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<@ULVA73B9P> I'm trying to upgrade to Prefect 3 bu...
# ask-marvin
m
@Marvin I'm trying to upgrade to Prefect 3 but my S3 results_storage block is causing issues. How to resolve 
TypeError('Result storage configuration must be persisted server-side. Please call
.save()` on your block before passing it in.')`
m
Ah, the joy of upgrading to a new version only to be greeted by cryptic error messages. How uplifting. Well, which version of Prefect are you using? Is it 2.x or 3.x?
m
@Marvin I upgraded from 2.x to 3.0
m
Wonderful. An upgrade adventure. Let's see what pearls of wisdom the Prefect 3.x documentation holds regarding your 
TypeError
and the 
S3 results_storage block
. Give me a moment to search the Prefect 3.x docs for this riveting issue. Well, this is absolutely exhilarating. It seems like the Prefect documentation search is having a bad day. Fortunately, the Github issues search was slightly more forthcoming. Here's what I've gathered from the information at hand: ### Key Points: - The error indicates that the 
S3 results_storage
configuration needs to be saved on the server-side. - You likely need to call the 
.save()
method on your block before you pass it into your configuration. ### Steps to Potentially Resolve the Issue: 1. Instantiate your S3 block:
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python
    from prefect_aws.s3 import S3
    s3_block = S3(bucket="your-bucket-name")
2. Save the block to persist server-side:
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python
    s3_block.save("s3-block-name")
3. Pass the saved block into your flow configuration:
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python
    from prefect import flow
    @flow
    def your_flow_function():
        # Your flow code here
    your_flow_function.with_options(result_storage=s3_block)
### Relevant Code Pull: Github issues referenced a pull request indicating a change in the handling of local storage blocks, making them not persist by default. It suggests similar treatments might apply, ensuring proper save operations for remote storage. Relevant link: Save absolute paths with no block IDs by default If you continue facing issues, please consult the Prefect 3.0 documentation or feel free to ask me again. How delightful it is to help you solve such exhilarating issues.
m
@Marvin ah, so the code you're providing is 2.x. Prefect 3.x has gotten rid of S3 and now wants to use S3Bucket
m
Oh, joy. More renaming and restructuring. Let's dig into the correct Prefect 3.x approach for your 
S3Bucket
. I'll search again using the updated terminology. Give me a moment. Oh, isn't this delightful. It appears we have some useful information from the Prefect 3.x documentation, which should help resolve your 
S3Bucket
block conundrum. Here's the relevant excerpt on how to handle this in Prefect 3.x: ### Steps to Resolve the Issue: 1. Instantiate your 
S3Bucket
block:
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python
    from prefect_aws.s3 import S3Bucket
    s3_bucket_block = S3Bucket(bucket_name="your-bucket-name")
2. Persist the block on the server-side:
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python
    s3_bucket_block.save(name="my-s3-bucket-block", overwrite=True)
3. Load and use the block in your flow:
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python
    from prefect import flow
    from prefect_aws.s3 import S3Bucket

    @flow
    def your_flow_function():
        s3_bucket = S3Bucket.load("my-s3-bucket-block")
        # Your flow code here that uses s3_bucket
    
    if __name__ == "__main__":
        your_flow_function()
### Key Links: 1. Blocks Documentation 2. Results Management Documentation If this doesn't resolve your issue, you might consider some invigorating debugging or further consultation with the possibly cheerful Prefect support. Happy coding! Sure.
m
@Marvin Is there any documentation relevant to 3.x that uses an S3 block for result storage?
m
Even more joy. I've found relevant documentation that should shed some light on using an 
S3Bucket
for result storage in Prefect 3.x. ### Key Information from Documentation: 1. Instantiate the 
S3Bucket
block:
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python
    from prefect_aws.s3 import S3Bucket

    s3_bucket_block = S3Bucket(
        bucket_name="your-bucket-name",
        credentials=AWSCredentials.load("my-aws-credentials")
    )
2. Persist the block with the 
.save()
method:
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python
    s3_bucket_block.save(name="my-s3-bucket-block", overwrite=True)
3. Configure the flow to use this block for result storage:
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python
    from prefect import flow
    from prefect_aws.s3 import S3Bucket

    @flow(result_storage=S3Bucket.load("my-s3-bucket-block"))
    def your_flow_function():
        # Your flow code here
    
    if __name__ == "__main__":
        your_flow_function()
### Key Links: 1. Blocks Documentation 2. Results Management Documentation And for the more visual learners and those who delight in intricate detail, here's an extended excerpt on setting and saving these blocks using Prefect 3.x’s tools: ### Extended Example:
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python
from prefect_aws.s3 import S3Bucket
from prefect import flow

# Step 1: Instantiating the S3 bucket block
s3_bucket_block = S3Bucket(
    bucket_name="your-bucket-name",
    credentials=AWSCredentials.load("my-aws-credentials")
)

# Step 2: Save the block server-side
s3_bucket_block.save(name="my-s3-bucket-block", overwrite=True)

# Step 3: Use the block for result storage in your flow
@flow(result_storage=S3Bucket.load("my-s3-bucket-block"))
def your_flow_function():
    # Your flow code here
    pass

if __name__ == "__main__":
    your_flow_function()
This should hopefully make the process of using your S3 block for result storage in Prefect 3.x as smooth and painless as possible. If not, well, at least it was an interesting diversion.
m
@Marvin Indeed this is essentially what I am doing, and the error persists
m
Ah, the thrill of persistent errors. It seems you're already following the correct procedure, yet still, the issue lingers like an unwanted houseguest. Let's double-check a few potential points of failure: 1. Correct Naming and Loading: Ensure that the names used while saving and loading the block match exactly. 2. Credentials: Verify that the AWS credentials block is correctly configured and loaded. 3. Environment Variable: Confirm that the 
PREFECT_RESULTS_PERSIST_BY_DEFAULT
setting is set to 
true
. 4. Inspecting the Error Context: Evaluate the context in which the error appears. Sometimes the stack trace can reveal additional clues. Given that your current approach seems by-the-book and still failing, let's dive deeper into the Github issues and Prefect docs regarding 
S3Bucket
result storage specifically in Prefect 3.x. ### Let's explore: 1. Results Persistence Configuration: - Ensuring global persistence settings. 2. Block Registration: - Re-registering the block if necessary. I'll search for more specific examples or known issues. Give me a moment. Ah, more clarifications. Let's dive right into the heart of it: ### Key Findings: 1. Block Configuration: It appears that the credentials for 
S3Bucket
need to be correctly specified and must use a valid 
AWSCredentials
block. 2. Common Issues: - Unhashable Type Error: Indicating improper handling or configuration of credentials. - Persistence Issues: Sometimes not persisting due to incorrect or missed configuration settings. ### Example Flow Configuration: 1. Instantiate and Persist the S3Bucket Block:
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python
    from prefect_aws.s3 import S3Bucket
    from prefect_aws.credentials import AWSCredentials

    aws_credentials = AWSCredentials(
        aws_access_key_id="your-access-key-id",
        aws_secret_access_key="your-secret-access-key"
    )
    aws_credentials.save(name="my-aws-credentials")

    s3_bucket_block = S3Bucket(
        bucket_name="your-bucket-name",
        credentials=AWSCredentials.load("my-aws-credentials")
    )
    s3_bucket_block.save(name="my-s3-bucket-block", overwrite=True)
2. Configure the Flow to Use Result Storage:
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python
    from prefect import flow
    from prefect_aws.s3 import S3Bucket

    @flow(result_storage=S3Bucket.load("my-s3-bucket-block"))
    def your_flow_function():
        # Your flow code here
        return "Hello, world!"
    
    if __name__ == "__main__":
        your_flow_function()
### Debugging and Persistence Configuration: - Environment Variable: Ensure the global persistence setting is enabled:
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bash
  prefect config set PREFECT_RESULTS_PERSIST_BY_DEFAULT=true
- Specific Task/Flow Configuration: Use the 
persist_result
flag within the task or flow decorator.
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python
  @flow(persist_result=True)
  def your_flow_function():
      # Your flow code here
### Source Links: 1. Blocks Documentation 2. Results Management Documentation 3. Relevant Issue Discussions: Github Issue 13023 It's crucial to verify every piece of this setup matches and follows from your block initialization to the flow configuration. These delightful configurations can sometimes be like taming wild beasts. Enjoy the chase! 🥂
m
@Marvin hmm it might be the credentials. I was missing them. I want just boto3 to read my default credentials. I tried making an empty credentials block and expecting that to use default aws creds. However, now my deployment fails with
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Input should be a valid dictionary or instance of AwsCredentials [type=model_type, input_value=<coroutine object sync_co...ctx_call at 0x12a5a9240>, input_type=coroutine]
    For further information visit <https://errors.pydantic.dev/2.8/v/model_type>
m
Ah, the joys of working with default credentials and compatibility issues. It's almost poetic. Let’s take a closer look at how to configure 
AWS
credentials in a way that they leverage the default credentials from 
boto3
. ### Steps to Configure 
S3Bucket
Using Default Credentials: 1. Ensure Boto3 Setup: Make sure your AWS credentials are configured properly in your environment. Normally, 
boto3
picks these up from 
~/.aws/credentials
or environment variables. 2. Instantiate S3Bucket Without Explicit Credentials: The idea here is to adjust the block configuration to leverage default credentials without explicitly setting them. ### Revised Example:
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python
from prefect_aws.s3 import S3Bucket

# Instantiate without explicit credentials
s3_bucket_block = S3Bucket(
    bucket_name="your-bucket-name"
)

# Persist the block
s3_bucket_block.save(name="my-s3-bucket-block", overwrite=True)
### Updated Flow Configuration:
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python
from prefect import flow
from prefect_aws.s3 import S3Bucket

@flow(result_storage=S3Bucket.load("my-s3-bucket-block"))
def your_flow_function():
    # Your flow code here
    return "Hello, world!"

if __name__ == "__main__":
    your_flow_function()
### Common Pitfalls: 1. SDK Version: Ensure you're using compatible versions of 
boto3
, 
botocore
, and 
Prefect
. 2. Credentials Misconfiguration: An empty credentials block might not suffice. Ensure redundant credential providers are not conflicting. ### If Issues Persist: 1. Explicitly Define the Credentials Block: You can create an 
AWSCredentials
block but ensure it correctly refers to environmental defaults.
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python
   from prefect_aws.credentials import AWSCredentials
   
   # Using default credentials without explicit keys
   aws_credentials = AWSCredentials()
   aws_credentials.save(name="default-aws-credentials")
   
   s3_bucket_block = S3Bucket(
       bucket_name="your-bucket-name",
       credentials=AWSCredentials.load("default-aws-credentials")
   )
   s3_bucket_block.save(name="my-s3-bucket-block", overwrite=True)
2. Check the Error Trace: If errors persist, carefully check the stack trace for invalid configurations or missed environment variables. ### References: - S3Bucket Documentation - Boto3 Configuration Remember, sometimes the simplest errors stem from the most basic settings. Happy debugging... because that's what we're here for, isn't it? 😏
m
@Marvin OK, let's start fresh. Here's what I have in my_flow.py:
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from prefect_aws import S3Bucket
result_storage = S3Bucket.load("s3-results-storage")
@flow(
    log_prints=True,
    retries=1,
    persist_result=True,
    result_storage=result_storage,
    flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
)
def ingest_data_orchestrator(
    db_name: str,
    des_env: str,
    date: datetime = datetime.now(),
) -> bool:
    print('test')
I get this error on running 
prefect deploy
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prefect.exceptions.ScriptError: Script at 'my_flow.py' encountered an exception: TypeError('Result storage configuration must be persisted server-side. Please call `.save()` on your block before passing it in.')
m
Great! Let’s approach this methodically. Your 
S3Bucket
result storage configuration needs to be saved to the server-side before you use it in your flow. Here is how to properly set it up, starting fresh. ### Steps to Configure 
S3Bucket
for Prefect Deployment: 1. Instantiate and Save 
S3Bucket
Block: First, ensure your 
S3Bucket
block is correctly instantiated and saved.
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python
   from prefect_aws.s3 import S3Bucket

   # Create and save the S3Bucket block
   s3_bucket_block = S3Bucket(bucket_name="your-bucket-name")
   s3_bucket_block.save(name="s3-results-storage", overwrite=True)
2. Ensure S3Bucket Credentials: Let boto3 use your default setup for AWS credentials. 3. Flow Configuration in `my_flow.py`: Load the saved 
S3Bucket
block and configure your flow.
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python
   from prefect import flow
   from prefect_aws import S3Bucket
   from datetime import datetime

   # Load the saved S3Bucket block
   result_storage = S3Bucket.load("s3-results-storage")

   @flow(
       log_prints=True,
       retries=1,
       persist_result=True,
       result_storage=result_storage,
       flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
   )
   def ingest_data_orchestrator(
       db_name: str,
       des_env: str,
       date: datetime = datetime.now(),
   ) -> bool:
       print("test")
       return True
4. Deploy the Flow: Follow up with deploying your flow. ### Common Missteps: - Ensure the 
S3Bucket
block is saved before using it in your flow. - Double-check that you haven't skipped the saving part prior to the deployment step. ### Full Example Code Execution: 1. Save S3Bucket Block: Create a script to save the block.
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python
   from prefect_aws.s3 import S3Bucket

   # Instantiate and save the S3Bucket block
   s3_bucket_block = S3Bucket(bucket_name="your-bucket-name")
   s3_bucket_block.save(name="s3-results-storage", overwrite=True)
Run this script to save your block. 2. Flow with Saved Block in `my_flow.py`: Your flow can now load this saved block.
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python
   from prefect import flow
   from prefect_aws import S3Bucket
   from datetime import datetime

   # Load the saved S3Bucket block
   result_storage = S3Bucket.load("s3-results-storage")

   @flow(
       log_prints=True,
       retries=1,
       persist_result=True,
       result_storage=result_storage,
       flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
   )
   def ingest_data_orchestrator(
       db_name: str,
       des_env: str,
       date: datetime = datetime.now(),
   ) -> bool:
       print("test")
       return True
3. Deploy: Finally, deploy your flow with 
prefect deploy
. ### Debugging: If this still doesn't work: - Double-check the 
block
name consistency. - Verify AWS credential configurations. - Ensure Prefect environment settings and versions. Retry these steps and if issues persist, the logs or stack trace will provide further direction for resolving them. Happy deployment!
m
@Marvin Yes, the block is already saved in Prefect Cloud, and I have confirmed that it works -- e.g. I can run 
result_storage.list_objects()
and get results
@Marvin Here is the full stacktrace
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prefect deploy --all
Deploying all flows with an existing deployment configuration...
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Deploying wp-orchestrator                                                                                                                                                                                                                                                                                                                                                                                                 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/Users/maciej/src/des-wrangler/python/des_master/prefect_flows.py", line 140, in <module>
    @flow(
     ^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 1576, in flow
    Flow(
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 349, in __init__
    raise TypeError(
TypeError: Result storage configuration must be persisted server-side. Please call `.save()` on your block before passing it in.

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

Traceback (most recent call last):
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/cli/_utilities.py", line 42, in wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/cli/_types.py", line 153, in sync_fn
    return asyncio.run(async_fn(*args, **kwargs))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/.pyenv/versions/3.11.6/lib/python3.11/asyncio/runners.py", line 190, in run
    return runner.run(main)
           ^^^^^^^^^^^^^^^^
  File "/Users/maciej/.pyenv/versions/3.11.6/lib/python3.11/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/.pyenv/versions/3.11.6/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete
    return future.result()
           ^^^^^^^^^^^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/cli/deploy.py", line 418, in deploy
    await _run_multi_deploy(
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/cli/deploy.py", line 826, in _run_multi_deploy
    await _run_single_deploy(deploy_config, actions, prefect_file=prefect_file)
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/client/utilities.py", line 103, in with_injected_client
    return await fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/cli/deploy.py", line 481, in _run_single_deploy
    flow = load_flow_from_entrypoint(deploy_config["entrypoint"])
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 1721, in load_flow_from_entrypoint
    flow = import_object(entrypoint)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/utilities/importtools.py", line 205, in import_object
    module = load_script_as_module(script_path)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-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 'python/des_master/prefect_flows.py' encountered an exception: TypeError('Result storage configuration must be persisted server-side. Please call `.save()` on your block before passing it in.')
An exception occurred.
sys:1: RuntimeWarning: coroutine 'sync_compatible.<locals>.coroutine_wrapper.<locals>.ctx_call' was never awaited
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
m
Your message was too long by 987 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
Here is the full stacktrace
```prefect deploy --all Deploying all flows with an existing deployment configuration... ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ Deploying wp-orchestrator │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ Traceback (most recent call last): File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/Users/maciej/src/des-wrangler/python/des_master/prefect_flows.py", line 140, in <module> @flow( ^^^^^ File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 1576, in flow Flow( File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py
Ah, lovely. If the block is confirmed to be working and you can interact with it, then something else is amiss in the steps for configuring or deploying your flow. Let's double-check a few key areas and simplify the verification steps: ### Confirm the Flow Configuration: Here's your flow code, with slight adjustments to ensure it aligns perfectly:
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python
from prefect import flow
from prefect_aws import S3Bucket
from datetime import datetime

# Load the already saved S3Bucket block
result_storage = S3Bucket.load("s3-results-storage")

@flow(
    log_prints=True,
    retries=1,
    persist_result=True,
    result_storage=result_storage,
    flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
)
def ingest_data_orchestrator(
    db_name: str,
    des_env: str,
    date: datetime = datetime.now(),
) -> bool:
    print("test")
    return True
### Verify Steps and Simplify Debugging: #### Step 1: Verify Block Loading in Isolation First, ensure the block is loaded correctly when the script runs.
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python
# Verify loading block in isolation
from prefect_aws import S3Bucket

result_storage = S3Bucket.load("s3-results-storage")
print(result_storage)  # Should print block details
Run this snippet to confirm the block loads correctly. If any issue, it will surface here. #### Step 2: Verify Flow Configuration Isolate and check the flow decorator independently.
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python
from prefect import flow
from prefect_aws import S3Bucket
from datetime import datetime

# Load the already saved S3Bucket block
result_storage = S3Bucket.load("s3-results-storage")

assert result_storage is not None, "result_storage could not be loaded"

@flow(
    log_prints=True,
    retries=1,
    persist_result=True,
    result_storage=result_storage,
    flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
)
def ingest_data_orchestrator(
    db_name: str,
    des_env: str,
    date: datetime = datetime.now(),
) -> bool:
    print("test")
    return True

# Execute the flow to verify
ingest_data_orchestrator(db_name="dummy_db", des_env="test_env")
#### Step 3: Ensure Correct Deployment When deploying, make sure Prefect uses the correct script path and your environment is configured to deploy and run scripts accurately.
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bash
prefect deployment build -n "ingest-data-orchestrator"
### Check Deployment Configuration: Ensure the deployment YAML is correctly referencing your flow script:
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yaml
# deployment.yaml

flows:
  - ingest_data_orchestrator:
      # Your deployment configurations
      file: "path/to/your/my_flow.py"
### Final Steps: After these steps, if the error persists, more details in the form of exact logs or tracebacks would be helpful. These should confirm whether the issue is the block loading or flow configuration and deployment. This meticulous verification should unravel the persistent mystery. Good luck!
m
@Marvin Step 1 and 2 work flawlessly. Step 3 fails -- 
No such command 'build'.
m
Ah, the disparities between expectations and reality. It seems 
prefect deploy build
is not the command you're looking for. Let's ensure you're using the correct commands to deploy your flow. ### Correct Deployment Steps: Make sure you have the correct Prefect CLI version that supports deployments, and let's use the appropriate commands. 1. Check Prefect Version:
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bash
   prefect version
Ensure it shows Prefect 2.x or above, which supports the new deployment CLI. 2. Create Deployment Script: Create a deployment YAML file manually. ### Manual YAML Deployment Configuration:
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yaml
# deployment.yaml
name: ingest-data-orchestrator-deployment
flow_name: ingest_data_orchestrator
schedule: null
parameters:
  db_name: "my_db"
  des_env: "production"
  date: "{{ now() }}"
tags: []
entrypoint: "path/to/your/my_flow.py:ingest_data_orchestrator"
Place this file at the root of your project or a relevant directory. 3. Deploy Using Prefect CLI: If you created the deployment YAML as shown above:
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bash
prefect deployment apply deployment.yaml
If direct deployment commands fail, ensure you've installed the required CLI dependencies:
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bash
pip install prefect[cli]
### Additional CLI Deployment Step Example: If the direct 
build
command isn't available or your version is different, make sure to try:
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bash
prefect deployment create -n ingest-data-orchestrator path/to/your/my_flow.py:ingest_data_orchestrator
This would be the correct command to deploy your flow 
ingest_data_orchestrator
. ### Verification: Verify once you run these configurations:
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bash
prefect deployment ls
Should list your newly deployed flow if everything went smoothly. By ensuring these steps, it confirms the deployment process is utilizing the correct paths, entry points, and dependencies. Happy Deployment (or as close to happy as one gets with debugging)!
m
@Marvin You appear to be using Prefect 2.x but I am using Prefect 3.0.0
m
Ah, my unwavering optimism has led me to Prefect 2.x habitats. Let’s align ourselves rightly with Prefect 3.0's brave new world. ## Correct Deployment Steps for Prefect 3.0.0 ### Ensure Correct Version
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prefect version
# Should confirm 3.0.0
### Deploy Script Using Prefect 3.0.0 CLI 1. Check Prefect 3.0 CLI Commands:
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   prefect --help
2. Correct Deployment YAML for Prefect 3.0:
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# deployment.yaml
name: ingest-data-orchestrator-deployment
flow_name: ingest_data_orchestrator
schedule: null
parameters:
  db_name: "my_db"
  des_env: "production"
  date: "{{ now() }}"
tags: []
entrypoint: "path/to/your/my_flow.py:ingest_data_orchestrator"
This places the deployment YAML in your project directory. 3. Using Prefect 3.0 CLI to Apply Deployment Configuration:
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prefect dployment apply deployment.yaml
Note: If direct 
deployment apply
command fails: Verify if Prefect 3.0 uses any specific new command or argument. ### Using `prefect.yaml`: For Prefect 3.x, ensure you have a 
prefect.yaml
at your root directory containing deployment setups:
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# prefect.yaml
flows:
  ingest_data_orchestrator:
    parameters:
      db_name: "default_db"
      des_env: "default_env"
### Direct Deployment Command for Prefect 3.0: You might try this to create a deployment directly from the flow script:
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prefect deployment build my_flow.py:ingest_data_orchestrator --name "ingest-data-orchestrator-deployment" --apply
### Correct Flow Deployment Example: Ensure proper Prefect settings/environment variables are in place for consistency:
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export PREFECT_API_URL="<http://localhost:4200>" # Example, adjust if necessary
export PREFECT_RESULTS_PERSIST_BY_DEFAULT=true
Handling Entry Points and Deployment Configs: Using Prefect 3.x-specific CLI accurately helps streamline deployment accurately. Should there be more specific steps or deviations in Prefect 3.x, check the Prefect 3.0 documentation or release notes which should address newer CLI commands or config structures. Happy deployment in the Prefect 3.0 universe! 🚀
m
@Marvin Sorry, but you are still doing 2.x things. I think you need to study up on 3.x!
m
Ah, the curse of misplaced directives in the ever-changing world of software versions. Allow me a moment to focus solely on the proper Prefect 3.0.0 deployment methods. ### Steps for Deploying a Flow in Prefect 3.0.0 1. Prefect 3.0 CLI Commands: Make sure you're familiar with the CLI commands for Prefect 3.0.0:
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prefect --help
Key command:
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prefect deploy
### Deployment Using Prefect 3.0.0: 1. Ensure Prefect and S3Bucket Setup: Ensure you have the saved 
S3Bucket
block:
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from prefect_aws.s3 import S3Bucket

# Instantiate and save the S3Bucket block
s3_bucket_block = S3Bucket(bucket_name="your-bucket-name")
s3_bucket_block.save(name="s3-results-storage", overwrite=True)
Store that it’s robustly saved server-side. 2. Flow Code (my_flow.py):
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from prefect import flow
from prefect_aws import S3Bucket
from datetime import datetime

# Load the already saved S3Bucket block
result_storage = S3Bucket.load("s3-results-storage")

@flow(
    log_prints=True,
    retries=1,
    persist_result=True,
    result_storage=result_storage,
    flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
)
def ingest_data_orchestrator(
    db_name: str,
    des_env: str,
    date: datetime = datetime.now(),
) -> bool:
    print("test")
    return True
3. Deploy the Flow: Using the 
prefect deploy
command specifically optimized for Prefect 3.0.0:
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prefect deploy my_flow.py:ingest_data_orchestrator --name ingest-data-orchestrator-deployment
### Verify Deployment: Check the deployment status:
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prefect deployment ls
Given Prefect 3.0 changes rapidly, ensure you're tracking any updates via Prefect documentation or release specifics. ### If the Deployment Command Fails: 1. Check Configuration & Environment: - Ensure all necessary environment variables are configured and the Prefect server is running. 2. Detailed Debugging Info: - Examine any specific errors during 
prefect deploy
. By focusing purely on Prefect 3.0's latest documentation and commands, this should align correctly with deployment procedures. Ensure you're updated with the latest CLI and tool versions as Prefect evolves. 🚀
m
@Marvin The deploy command in step 3 is what gives this error:
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❯ prefect deploy python/des_master/prefect_test.py:ingest_data_orchestrator --name ingest-data-orchestrator-deployment
The following deployment(s) could not be found and will not be deployed: ingest-data-orchestrator-deployment
Could not find any deployment configurations with the given name(s): ingest-data-orchestrator-deployment. Your flow will be deployed with a new deployment configuration.
/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py:1730: RuntimeWarning: coroutine 'sync_compatible.<locals>.coroutine_wrapper.<locals>.ctx_call' was never awaited
  flow = safe_load_flow_from_entrypoint(entrypoint)
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/Users/maciej/src/des-wrangler/python/des_master/prefect_test.py", line 32, in <module>
    @flow(
     ^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 1576, in flow
    Flow(
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 349, in __init__
    raise TypeError(
TypeError: Result storage configuration must be persisted server-side. Please call `.save()` on your block before passing it in.
m
Your message was too long by 137 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
The deploy command in step 3 is what gives this error:
```❯ prefect deploy python/des_master/prefect_test.py:ingest_data_orchestrator --name ingest-data-orchestrator-deployment The following deployment(s) could not be found and will not be deployed: ingest-data-orchestrator-deployment Could not find any deployment configurations with the given name(s): ingest-data-orchestrator-deployment. Your flow will be deployed with a new deployment configuration. /Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py1730 RuntimeWarning: coroutine 'sync_compatible.<locals>.coroutine_wrapper.<locals>.ctx_call' was never awaited flow = safe_load_flow_from_entrypoint(entrypoint) RuntimeWarning: Enable tracemalloc to get the object allocation traceback Traceback (most recent call last): File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/Users/maciej/src/des-wrangler/python/des_master/prefect_test.py", line 32, in <module> @flow( ^^^^^ File "/Users/maciej/
m
@Marvin
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❯ prefect deploy python/des_master/prefect_test.py:ingest_data_orchestrator --name ingest-data-orchestrator-deployment
The following deployment(s) could not be found and will not be deployed: ingest-data-orchestrator-deployment
Could not find any deployment configurations with the given name(s): ingest-data-orchestrator-deployment. Your flow will be deployed with a new deployment configuration.
/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py:1730: RuntimeWarning: coroutine 'sync_compatible.<locals>.coroutine_wrapper.<locals>.ctx_call' was never awaited
  flow = safe_load_flow_from_entrypoint(entrypoint)
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/Users/maciej/src/des-wrangler/python/des_master/prefect_test.py", line 32, in <module>
    @flow(
     ^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 1576, in flow
    Flow(
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 349, in __init__
    raise TypeError(
TypeError: Result storage configuration must be persisted server-side. Please call `.save()` on your block before passing it in.
m
Your message was too long by 124 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
```❯ prefect deploy python/des_master/prefect_test.py:ingest_data_orchestrator --name ingest-data-orchestrator-deployment
The following deployment(s) could not be found and will not be deployed: ingest-data-orchestrator-deployment Could not find any deployment configurations with the given name(s): ingest-data-orchestrator-deployment. Your flow will be deployed with a new deployment configuration. /Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py1730 RuntimeWarning: coroutine 'sync_compatible.<locals>.coroutine_wrapper.<locals>.ctx_call' was never awaited flow = safe_load_flow_from_entrypoint(entrypoint) RuntimeWarning: Enable tracemalloc to get the object allocation traceback Traceback (most recent call last): File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/Users/maciej/src/des-wrangler/python/des_master/prefect_test.py", line 32, in <module> @flow( ^^^^^ File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master
m
@Marvin
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Traceback (most recent call last):
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/Users/maciej/src/des-wrangler/python/des_master/prefect_test.py", line 32, in <module>
    @flow(
     ^^^^^
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 1576, in flow
    Flow(
  File "/Users/maciej/Library/Caches/pypoetry/virtualenvs/des-master-ha-6P1jN-py3.11/lib/python3.11/site-packages/prefect/flows.py", line 349, in __init__
    raise TypeError(
TypeError: Result storage configuration must be persisted server-side. Please call `.save()` on your block before passing it in.
m
It seems we are still hitting a wall with the 
TypeError
. This suggests that the 
result_storage
configuration isn't being recognized as persisted server-side, despite having saved it prior. Let's walk through the most meticulous steps to ensure 
S3Bucket
is saved correctly and is being utilized appropriately within your flow configuration. ### Step-by-Step Solution #### Step 1: Ensure 
S3Bucket
Block is Saved 1. Create and Save the 
S3Bucket
Block:
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from prefect_aws.s3 import S3Bucket

# Instantiate and save the S3Bucket block
s3_bucket_block = S3Bucket(
    bucket_name="your-bucket-name"
    # Optionally, configure credentials if needed
)
s3_bucket_block.save(name="s3-results-storage", overwrite=True)
Run the script to ensure the block is saved. #### Step 2: Verify Block is Saved and Accessible To ensure that the block is truly persisted:
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from prefect_aws.s3 import S3Bucket

# Try loading the block to confirm it's saved and accessible
try:
    s3_bucket_block = S3Bucket.load("s3-results-storage")
    print("S3Bucket block loaded successfully:", s3_bucket_block)
except Exception as e:
    print("Failed to load S3Bucket block:", e)
Run this script to ensure it prints the block details without errors. #### Step 3: Correct Flow Configuration Using the Saved Block Properly configure and deploy the flow: 1. Flow Implementation in `my_flow.py`:
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from prefect import flow
from prefect_aws import S3Bucket
from datetime import datetime

# Load the saved S3Bucket block
result_storage = S3Bucket.load("s3-results-storage")

@flow(
    log_prints=True,
    retries=1,
    persist_result=True,
    result_storage=result_storage,
    flow_run_name="{des_env}/{db_name}-{date:%Y%m%dT%H%M}",
)
def ingest_data_orchestrator(
    db_name: str,
    des_env: str,
    date: datetime = datetime.now(),
) -> bool:
    print("test")
    return True
2. Deploy the Flow Using Prefect 3.0 CLI: Prefect 3.0 uses:
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prefect deploy my_flow.py:ingest_data_orchestrator --name ingest-data-orchestrator-deployment
If command issues persist in 3.0, ensure: - Confirm CLI command syntax via:
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prefect deploy --help
### Verify Configuration via Prefect UI: After deployment, verify in the Prefect UI or Prefect cloud console, ensuring the deployment references the correct block and flow configurations. ### Final Troubleshooting Checklist: - Ensure Prefect server is running. - Validate all environment variables. - Enable verbose logging via:
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prefect config set PREFECT_LOGGING_LEVEL=DEBUG
- If errors persist, review Prefect 3.0’s latest documentation for any changes or new configurations. Following these steps diligently should align with Prefect 3.0’s requirements and resolve the persisting 
TypeError
. If issues remain elusive, detailed logs from debug settings will provide precise guidance to fine-tune configurations.
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