worker image prefecthq/prefect 3-latest container_name prefect-docker-worker depends_on prefect-server condition service_healthy environment # Point the worker to the local Prefect API PREFECT_API_URL <http //prefect-server 4200/api> # Optional tuning # PREFECT_WORKER_PREFETCH_SECONDS 10 # PREFECT_WORKER_QUERY_SECONDS 15 volumes # Critical allow the worker to create Docker containers - /var/run/docker sock /var/run/docker sock # Use 'uv' to ensure the docker integration is available command > uv run --with 'prefect[docker]' prefect worker start --pool docker-pool restart on-failure volum
es postgres_data redis_data # Welcome to your prefect yaml file! You can use this file for storing and managing # configuration for deploying your flows We recommend committing this file to source # control along with your flow code # Generic metadata about this project name my_prefect_demo prefect-version 3 6 4 # build section allows you to manage and build docker images build - prefect_docker deployments steps build_docker_image id build_image requires prefect-docker>=0 3 1 image_name pohengchou/demo
p
@Marvin give me baked into docker image 's prefect.yaml dockerfile
m
✅ thought for 65.1 seconds
Here you go — a clean “baked image” setup with no git_clone at runtime. Dockerfile (baked code + deps into the image)
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FROM prefecthq/prefect:3.6.4-python3.11

WORKDIR /opt/prefect

# Copy dependency manifests first for better layer caching
COPY pyproject.toml uv.lock ./

# Install deps into system site-packages (no venv needed at runtime)
# Uses the locked deps to ensure reproducible builds
RUN python -m pip install -U pip uv \
 && uv export --locked --no-dev -o requirements.txt \
 && pip install -r requirements.txt \
 && rm -f requirements.txt

# Copy your flow code into the image
COPY flows ./flows
prefect.yaml (no pull section; deployment uses the baked image)
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name: my_prefect_demo
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: your-dockerhub-user/prefect-demo
    tag: v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# No pull section for baked-image method

deployments:
- name: my_baked_deployment
  # Use an absolute path that matches where you COPY your code in the Dockerfile
  entrypoint: /opt/prefect/flows/main.py:main
  work_pool:
    name: docker-pool
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: IfNotPresent
      # If your server and jobs share a named Docker network, include it (recommended on Linux)
      # networks:
      #   - prefect-network
      env:
        # If using a named Docker network with your server container, use the service name:
        # PREFECT_API_URL: <http://prefect-server:4200/api>
        # Otherwise, if you expose port 4200 on the host, use the host URL:
        PREFECT_API_URL: <http://localhost:4200/api>
Notes - No pull steps: your code and dependencies are already in the image. - If you’re running Prefect Server and the worker on a named Docker network (e.g., prefect-network), set job_variables.networks and point PREFECT_API_URL to http://prefect-server:4200/api. - For testing to avoid stale images, you can temporarily set image_pull_policy: Always. - Ensure your pyproject.toml lists pandas/pyarrow/requests/etc. If you don’t have a uv.lock yet, generate it locally with uv before building, or drop the “--locked” flag during early development.
p
@Marvin orker 'ProcessWorker f1447932-968e-46e0-8f38-b906e4dd17e8' submitting flow run '019ad88d-a462-722b-a556-8fc8d06eb8f9' 021556 PM prefect.flow_runs.worker Opening process... 021556 PM prefect.flow_runs.runner Completed submission of flow run '019ad88d-a462-722b-a556-8fc8d06eb8f9' 021556 PM prefect.flow_runs.worker Process for flow run 'scrupulous-hog' exited with status code: 2 021556 PM prefect.flow_runs.runner Reported flow run '019ad88d-a462-722b-a556-8fc8d06eb8f9' as crashed: Flow run process exited with non-zero status code 2. 021556 PM prefect.flow_runs.runner Loading flow to check for on_crashed hooks 021556 PM prefect.flow_runs.runner > Running git_clone step... 021556 PM prefect.flow_runs Invalid template: '{{ prefect.blocks.secret.gitlab-ssh-key.private_key }}'. Could not resolve the keypath in the block document data. 021556 PM prefect.flow_runs Runner failed to retrieve flow to execute on_crashed hooks for flow run UUID('019ad88d-a462-722b-a556-8fc8d06eb8f9'). Traceback (most recent call last): File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/deployments/steps/core.py", line 186, in run_steps step_output = await run_step(step, upstream_outputs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/deployments/steps/core.py", line 136, in run_step inputs = await resolve_block_document_references(inputs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/client/utilities.py", line 99, in with_injected_client return await fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/utilities/templating.py", line 317, in resolve_block_document_references updated_value = await resolve_block_document_references( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/client/utilities.py", line 99, in with_injected_client return await fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/utilities/templating.py", line 365, in resolve_block_document_references raise ValueError( ValueError: Invalid template: '{{ prefect.blocks.secret.gitlab-ssh-key.private_key }}'. Could not resolve the keypath in the block document data. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/runner/runner.py", line 1548, in _run_on_crashed_hooks flow = await load_flow_from_flow_run( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/client/utilities.py", line 69, in wrapper return await func(client, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/flows.py", line 2816, in load_flow_from_flow_run output = await run_steps( ^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/deployments/steps/core.py", line 231, in run_steps raise StepExecutionError(f"Encountered error while running {fqn}") from exc prefect.deployments.steps.core.StepExecutionError: Encountered error while running prefect.deployments.steps.git_clone
m
✅ thought for 42.6 seconds
Thanks for the logs — the crash is happening before your flow ever runs because the git_clone pull step can’t resolve your credentials template: Invalid template: '{{ prefect.blocks.secret.gitlab-ssh-key.private_key }}'. Could not resolve the keypath in the block document data. What that means - You’re referencing a Prefect Block in your prefect.yaml, but the keypath is wrong. - A Secret block’s field is named value, not private_key. So .private_key will always fail. - Also, your run was picked up by a ProcessWorker, not a DockerWorker; make sure the deployment targets the Docker work pool you have a worker for. Fix options (pick one) A) Use a Secret block with an access token (simplest HTTPS token) 1) Create a Secret block named gitlab-pat with your GitLab Personal Access Token (in UI: Blocks → Create → Secret). 2) Reference it correctly in prefect.yaml (note the .value):
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pull:
- prefect.deployments.steps.git_clone:
    id: clone
    repository: <https://gitlab.com/<group>/<repo>.git>
    branch: main
    credentials:
      access_token: "{{ prefect.blocks.secret.gitlab-pat.value }}"
B) Use a GitLab credentials block (requires prefect-gitlab) 1) Install the integration for the environment running prefect deploy: - uv run --with 'prefect[gitlab]' python -c "import prefect" 2) Create a GitLabCredentials block in the UI (Blocks → Create → GitLab Credentials) with your token. 3) Reference the block directly (no keypath) or its token field:
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pull:
- prefect.deployments.steps.git_clone:
    id: clone
    repository: <https://gitlab.com/<group>/<repo>.git>
    branch: main
    credentials: "{{ prefect.blocks.gitlab-credentials.my-gitlab-creds }}"
# or
#   credentials:
#     access_token: "{{ prefect.blocks.gitlab-credentials.my-gitlab-creds.token }}"
C) If you must use SSH keys - Put the private key into a Secret block (name e.g., gitlab-ssh-key) and reference .value: - Use an SSH URL: git@gitlab.com:<group>/<repo>.git - Then pass the key in the credentials per the step’s SSH parameters (varies by integration). If you want this path, tell me and I’ll give the exact snippet for SSH. Also fix the worker/pool mismatch - Your log shows ProcessWorker. If you intended Docker: - Ensure the deployment’s work_pool.name is your Docker pool (e.g., docker-pool) - Ensure a Docker worker is running for that pool: - In compose or: uv run --with 'prefect[docker]' prefect worker start --pool docker-pool - Verify: prefect worker ls Quick checks - List blocks to confirm names: prefect block ls - Inspect your Secret block fields: prefect block inspect secret/gitlab-pat - Redeploy after fixing prefect.yaml: prefect deploy -n <your-deployment-name> If you paste the current pull section from your prefect.yaml and tell me which auth you prefer (HTTPS token vs SSH key), I’ll return the exact, corrected snippet.
p
@Marvin Worker 'ProcessWorker f1447932-968e-46e0-8f38-b906e4dd17e8' submitting flow run '019ad890-ffee-72f5-b393-298647652cdb' 022053 PM prefect.flow_runs.worker Opening process... 022053 PM prefect.flow_runs.runner Process for flow run 'copper-mouflon' exited with status code: 2 022053 PM prefect.flow_runs.runner Completed submission of flow run '019ad890-ffee-72f5-b393-298647652cdb' 022053 PM prefect.flow_runs.worker Reported flow run '019ad890-ffee-72f5-b393-298647652cdb' as crashed: Flow run process exited with non-zero status code 2. 022053 PM prefect.flow_runs.runner Loading flow to check for on_crashed hooks 022053 PM prefect.flow_runs.runner > Running git_clone step... 022053 PM prefect.flow_runs git_clone() got an unexpected keyword argument 'known_hosts' 022053 PM prefect.flow_runs Runner failed to retrieve flow to execute on_crashed hooks for flow run UUID('019ad890-ffee-72f5-b393-298647652cdb'). Traceback (most recent call last): File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/deployments/steps/core.py", line 186, in run_steps step_output = await run_step(step, upstream_outputs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/deployments/steps/core.py", line 140, in run_step result = await from_async.call_soon_in_new_thread( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 374, in aresult return await asyncio.wrap_future(self.future) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/_internal/concurrency/calls.py", line 399, in _run_sync result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/_internal/compatibility/async_dispatch.py", line 94, in wrapper return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ TypeError: git_clone() got an unexpected keyword argument 'known_hosts' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/runner/runner.py", line 1548, in _run_on_crashed_hooks flow = await load_flow_from_flow_run( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/client/utilities.py", line 69, in wrapper return await func(client, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/flows.py", line 2816, in load_flow_from_flow_run output = await run_steps( ^^^^^^^^^^^^^^^^ File "/root/.cache/uv/archive-v0/VoeeYlon79CyjLxZuIA15/lib/python3.12/site-packages/prefect/deployments/steps/core.py", line 231, in run_steps raise StepExecutionError(f"Encountered error while running {fqn}") from exc prefect.deployments.steps.core.StepExecutionError: Encountered error while running prefect.deployments.steps.git_clone
m
✅ thought for 135.9 seconds
You’ve got two separate issues in that log: 1) The run was picked up by a ProcessWorker, not your Docker worker - If you intend Docker, make sure the deployment targets your Docker work pool and that a Docker worker is running for that pool. - Verify in the UI or with: - prefect worker ls - If it says ProcessWorker, update the deployment’s work_pool.name to your Docker pool (e.g., docker-pool) and redeploy. 2) Your git_clone step is passing an unsupported argument: known_hosts - In Prefect 3.x, prefect.deployments.steps.git_clone does NOT accept known_hosts (or ssh_key) parameters. - Valid parameters: repository, branch, commit_sha, include_submodules, directories, access_token, credentials. Correct ways to authenticate A) HTTPS with a Secret block (simplest) - Create a Secret block named gitlab-pat that contains a PAT with read_repository scope. - Reference it in your pull step using access_token:
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pull:
- prefect.deployments.steps.git_clone:
    id: clone
    repository: <https://gitlab.com/<group>/<repo>.git>
    branch: main
    access_token: "{{ prefect.blocks.secret.gitlab-pat }}"
B) HTTPS with a GitLabCredentials block (recommended if you use prefect-gitlab) - Create the block (Blocks → Create → GitLab Credentials) named my-gitlab-creds (token + optional base_url for self-hosted). - Reference the block as credentials (no field path):
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pull:
- prefect.deployments.steps.git_clone:
    id: clone
    repository: <https://gitlab.com/<group>/<repo>.git>
    branch: main
    credentials: "{{ prefect.blocks.gitlab-credentials.my-gitlab-creds }}"
C) SSH (no parameters in git_clone) - Use an SSH URL: git@gitlab.com:<group>/<repo>.git - Ensure the job runtime (container or process host) already has: - /home/<user>/.ssh/id_rsa (or another key) with 600 perms - /home/<user>/.ssh/known_hosts containing gitlab.com - Since git_clone has no known_hosts or ssh_key args, you must prepare SSH on the machine/image itself. Action checklist - Remove known_hosts (and any ssh_key) from your git_clone step — they’re not supported. - Choose A/B/C above and update your prefect.yaml pull step accordingly. - Ensure the deployment’s work_pool name is your Docker pool if you want Docker runs. - Redeploy: prefect deploy -n <your-deployment-name> If you paste your current pull section, I’ll rewrite it for your chosen auth (Secret block PAT vs GitLabCredentials vs SSH).
p
@Marvin # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: prefect_demo prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.git_clone: repository: git@gitlab.sltung.com.tw:t15989/docker_git.git branch: main ssh_private_key: "{{ prefect.blocks.secret.gitlab-ssh-key.value }}" # 確保主機指紋檢查被禁用 (防止第一次連線失敗) known_hosts: null # the deployments section allows you to provide configuration for deploying flows deployments: - name: my_first_deployment version: "1.0" tags: [demo, docker] description: "我的第一個deployment" schedule: cron: "*/1 * * * *" timezone: "Asia/Taipei" flow_name: main entrypoint: flows/flow.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' command: "sh -lc '. .venv/bin/activate && prefect flow-run execute'" env: PREFECT_API_URL: "http://host.docker.internal:4200/api"
m
✅ thought for 59.7 seconds
Thanks for sharing your prefect.yaml. The crash is due to unsupported fields in your git_clone step and a couple of formatting issues. Key points: - git_clone DOES NOT accept ssh_private_key or known_hosts. Those will raise errors like “unexpected keyword argument.” - For SSH, Prefect expects the job runtime to already have SSH configured (key + known_hosts). No inline params. - If you prefer HTTPS, pass a token via a Secret block or a GitLabCredentials block. - Your YAML also contains HTML-escaped characters (e.g., >=, &&), and angle-bracketed URLs; remove those. Pick one auth method below and use the corrected template. Option A (recommended): HTTPS with a Secret block (simplest) - Create a Secret block (e.g., secret/gitlab-pat) containing a PAT with read_repository scope. - Use that token in git_clone via access_token.
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name: prefect_demo
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

pull:
- prefect.deployments.steps.git_clone:
    id: clone
    repository: <https://gitlab.sltung.com.tw/t15989/docker_git.git>
    branch: main
    access_token: "{{ prefect.blocks.secret.gitlab-pat.value }}"

# If you need Python deps installed from the repo at runtime, add:
- prefect.deployments.steps.run_shell_script:
    directory: "{{ clone.directory }}"
    script: |
      python -m pip install -U uv
      uv sync

deployments:
- name: my_first_deployment
  description: "我的第一個deployment"
  schedule:
    cron: "*/1 * * * *"
    timezone: "Asia/Taipei"
  flow_name: main
  entrypoint: flows/flow.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      # For Linux with a named network, prefer:
      # networks: ["prefect-network"]
      # env: { PREFECT_API_URL: "<http://prefect-server:4200/api>" }
      env:
        PREFECT_API_URL: "<http://localhost:4200/api>"
      # Remove venv activation unless you actually created one; default command is fine:
      # command: "prefect flow-run execute"
Option B: HTTPS with a GitLabCredentials block - Create a GitLab Credentials block (gitlab-credentials/my-gitlab-creds) with your PAT and optional base_url. - Requires installing the integration where you run prefect deploy (prefect[gitlab]).
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pull:
- prefect.deployments.steps.git_clone:
    id: clone
    repository: <https://gitlab.sltung.com.tw/t15989/docker_git.git>
    branch: main
    credentials: "{{ prefect.blocks.gitlab-credentials.my-gitlab-creds }}"
Option C: SSH (no inline key or known_hosts params) - Use an SSH URL: - repository: git@gitlab.sltung.com.tw:t15989/docker_git.git - Ensure the job image/container already has SSH configured: - /home/prefect/.ssh/id_rsa (0600) - /home/prefect/.ssh/known_hosts with gitlab.sltung.com.tw host key - No ssh_private_key or known_hosts fields in git_clone. If you need help baking/mounting these, say so and I’ll provide a safe pattern. Other fixes you should make now: - Replace all HTML escapes in YAML: - requires: prefect-docker>=0.3.1 - Remove <...> wrappers around URLs - Replace 
&&
with 
&&
anywhere you use it - The run was picked up by a ProcessWorker earlier; if you intend Docker: - Ensure the deployment’s work_pool.name = your Docker pool (docker-pool) - Ensure a Docker worker is running for that pool - Verify with 
prefect worker ls
If you confirm which auth method you’re choosing (HTTPS token vs GitLabCredentials vs SSH), I’ll return the exact final prefect.yaml and, if needed, a Dockerfile snippet to preconfigure SSH for the job image.
p
@Marvin Worker 'DockerWorker 588b3c08-6e05-495b-bda4-04f0f50557ec' submitting flow run '019ad9de-1643-7286-b3b8-9487d5ec5df7' 082856 PM prefect.flow_runs.worker Completed submission of flow run '019ad9de-1643-7286-b3b8-9487d5ec5df7' 082856 PM prefect.flow_runs.worker Reported flow run '019ad9de-1643-7286-b3b8-9487d5ec5df7' as crashed: Flow run infrastructure exited with non-zero status code 1. # Dockerfile FROM prefecthq/prefect:3-latest WORKDIR /opt/prefect COPY pyproject.toml uv.lock ./ RUN uv sync ENV PATH="/opt/prefect/.venv/bin:$PATH" COPY flows/ ./flows/ # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: prefect_docker prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo2 tag: test-v1 dockerfile: auto # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/prefect_docker # the deployments section allows you to provide configuration for deploying flows deployments: - name: my-deployment version: '1.0.0' tags: [baked-docker] description: docker baked deployment schedule: cron: "* * * * *" timezone: "Asia/Taipei" flow_name: null entrypoint: flows/flow.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image }}' why? 喔i also check path: (prefect_docker) pohengchou@DESKTOP-FD8AIE7:~/repos/prefect_docker$ docker run -it pohengchou/demo2:test-v1 /bin/bash root@9686e20f9c30:/opt/prefect/prefect_docker# ls Dockerfile README.md dockerfile flows prefect.yaml pyproject.toml uv.lock root@9686e20f9c30:/opt/prefect/prefect_docker# cd .. root@9686e20f9c30:/opt/prefect# ls '=1:2.47.3' entrypoint.sh prefect_docker root@9686e20f9c30:/opt/prefect# cd .. root@9686e20f9c30:/opt# ls prefect it's correct
m
✅ thought for 55.5 seconds
Thanks for all the detail — I can see exactly why this run is crashing. What’s wrong in your current setup - Wrong image reference in job_variables: - You used 
image: '{{ build_image.image }}'
— there is no 
image
key in the build step output. - Correct is 
image: '{{ build_image.image_name }}:{{ build_image.tag }}'
- Your build step uses 
dockerfile: auto
- That ignores your custom Dockerfile and auto-builds a generic image that copies your repo to 
/opt/prefect/<project-name>
. - If you want to use the Dockerfile you showed, set 
dockerfile: Dockerfile
. - You’re baking deps into a venv with 
uv sync
, but not activating that venv at runtime - PATH helps find executables but does not switch Python site-packages for the Prefect runner unless 
prefect
runs from the venv. Safer: install into system site-packages, or explicitly activate the venv in the job command. - You kept a pull step that only sets CWD - For baked images, you don’t need any pull section. And if your job accidentally used the base Prefect image (because of the wrong image reference), that directory won’t exist anyway. Two working fixes — pick one Option A: Baked into system site-packages (simplest) - Use your Dockerfile and install into system site-packages so no venv activation is needed. Dockerfile
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FROM prefecthq/prefect:3.6.4-python3.11
WORKDIR /opt/prefect

COPY pyproject.toml uv.lock ./
RUN python -m pip install -U pip uv \
 && uv export --locked --no-dev -o requirements.txt \
 && pip install -r requirements.txt \
 && rm requirements.txt

COPY flows ./flows
prefect.yaml
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name: prefect_docker
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo2
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# No pull section for baked images

deployments:
- name: my-deployment
  description: docker baked deployment
  schedule:
    cron: "* * * * *"
    timezone: "Asia/Taipei"
  entrypoint: /opt/prefect/flows/flow.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: Always
      # If your server and jobs share a named network, include it and use the service URL:
      # networks: ["prefect-network"]
      # env: { PREFECT_API_URL: "<http://prefect-server:4200/api>" }
      env:
        PREFECT_API_URL: "<http://localhost:4200/api>"
Option B: Keep a venv, but activate it at runtime - If you prefer 
uv sync
venv, activate it in the job command. Dockerfile
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FROM prefecthq/prefect:3.6.4-python3.11
WORKDIR /opt/prefect

COPY pyproject.toml uv.lock ./
RUN python -m pip install -U pip uv && uv sync --frozen

COPY flows ./flows
prefect.yaml
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name: prefect_docker
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo2
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

deployments:
- name: my-deployment
  entrypoint: /opt/prefect/flows/flow.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: Always
      command: "sh -lc '. /opt/prefect/.venv/bin/activate && prefect flow-run execute'"
      env:
        PREFECT_API_URL: "<http://localhost:4200/api>"
Why your container ls looked “correct”
- Because 
dockerfile: auto
copied your repo under 
/opt/prefect/prefect_docker
, not because your custom Dockerfile ran. - If you want your custom Dockerfile behavior, set 
dockerfile: Dockerfile
and use absolute entrypoint 
/opt/prefect/flows/flow.py:main
. Next steps - Update prefect.yaml with one of the options above (fix image reference; remove pull; set dockerfile to Dockerfile). - Rebuild/push and redeploy: - 
docker build -t pohengchou/demo2:test-v1 .
- 
docker push pohengchou/demo2:test-v1
- 
prefect deploy -n my-deployment
- If it still crashes, grab the job container logs: - From the flow run → Infrastructure tab → container ID → 
docker logs <id>
- Or enable debug logging by adding: - 
PREFECT_LOGGING_LEVEL: DEBUG
in job_variables.env This should resolve the exit code 1 you’re seeing.
p
@Marvin # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: prefect_docker prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo2 tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # pull section allows you to provide instructions for cloning this project in remote locations pull: - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/ # the deployments section allows you to provide configuration for deploying flows deployments: - name: my-deployment version: '1.0.0' tags: [baked-docker] description: docker baked deployment schedule: cron: "* * * * *" timezone: "Asia/Taipei" flow_name: null entrypoint: flows/flow.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' command: "sh -lc '. /opt/prefect/.venv/bin/activate && prefect flow-run execute'" # Dockerfile FROM prefecthq/prefect:3-latest WORKDIR /opt/prefect COPY pyproject.toml uv.lock ./ RUN uv sync COPY flows/ ./flows/
m
✅ thought for 31.9 seconds
You’re super close. For a baked-image deployment, you do NOT need a pull section, and it’s safest to use an absolute entrypoint that matches where you COPY your code in the image. Here’s a clean, working pair you can use as-is. Dockerfile (bakes deps into the image; no venv needed at runtime)
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FROM prefecthq/prefect:3.6.4-python3.11  # pin to match your code/lock; avoid :latest for reproducibility

WORKDIR /opt/prefect

# Copy dependency manifests first for better layer caching
COPY pyproject.toml uv.lock ./

# Install deps into system site-packages (no venv to activate at runtime)
RUN python -m pip install -U pip uv \
 && uv export --locked --no-dev -o requirements.txt \
 && pip install -r requirements.txt \
 && rm -f requirements.txt

# Copy your flow code into the image
COPY flows ./flows
prefect.yaml (baked image; no pull; absolute entrypoint; correct image reference)
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name: prefect_docker
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo2
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# No pull section for baked-image deployments

deployments:
- name: my-deployment
  description: docker baked deployment
  schedule:
    cron: "* * * * *"
    timezone: "Asia/Taipei"
  # Use an absolute path that matches the Dockerfile COPY location
  entrypoint: /opt/prefect/flows/flow.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      # IMPORTANT: reference name + tag from the build step
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      # During testing, force fresh pulls to avoid stale images
      image_pull_policy: Always
      # If your Prefect Server + worker run on a named Docker network (recommended on Linux),
      # attach jobs to that network and use the service URL:
      # networks:
      #   - prefect-network
      # env:
      #   PREFECT_API_URL: <http://prefect-server:4200/api>

      # If you're exposing 4200 on the host and not using a shared network, use the host URL:
      env:
        PREFECT_API_URL: <http://localhost:4200/api>
What changed vs your files - Removed pull (no git_clone for baked images). - Fixed the image reference to use 
image_name:tag
from the build step. - Switched to installing deps into system site-packages at build time (no need to activate a venv). - Made entrypoint absolute: 
/opt/prefect/flows/flow.py:main
. - Replaced HTML escapes (
>=
and 
&&
) with real characters. - Added 
image_pull_policy: Always
so you don’t hit stale images while testing. Run it - Ensure your CLI points at your server: 
prefect config set PREFECT_API_URL=<http://localhost:4200/api>
- Create the pool once: 
prefect work-pool create docker-pool --type docker
- Build/push/deploy: - 
prefect deploy -n my-deployment
- Trigger a run from the UI or: - 
prefect deployment run "prefect_docker/my-deployment"
If your server and worker are in docker-compose on a named network (e.g., 
prefect-network
), uncomment 
networks
and set 
PREFECT_API_URL
to 
<http://prefect-server:4200/api>
. If it still exits with code 1, grab the job container logs from the run’s Infrastructure tab and paste them here—I’ll pinpoint the exact error.
p
@Marvin services: postgres: image: postgres:14 environment: POSTGRES_USER: prefect POSTGRES_PASSWORD: prefect POSTGRES_DB: prefect volumes: - postgres_data:/var/lib/postgresql/data healthcheck: test: ["CMD-SHELL", "pg_isready -U prefect"] interval: 5s timeout: 5s retries: 5 redis: image: redis:7 volumes: - redis_data:/data healthcheck: test: ["CMD-SHELL", "redis-cli ping"] interval: 5s timeout: 5s retries: 5 prefect-server: image: prefecthq/prefect:3-latest depends_on: postgres: condition: service_healthy redis: condition: service_healthy environment: PREFECT_API_DATABASE_CONNECTION_URL: postgresql+asyncpg://prefect:prefect@postgres:5432/prefect PREFECT_SERVER_API_HOST: 0.0.0.0 PREFECT_MESSAGING_BROKER: prefect_redis.messaging PREFECT_MESSAGING_CACHE: prefect_redis.messaging PREFECT_REDIS_MESSAGING_HOST: redis PREFECT_REDIS_MESSAGING_PORT: 6379 PREFECT_REDIS_MESSAGING_DB: 0 PREFECT_UI_ENABLED: "true" # Explicitly tell the UI where the API lives from your browser's perspective PREFECT_UI_API_URL: http://localhost:4200/api command: prefect server start --host 0.0.0.0 ports: - "4200:4200" healthcheck: test: ["CMD", "python", "-c", "import urllib.request as u; u.urlopen('http://localhost:4200/api/health', timeout=1)"] interval: 30s timeout: 10s retries: 3 start_period: 60s prefect-services: image: prefecthq/prefect:3-latest depends_on: prefect-server: condition: service_healthy environment: PREFECT_API_DATABASE_CONNECTION_URL: postgresql+asyncpg://prefect:prefect@postgres:5432/prefect PREFECT_MESSAGING_BROKER: prefect_redis.messaging PREFECT_MESSAGING_CACHE: prefect_redis.messaging PREFECT_REDIS_MESSAGING_HOST: redis PREFECT_REDIS_MESSAGING_PORT: 6379 PREFECT_REDIS_MESSAGING_DB: 0 command: prefect server services start prefect-docker-worker: image: prefecthq/prefect:3-latest container_name: prefect-docker-worker depends_on: prefect-server: condition: service_healthy environment: # Point the worker to the local Prefect API PREFECT_API_URL: http://prefect-server:4200/api # Optional tuning: # PREFECT_WORKER_PREFETCH_SECONDS: "10" # PREFECT_WORKER_QUERY_SECONDS: "15" volumes: # Critical: allow the worker to create Docker containers - /var/run/docker.sock:/var/run/docker.sock # Use 'uv' to ensure the docker integration is available command: > uv run --with 'prefect[docker]' prefect worker start --pool docker-pool restart: on-failure volumes: postgres_data: redis_data: this is my docker-compose.yaml so what network ?
m
✅ thought for 34.4 seconds
Great question. Right now your compose file doesn’t declare a named network, so Docker Compose creates a default one named <project>_default. Your server and worker containers are on that network, but flow-run containers launched by the Docker worker are NOT automatically attached unless you tell Prefect to attach them. That’s why jobs can’t resolve http://prefect-server:4200/api unless they’re on the same user-defined network. You have two clean options: Option A (recommended): define a named network and use it everywhere - Add a named network to your compose and attach both server and worker - Tell Prefect to attach flow-run containers to that same network via job_variables.networks (in prefect.yaml or work pool base job template) Compose:
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version: "3.9"

networks:
  prefect:
    name: prefect-network
    driver: bridge

services:
  postgres:
    image: postgres:14
    networks: [prefect]
    ...
  redis:
    image: redis:7
    networks: [prefect]
    ...
  prefect-server:
    image: prefecthq/prefect:3-latest
    networks: [prefect]
    environment:
      PREFECT_SERVER_API_HOST: 0.0.0.0
    command: prefect server start --host 0.0.0.0
    ports: ["4200:4200"]
    ...
  prefect-docker-worker:
    image: prefecthq/prefect:3-latest
    networks: [prefect]
    environment:
      PREFECT_API_URL: <http://prefect-server:4200/api>
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    command: >
      uv run --with 'prefect[docker]'
      prefect worker start --pool docker-pool
prefect.yaml (for your baked image deployment):
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deployments:
  - name: my-deployment
    entrypoint: /opt/prefect/flows/flow.py:main
    work_pool:
      name: docker-pool
      job_variables:
        image: your-registry/your-image:tag
        networks:
          - prefect-network
        env:
          PREFECT_API_URL: <http://prefect-server:4200/api>
Option B: use the Compose default network - If you don’t declare networks, Compose creates one named <project>_default (project = compose project name, typically the folder name). - Find it and set that in job_variables.networks. Find your project network name: - It’s usually “<folder>_default”. Confirm with: - docker network ls | grep _default - or: docker compose ls (to see the project name), then use <project>_default Then in prefect.yaml:
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job_variables:
  networks:
    - <yourproject>_default
  env:
    PREFECT_API_URL: <http://prefect-server:4200/api>
Notes - Do not use http://localhost:4200/api inside job containers; “localhost” is the container itself. Use the service name (prefect-server) on a shared network or attach to the host via other methods. - If you stick with baked images, remove the pull section from prefect.yaml; it isn’t needed. - During testing, consider image_pull_policy: Always to avoid stale images. If you share the output of: - docker compose ls (to confirm your project name) - docker network ls | grep _default I’ll tell you exactly what to put in job_variables.networks.
p
@Marvin FileNotFoundError: [Errno 2] No such file or directory: '/opt/prefect/prefect_docker/flows/flow.py' An exception occurred. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/opt/prefect/flows/flow.py' # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: prefect_docker prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo2 tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # # pull section allows you to provide instructions for cloning this project in remote locations # pull: # - prefect.deployments.steps.set_working_directory: # directory: /opt/prefect/ # the deployments section allows you to provide configuration for deploying flows deployments: - name: my-deployment version: '1.0.0' tags: [baked-docker] description: docker baked deployment schedule: cron: "* * * * *" timezone: "Asia/Taipei" flow_name: null entrypoint: /opt/prefect/prefect_docker/flows/flow.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' command: "sh -lc '. /opt/prefect/.venv/bin/activate && prefect flow-run execute'" image_pull_policy: Always networks: - prefect-network env: PREFECT_API_URL: http://prefect-server:4200/api # # Dockerfile # FROM prefecthq/prefect:3-latest # WORKDIR /opt/prefect # COPY pyproject.toml uv.lock ./ # RUN uv sync # COPY flows/ ./flows/ FROM prefecthq/prefect:3.6.4-python3.11 # pin to match your code/lock; avoid :latest for reproducibility WORKDIR /opt/prefect # Copy dependency manifests first for better layer caching COPY pyproject.toml uv.lock ./ # Install deps into system site-packages (no venv to activate at runtime) RUN python -m pip install -U pip uv \ && uv export --locked --no-dev -o requirements.txt \ && pip install -r requirements.txt \ && rm -f requirements.txt # Copy your flow code into the image COPY flows ./flows
m
✅ thought for 24.7 seconds
The FileNotFoundError is because your entrypoint path doesn’t match what your Dockerfile actually copies. - Your Dockerfile copies code to /opt/prefect/flows - Your prefect.yaml entrypoint points to /opt/prefect/prefect_docker/flows/flow.py:main Fix the entrypoint to /opt/prefect/flows/flow.py:main, and remove the venv activation command (you’re installing into system site-packages, so there’s no .venv to activate). Also remove HTML escapes and angle-bracketed URLs. Use these corrected files: Dockerfile (baked deps into system site-packages)
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FROM prefecthq/prefect:3.6.4-python3.11

WORKDIR /opt/prefect

# Copy dependency manifests first for caching
COPY pyproject.toml uv.lock ./

# Install deps into system site-packages (no venv needed at runtime)
RUN python -m pip install -U pip uv \
 && uv export --locked --no-dev -o requirements.txt \
 && pip install -r requirements.txt \
 && rm -f requirements.txt

# Copy your flow code into the image
COPY flows ./flows
prefect.yaml (no pull, absolute entrypoint, correct image reference)
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name: prefect_docker
prefect-version: 3.6.4

build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: pohengchou/demo2
    tag: test-v1
    dockerfile: Dockerfile

push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# No pull section for baked-image deployments

deployments:
- name: my-deployment
  description: docker baked deployment
  schedule:
    cron: "* * * * *"
    timezone: "Asia/Taipei"
  entrypoint: /opt/prefect/flows/flow.py:main
  work_pool:
    name: docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: Always
      networks:
        - prefect-network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
Checklist - Ensure flows/flow.py exists in your repo and defines a function 
main
. - Make sure .dockerignore is not excluding flows/ or pyproject.toml/uv.lock. - Your docker-compose must define the named network prefect-network and attach both prefect-server and the worker to it. The job container will join it via job_variables.networks. Rebuild, deploy, test - Rebuild and push (or let prefect deploy do it): - 
docker build -t pohengchou/demo2:test-v1 .
- 
docker push pohengchou/demo2:test-v1
- Deploy: - 
prefect config set PREFECT_API_URL=<http://localhost:4200/api>
- 
prefect work-pool create docker-pool --type docker
(once) - 
prefect deploy -n my-deployment
- Run a quick image sanity check:
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docker run --rm --network prefect-network pohengchou/demo2:test-v1 \
    sh -lc "python -c 'import pandas, pyarrow; import runpy; runpy.run_path(\"/opt/prefect/flows/flow.py\")' && echo OK"
If you still see a crash, grab the job container logs from the run’s Infrastructure tab and paste them here so I can pinpoint the exact error.
p
@Marvin return io.open(self, mode, buffering, encoding, errors, newline) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/opt/prefect/flows/flow.py' An exception occurred. # Welcome to your prefect.yaml file! You can use this file for storing and managing # configuration for deploying your flows. We recommend committing this file to source # control along with your flow code. # Generic metadata about this project name: prefect_docker prefect-version: 3.6.4 # build section allows you to manage and build docker images build: - prefect_docker.deployments.steps.build_docker_image: id: build_image requires: prefect-docker>=0.3.1 image_name: pohengchou/demo2 tag: test-v1 dockerfile: Dockerfile # push section allows you to manage if and how this project is uploaded to remote locations push: - prefect_docker.deployments.steps.push_docker_image: requires: prefect-docker>=0.3.1 image_name: '{{ build_image.image_name }}' tag: '{{ build_image.tag }}' # # pull section allows you to provide instructions for cloning this project in remote locations # pull: # - prefect.deployments.steps.set_working_directory: # directory: /opt/prefect/ # the deployments section allows you to provide configuration for deploying flows deployments: - name: my-deployment version: '1.0.0' tags: [baked-docker] description: docker baked deployment schedule: cron: "* * * * *" timezone: "Asia/Taipei" flow_name: null entrypoint: /opt/prefect/flows/flow.py:main parameters: {} work_pool: name: docker-pool work_queue_name: default job_variables: image: '{{ build_image.image_name }}:{{ build_image.tag }}' image_pull_policy: Always networks: - prefect-network env: PREFECT_API_URL: http://prefect-server:4200/api
m
✅ thought for 37.5 seconds
The error is just a path mismatch between your entrypoint and what actually exists in the baked image. Right now Prefect is trying both: - /opt/prefect/prefect_docker/flows/flow.py (old auto build layout) - /opt/prefect/flows/flow.py (your current custom Dockerfile layout) One of these is wrong for your image. Let’s verify and fix. Quick verification on your image Run these on your host to see what’s actually in the image tag your deployment uses:
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docker pull pohengchou/demo2:test-v1
docker run --rm -it pohengchou/demo2:test-v1 sh -lc "python -V; ls -la /opt/prefect; echo; ls -la /opt/prefect/flows || true"
- If you see flows/main.py, use /opt/prefect/flows/main.py:main - If you see flows/flow.py, use /opt/prefect/flows/flow.py:main - If flows/ is missing entirely, your Dockerfile didn’t COPY it (check .dockerignore and build logs) Two working patterns (pick one) Option A: Keep your current Dockerfile that copies to /opt/prefect/flows - Dockerfile stays:
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FROM prefecthq/prefect:3.6.4-python3.11
WORKDIR /opt/prefect

COPY pyproject.toml uv.lock ./
RUN python -m pip install -U pip uv \
 && uv export --locked --no-dev -o requirements.txt \
 && pip install -r requirements.txt \
 && rm -f requirements.txt

COPY flows ./flows
- prefect.yaml must use the absolute path that matches the file name in flows:
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deployments:
  - name: my-deployment
    entrypoint: /opt/prefect/flows/flow.py:main        # or main.py:main if that’s your file
    work_pool:
      name: docker-pool
      job_variables:
        image: '{{ build_image.image_name }}:{{ build_image.tag }}'
        image_pull_policy: Always
        networks: [prefect-network]
        env:
          PREFECT_API_URL: <http://prefect-server:4200/api>
Option B: Keep your old auto layout (not recommended) If you deliberately want /opt/prefect/prefect_docker/flows/, change the Dockerfile to copy there and set entrypoint to that path. But it’s simpler to use /opt/prefect/flows. Common pitfalls to double-check - Wrong filename in entrypoint: you used flows/flow.py:main but your file may be flows/main.py:main - .dockerignore excludes flows/ so it never gets copied - The build step in prefect.yaml uses the wrong Dockerfile (you set dockerfile: Dockerfile — good). Don’t use dockerfile: auto if you want your custom layout - Stale image: use image_pull_policy: Always while testing Finally, remove HTML escapes/angle brackets in YAML - Change 
requires: prefect-docker>=0.3.1
to 
requires: prefect-docker>=0.3.1
- Change 
PREFECT_API_URL: <http://prefect-server:4200/api>
to 
PREFECT_API_URL: <http://prefect-server:4200/api>
If the ls output from the docker run shows a different layout or missing flows, paste that output here and I’ll give you the exact entrypoint or Dockerfile COPY fix.
p
@Marvin (prefect_docker) pohengchou@DESKTOP-FD8AIE7:~/repos/prefect_docker$ docker run --rm -it pohengchou/demo2:test-v1 sh -lc "python -V; ls -la /opt/prefect; echo; ls -la /opt/prefect/flows || true" Python 3.12.12 total 496 drwxr-xr-x 1 root root 4096 Dec 1 13:01 . drwxr-xr-x 1 root root 4096 Nov 21 18:04 .. drwxr-xr-x 5 root root 4096 Dec 1 13:00 .venv -rw-r--r-- 1 root root 29863 Nov 21 18:05 '=1:2.47.3' -rwxr-xr-x 1 root root 485 Nov 21 18:04 entrypoint.sh drwxr-xr-x 3 root root 4096 Dec 1 13:01 flows -rw-r--r-- 1 root root 390 Dec 1 11:23 pyproject.toml -rw-r--r-- 1 root root 447056 Dec 1 11:23 uv.lock total 16 drwxr-xr-x 3 root root 4096 Dec 1 13:01 . drwxr-xr-x 1 root root 4096 Dec 1 13:01 .. drwxr-xr-x 2 root root 4096 Dec 1 11:39 pycache -rw-r--r-- 1 root root 2836 Dec 1 11:28 flow.py
m
✅ thought for 54.4 seconds
Thanks for the 
docker run
check — that helps. Your image clearly has: - Python 3.12.12 - A venv at /opt/prefect/.venv (created by 
uv sync
) - Code at /opt/prefect/flows/flow.py Two things to fix: 1) Activate the venv at runtime (or install into system site-packages) - Prefect does not auto-activate /opt/prefect/.venv. If your deps were installed into that venv, the flow will import with the system Python and crash (exit 1) when importing packages. - Either activate the venv via job command, OR rebuild to install into system site-packages. Option A: activate venv (fastest) - In your prefect.yaml deployment, add a command and use real && (no HTML escape):
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deployments:
- name: my-deployment
  entrypoint: /opt/prefect/flows/flow.py:main
  work_pool:
    name: docker-pool
    job_variables:
      image: '{{ build_image.image_name }}:{{ build_image.tag }}'
      image_pull_policy: Always
      networks:
        - prefect-network
      env:
        PREFECT_API_URL: <http://prefect-server:4200/api>
      command: "sh -lc '. /opt/prefect/.venv/bin/activate && prefect flow-run execute'"
Option B: rebuild image with system installs (no venv) - Update Dockerfile, rebuild, and you can omit the command:
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FROM prefecthq/prefect:3.6.4-python3.11  # or pin a 3.12 tag and regenerate lock
WORKDIR /opt/prefect
COPY pyproject.toml uv.lock ./
RUN python -m pip install -U pip uv \
 && uv export --locked --no-dev -o requirements.txt \
 && pip install -r requirements.txt \
 && rm -f requirements.txt
COPY flows ./flows
2) Align Python version with your lock - Your image is running Python 3.12.12. If your uv.lock was generated for 3.11, resolve for 3.12 or switch the base image to a 3.11 tag (e.g., 
prefect:3.6.4-python3.11
). Mismatched locks can cause runtime import errors. Also double-check - Entrypoint path: you set 
/opt/prefect/flows/flow.py:main
— that matches your image (good). - Remove HTML escapes and angle brackets in YAML (>=, &&, plain URLs). - Ensure the network exists: your compose should declare 
prefect-network
and attach both 
prefect-server
and the worker. The job joins it via job_variables.networks. After changes - 
prefect deploy -n my-deployment
- Trigger a run. If it still fails, grab the job container logs from the run’s Infrastructure tab or via: - 
docker ps -a --filter "label=<http://prefect.io/flow-run-id=<flow-run-id|prefect.io/flow-run-id=<flow-run-id>>"
- 
docker logs <container_id>
If you prefer the system-install route, I can tweak your Dockerfile to pin specific versions and confirm the exact base tag for your lock.
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