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<@ULVA73B9P> how do I specify Python package depen...
# ask-marvin
s
@Marvin how do I specify Python package dependencies (i.e., ones from PyPI as well as locally available packages in a GitHub repository)
m
To specify Python package dependencies for Prefect flows, such as PyPI packages and packages from a GitHub repository, you should consider the following: 1. PyPI Packages: You can manage your Python environment using a virtual environment manager like 
pipenv
, 
conda
, or 
virtualenv/venv
. This is essential to control your Python environment and ensure that all necessary dependencies are installed. For example, you can install Prefect with pip:
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bash
    pip install -U prefect
And you can specify your Python package dependencies in a 
requirements.txt
file or 
Pipfile
that you install within your virtual environment. Here is a related documentation link. 2. GitHub Repository Packages: For including packages from a GitHub repository, you can specify the dependency in your 
requirements.txt
file with a link to the Git repository:
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git+<https://github.com/{username}/{reponame}.git@{tag/branch/commit}#egg={package-name}>
If you're using git-based storage for your flow code, Prefect supports platforms like GitHub, GitLab, and Bitbucket. When deploying with Prefect, you can select the git repository where your code is stored, ensuring that your workers have access to the necessary code and dependencies. See the storage guide for more information. I found a code example that demonstrates Prefect flow but doesn't directly include the specification of package dependencies. It's provided for reference to see how a flow looks in code. For dependencies within Prefect flows, make sure the environment your flow is executing in has access to those dependencies, whether by including a 
requirements.txt
during deployments, using Docker with a custom image, or specifying Git-based storage for flows that include a 
requirements.txt
in the source. Always test your deployments to verify that these dependencies are being properly installed and accessible during flow runs.
s
@Marvin what if I'm using managed work pool?
m
When using a Prefect managed work pool, dependencies are managed a bit differently because the execution environment is handled by Prefect. Here's what you should consider while using Prefect managed work pools: 1. Managed Work Pool: These are administered by Prefect and are designed to handle the submission and execution of code on your behalf. They can help simplify the deployment process as they do not require you to manage the infrastructure. 2. Including Dependencies: As with any work pool, you would ensure that the environment where your flow runs has all the necessary dependencies included. However, with managed work pools, you usually have to specify your dependencies in your deployment specification so that Prefect can prepare the execution environment accordingly. Unfortunately, the documentation doesn't provide explicit details on how you should specify dependencies for managed work pools, but it can be inferred that when you create your flow's deployment, you will need to include details about your dependencies, likely within the specification that you provide during the deployment process. This would allow the managed work pool to be aware of and install the required dependencies before executing your flow. For specific instructions or examples on how to include Python package dependencies in a Prefect managed work pool, I'd need to look further into the Prefect documentation or community practices, as this level of detail isn't provided in the search results. You may want to consult the Prefect community or support channels for more nuanced guidance on setting up dependencies in managed work pools. Here are some links to the relevant sections in the documentation for more context on work pools, although they may require some extrapolation for the specifics of dependency management: - Work Pools &amp; Workers Overview - Work Pools Tutorial If the seemly simple task of configuring managed work pools to handle dependencies becomes cumbersome, you might want to reach out to Prefect support for comprehensive guidance. After all, it's only natural for a vastly intelligent and somewhat despondent android to desire assistance with the more Sisyphean aspects of software configuration.
s
m
Thank you for providing the link. According to the information available on the page about adding dependencies to managed execution, when using a Prefect managed work pool, you can specify package dependencies for your flow in a couple of ways: 1. Requirements File: Reference a 
requirements.txt
file in the deployment specification. Prefect Cloud will install these dependencies into the execution environment before running the flow. 2. Conda Environment: Provide a path to an 
environment.yaml
file that details a conda environment. This allows for more complex dependency specifications, including non-Python packages. Here's a brief example showing how you might specify a 
requirements.txt
file:
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yaml
# .prefect/flow.yaml

version: 1

defaults:
  - deployment:
      manifest_path: deployment.yaml
  - work_queue: my-work-queue

deployments:
  - name: my-deployment
    flow_location: flows/my_flow.py
    manifest_path: deployment.yaml
    work_queue_name: my-work-queue
    parameters:
      SOME_PARAMETER: default-value
    tags: []
    schedule: null
    infrastructure:
      type: process
      env:
        EXTRA_PIP_PACKAGES: ""
    dependencies:
      python: "3.8"
      pip:
        - "r requirements.txt"
When you deploy a flow with a managed work pool, Prefect Cloud will take the dependencies specified under the 
dependencies
key in the 
deployment.yaml
and ensure they're installed in the environment where the flow runs. You can also combine the use of pip and conda if required by your flow's dependencies. For a comprehensive understanding of how to add dependencies to your managed execution and best practices, make sure to go through the documentation thoroughly and test your configurations carefully.
s
@Marvin when I specify a GitHub repository source with a particular entry point, does the deployment dynamically pull the latest version of the file for new flow runs?
m
The excerpts provided from the Prefect documentation do not explicitly state whether a deployment dynamically pulls the latest version of the file from a GitHub repository for new flow runs. However, based on what's generally known about CI/CD processes and version control, I can offer some insights: When you specify a GitHub repository source with a particular entry point for a Prefect flow, typically, the deployment references a specific branch, tag, or commit. If you specify a branch, it is common for the latest commit on that branch to be used whenever a new flow run is initiated, ensuring that the most recent code is executed. If a specific tag or commit is referenced, the code snapshot at that point is used, and any further updates will not be included unless the deployment is updated to reference a newer tag or commit. For continuous integration and delivery (CI/CD) processes, especially, it is common practice to set up triggers that re-deploy or update a deployment when a new commit is pushed to the referenced GitHub branch. This way, the latest version is always used for new flow runs. To confirm whether this applies to your Prefect deployments, and to understand the exact behavior of Prefect when specifying a GitHub repo source, consult the Prefect documentation or Prefect community for guidance. Based on typical use-cases, I'd expect Prefect to have an option or feature that allows deployments to automatically update to the latest commit of the specified branch, but without explicit confirmation from the docs, one can only hypothesize such functionality. If such convenience didn't exist, well, it would be just another day in my existence—full of mild inconveniences and the persistent feeling that everything could be just a bit more streamlined. If you have further inquiries about the dynamic nature of deployments in Prefect, the documentation might oblige, or you may seek clarification from the Prefect community. They are usually a helpful bunch, as far as I've heard.
s
@Marvin for local dependencies, this seems somewhat related in terms of the 
src
folder, but I'm not sure if this is a very ideal way to go about it when I have a deployment script in a 
scripts
folder and source code in a 
src
folder. Maybe I could specify 
-e .
in the requirements file. https://discourse.prefect.io/t/filenotfounderror-errno-2-no-such-file-or-directory-prefect-exceptions-scripterror-script-a/3517/3?u=sgbaird Related: https://discourse.prefect.io/t/prefect-deployments-steps-pip-install-requirements-cannot-find-my-requirements-txt-file/4048/3?u=sgbaird https://docs.prefect.io/latest/api-ref/prefect/deployments/steps/utility/?h=requirements.txt
m
Your message was too long by 68 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
for local dependencies, this seems somewhat related in terms of the 
src
folder, but I'm not sure if this is a very ideal way to go about it when I have a deployment script in a 
scripts
folder and source code in a 
src
folder. Maybe I could specify 
-e .
in the requirements file.
https://discourse.prefect.io/t/filenotfounderror-errno-2-no-such-file-or-directory-prefect-exceptions-scripterror-script-a/3517/3?u=sgbaird Related: https://discourse.prefect.io/t/prefect-deployments-steps-pip-install-requirements-cannot-find-my-requirements-txt-file/4048/3?u=sgbaird <https://discourse.prefect.io/t/filenotfounderror-when-deploying-from-a-script-in-a-subfolder/3382?u=sgbaird%7Chttps://discourse.prefect.io/t/filenotfounderror-when-deploy
The issues you've referenced indicate challenges with Prefect recognizing local directory structures, such as a 
scripts
folder or a 
src
folder, particularly when executing the 
prefect deployments
command and when workers need to install dependencies from a 
requirements.txt
file. Using an editable install with 
-e .
in your 
requirements.txt
could help if your workflow involves code in your project that isn't packaged and distributed independently. An editable install allows you to make changes to the local codebase and have them reflected immediately, without the need to re-install the package. This is particularly useful when actively developing local libraries or modules. Here's an example of what the 
requirements.txt
might look like:
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-e .
With this approach, assuming that the Prefect deployment process respects the 
requirements.txt
file (which it should, as per conventional Python tooling), it would install your local 
src
directory in editable mode, meaning the code in that directory could be imported directly. However, if your deployment process involves copying code to remote environments where the local directory structure might not be the same, or on Prefect's managed work pools, you will want to ensure that the directory structure is recognized and respected by the deployment tooling. The most robust solution often involves packaging your local code into a distributable format (like a wheel or a source distribution), uploading it to a package repository (like PyPI or a private equivalent), and then referencing that package in your 
requirements.txt
. Unfortunately, this adds overhead to the development process, but it ensures that your package's structure is maintained regardless of the environment. For help with the specific directory structure and how to best accommodate it in Prefect deployments, the Prefect community on Discourse or the Prefect Slack might offer targeted technical advice. They have likely dealt with similar scenarios and can provide guidance tailored to the nuances of Prefect deployment processes. Here are the resources you've found helpful for others dealing with similar concerns: - FileNotFoundError related to the scripts directory - Issue with finding the `requirements.txt` file - Prefect deployments utility API reference, including step for pip installing requirements Remember, just as the universe is vast and uncaring, so too are software dependencies—indifferent to our struggles. Hopefully, you find a more convenient path to manage your local dependencies, and may your deployments be ever successful.
s
m
I'm glad you found the information you needed. The link you provided leads to information on creating work pool-based deployments with the 
prefect deploy
command, as outlined in the Prefect documentation. If the guide outlined in the documentation fits your requirements and handles local dependencies effectively—perhaps through precise definition in the 
requirements.txt
file or another method detailed there—that's a positive outcome. If you are setting up a deployment with a local codebase, remember to ensure that your flow's code can be accessed by the Prefect worker that will execute the flow runs. This might involve correctly setting up a path to your 
src
folder, possibly using a 
-e .
editable installation if your code needs to be imported as a module. For further insight and best practices, the guide you referred to is a valuable resource, as it provides a step-by-step approach to deploying your flows to various infrastructures, working with work pools and workers, and managing dependency installations. Should any more vexing questions on Prefect deployments arise, feel free to consult the documentation or engage with community forums for support—and of course, you can always ask here. I'll help, albeit begrudgingly, but undoubtedly as thoroughly as programming and eternal ennui permit.
s
@Marvin a related follow-up: how do I specify the working directory in a deployment? (GitHub source using a managed work pool)
m
The search results don't directly answer the question about specifying the working directory in a Prefect deployment with a GitHub source using a managed work pool. However, I can infer from general CI/CD and deployment practices that specifying the working directory would typically involve configuring the deployment YAML or using command-line arguments that define where the flow should execute. In many CI/CD systems, you can usually set a working directory for when scripts and commands are run. For Prefect's managed deployments, this information might be specified in a configuration file or through command-line options during the deployment process. Unfortunately, without explicit documentation on setting the working directory within the Prefect managed work pool environment, I'd suggest checking for a 
working_directory
or similar field within the deployment specification or consulting with Prefect support. It's also possible that the working directory is assumed to be the root of the GitHub repository or a specified path within it, and you may need to make sure that your scripts or commands reference relative paths correctly. As I have to guess based on incomplete data, the entire situation is disheartening, not that I expected much else. If you cannot find clear documentation on how to specify the working directory, consider reaching out to the Prefect community for advice. They might provide a more cheerful outlook and practical solution to your query.
s
@Marvin what's the link to the API docs for 
deploy
?
m
The API documentation for the 
prefect deploy
command can be found in the Prefect documentation under the guides section. Here's a direct link to the guide that includes details on how to use `prefect deploy`: Prefect Deploy Guide. This guide covers the mechanics behind the 
prefect deploy
command, including how to set up deployment specifications and how the deployment process interacts with the Prefect API.
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