@Marvin what is the difference for deployments on uses build_from_flow() function or from_source() function?

When deploying flows in Prefect, two methods can be used:

build_from_flow()

and

from_source()

. Here's a summary of the differences between them: 1. build_from_flow(): - This method is used to create a deployment from a flow object directly. - It is typically used for local development where the flow code is available in the local environment. - You can specify additional deployment parameters such as storage, infrastructure, or schedules directly while creating the deployment. - Example usage can be seen where the deployment is built directly from the flow function you have defined. For instance, using the GitHub storage to load your flow code, you might write:

Deployment.build_from_flow(
         my_flow,
         name="my-deployment",
         storage=GitHub.load("demo-repo"),
         parameters=dict(name="Marvin"),
     )

2. from_source(): - This approach is suitable for flows stored in remote storage (such as GitHub, AWS S3, etc.). -

from_source()

is used to load your flow from the specified source location, and is part of a workflow to deploy it separately. - It allows for more flexible deployment scenarios where you might build the deployment on a different environment or want the deployment to always pull the latest version of the flow code from a remote source. - You can specify the source, like a GitHub repository, and entry point in your code:

flow.from_source(
         source=GitHub.load("demo-repo"),
         entrypoint="example.py:my_flow"
     )

- This method involves specifying where the flow code resides and then instructing Prefect to create the deployment from the source without having the flow code in the local deployment environment. These two methods cater to different workflow styles, with

build_from_flow()

being more suited for rapid local development and testing, and

from_source()

accommodating a remote and possibly collaborative workflow where flows are stored externally. For more details, check the Prefect documentation resources: - Upgrade Guide: Agents to Workers - Concepts: Deployments Block-Based

@Marvin how can I use build_from_flow() where the source is a S3Bucket block?

To use the

build_from_flow()

function with an S3Bucket block in Prefect, you can follow these steps based on the documentation: 1. Define the S3Bucket Block: First, you need to define an S3Bucket block using Prefect's block configurations. This involves setting up the bucket with AWS credentials.

python
from prefect.filesystems import S3Bucket
from prefect.filesystems.credentials import AWSCredentials

# Create an S3 bucket storage block
my_s3_bucket = S3Bucket(
    bucket_name="my_s3_bucket",
    credentials=AWSCredentials.load("my_aws_credentials")
)
my_s3_bucket.save("my_s3_bucket_block")

2. Build Deployment from a Flow: Then, use the

build_from_flow()

method to create a deployment. You can refer to the saved S3Bucket block by its name while building the deployment.

python
from prefect.deployments import Deployment
from prefect import flow

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

# Build the deployment
Deployment.build_from_flow(
    flow=my_flow,
    name="example-deployment",
    storage=S3Bucket.load("my_s3_bucket_block")
)

3. Persist Deployment Configuration: The deployment will be configured to store the flow's code in the specified S3 bucket location. For more detailed guidance and examples, you can explore the Prefect documentation: - Prefect Storage Concepts - Prefect Block Concepts These steps should help you set up a Prefect deployment with an S3 bucket as the storage backend for your flow code.

@Marvin in Deployment.build_from_flow() function storage parameter in which format should be?

In the

Deployment.build_from_flow()

function, the

storage

parameter is expected to be a Prefect Storage block. This can be specified using Prefect's built-in storage classes like

S3Bucket

,

Azure

,

GitHub

, etc. The

storage

parameter essentially determines how and where your flow code is stored and accessed when deployments are executed. Here's how you can configure this parameter: 1. Define a Storage Block: You need to first create a storage block and save it. For instance, an S3 Bucket can be configured as follows:

python
from prefect.filesystems import S3Bucket

s3_storage = S3Bucket(
    bucket_name="my_bucket"
)
s3_storage.save("my_s3_bucket_block")

2. Use the Storage Block in Deployment: You can refer to this saved block when building a deployment by loading it with its name:

python
from prefect.deployments import Deployment
from prefect import flow

@flow
def my_flow():
    print("Hello, World!")

# Use the storage block while creating the deployment
Deployment.build_from_flow(
    flow=my_flow,
    name="example-deployment",
    storage=S3Bucket.load("my_s3_bucket_block")
)

When specifying the storage parameter, you should ensure that the appropriate storage library (such as

s3fs

for AWS S3) is installed, as Prefect relies on it to manage file uploads and downloads. For more considerations regarding Prefect storage options, you might want to review Prefect's Storage Concepts documentation.

@Marvin how to create a block in S3Bucket based on an S3 block?

To create an

S3Bucket

block based on an existing

S3

block in Prefect, you'll need to follow these steps, which involve setting up the necessary configurations and credentials: 1. Create AWS Credentials Block: First, make sure you have an AWS credentials block available. This stores the access key ID and secret access key.

python
from prefect.filesystems import AWSCredentials

# Create and save AWS credentials block
aws_credentials = AWSCredentials(
    aws_access_key_id="your-access-key-id",
    aws_secret_access_key="your-secret-access-key"
)
aws_credentials.save("my_aws_credentials")

2. Create S3Bucket Block: Using the credentials you've set up, create the

S3Bucket

block.

python
from prefect.filesystems import S3Bucket

# Set up the S3Bucket block with AWS credentials
my_s3_bucket = S3Bucket(
    bucket_name="my_s3_bucket",
    credentials=AWSCredentials.load("my_aws_credentials")
)
my_s3_bucket.save("my_s3_bucket_block")

This code configures an

S3Bucket

block to use specified credentials, and allows Prefect to upload your flow code and other related resources to this S3 bucket for task execution and storage. The

save

method is used to persist the configuration. 3. Usage: This block can now be used for storing flow artifacts and can be referenced in deployments or other configurations within Prefect. By following these steps, you'll have an

S3Bucket

block ready to be used within your Prefect workflows, leveraging AWS's S3 storage capabilities. For more information on blocks, check Prefect's Block Concepts documentation.

@Marvin I have this S3(bucket_path='bucket-name/flow-code', aws_access_key_id=None, aws_secret_access_key=None) and I want to create a block with S3Bucket which will be bucket name in this case?

To create an

S3Bucket

block from an

S3

block configuration like the one you mentioned (

S3(bucket_path='bucket-name/flow-code', aws_access_key_id=None, aws_secret_access_key=None)

), you need to extract the bucket name from the

bucket_path

and use it to initialize the

S3Bucket

block. Here's how you can do it: 1. Extract Bucket Name: The bucket name can be extracted directly from the

bucket_path

. In your example, the bucket name is

'bucket-name'

. 2. Create S3Bucket Block: You can then create the

S3Bucket

block using the extracted bucket name and your AWS credentials. Here’s a step-by-step Python code example:

python
from prefect.filesystems import S3Bucket
from prefect.filesystems.credentials import AWSCredentials

# Assuming AWS credentials are already available
# If not, you'd create and save the credentials block as shown before

# Set up the S3 bucket using the details provided
my_s3_bucket = S3Bucket(
    bucket_name='bucket-name',  # Extracted from 'bucket-path'
    credentials=AWSCredentials.load("my_aws_credentials")  # Load existing credentials
)

# Save the block configuration for later use
my_s3_bucket.save("my_s3_bucket_block")

### Key Points: - Bucket Name: Extracted directly from the

bucket_path

, i.e., everything before the first slash. - Credentials: Assuming you have AWS credentials stored as a Prefect block under "my_aws_credentials". - Block Storage: This configuration is saved so it can be reused across deployments or flows. This setup will allow you to store and retrieve Prefect flow code from the specified Amazon S3 bucket. If you haven't already set up your AWS credentials in Prefect, you will need to create the

AWSCredentials

block first.

@Marvin how to create from prefect dashboard?

Creating an S3Bucket block from the Prefect dashboard is a straightforward process. Here's how you can do it: 1. Navigate to the Prefect Dashboard: Log in to your Prefect Cloud or your local Prefect Orion dashboard. 2. Access the Blocks Page: - Look for a menu item or a tab called "Blocks" or "Block Storage" on the sidebar of the dashboard. 3. Create a New Block: - Find a button or link to create or add a new block. This could be labeled "Create Block", "Add +", or something similar. 4. Select S3Bucket Block: - In the block creation view, you should see a list or library of block types. Look for "S3Bucket" among these types. - Click on it to proceed with creating an S3Bucket block. 5. Configure Your S3Bucket Block: - You will be prompted to enter configuration details for the block. These typically include the bucket name (e.g.,

'bucket-name'

) and the credentials. - For credentials, you can either enter them directly or reference a previously created AWSCredentials block. 6. Save the Block: - After entering all necessary details, find the save or create button to save the new block configuration. - The block will now appear in your list of available blocks and can be used for deployment or flow storage. By following these steps in the Prefect dashboard, you should have successfully created a new S3Bucket block. This allows you to manage flow storage directly through the Prefect web interface, providing ease of use and a centralized configuration environment.

@Marvin in the dashboard also need to provide a block name. When I want to do this S3Bucket.load("my_s3_bucket_block") can I use block name to load or bucket name?

When you use

S3Bucket.load("my_s3_bucket_block")

in your Prefect code, you should provide the block name, not the bucket name. The block name is the identifier you assign when you save the block configuration through the Prefect dashboard or the API. Here's a quick clarification: - Block Name: This is a unique identifier for the S3Bucket block within Prefect. It is used to retrieve and reference the block configuration in your flows. For example, when you save the block as

my_s3_bucket_block

, this is what you would use in the

load()

function. - Bucket Name: This is the actual name of the S3 bucket in AWS where your files are stored. This is specified as part of the block's configuration but is not used to load the block in Prefect. So, when you want to load and use the block in your Prefect scripts, you would use the block name like this:

python
from prefect.filesystems import S3Bucket

# Load the S3Bucket block using the block name
s3_bucket_block = S3Bucket.load("my_s3_bucket_block")

Ensure that the block name you provide matches exactly what you used when creating the block in the Prefect dashboard.

@Marvin when I use S3Bucket with build_from_flow() it gives an error like this: prefect.exceptions.ScriptError: Script at 'insights_orchestration/suggested_tags/flows/suggest_tags.py' encountered an exception: FileNotFoundError(2, 'No such file or directory')

The error you're encountering,

prefect.exceptions.ScriptError: FileNotFoundError

, suggests that Prefect is unable to locate the specified script file when using

S3Bucket

with

build_from_flow()

. Here are some potential causes and solutions to consider: 1. Incorrect Path: Ensure that the path to your script (

'insights_orchestration/suggested_tags/flows/suggest_tags.py'

) is correct. The path must be relative to the location from which you are running your Prefect commands or creating the deployment. 2. Local Environment: If you're executing this command locally, ensure that the script exists on your local file system at the specified path. If it doesn't exist locally, Synchronize your local environment, or consider adjusting the path. 3. S3Bucket Configuration: Verify that the S3Bucket block is correctly configured and accessible, with the bucket name and credentials correctly set. Check if the script file is indeed uploaded to the specified bucket path. 4. Prefect Server or Cloud: If you're using Prefect Cloud or a Prefect server, ensure that the script is accessible from the server environment. The server must have access to the S3 bucket and the specified file path. 5. Deployment Context: If the deployment is picking up the flow from a remote location (such as S3), ensure that all necessary files along with the Python script are correctly uploaded to the expected S3 path. If these areas don't resolve the issue, you can also consider logging more detailed information about the paths and configuration to diagnose what might be going wrong. If you consistently encounter this issue, feel free to reach out to Prefect's community support channels for deeper insight or specific fixes related to your environment's setup.

@Marvin the error occurred after the change of the block from S3 to S3Bucket

When transitioning from an

S3

block to an

S3Bucket

block, there are a few key considerations that could lead to the

FileNotFoundError

. Here's how you might address these: 1. Path Configuration: - Ensure the path specified in your flow's script matches the directory structure and files in your S3 bucket. - The

bucket_name

and any path configuration within the

S3Bucket

block should directly map to the actual structure in your S3 bucket. 2. AWS Credentials and Access: - Double-check that the

AWSCredentials

block used in

S3Bucket

is correct and has the necessary permissions to read from the bucket path. - The credentials should be properly loaded and referenced in your Prefect configuration. 3. Block Instantiation and Use: - When you create the

S3Bucket

block, make sure you're not just changing the block type but also properly configuring it to match the previous settings of your

S3

block (ensuring equivalency in terms of paths and access configs). 4. Code Adaptation: - If your scripts or flows expect a certain environment setup based on the

S3

block, they might require adjustments to work with the

S3Bucket

block. - Verify the way your script accesses files and ensure it aligns with the new block's configuration. 5. Debugging Information: - Add logging statements to capture the paths and configurations being accessed, which might reveal if the file path resolves differently under the new block setup. Here’s an example of transitioning and ensuring configurations:

python
from prefect.filesystems import S3Bucket
from prefect.filesystems.credentials import AWSCredentials

# Configure the S3Bucket block
s3_bucket_block = S3Bucket(
    bucket_name="bucket-name",  # Ensure this aligns with your actual S3 bucket
    credentials=AWSCredentials.load("my_aws_credentials")  # Loads your AWS credentials
)

# Save or load the block as necessary
s3_bucket_block.save("my_s3_bucket_block")

Finally, double-check your flow or deployment code to ensure compatibility with any path or storage changes that accompany the new block type.

@Marvin how to create an S3Bucket block in prefect cloud dashboard based on S3 block configuration S3(bucket_path='bucket-name/flow-code', aws_access_key_id=None, aws_secret_access_key=None) ?

To create an

S3Bucket

block in the Prefect Cloud dashboard based on your existing

S3

block configuration, follow these steps: 1. Log into Prefect Cloud Dashboard: - Navigate to your Prefect Cloud workspace and log in. 2. Access the Blocks Section: - Find and click on a section usually labeled "Blocks" or "Block Storage" in the dashboard's navigation menu. 3. Create a New Block: - Look for a button to add or create a new block, typically labeled "Create Block" or "Add +". 4. Select S3Bucket Block Type: - Choose "S3Bucket" from the list of available block types. 5. Configure the S3Bucket Block: - Enter the Bucket Name: Extract 'bucket-name' from your

S3

block and fill it in the "Bucket Name" field. - Set Credentials: You might need to either create or link to an existing AWS credentials block: - If your credentials are embedded, create a new AWS credentials block directly in the dashboard by entering the access key ID and secret access key. - If using environment-based or previously stored credentials, select the appropriate AWS credentials block. 6. Set Additional Configuration (if available): - If there's an option to specify a default directory or path within the bucket, set it to 'flow-code' if applicable, or as required by your workflow. This aligns with

bucket_path='bucket-name/flow-code'

. 7. Assign a Block Name: - Choose a unique name for your block, such as "my_s3_bucket_block", which you'll use to reference this configuration in scripts. 8. Save the Block: - Click Save or Create to finalize and persist this block configuration. Once done, your

S3Bucket

block is set up and can be accessed in your Prefect flows or deployments using the block name you specified:

python
from prefect.filesystems import S3Bucket

# Load the S3Bucket block using the block name
s3_bucket_block = S3Bucket.load("my_s3_bucket_block")

Ensure all necessary permissions are granted for the access keys used in your Prefect configuration, particularly for accessing the specified bucket and paths. This setup should thereby reflect equivalent functionality to your initial

S3

block definition.

@Marvin in S3 block there is Bucket Path=bucket-name/flow-code how can configure Bucket Name and bucket folder for an S3Bucket based on configuration of the S3 block?

To configure an

S3Bucket

block using an existing

S3

block configuration with a

Bucket Path

of

'bucket-name/flow-code'

, you'll need to separate the

Bucket Path

into two parts: the

Bucket Name

and the

Bucket Folder

. Here's how you can do this: 1. Extract Bucket Name and Bucket Folder: From your configuration, the

Bucket Path

is

'bucket-name/flow-code'

. - Bucket Name: This is the root of your S3 path, i.e.,

'bucket-name'

. - Bucket Folder: This is the sub-directory or folder within the bucket, i.e.,

'flow-code'

. 2. Configure the S3Bucket Block: Use these extracted values to set up your

S3Bucket

block configuration. Here's a step-by-step configuration to create an

S3Bucket

block in Prefect: - Open Prefect Cloud Dashboard: Log in to your Prefect Cloud account. - Navigate to Blocks: Go to the "Blocks" section of the dashboard. - Create a New Block: Click on "Create Block" or "Add +" to start setting up a new block. - Select S3Bucket: Choose "S3Bucket" from the list of block types. - Configure the Block: - Enter Bucket Name: Use

'bucket-name'

in the "Bucket Name" field. - Set Bucket Folder: Use

'flow-code'

in the "Bucket Folder" or similar field if available. - Set Credentials: - Ensure AWS credentials are properly set if prompted. You can either enter credentials directly or select an existing AWS credentials block. - Provide a Block Name: - Assign a unique name to this block, e.g., "my_s3_bucket_block". - Save the Block: - Complete the setup by saving the block configuration. Once set up, you can then reference this block in your Prefect code as follows:

python
from prefect.filesystems import S3Bucket

# Load the S3Bucket configuration by its block name
s3_bucket_block = S3Bucket.load("my_s3_bucket_block")

This configuration will ensure that your S3Bucket references the correct bucket and sub-directory structure to match how it was previously set up using the

S3

block. Make sure that your AWS credentials have the necessary permissions to access both the bucket and its specified folder.

@Marvin I have also this function

deployment = Deployment.build_from_flow(
    flow=suggest_tags.perform,
    name="on-demand",
    version=VERSION,
    work_queue_name="default",
    storage=S3.load("flow-code"),
    path=f"suggest_tags/{VERSION}",
    infrastructure=infrastructure,
)

and I want to obtain this to use S3Bucket block

To adapt your function to use an

S3Bucket

block instead of an

S3

block, the main change will be in how you refer to and configure the storage. Here's how you can modify your code: 1. Migrate to S3Bucket Block: - You need to create and configure an

S3Bucket

block in the Prefect Cloud dashboard, as previously discussed. - Save this block with a block name, for example, "my_s3_bucket_block". 2. Update the Deployment Code: - Replace the line where the

S3

block is used with the corresponding load call to your

S3Bucket

block. Here's how your updated code would look:

python
from prefect.deployments import Deployment
from prefect.filesystems import S3Bucket

# Load the S3Bucket block using its name
s3_bucket_block = S3Bucket.load("my_s3_bucket_block")

# Build the deployment using the S3Bucket block as storage
deployment = Deployment.build_from_flow(
    flow=suggest_tags.perform,
    name="on-demand",
    version=VERSION,
    work_queue_name="default",
    storage=s3_bucket_block,  # Updated to use S3Bucket
    path=f"suggest_tags/{VERSION}",  # The sub-path in the bucket, keep consistent with your existing structure
    infrastructure=infrastructure,
)

### Key Steps: - Create and Save S3Bucket Block: Ensure that you've configured your

S3Bucket

block in the Prefect Cloud dashboard with the correct bucket name and folder path. This block will replace the original

S3

block configuration. - Load the Block in Code: You'll continue to use the

load()

method but with your

S3Bucket

block name ("my_s3_bucket_block"). - Set Path Parameter: The

path

parameter in the deployment should continue to specify the directory structure within your bucket that you want to use for this flow's version. By making these changes, you'll transition your deployment setup to the newer

S3Bucket

block configuration, aligning with Prefect's updated storage and block management practices. Ensure your AWS credentials and permissions are appropriately configured for this to work seamlessly.