Thanks for the detailed logs — the crash is because the container that ran your flow doesn’t have pandas installed: ModuleNotFoundError: No module named 'pandas' You’re using a Docker work pool and I can see the git_clone pull step ran, but nothing installed your project’s dependencies inside the container. In Prefect 3.x you have three supported ways to make packages like pandas available: Option A (quickest): Install from requirements during pull Add the pip_install_requirements pull step to your prefect.yaml so dependencies are installed after git_clone and before your flow loads.

pull:
  - prefect.deployments.steps.git_clone:
      id: repo
      repository: <https://github.com/your-org/your-repo.git>
      # branch: main   # optional
  - prefect.deployments.steps.pip_install_requirements:
      directory: "{{ repo.directory }}"
      requirements_file: requirements.txt  # update if your file lives elsewhere

deployments:
  - name: my-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: your-docker-pool

Notes: - Step name is exactly: prefect.deployments.steps.pip_install_requirements - It expects a requirements.txt. If you only have a pyproject.toml, consider Option B or C. Option B: Install at container startup via environment variable If you’re using a Prefect base image (e.g., prefecthq/prefect:3-latest), you can have the container install packages on startup using EXTRA_PIP_PACKAGES. - Per-deployment (recommended for targeted changes):

deployments:
  - name: my-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: your-docker-pool
      job_variables:
        env:
          EXTRA_PIP_PACKAGES: "pandas==2.2.2 numpy"

- Or set this at the work pool level (applies to all jobs in that pool) via the UI by editing the pool’s base job template env and adding EXTRA_PIP_PACKAGES. Option C (best for production): Bake deps into your Docker image Build and use a Docker image that already contains your code and dependencies. You can do this in prefect.yaml with the docker build step and point your deployment at that image:

build:
  - prefect_docker.deployments.steps.build_docker_image:
      requires: prefect-docker>=0.3.0
      image_name: your-registry/your-image
      tag: latest
      dockerfile: auto        # Prefect will generate a Dockerfile
      # By default this installs from requirements.txt in your project root

deployments:
  - name: my-deployment
    entrypoint: flows/flow.py:your_flow
    work_pool:
      name: your-docker-pool
      job_variables:
        image: "{{ build-image.image }}"

This yields the fastest, most reproducible runs. What I recommend right now - If you just need to unblock quickly: add Option A’s pip_install_requirements pull step (since you’re already using git_clone), or Option B’s EXTRA_PIP_PACKAGES with pandas. - Longer term: switch to Option C to bake your requirements into the image. A couple of quick questions to tailor the fix: - Do you have a requirements.txt in the repo (and where is it located)? - Are you deploying with a prefect.yaml (looks like it), or via Python’s flow.deploy? - Do you want a quick fix (runtime install) or a more robust image build setup? Share your current prefect.yaml (redact secrets) and I can suggest the exact minimal changes.