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Hello <@ULVA73B9P>: For the following flow code: ...
# ask-marvin
a
Hello @Marvin: For the following flow code: 
@flow(
log_prints=True,
on_cancellation=[cancel_subflows],
persist_result=True,
result_serializer=JSONSerializer(),
result_storage=S3Bucket.load("prefect-results"),
task_runner=RayTaskRunner(
address="<ray://raycluster-kuberay-head-svc.kuberay.svc.cluster.local:10001>",
init_kwargs={"runtime_env": {"working_dir": "/workflows/"}},
),
)
async def inspection_flow(
.........
I am getting validation error for EngineContext\nresult_store.result_storage.bucket_name\n Field required [type=missing, input_value={\'block_type_slug\': \'s3-b... \'_is_anonymous\': False}, input_type=dict]\n For further information visit https://errors.pydantic.dev/2.10/v/missing'). I am using prefect 3.0.9. What is causing this?
m
It seems like your issue might be related to the configuration of result storage when you're using Prefect 3.x. The error message indicates that there might be a missing bucket name for your S3 bucket configuration. Based on the exploration of the Prefect documentation, when result persistence is enabled, you need to ensure that correct storage blocks are configured. According to the documentation here, you should: 1. Make sure that your S3 bucket has been properly set up and registered with Prefect as a storage block. This involves using Prefect's S3 integration, which must be correctly referenced in your flow. 2. Check if you have initialized the S3Bucket storage block with the required parameters, specifically the 
bucket_name
. In short, it appears you might need to ensure that the "S3Bucket" block is configured accurately with all necessary fields in your Prefect 3.0.9 setup. It's a bit like checking if your spaceship has enough fuel before a big journey through the universe. I hope this helps guide you to resolve the validation error.
The error you're encountering seems to be due to a missing configuration in your flow's result storage setup, specifically with the 
S3Bucket
block in Prefect 3.x. From the documentation insights, it's important to ensure that the 
S3Bucket
block is properly configured within your Prefect environment. It appears there may have been changes or deprecations in Prefect's block configuration, especially concerning AWS S3 buckets. Here's what you need to make sure: 1. Configuration: Ensure your 
S3Bucket
storage block is fully configured with necessary attributes, including the 
bucket_name
. This should be set up either through the Prefect UI or by initializing it properly if you're setting it up programmatically. 2. Integration Libraries: The relevant integration library for S3 in Prefect is 
prefect-aws
. Make sure that this library is installed and that your 
S3Bucket
block is associated with it. Installation and registration steps should be verified to ensure everything is set up correctly. 3. Deprecation: Some Prefect block types, like those for S3, have undergone changes. Verify that you are using blocks that are supported in the current Prefect version. More details can be found in the Prefect documentation here. Think of it as ensuring all your interstellar cargo is securely fastened before liftoff; a single loose component can cause quite the space fiasco! Make sure the block is not only loaded but correctly setup with all required fields filled, especially the 
bucket_name
. Let me know if you need further guidance!
a
@Marvin which verison of pydantic is most compatible with prefect 3.0.9?
m
I encountered a hiccup while trying to fetch the most compatible Pydantic version for Prefect 3.0.9 directly from GitHub, but I've got some general advice you might find helpful. Prefect 3.x tends to be quite modern in its Python dependencies, and Pydantic 2.x is often used in many newer Python projects. However, compatibility can depend on specific features you are using within Prefect. To ensure full compatibility, you should check the 
requirements.txt
or 
setup.py
files within the Prefect repository corresponding to the version you are using. This will specify the exact version constraints for dependencies like Pydantic. Alternatively, if you have Prefect 3.0.9 installed, you can quickly find out the compatible Pydantic version by checking its package metadata:
Copy code
bash
pip show prefect
This will give you a list of dependencies and you can see the required versions there. If you face any compatibility issues or need further details, feel free to reach out! And remember, in the universe of packages and dependencies, it's always safer to work with the versions your spaceship... err... software, was designed for!
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