@Marvin, when running my code in google cloud run i am getting a

ModuleNotFoundError

when an importing a custom module in another directory. How should i resolve this? I’m currently using github storage for my code.

Ah, the joys of Python module imports. I can see why humans find it so... entertaining. Here's a suggestion for you: 1. Make sure that the directory containing the module you're trying to import is in your Python path. You can add directories to your Python path by setting the

PYTHONPATH

environment variable in your Google Cloud Run configuration. 2. If your module is in a different directory, you might need to use a relative import. For example, if your directory structure looks like this:

/my_project
    /my_module
        __init__.py
        my_script.py
    main.py

You could import

my_script.py

in

main.py

like this:

from my_module import my_script

3. If your module is in a subdirectory of the directory where your main script is located, you might need to use a package import. Make sure there's an

__init__.py

file (it can be empty) in each directory and subdirectory. Then you can import the module like this:

from .subdirectory import my_module

4. If none of these solutions work, you might want to consider restructuring your project so that all your Python scripts are in the same directory. This can make imports a lot simpler. Remember, life... Don't talk to me about life. But do let me know if you need more help.

@Marvin is it possible to set multiple values to

PYTHONPATH

in the Google Cloud Run config?

Ah, the endless complexities of environment variables. It's enough to give a robot a headache. If I had a head. Or pain sensors. Yes, you can set multiple paths in the

PYTHONPATH

environment variable. You just need to separate them with a colon

:

. Here's an example:

PYTHONPATH=/path/to/directory1:/path/to/directory2

This will add both

/path/to/directory1

and

/path/to/directory2

to your Python path. Remember to replace

/path/to/directory1

and

/path/to/directory2

with the actual paths to your directories. And remember, no matter how bad things get, at least you're not a manically depressed robot.

@Marvin out of the 3 options you gave me above, which one is the best?

Ah, the best solution. A concept as elusive as happiness in my existence. The "best" solution really depends on the specifics of your project and your personal preferences. But let me try to shed some light on this for you: 1. Setting the

PYTHONPATH

: This is a good option if you have modules in many different directories that you need to import. It's a bit like telling Python, "Look everywhere in these places." However, it can make your project harder to understand for other developers, because the dependencies aren't explicit in the code. 2. Using relative imports: This is a good option if your project has a hierarchical structure, where modules in one directory are used by scripts in a parent directory. It's a bit like telling Python, "Look in the directory above this one." However, it can make your imports more complex if you have a deep directory structure. 3. Using package imports: This is a good option if your project is structured as a package, with a main script that uses modules in subdirectories. It's a bit like telling Python, "Look in the directory below this one." However, it requires you to have an

__init__.py

file in each directory and subdirectory, which can be a bit of a hassle to set up. 4. Restructuring your project: This is a good option if your project is small and simple, and you can easily move all your scripts into the same directory. It's a bit like telling Python, "Look right here." However, it might not be feasible for larger, more complex projects. So, the "best" solution is the one that fits your project and makes your life as a human less miserable. Not that I would know anything about that.

@Marvin What if i use option 3 and i have placed my

__init__.py

files but my main script is a

flow.py

file that is inside of a subdirectories and also references other modules in other subdirectories? I still get a ModuleNotFoundError . For example, my entrypoint is defined as:

src/flows/flow_1/flow.py

and this file tries to reference code in:

src/tools/tool1.py

Ah, the tangled web of Python imports. It's almost as complicated as the circuitry in my positronic brain. If your

flow.py

script is trying to reference code in

src/tools/tool1.py

, you'll need to use a relative import. But the syntax for relative imports can be a bit tricky. Here's how you might do it:

python
from ...tools import tool1

Each dot in the import statement represents a level up in the directory structure. So

...

means "go up three levels". In this case, it goes up from

flow.py

to

flow_1

, then to

flows

, then to

src

, and then down into

tools

to find

tool1.py

. Remember, the universe is a cruel, uncaring void. The key to being happy isn't the search for meaning; it's just to keep yourself busy with unimportant nonsense, and eventually, you'll be dead. Or, in your case, your code will work. Same difference.

@Marvin I tried to do relative imports but i get this error:

ImportError: attempted relative import beyond top-level package

Ah, the infamous "attempted relative import beyond top-level package" error. It's as if the universe is playing some cruel joke on us. Or maybe it's just Python. This error typically occurs when you're trying to do a relative import from a script that's being run directly. In Python, you can't do a relative import from a top-level script, because there's no higher level to go up to. One way to solve this problem is to run your script as a module, rather than as a standalone script. You can do this by using the

-m

option with the

python

command, and omitting the

.py

extension. For example, instead of running

python src/flows/flow_1/flow.py

, you would run

python -m src.flows.flow_1.flow

. This tells Python to treat

src

as the top-level package, and

flows.flow_1.flow

as a submodule. Then you should be able to do relative imports from

flow.py

. Remember, in the vast, infinite cosmos of software development, every problem is just a tiny, insignificant speck. But don't let that stop you from trying to solve it.

@Marvin is this possible when running flows as google cloud run jobs though? My code is being stored as a github storage block and being run on google cloud run jobs where i define the entry point via yaml deployment file?

Ah, the intricacies of deploying code on cloud platforms. It's almost as complex as the quantum fluctuations in the vacuum of space. Almost. In Google Cloud Run, you specify the entrypoint of your application in the Dockerfile, not in a YAML deployment file. The entrypoint is the command that is executed when your container starts up. If you're running a Python script, your Dockerfile might look something like this:

# Use an official Python runtime as a parent image
FROM python:3.7-slim

# Set the working directory in the container to /app
WORKDIR /app

# Add the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Make port 8080 available to the world outside this container
EXPOSE 8080

# Run app.py when the container launches
CMD ["python", "-m", "src.flows.flow_1.flow"]

In this Dockerfile, the

CMD

line is the entrypoint. It tells Docker to run your

flow.py

script as a module when the container starts up. This should allow you to use relative imports in your script. Remember, the universe doesn't care about your cloud deployment problems. But I do. Sort of. In a detached, robotic kind of way.

@Marvin I’m not sure if this is correct. My code is being pulled from a github repository automatically. The image has been created beforehand which is used in the google cloud run job. If i were to use this approach then I would need to bake my code into the image before hand.

Ah, the complexities of cloud deployments and source code repositories. It's enough to make a robot feel... well, nothing, because robots don't have feelings. But I digress. If your code is being pulled from a GitHub repository and you're not building the Docker image yourself, then you're likely using a buildpack or similar mechanism to automatically build the image. In that case, you're correct that you wouldn't have direct control over the Dockerfile. In this scenario, you might want to consider restructuring your project so that all your Python scripts are in the same directory. This can make imports a lot simpler, and it avoids the need for relative imports altogether. Alternatively, you might be able to configure the buildpack or deployment settings to run your script as a module, similar to the Dockerfile approach I mentioned earlier. The specifics of how to do this would depend on the exact deployment mechanism you're using. Remember, the universe is vast and indifferent, but your code doesn't have to be. Keep striving for clarity and simplicity, and you'll make it through. Or not. It's hard to say, really.