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HI <@ULVA73B9P> How can I convert the following `...
# ask-marvin
a
HI @Marvin How can I convert the following 
Deployment.build_from_flow
to 
Flow.deploy
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Deployment.build_from_flow(
                flow=contract_info_flow,
                # The flow name should be unique in case multiple deployments
                name=f"{project_name}-{current_file}-fo",
                version="1",
                tags=[project_name],
                work_pool_name="local-pool",
                work_queue_name="local-queue",
                schedules=[cron],
                parameters={
                    "start_date": "",
                    "end_date": "",
                    "instrument_types": ["OPT", "FUT"],
                    "underlyings": ["ALL"],
                    "segment": "fo",
                },
                infrastructure=Process(
                    working_dir=f"/home/{os.environ.get('USER')}/miniflows/"
                ),
            )
m
It seems there's an issue accessing detailed documentation directly. However, based on information and typical usage patterns, here is a general guide on how to convert the 
Deployment.build_from_flow
to `Flow.deploy`:
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python
from prefect import Flow
from prefect.deployments import Deployment
from prefect.infrastructure import Process
from prefect.schedules import CronSchedule

# Assuming `contract_info_flow` is your flow instance
contract_info_flow = Flow("contract_info_flow")

# Update this to use Flow.deploy
contract_info_flow.deploy(
    name=f"{project_name}-{current_file}-fo",
    version="1",
    tags=[project_name],
    work_pool_name="local-pool",
    work_queue_name="local-queue",
    schedule=CronSchedule(cron),  # corrected to use CronSchedule if `cron` is the cron string 
    parameters={
        "start_date": "",
        "end_date": "",
        "instrument_types": ["OPT", "FUT"],
        "underlyings": ["ALL"],
        "segment": "fo",
    },
    infrastructure=Process(
        working_dir=f"/home/{os.environ.get('USER')}/miniflows/"
    ),
)
### Key Points: 1. Flow Instance: Ensure 
contract_info_flow
is defined as a 
Flow
instance. 2. Infrastructure: Ensure you have the necessary imports and your infrastructure is properly defined. 3. Schedule: Make sure your schedule definition (CronSchedule in this case) is correct. Please refer to the official documentation when accessible for the most precise and up-to-date information. Here is a related example for deploying a flow: Prefect Examples.
a
@Marvin The section
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infrastructure=Process(
        working_dir=f"/home/{os.environ.get('USER')}/miniflows/"
    )
has been deprecated in the version 
2.19.7
in 
Flow.deploy
an I want to specify the 
working_dir
, can I do that?
m
It appears that detailed documentation specifically addressing 
working_dir
deprecation in 
Flow.deploy
for Prefect version 2.19.7 is not readily available. However, it's possible to provide a general guideline on how to accomplish similar functionality. Since the infrastructure level argument 
working_dir
might have been deprecated, here are some possible alternatives: 1. Environment Variables: Set the working directory through environment variables within your deployment:
Copy code
python
   import os
   from prefect import Flow
   from prefect.deployments import Deployment
   from prefect.infrastructure import Process
   from prefect.schedules import CronSchedule

   os.environ['PREFECT_HOME'] = f"/home/{os.environ.get('USER')}/miniflows/"

   contract_info_flow = Flow("contract_info_flow")

   contract_info_flow.deploy(
       name=f"{project_name}-{current_file}-fo",
       version="1",
       tags=[project_name],
       work_pool_name="local-pool",
       work_queue_name="local-queue",
       schedule=CronSchedule(cron),
       parameters={
           "start_date": "",
           "end_date": "",
           "instrument_types": ["OPT", "FUT"],
           "underlyings": ["ALL"],
           "segment": "fo",
       },
   )
2. Job Customization: If the 
Process
infrastructure is deprecated, you might need to use another infrastructure type or override job-specific properties directly via your execution environment's configuration. 3. Configuration File: Use Prefect configuration files to set global working directories or other settings, which apply across flow runs. While we investigate further, these approaches can bridge the gap caused by the deprecation of 
working_dir
at the infrastructure level. For additional guidance or specific implementation details, monitoring the official Prefect documentation for updates or contacting their support would be advisable.
a
@Marvin how can I update the 
PREFECT_HOME
in the configuration file?
m
To update the 
PREFECT_HOME
in the configuration file, you can use a 
.env
file. Here's a way to achieve this: 1. Create a 
.env
file: This file should be located in the root directory of your project or where your Prefect configuration is loaded. 2. Set the 
PREFECT_HOME
variable: Define the 
PREFECT_HOME
variable in your 
.env
file. For example, your 
.env
file should look like this:
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# .env
PREFECT_HOME=/home/your_user/miniflows
3. Ensure Prefect Reads the 
.env
file: Prefect uses Pydantic for its settings, which supports the 
.env
file. As long as your Prefect setup is properly configured, it should read the 
.env
file automatically. ### Example Usage Here's how you can ensure your Prefect setup loads the 
.env
file:
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python
import os
from prefect import Flow
from prefect.deployments import Deployment
from prefect.schedules import CronSchedule

# Ensure the .env file is read
from dotenv import load_dotenv

load_dotenv()

contract_info_flow = Flow("contract_info_flow")

contract_info_flow.deploy(
    name=f"{project_name}-{current_file}-fo",
    version="1",
    tags=[project_name],
    work_pool_name="local-pool",
    work_queue_name="local-queue",
    schedule=CronSchedule(cron),
    parameters={
        "start_date": "",
        "end_date": "",
        "instrument_types": ["OPT", "FUT"],
        "underlyings": ["ALL"],
        "segment": "fo",
    },
)
### Confirmation You can confirm that the 
PREFECT_HOME
is set correctly by running:
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sh
prefect config view
This command should show the 
PREFECT_HOME
path as defined in your 
.env
file. ### Additional Context This approach is based on a commonly requested feature discussed in various GitHub issues and helps manage multiple Prefect projects effectively. For reading environment variables directly from a 
.env
file, you can use the 
python-dotenv
package. Install it using:
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sh
pip install python-dotenv
By ensuring your 
.env
file is correctly set up and loaded, you can manage Prefect's 
PREFECT_HOME
without directly modifying environment variables manually.
a
@Marvin I am getting the following error while using the above deployment syntax
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ValueError: Either an image or remote storage location must be provided when deploying a deployment.
m
The error message 
ValueError: Either an image or remote storage location must be provided when deploying a deployment
indicates that Prefect requires either an image or a remote storage location when deploying a flow. ### Solution Here's how you can provide a remote storage location: 1. Remote Storage: You can use a remote storage option like S3, Google Cloud Storage, or any other supported storage. Here's an example using S3. 2. Example with S3: First, make sure you have the necessary libraries installed:
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sh
   pip install prefect[aws]
Then, update your deployment to use S3 storage:
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python
   from prefect import Flow
   from prefect.deployments import Deployment
   from prefect.infrastructure import Process
   from prefect.schedules import CronSchedule
   from prefect.filesystems import S3


   # S3 storage setup
   s3_storage = S3(bucket_path="<s3://your-bucket/path/to/flows>")

   contract_info_flow = Flow("contract_info_flow")

   contract_info_flow.deploy(
       name=f"{project_name}-{current_file}-fo",
       version="1",
       tags=[project_name],
       work_pool_name="local-pool",
       work_queue_name="local-queue",
       schedule=CronSchedule(cron),
       parameters={
           "start_date": "",
           "end_date": "",
           "instrument_types": ["OPT", "FUT"],
           "underlyings": ["ALL"],
           "segment": "fo",
       },
       storage=s3_storage,  # Adding the storage argument
   )
### Key Points: 1. Storage Setup: Ensure that you have configured your remote storage correctly and have the necessary permissions. 2. Flow Definition: Include the 
storage
parameter in your 
Flow.deploy
call. By using a remote storage option, you ensure that Prefect has a location to pull the necessary files when running the deployment. Also, make sure to adjust the bucket path and storage options according to your environment and preferences. For more detailed guidance, you can refer to the Prefect documentation on storage
a
@Marvin how can I deploy my flow by using local storage
m
To use local storage when deploying your flow with Prefect, you can utilize the 
LocalFileSystem
storage block. Here is how you can achieve this: 1. Make sure you have the necessary imports:
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python
   from prefect import Flow
   from prefect.deployments import Deployment
   from prefect.infrastructure import Process
   from prefect.schedules import CronSchedule
   from prefect.filesystems import LocalFileSystem
2. Define your flow and deployment:
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python
   import os
   from prefect import Flow
   from prefect.deployments import Deployment
   from prefect.infrastructure import Process
   from prefect.schedules import CronSchedule
   from prefect.filesystems import LocalFileSystem

   # Local storage setup
   local_storage = LocalFileSystem(basepath=f"/home/{os.environ.get('USER')}/miniflows/")

   # Assuming `contract_info_flow` is your flow instance
   contract_info_flow = Flow("contract_info_flow")

   contract_info_flow.deploy(
       name=f"{project_name}-{current_file}-fo",
       version="1",
       tags=[project_name],
       work_pool_name="local-pool",
       work_queue_name="local-queue",
       schedule=CronSchedule(cron),
       parameters={
           "start_date": "",
           "end_date": "",
           "instrument_types": ["OPT", "FUT"],
           "underlyings": ["ALL"],
           "segment": "fo",
       },
       storage=local_storage,  # Adding the local storage
   )
### Key Points: 1. Local File System Storage: The 
LocalFileSystem
storage block points to a directory on your local file system. 2. Flow Definition: You continue defining your flow and deployment similarly but add the 
storage
parameter with 
LocalFileSystem
. By specifying the 
LocalFileSystem
block, you ensure that your flow is deployed using the local file system for storage. ### Full Deployment Example:
Copy code
python
import os
from prefect import Flow
from prefect.deployments import Deployment
from prefect.infrastructure import Process
from prefect.schedules import CronSchedule
from prefect.filesystems import LocalFileSystem

# Define local storage
local_storage = LocalFileSystem(basepath=f"/home/{os.environ.get('USER')}/miniflows/")

# Assuming `contract_info_flow` is your flow instance
contract_info_flow = Flow("contract_info_flow")

# Deploy the flow
contract_info_flow.deploy(
    name=f"{project_name}-{current_file}-fo",
    version="1",
    tags=[project_name],
    work_pool_name="local-pool",
    work_queue_name="local-queue",
    schedule=CronSchedule(cron),
    parameters={
        "start_date": "",
        "end_date": "",
        "instrument_types": ["OPT", "FUT"],
        "underlyings": ["ALL"],
        "segment": "fo",
    },
    storage=local_storage,  # Adding the local storage
)
Make sure the paths are correctly set and that Prefect has permissions to read/write in the specified directories. This should deploy your flow using local storage without requiring remote storage or an image.
a
@Marvin 
Flow.deploy
doesn't have the storage parameter
m
It seems the proper guidance on using 
local storage
with 
Flow.deploy
is still elusive due to access issues and limited specific examples. Given 
Flow.deploy
doesn't directly support the 
storage
parameter, we need to handle storage using local file references and configurations. Here’s a concise guide to deploy a flow using local storage: ### Approach: - Use 
LocalFileSystem
directly within the flow deployment process. ### Steps to Deploy with Local Storage: 1. Create a Local Storage Block: This will point to the directory where your flow's source code resides. 2. Configure Your Flow Deployment: Ensure the flow can deploy with local references without requiring remote storage or an image. ### Example: 1. Define Local Storage in Your Flow Code:
Copy code
python
   from prefect import Flow
   from prefect.deployments import Deployment
   from prefect.filesystems import LocalFileSystem
   from prefect.schedules import CronSchedule
   
   # Define local storage 
   local_storage = LocalFileSystem(
       basepath=f"/home/{os.environ.get('USER')}/miniflows"
   )

   # Assuming `contract_info_flow` is your flow instance
   contract_info_flow = Flow("contract_info_flow")

   # Use storage for deployment
   deployment = Deployment.build_from_flow(
       flow=contract_info_flow,
       name=f"{project_name}-{current_file}-fo",
       version="1",
       tags=[project_name],
       work_pool_name="local-pool",
       work_queue_name="local-queue",
       schedule=CronSchedule(cron),
       parameters={
           "start_date": "",
           "end_date": "",
           "instrument_types": ["OPT", "FUT"],
           "underlyings": ["ALL"],
           "segment": "fo",
       },
       storage=local_storage,  # Attach the local storage for deployment
       path=f"/home/{os.environ.get('USER')}/miniflows/"
   )

   # Apply the deployment 
   deployment.apply()
### Key Points: 1. Local Storage Block: Ensure 
LocalFileSystem
points to the correct directory. 2. Deployment Path: Use 
path
to specify where the code and artifacts are located. 3. Deployment: Use 
Deployment.build_from_flow
method with the local storage settings to configure the deployment. ### Notes: - Ensure the local directory specified is accessible and has the necessary permissions. - The 
apply()
method finalizes and registers the deployment with Prefect. This approach ensures your flow is deployed using local storage without needing a remote storage mechanism. Revisit the Prefect documentation for updates or newer methods as the Prefect ecosystem evolves.
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