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<@ULVA73B9P> In the deployment yaml file, there is...
# ask-marvin
j
@Marvin In the deployment yaml file, there is a step pip_install_requirements to install requirements from a requirements.txt file. How to install requirements from a pyproject.toml ?
m
✅ thought for 47.3 seconds
Based on the research and GitHub issues, I can provide some additional important details about using pyproject.toml with Prefect deployments: 1. Alternative Approach: If you're using a modern Python environment manager like 
uv
, you can also specify your dependencies directly in the deployment YAML with more granular control:
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yaml
deployments:
  - name: your-deployment-name
    dependencies:
      - source: pyproject.toml
        groups: ["main"]  # or specific groups you need
      - source: package  # for direct package specifications
        specs: ["pandas>=2.0.0"]
2. Best Practices: - Keep your pyproject.toml in the root directory of your project - Make sure all required dependencies are properly defined in the 
[project]
section - Use 
[project.optional-dependencies]
for organizing different sets of dependencies - If you're using development dependencies, put them in 
[project.optional-dependencies.dev]
3. Important Note: If you're using a Docker-based deployment, make sure your Dockerfile correctly copies the pyproject.toml file and installs the dependencies. Here's a simple example:
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dockerfile
COPY pyproject.toml .
RUN pip install -e .  # This will install from pyproject.toml
4. Troubleshooting: If you encounter any issues with dependencies not being installed: - Verify that your pyproject.toml is being correctly copied to the working directory - Check that all dependency specifications are valid - Ensure your deployment configuration correctly references the pyproject.toml location The pyproject.toml approach is generally more robust than requirements.txt as it provides better dependency resolution and more structured way to manage different groups of dependencies. It's also the more modern approach recommended by Python packaging standards. Would you like me to provide more specific details about any part of this setup, or do you have a particular use case you'd like me to address?
j
@Marvin How to change the working directory ?
m
✅ thought for 51.2 seconds
There are several ways to change the working directory in a Prefect deployment. Here are the main approaches: 1. Using the 
set_working_directory
deployment step in prefect.yaml:
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yaml
deployments:
  - name: my-deployment
    entrypoint: flows/my_flow.py:my_flow
    pull:
      - prefect.deployments.steps.set_working_directory:
          directory: "/path/to/your/working/directory"
2. Using relative paths in your deployment configuration:
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yaml
deployments:
  - name: my-deployment
    entrypoint: ./relative/path/to/flows/my_flow.py:my_flow
    work_pool:
      name: my-pool
3. Setting the working directory when running a flow locally:
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python
from prefect import flow
import os

@flow
def my_flow():
    # Change working directory at runtime
    os.chdir("/path/to/your/working/directory")
    # Your flow code here
4. Using absolute paths in your deployment configuration:
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yaml
deployments:
  - name: my-deployment
    entrypoint: /absolute/path/to/flows/my_flow.py:my_flow
Important considerations: 1. Docker Environments: If you're running in Docker, make sure the working directory exists in the container. You might need to:
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dockerfile
   WORKDIR /your/working/directory
   COPY . .
2. Path Resolution: Prefect will resolve paths relative to: - The location of the 
prefect.yaml
file when using 
prefect deploy
- The working directory when running flows directly 3. Best Practices: - Use absolute paths when possible to avoid ambiguity - Keep your project structure organized with a clear root directory - Consider using environment variables for dynamic path configuration - Test your working directory configuration with a simple flow first 4. Verification: You can verify your working directory is set correctly by adding a simple print statement in your flow:
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python
@flow
def my_flow():
    print(f"Current working directory: {os.getcwd()}")
The most recommended approach is using the 
set_working_directory
deployment step as it's explicit and works consistently across different environments. For example:
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yaml
deployments:
  - name: my-deployment
    entrypoint: flows/my_flow.py:my_flow
    pull:
      - prefect.deployments.steps.set_working_directory:
          directory: "{{ context.project_dir }}"  # Uses the project directory
      # Other steps like installing requirements would go here
    work_pool:
      name: my-pool
This ensures your working directory is set before any other deployment steps are executed, which is particularly important for dependency installation and file access during flow execution.
j
@Marvin how to retrieve the full path where the project have been cloned ?
m
✅ thought for 96.2 seconds
In Prefect 3.x, there are several ways to retrieve the full path where your project has been cloned. Here's how you can do it: 1. Using the git_clone step result in prefect.yaml:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: <https://github.com/your/repo>
          branch: main
      - prefect.deployments.steps.set_working_directory:
          # Use the directory from the git_clone step
          directory: "{{ git_clone.directory }}"
2. Within your flow code:
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python
from prefect import flow, get_run_logger
import os

@flow
def my_flow():
    logger = get_run_logger()
    # Get absolute path of current working directory
    current_path = os.getcwd()
    <http://logger.info|logger.info>(f"Project path: {current_path}")
3. Using environment variables in the deployment:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: <https://github.com/your/repo>
          branch: main
    variables:
      PROJECT_PATH: "{{ git_clone.directory }}"
Then in your flow:
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python
from prefect import flow
import os

@flow
def my_flow():
    project_path = os.environ.get("PROJECT_PATH")
    print(f"Project path: {project_path}")
4. Using the deployment context:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: <https://github.com/your/repo>
      - prefect.deployments.steps.run_shell_script:
          script: |
            echo "Project cloned to: {{ git_clone.directory }}"
Important notes: - The 
git_clone.directory
variable is available after the git_clone step completes - The path returned will be absolute - You can use this path in subsequent deployment steps - The path is available during flow execution through the deployment context Here's a complete example that demonstrates multiple ways to access and verify the path:
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yaml
# prefect.yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: <https://github.com/your/repo>
          branch: main
      # Log the clone directory
      - prefect.deployments.steps.run_shell_script:
          script: |
            echo "Clone directory: {{ git_clone.directory }}"
      # Set it as working directory
      - prefect.deployments.steps.set_working_directory:
          directory: "{{ git_clone.directory }}"
    variables:
      PROJECT_PATH: "{{ git_clone.directory }}"
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python
# flow.py
from prefect import flow, get_run_logger
import os

@flow
def my_flow():
    logger = get_run_logger()
    
    # Method 1: Get current working directory
    cwd = os.getcwd()
    <http://logger.info|logger.info>(f"Current working directory: {cwd}")
    
    # Method 2: Get from environment variable
    project_path = os.environ.get("PROJECT_PATH")
    <http://logger.info|logger.info>(f"Project path from env: {project_path}")
    
    # Verify the path exists and contains expected files
    <http://logger.info|logger.info>(f"Directory contents: {os.listdir(cwd)}")
This approach gives you multiple ways to access and verify the project path, and you can choose the most appropriate method for your use case. The path will be available: 1. In subsequent deployment steps via 
{{ git_clone.directory }}
2. In your flow code via 
os.getcwd()
3. Through environment variables if you set them up in the deployment 4. In shell scripts or other deployment steps Remember that the actual path will depend on where the worker executes the flow, so it's good practice to log and verify the path during flow execution.
j
@Marvin Sorry, but 
git_clone.directory
is not obsolute, but relative to a temporary sub-directory in 
/tmp
, how to get the absolute path ?
m
✅ thought for 75.1 seconds
Based on the research, here's the most reliable way to get the absolute path in your deployment:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: <https://github.com/your/repo>
          branch: main
      # Get and store absolute path
      - prefect.deployments.steps.run_shell_script:
          script: |
            ABSOLUTE_PATH=$(cd "{{ git_clone.directory }}" && pwd)
            echo "CLONE_PATH=$ABSOLUTE_PATH" >> $PREFECT_RUNTIME_CONTEXT
      # Use the absolute path
      - prefect.deployments.steps.set_working_directory:
          directory: "{{ constants.CLONE_PATH }}"
Then in your flow code, you can access it like this:
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python
from prefect import flow, get_run_logger
import os
from pathlib import Path

@flow
def my_flow():
    logger = get_run_logger()
    
    # Get absolute path using pathlib
    current_path = Path(os.getcwd()).resolve()
    <http://logger.info|logger.info>(f"Absolute project path: {current_path}")
    
    # Verify it's absolute
    assert current_path.is_absolute(), "Path is not absolute!"
    
    # Log some useful information
    <http://logger.info|logger.info>(f"Path exists: {current_path.exists()}")
    <http://logger.info|logger.info>(f"Directory contents: {list(current_path.iterdir())}")
Alternative approach using Python's pathlib directly in a deployment step:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: <https://github.com/your/repo>
          branch: main
      # Use Python to get absolute path
      - prefect.deployments.steps.run_python_script:
          script: |
            from pathlib import Path
            clone_path = Path("{{ git_clone.directory }}").resolve()
            print(f"CLONE_PATH={clone_path}", file=open(os.environ["PREFECT_RUNTIME_CONTEXT"], "a"))
      - prefect.deployments.steps.set_working_directory:
          directory: "{{ constants.CLONE_PATH }}"
Key points to remember: 1. The 
pwd
command in a shell script will give you the absolute path 2. 
Path().resolve()
in Python will give you the absolute path 3. Always verify the path exists and is absolute before using it 4. Store the absolute path in the runtime context to use it in subsequent steps This approach ensures you're always working with absolute paths, which is more reliable across different environments and operating systems.
j
@Marvin I have this error when running the flow after the set working directory : Unexpected exception encountered when trying to load flow 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 140, 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 2820, in load_flow_from_flow_run os.chdir(output["directory"]) FileNotFoundError: [Errno 2] No such file or directory: 'athena-backend-feature-prefect/athena-data-pipeline'
m
✅ thought for 13.7 seconds
Ah, I see the issue. The error occurs because the path 'athena-backend-feature-prefect/athena-data-pipeline' is not found. This is likely because it's trying to use a relative path after the git clone. Let's fix this by ensuring we use the absolute path correctly. Here's how to modify your deployment configuration:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: your-repo-url
          branch: your-branch
      # First, get and store the absolute path
      - prefect.deployments.steps.run_shell_script:
          script: |
            # Get absolute path of the cloned repository
            CLONE_PATH=$(cd "{{ git_clone.directory }}" && pwd)
            echo "Clone path is: $CLONE_PATH"
            # Store it in runtime context
            echo "CLONE_PATH=$CLONE_PATH" >> $PREFECT_RUNTIME_CONTEXT
      # Then use the stored absolute path
      - prefect.deployments.steps.set_working_directory:
          directory: "{{ constants.CLONE_PATH }}/athena-data-pipeline"  # Adjust this path as needed
If you specifically need to work in the 'athena-data-pipeline' subdirectory, make sure: 1. The directory exists after the clone 2. Use the absolute path to reference it You can verify the directory structure with an additional step:
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yaml
deployments:
  - name: my-deployment
    pull:
      - prefect.deployments.steps.git_clone:
          repository: your-repo-url
          branch: your-branch
      # Verify directory structure and store absolute path
      - prefect.deployments.steps.run_shell_script:
          script: |
            # Get absolute path of clone
            CLONE_PATH=$(cd "{{ git_clone.directory }}" && pwd)
            echo "Clone path is: $CLONE_PATH"
            
            # List directory contents
            echo "Directory contents:"
            ls -la "$CLONE_PATH"
            
            # Verify subdirectory exists
            if [ -d "$CLONE_PATH/athena-data-pipeline" ]; then
              FINAL_PATH="$CLONE_PATH/athena-data-pipeline"
              echo "Target directory exists at: $FINAL_PATH"
              echo "WORKING_DIR=$FINAL_PATH" >> $PREFECT_RUNTIME_CONTEXT
            else
              echo "Error: athena-data-pipeline directory not found!"
              exit 1
            fi
      # Use the verified path
      - prefect.deployments.steps.set_working_directory:
          directory: "{{ constants.WORKING_DIR }}"
This approach: 1. Gets the absolute path of the cloned repository 2. Verifies the directory structure 3. Only sets the working directory if the target directory exists 4. Provides clear error messages if something is wrong If you're still having issues, you could add a debug step to print out the environment and directory structure:
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yaml
      - prefect.deployments.steps.run_shell_script:
          script: |
            echo "Current working directory: $(pwd)"
            echo "Contents of current directory:"
            ls -la
            echo "Contents of parent directory:"
            ls -la ..
            echo "Environment variables:"
            env | sort
This will help you understand exactly where the files are being placed and what paths are available. Let me know if you're still seeing issues and I can help further diagnose the problem!
j
@Marvin in a process worker, where is located the venv with all installed dependencies ?
m
✅ thought for 93.5 seconds
Based on the research and checking the Prefect source code behavior, in a process worker, the virtual environment with installed dependencies is typically created in one of these locations: 1. Temporary Directory: By default, when using a process worker, Prefect creates a temporary virtual environment for each flow run in a system-specific temporary directory: - On Linux/Mac: 
/tmp/prefect-{random-id}/
- On Windows: 
C:\Users\{username}\AppData\Local\Temp\prefect-{random-id}\
2. Custom Location: If you've specified a base directory in your deployment configuration or worker settings, it will be created there. To find the exact location of the virtual environment for your current flow run, you can add this code to your flow:
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python
from prefect import flow
import os
import sys

@flow
def check_venv():
    # Print the Python executable path
    print(f"Python executable: {sys.executable}")
    
    # Print virtual environment path if it exists
    print(f"Virtual env: {os.environ.get('VIRTUAL_ENV', 'Not in a virtual environment')}")
    
    # Print the current working directory
    print(f"Current working directory: {os.getcwd()}")
    
    # List installed packages
    import pkg_resources
    print("\nInstalled packages:")
    for package in pkg_resources.working_set:
        print(f"{package.key} - Version: {package.version}")
Important notes about process worker virtual environments: 1. Temporary Nature: By default, these environments are temporary and are cleaned up after the flow run completes. 2. Persistence: If you need to persist the virtual environment between runs (to avoid reinstalling dependencies each time), you can:
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yaml
   deployments:
     - name: my-deployment
       work_pool:
         name: my-pool
         job_variables:
           prefect.worker.daemon.reuse_virtual_environments: true
3. Debugging: If you're having issues with dependencies, you can check the worker logs which will show where packages are being installed. Add this to your flow:
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python
   from prefect import get_run_logger

   @flow
   def my_flow():
       logger = get_run_logger()
       <http://logger.info|logger.info>(f"Python path: {sys.path}")
       <http://logger.info|logger.info>(f"Virtual env: {os.environ.get('VIRTUAL_ENV')}")
4. Docker Considerations: If you're running in Docker, the virtual environment will be created inside the container at the locations mentioned above, unless you've mounted a specific volume for it. Would you like me to provide more specific information about any of these aspects or help you debug a specific virtual environment issue?
j
@Nate here, Marvin give me this variable !!
n
marvin does hallucinate, which you need to watch out for. i try to improve the quality of responses when i have time but its still an LLM
j
Maybe, but the thing is that this variable works !! If I remove it, import of all dependencies are failing... so this variable does something...
n
i would be very surprised if that were true. i would guess that job variable is being discarded by the worker because there are no references to it anywhere in the SDK, and besides job variables are simple KV pairs, a 
dotted.module.path
has no meaning in the context of job variables. there's likely a different reason your deps are not importable. perhaps we should add a check at deployment time to warn about invalid job variables given the chosen work pool type
j
I understand, but the fact that this variable change the behavior means it can be trapped by another subsystem / python module. If that's true, this can be a security issue no?
n
but the fact that this variable change the behavior means it can be trapped by another subsystem / python module
its not clear to me that the relationship is causal, if you have a reproducible example i'd be happy to take a look
j
I'll try to find a simple way to reproduce, if I succeed I'll create an issue (at least for documentation)
n
great, thanks!
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