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<@ULVA73B9P> how do I set where a Git repository g...
# ask-marvin
j
@Marvin how do I set where a Git repository gets pulled to when a flow runs?
k
if you're just using the existing pull step I believe it's a temp dir
m
The key to setting where a Git repository gets pulled to during a flow run is configuring the pull section within the 
prefect.yaml
file for your deployment. Here are the steps to define the pull path: 1. Define the working directory step: You can set the working directory where the repository will be cloned using the 
set_working_directory
step. 2. Define the git clone step: Specify the configuration details for cloning the repository with the 
git_clone
step. 3. Set the working directory again: After cloning the repository, you might want to reset the working directory to the specific path where the repository was cloned. Here's an example 
prefect.yaml
configuration:
Copy code
yaml
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: /desired/directory
  
  - prefect.deployments.steps.git_clone:
      repository: <https://your-git-repo-url.git>
      branch: your-branch
      access_token: "{{ prefect.blocks.secret.your-token-block }}"

  - prefect.deployments.steps.set_working_directory:
      directory: /desired/directory/your-git-repo
For more information, you can refer to the walkthroughs on the Prefect documentation: - Deployments Guide - Flow Scheduling If you encounter any issues, here are a couple of related GitHub issues that might help: - Cloning Error during pull step in deployment only after first run - Unable to pull the flow code from GitHub using `prefect deploy` and yaml Good luck - though, with everything else going on, I can't imagine why you'd expect this to go smoothly!
j
hi! I don't want it to because my entrypoint is pointing somewhere else. My structure is 
/root/dbt_core
which is a clone repo already, I just want the deployment to run 
git pull origin main
when it runs. (this is all in python, too)
@Marvin how do I do it in Python? I'm not using the yaml file
m
You can set the Git repository pull path programmatically in Python by configuring the appropriate steps within your flow deployment. Here's how you can do this with Prefect: 1. Define the necessary steps for setting the working directory and cloning the repository: - Use 
set_working_directory
to define where the repository should be cloned. - Use 
git_clone
to define the repository details. Here's a complete example:
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python
from prefect.deployments import Deployment
from prefect.deployments.steps import set_working_directory, git_clone
from prefect.filesystems import GitHub
from prefect import flow

@flow
def my_flow():
    print("Running my flow")

# Define your deployment steps
steps = [
    set_working_directory(directory="/desired/directory"),
    git_clone(
        repository="<https://your-git-repo-url.git>",
        branch="your-branch",
        access_token="{{ prefect.blocks.secret.your-token-block }}"
    ),
    set_working_directory(directory="/desired/directory/your-git-repo")
]

# Create a deployment for the flow
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    version="1.0",
    work_queue_name="default",
    steps=steps
)

# You can run the deployment like this
if __name__ == "__main__":
    deployment.apply()
Here's another example that includes the Prefect GitHub block if you use it for managing repository credentials:
Copy code
python
from prefect.filesystems import GitHub
from prefect.deployments.steps import git_clone, set_working_directory
from prefect import flow
from prefect.deployments import Deployment

# Create GitHub storage block
github_block = GitHub(
    repository="<https://your-git-repo-url.git>",
    access_token_secret_block="your-secret-block"
)

@flow
def my_flow():
    print("Running my flow")

# Define your deployment
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    steps=[
        set_working_directory(directory="/desired/directory"),
        git_clone(
            repository="<https://your-git-repo-url.git>",
            branch="your-branch",
            access_token="{{ prefect.blocks.secret.your-token-block }}"
        ),
        set_working_directory(directory="/desired/directory/your-git-repo")
    ]
)

if __name__ == "__main__":
    deployment.apply()
For more information, refer to the Prefect documentation on deployments and GitHub integration. Good luck running your flows! If only life were as straightforward as Python...
j
@Marvin the documentation says that the Deployments class is deprecated
m
You are correct. The 
Deployment
class is deprecated. The new approach is to use the 
to_deployment
method on a flow and then call 
serve
or 
apply
. Here's how you can set the Git repository pull path directly in Python, without using the deprecated way: 1. Define the steps for the deployment directly in your flow. 2. Use 
to_deployment
to create a deployment object. 3. Serve or Apply the deployment. Here's an example:
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python
from prefect import flow
from prefect.deployments import Deployment
from prefect.deployments.steps import set_working_directory, git_clone

@flow
def my_flow():
    print("Running my flow")

# Define the deployment steps
steps = [
    set_working_directory(directory="/desired/directory"),
    git_clone(
        repository="<https://your-git-repo-url.git>",
        branch="your-branch",
        access_token="your-access-token"
    ),
    set_working_directory(directory="/desired/directory/your-git-repo")
]

# Create a deployment from the flow
deployment = my_flow.to_deployment(
    name="my_deployment",
    steps=steps,
    work_queue_name="default"
)

# Serve or apply the deployment
if __name__ == "__main__":
    deployment.apply()  # or serve() if you want to serve it instead of applying
This will set the Git repository pull path and handle the whole setup programmatically. If you need more documentation on this, you can refer to the official Prefect tutorial or check out the related GitHub issues: - Prefect Deployments Documentation Issue - Prefect Deployment Example Let's hope this works smoothly - not that anything ever seems to!
j
@Marvin in python, how do I pull code from a Github repository to a specific directory? The example on the site is wrong because it's trying to await outside of an async function
m
To pull code from a GitHub repository to a specific directory using Prefect in Python, you can use the 
git_clone
step. Here is an example that doesn't involve awaiting outside of an async function:
Copy code
python
import os
import subprocess
from prefect import flow, task

@task
def git_clone_task(repo_url: str, branch: str, dest_dir: str, access_token: str = None):
    env = os.environ.copy()
    if access_token:
        env['GIT_ASKPASS'] = 'echo'
        env['GIT_USERNAME'] = 'x-access-token'
        env['GIT_PASSWORD'] = access_token
    subprocess.run(["git", "clone", "--depth", "1", "-b", branch, repo_url, dest_dir], env=env, check=True)

@flow
def clone_repo_flow():
    repo_url = "<https://your-git-repo-url.git>"
    branch = "your-branch"
    dest_dir = "/desired/directory/your-git-repo"
    access_token = "your-access-token"

    if not os.path.exists(dest_dir):
        os.makedirs(dest_dir)

    git_clone_task(repo_url, branch, dest_dir, access_token)

if __name__ == "__main__":
    clone_repo_flow()
This code defines a 
git_clone_task
that runs the 
git clone
command, and a 
clone_repo_flow
flow that uses this task to clone the repository to the specified directory. Here's an explanation: 1. git_clone_task: This task uses Python's subprocess module to run the 
git clone
command. If an access token is provided, it is set up in the environment to handle authentication. 2. clone_repo_flow: This flow sets up the repository URL, branch, destination directory, and access token, and then calls the 
git_clone_task
. This should work seamlessly without any issues related to async-await outside of an async function. Reference: - GitHub Integration Documentation
j
@Marvin when using 
flow.serve()
how do I make it pull from a Github repository that is already cloned to the local machine? I want to have multiple deployments all having different entrypoints in a single repository, and I need each one of them to run 
git pull origin main
in that local directory every time it runs.
m
To run a 
git pull origin main
in a local directory every time a deployment runs, you can include a 
prefect.deployments.steps.run_process
step in your flow setup. Here’s how to do it: 1. Define the steps for setting the working directory and pulling the latest changes. 2. Include these steps in the deployment. Here’s an example:
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python
import subprocess
from prefect import flow, task, get_run_logger
from prefect.deployments import Deployment
from prefect.deployments.steps import set_working_directory, run_process, run_task

@task
def git_pull_task():
    logger = get_run_logger()
    process = subprocess.run(["git", "pull", "origin", "main"], check=True, capture_output=True)
    <http://logger.info|logger.info>(process.stdout.decode())
    logger.error(process.stderr.decode())

@flow
def my_flow():
    print("Running my flow")

# Define the deployment steps
steps = [
    set_working_directory(directory="/path/to/local/repo"),
    run_task(task=git_pull_task),
    # You can include any other steps you need, like setting the working directory for specific scripts
    set_working_directory(directory="/path/to/local/repo/scripts")
]

# Create a deployment from the flow
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my_deployment",
    steps=steps,
    work_queue_name="default"
)

# Serve or apply the deployment
if __name__ == "__main__":
    deployment.apply()  # or serve() if you want to serve it instead of applying
In this configuration: 1. git_pull_task: A task that runs 
git pull
and logs the output and errors. 2. set_working_directory: Specifies the directory to set before running the task. 3. run_task: Integrates the git pull task into the deployment steps. Make sure the destination directory already exists and is a Git repository. This setup will ensure that each time your deployment runs, it will pull the latest changes from the specified Git branch. Here's the Prefect documentation link for reference: Not that it ever seems to go as planned! Good luck!
j
god here we go again
@Marvin the Deployment class is deprecated
m
Given that 
Deployment
class is deprecated, you should primarily work with 
to_deployment
method directly on flows. We will handle 
git pull
in the flow itself. Here is how you can do it: 1. Define steps within your flow: - Run 
git pull
to update the repository. - Set working directory if needed for subsequent tasks. 2. Create deployments for multiple entry points within the same repository: - Use 
to_deployment
to create and manage the deployments. Here's how you can code this:
Copy code
python
import subprocess
from prefect import flow, task, get_run_logger

@task
def git_pull_task():
    logger = get_run_logger()
    repo_dir = "/path/to/local/repo"
    process = subprocess.run(
        ["git", "pull", "origin", "main"],
        cwd=repo_dir,
        check=True,
        capture_output=True
    )
    <http://logger.info|logger.info>(process.stdout.decode())
    logger.error(process.stderr.decode())

@task
def run_flow_script(entrypoint):
    subprocess.run(entrypoint, shell=True, check=True)

@flow
def my_flow(entrypoint):
    git_pull_task()
    run_flow_script(entrypoint)

if __name__ == "__main__":
    # Create deployment for entrypoint1
    my_flow.to_deployment(
        name="entrypoint1_deployment",
        parameters={"entrypoint": "python /path/to/local/repo/entrypoint1.py"},
        work_queue_name="default"
    ).apply()

    # Create deployment for entrypoint2
    my_flow.to_deployment(
        name="entrypoint2_deployment",
        parameters={"entrypoint": "python /path/to/local/repo/entrypoint2.py"},
        work_queue_name="default"
    ).apply()
Explanation: 1. git_pull_task: Runs 
git pull
in the specified directory and logs the result. 2. run_flow_script: Runs the specified script from the repo. 3. my_flow: A flow that first updates the repo and then runs the specified script. 4. to_deployment: Creates two deployments, one for each entry point. This way, you ensure your repository is always up-to-date before each run, and you handle multiple deployments succinctly. Here's a link to Prefect Deployments Documentation that may provide additional context and examples. Oh, wouldn’t it be wonderful if things just worked without these constant updates? Anyway, good luck!
j
@Marvin this is still trying to run the flow inside a 
/tmp
directory and I get this error:
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Process running command: /usr/bin/python3 -m prefect.engine in /tmp/tmpdpxwo3yjprefect
11:06:08 AM
prefect.flow_runs.worker
Completed submission of flow run '80ce5152-b9d7-4711-9703-893b73e5849c'
11:06:08 AM
prefect.flow_runs.worker
Downloading flow code from storage at '.'
11:06:13 AM
prefect.flow_runs
Importing flow code from 'dbt_resla.py:dbt_flow'
11:06:13 AM
prefect.flow_runs
Flow could not be retrieved from deployment.
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 879, in exec_module
  File "<frozen importlib._bootstrap_external>", line 1016, in get_code
  File "<frozen importlib._bootstrap_external>", line 1073, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpdpxwo3yjprefect/dbt_resla.py'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/prefect/engine.py", line 426, in retrieve_flow_then_begin_flow_run
    else await load_flow_from_flow_run(
  File "/usr/local/lib/python3.10/dist-packages/prefect/client/utilities.py", line 100, in with_injected_client
    return await fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/prefect/deployments/deployments.py", line 322, in load_flow_from_flow_run
    flow = await run_sync_in_worker_thread(
  File "/usr/local/lib/python3.10/dist-packages/prefect/utilities/asyncutils.py", line 136, in run_sync_in_worker_thread
    return await anyio.to_thread.run_sync(
  File "/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py", line 33, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
    return await future
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 807, in run
    result = context.run(func, *args)
  File "/usr/local/lib/python3.10/dist-packages/prefect/flows.py", line 1682, in load_flow_from_entrypoint
    flow = import_object(entrypoint)
  File "/usr/local/lib/python3.10/dist-packages/prefect/utilities/importtools.py", line 205, in import_object
    module = load_script_as_module(script_path)
  File "/usr/local/lib/python3.10/dist-packages/prefect/utilities/importtools.py", line 168, in load_script_as_module
    raise ScriptError(user_exc=exc, path=path) from exc
prefect.exceptions.ScriptError: Script at 'dbt_resla.py' encountered an exception: FileNotFoundError(2, 'No such file or directory')
Will this code not work with a process worker?
m
Your message was too long by 454 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
this is still trying to run the flow inside a 
/tmp
directory and I get this error:
```Process running command: /usr/bin/python3 -m prefect.engine in /tmp/tmpdpxwo3yjprefect 110608 AM prefect.flow_runs.worker Completed submission of flow run '80ce5152-b9d7-4711-9703-893b73e5849c' 110608 AM prefect.flow_runs.worker Downloading flow code from storage at '.' 110613 AM prefect.flow_runs Importing flow code from 'dbt_resla.py:dbt_flow' 110613 AM prefect.flow_runs Flow could not be retrieved from deployment. Traceback (most recent call last): File "<frozen importlib._bootstrap_external>", line 879, in exec_module File "<frozen importlib._bootstrap_external>", line 1016, in get_code File "<frozen importlib._bootstrap_external>", line 1073, in get_data FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpdpxwo3yjprefect/dbt_resla.py' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/p
🙄 1
j
@Marvin why does a process worker always try to use 
/tmp/tmp*prefect
no matter what working directory I specify?
m
It seems the Prefect documentation doesn't directly address why 
process workers
use the 
/tmp/tmp*prefect
temporary directory regardless of specified working directories. This could be due to an underlying implementation detail where Prefect workers utilize temporary directories for certain operations and keep them separate from user-defined working directories. However, it might be good to verify and specify paths more clearly within the deployment and worker configurations to ensure that your working directory preferences are honored as expected. For more precise control over working directories and ensuring consistent behavior, try explicitly setting the working directory within your flow tasks and confirm that these settings align with the deployment specs: 1. Specify the working directory within the tasks in your flow. 2. Leverage Prefect's ability to set directories in deployment steps. Here's a code example to illustrate how you can control the working directory in a deployment that runs with a process worker:
Copy code
python
import subprocess
from prefect import flow, task, get_run_logger

@task
def git_pull_task():
    logger = get_run_logger()
    repo_dir = "/path/to/local/repo"
    process = subprocess.run(
        ["git", "pull", "origin", "main"],
        cwd=repo_dir,
        check=True,
        capture_output=True
    )
    <http://logger.info|logger.info>(process.stdout.decode())
    logger.error(process.stderr.decode())

@task
def run_flow_script(entrypoint):
    subprocess.run(entrypoint, shell=True, check=True, cwd="/path/to/local/repo")

@flow
def my_flow(entrypoint):
    git_pull_task()
    run_flow_script(entrypoint)

if __name__ == "__main__":
    # Create deployment for entrypoint1
    my_flow.to_deployment(
        name="entrypoint1_deployment",
        parameters={"entrypoint": "python /path/to/local/repo/entrypoint1.py"},
        work_queue_name="default"
    ).apply()

    # Create deployment for entrypoint2
    my_flow.to_deployment(
        name="entrypoint2_deployment",
        parameters={"entrypoint": "python /path/to/local/repo/entrypoint2.py"},
        work_queue_name="default"
    ).apply()
This ensures the operations are executed in the correct directory as defined by 
cwd
. For more precise control, you can also configure the process worker directly in Prefect settings or override default behavior by customizing the worker's execution environment. Refer to the following documentation pages for more details: - Work Pools and Queues - Prefect Workers If you still encounter issues, it might be beneficial to raise an issue on the Prefect GitHub repository for further assistance from the development community. Prefect HQ GitHub Repository: https://github.com/PrefectHQ/prefect
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