https://prefect.io logo
Join Slack
Powered by
<@ULVA73B9P> migrating to prefect 3 and i'm seeing...
# ask-marvin
y
@Marvin migrating to prefect 3 and i'm seeing couple of issues like: PrefectImportError: prefect.deployments:Deployment has been removed
m
✅ thought for 38.1 seconds
Here's how to fix this issue and migrate your deployment code to Prefect 3.x: 1. Old Way (2.x) that no longer works:
Copy code
python
from prefect.deployments import Deployment
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    ...
)
2. New Way (3.x):
Copy code
python
from prefect import flow

@flow
def my_flow():
    ...

if __name__ == "__main__":
    my_flow.from_source(
        source="<https://github.com/your-org/your-repo>",
        entrypoint="path/to/flow.py:my_flow"
    ).deploy(
        name="my-deployment",
        work_pool_name="my-work-pool"
    )
Key changes to note: 1. The entire 
Deployment
class has been removed. Instead, deployment functionality is now a method on the flow object itself. 2. CLI changes: - ❌ 
prefect deployment build
(removed) - ✅ 
prefect deploy
(new command) 3. Configuration is now primarily handled through 
prefect.yaml
files, which provide a cleaner way to manage deployment settings. 4. Work pools and workers replace the old agent system. You'll need to: - Create a work pool (
prefect work-pool create
) - Start a worker (
prefect worker start -p "my-work-pool"
) For a complete migration, you'll want to: 1. Update your deployment scripts to use the new 
.from_source().deploy()
pattern 2. Set up appropriate work pools for your deployment targets 3. Replace any agent configurations with worker configurations 4. Update your CI/CD pipelines to use 
prefect deploy
instead of 
prefect deployment build
You can find the complete migration guide in the Prefect 3.x documentation. Would you like me to provide more specific examples for any part of this migration process?
y
@Marvin do the rest of the keyword arguments stay the same and are compatible with each other?
m
✅ thought for 57.8 seconds
Let me break down the key differences in deployment arguments between 2.x and 3.x: ### Major Parameter Changes 2.x Parameters | 3.x Equivalent | Notes --------------|----------------|------- 
name
| 
name
| ✅ Still used, works the same way 
storage
| N/A | ❌ Removed, now handled via 
flow.from_source()
infrastructure
| 
work_pool_name
| 🔄 Replaced with work pool concept 
infra_overrides
| 
job_variables
| 🔄 Renamed and slightly different usage 
tags
| 
tags
| ✅ Still used, works the same way 
version
| N/A | ❌ Removed, now handled automatically 
schedule
| 
schedule
| ✅ Still used, works similarly 
parameters
| 
parameters
| ✅ Still used, works the same way ### New 3.x Parameters - `work_pool_name`: Specifies which work pool will run the deployment - `image`: For container-based deployments - `build`: Boolean to control if Docker image should be built - `push`: Boolean to control if Docker image should be pushed - `enforce_parameter_schema`: New option for parameter validation ### Example Comparison:
Copy code
python
# 2.x (OLD, no longer works)
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    infrastructure=DockerContainer.load("my-container"),
    infra_overrides={"env": {"MY_VAR": "value"}},
    tags=["prod"],
    parameters={"param1": "value1"}
)

# 3.x (NEW)
my_flow.from_source(
    source="<https://github.com/your-org/your-repo>",
    entrypoint="path/to/flow.py:my_flow"
).deploy(
    name="my-deployment",
    work_pool_name="my-docker-pool",
    job_variables={"env": {"MY_VAR": "value"}},
    tags=["prod"],
    parameters={"param1": "value1"}
)
### Important Notes: 1. Configuration Location: Many deployment settings that were previously in code are now recommended to be placed in 
prefect.yaml
files. 2. Infrastructure Management: Instead of infrastructure blocks, you now use: - Work pools for infrastructure configuration - Job variables for runtime overrides - Work queue configuration for execution control 3. Storage Changes: Rather than using storage blocks, source code location is now specified using 
from_source()
, which supports: - Git repositories - Local files - Remote URLs 4. Schedules: While schedules still exist, they're now typically defined in 
prefect.yaml
or using the UI, though they can still be specified in code. Would you like me to elaborate on any specific parameter or provide examples of how to migrate specific deployment configurations?
y
@Marvin does that mean i no longer have a deployment object? not too sure how to refactor existing codes
m
✅ thought for 50.8 seconds
You're correct - there's no longer a Deployment object in Prefect 3.x. Let me show you how to refactor common deployment-related operations: ### 1. Creating/Updating Deployments
Copy code
python
# Old way (2.x)
deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    parameters={"param1": "value1"},
    tags=["prod"]
)
deployment.apply()

# New way (3.x)
@flow
def my_flow(param1: str = "value1"):
    ...

if __name__ == "__main__":
    my_flow.from_source(
        source="<https://github.com/your-org/your-repo>",
        entrypoint="path/to/flow.py:my_flow"
    ).deploy(
        name="my-deployment",
        tags=["prod"]
    )
### 2. Loading/Accessing Deployments
Copy code
python
# Old way (2.x)
deployment = Deployment.load("flow-name/deployment-name")
deployment.update()

# New way (3.x)
from prefect.client import get_client

async def get_deployment():
    client = get_client()
    deployment = await client.read_deployment_by_name(
        "flow-name/deployment-name"
    )
    return deployment
### 3. Running Deployments
Copy code
python
# Old way (2.x)
deployment = Deployment.load("flow-name/deployment-name")
deployment.run()

# New way (3.x)
from prefect.deployments import run_deployment

# Synchronous
run_deployment(
    name="flow-name/deployment-name",
    parameters={"param1": "new_value"}
)

# Asynchronous
await run_deployment(
    name="flow-name/deployment-name",
    parameters={"param1": "new_value"}
)
### 4. Managing Deployment Configuration The preferred way in 3.x is to use `prefect.yaml`:
Copy code
yaml
# prefect.yaml
deployments:
  - name: my-deployment
    entrypoint: flow.py:my_flow
    work_pool:
      name: my-work-pool
    schedule:
      interval: 3600
    parameters:
      param1: value1
    tags:
      - prod
Then deploy using CLI:
Copy code
bash
prefect deploy
### 5. Programmatic Deployment Management If you need to manage deployments programmatically:
Copy code
python
# 3.x way
from prefect.client import get_client

async def manage_deployment():
    client = get_client()
    
    # List deployments
    deployments = await client.read_deployments()
    
    # Read specific deployment
    deployment = await client.read_deployment_by_name("flow-name/deployment-name")
    
    # Delete deployment
    await client.delete_deployment("deployment-id")
### Key Points for Refactoring: 1. Move Configuration to YAML: - Shift deployment configuration to 
prefect.yaml
files where possible - This is the recommended approach for managing deployments in 3.x 2. Use Client API: - For programmatic operations, use the Prefect client API - Most operations are async by default 3. Deployment Creation: - Use 
flow.from_source().deploy()
for creating deployments - Or use the 
prefect deploy
CLI command with 
prefect.yaml
4. Running Deployments: - Use 
run_deployment()
function instead of deployment object methods - Supports both sync and async execution 5. Work Pools: - Replace infrastructure configurations with work pool settings - Configure work pools via UI or CLI Would you like me to provide more specific examples for any particular deployment operations you're trying to refactor?
y
@Marvin so my workload had deployment = Deployment.build_from_flow( flow=my_flow, name="my-deployment", parameters={"param1": "value1"}, tags=["prod"] ) return deployment making as few amount of change as possible, how do i retain the same behavior in prefect 3?
m
✅ thought for 44.6 seconds
For the minimal possible change while maintaining similar behavior, you have two options. Here's both, from least to most change required: ### Option 1: Using Deployment Proxy Object (Minimal Change)
Copy code
python
from prefect.client import get_client
from prefect.deployments import run_deployment

class DeploymentProxy:
    def __init__(self, name, parameters=None, tags=None):
        self.name = name
        self.parameters = parameters or {}
        self.tags = tags or []
    
    def apply(self):
        # Deploy the flow when apply is called
        return self.flow.from_source(
            source=".",  # Assumes local source
            entrypoint="path/to/your/flow.py:my_flow"
        ).deploy(
            name=self.name,
            parameters=self.parameters,
            tags=self.tags
        )

def build_deployment(flow, name, parameters=None, tags=None):
    proxy = DeploymentProxy(
        name=name,
        parameters=parameters,
        tags=tags
    )
    proxy.flow = flow
    return proxy

# Your existing code stays almost the same:
deployment = build_deployment(
    flow=my_flow,
    name="my-deployment",
    parameters={"param1": "value1"},
    tags=["prod"]
)

return deployment
### Option 2: Direct Return of Deployment Name (More Pythonic for 3.x)
Copy code
python
def build_deployment(flow, name, parameters=None, tags=None):
    deployment_name = flow.from_source(
        source=".",  # Assumes local source
        entrypoint="path/to/your/flow.py:my_flow"
    ).deploy(
        name=name,
        parameters=parameters or {},
        tags=tags or []
    )
    return deployment_name

# Usage remains similar:
deployment = build_deployment(
    flow=my_flow,
    name="my-deployment",
    parameters={"param1": "value1"},
    tags=["prod"]
)

return deployment
Important Notes: 1. Option 1 maintains the most similar interface but adds complexity 2. Option 2 is simpler but changes the return type 3. Both options require you to specify the 
entrypoint
parameter 4. You'll need to adjust the 
source
parameter based on where your flow code lives The key differences from 2.x: - The deployment is created immediately rather than being built and applied separately - There's no direct equivalent to the Deployment object - You'll need to specify where your flow code lives via the 
source
and 
entrypoint
parameters Would you like me to explain more about either option or provide examples of how to use the returned deployment in your downstream code?
y
@Marvin is load_existing still a valid kwarg?
m
✅ thought for 29.7 seconds
No, 
load_existing
is not a valid parameter in Prefect 3.x. The concept of loading existing deployments has been replaced with a different approach. If you need to work with existing deployments in 3.x, you have these options: 1. Read/Load an existing deployment:
Copy code
python
from prefect.client import get_client

async def get_existing_deployment(name):
    client = get_client()
    deployment = await client.read_deployment_by_name(name)
    return deployment
2. Update an existing deployment:
Copy code
python
# Using the CLI (recommended approach):
prefect deploy

# Or programmatically:
flow.from_source(
    source=".",
    entrypoint="path/to/flow.py:my_flow"
).deploy(
    name="existing-deployment-name"  # If the name exists, it will update the deployment
)
3. Check if a deployment exists:
Copy code
python
from prefect.client import get_client

async def deployment_exists(name):
    client = get_client()
    try:
        await client.read_deployment_by_name(name)
        return True
    except Exception:
        return False
The key difference in 3.x is that deployments are more declarative - you specify what you want the deployment to look like, and Prefect handles creating or updating it accordingly. There's no need for explicit 
load_existing
functionality because: 1. If you deploy with an existing name, it updates the deployment 2. If you need to read an existing deployment, you use the client API 3. If you need to modify an existing deployment, you can use the client API or simply deploy again with updated parameters Would you like me to provide more detailed examples of any of these approaches?
y
@Marvin was there a kwarg for 
source
in prefect 2?
m
✅ thought for 35.9 seconds
No, there wasn't a 
source
parameter in Prefect 2.x's 
Deployment.build_from_flow()
. Instead, Prefect 2.x used the 
storage
parameter with storage blocks. Here's the comparison:
Copy code
python
# Prefect 2.x way
from prefect.deployments import Deployment
from prefect.filesystems import GitHub

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    storage=GitHub.load("my-github-block"),  # Used storage parameter
    parameters={"param1": "value1"}
)

# Prefect 3.x way
my_flow.from_source(
    source="<https://github.com/owner/repo>",  # Uses source parameter
    entrypoint="path/to/flow.py:my_flow"
).deploy(
    name="my-deployment",
    parameters={"param1": "value1"}
)
The key differences: - 2.x used 
storage
parameter with storage blocks (GitHub, S3, Azure, etc.) - 3.x uses 
source
parameter with direct URLs or paths - Storage blocks are removed in 3.x in favor of the simpler 
source
parameter If you're migrating code, you'll need to: 1. Remove references to storage blocks 2. Replace 
storage=...
with the appropriate 
source="..."
in the new deployment pattern 3. Add the required 
entrypoint
parameter to specify where your flow is located Would you like me to provide examples of how to migrate specific storage block configurations to the new 
source
parameter format?
y
@Marvin is there a way to convert storage into source automatically
m
✅ thought for 48.1 seconds
While there isn't an automated tool to convert storage blocks to source configurations, I can show you how to programmatically extract the information from your existing storage blocks and convert them to source URLs. Here's a helper function you can use:
Copy code
python
from prefect.client import get_client

async def convert_storage_to_source(block_name: str) -> str:
    """Convert a 2.x storage block to a 3.x source URL."""
    client = get_client()
    
    try:
        # Get the block document
        block_doc = await client.read_block_document_by_name(block_name)
        
        # Extract information based on block type
        block_type = block_doc.block_type_name
        
        if "github" in block_type.lower():
            # Extract GitHub details
            data = block_doc.data
            repository = data.get("repository")
            reference = data.get("reference", "main")
            
            return f"<https://github.com/{repository}@{reference}>"
            
        elif "s3" in block_type.lower():
            data = block_doc.data
            bucket = data.get("bucket_name")
            key = data.get("base_path", "")
            
            return f"s3://{bucket}/{key}"
            
        elif "azure" in block_type.lower():
            data = block_doc.data
            container = data.get("container")
            
            return f"azure://{container}"
            
        else:
            return "."  # Local filesystem default
            
    except Exception as e:
        print(f"Could not convert block {block_name}: {e}")
        return "."

# Usage example:
async def migrate_deployment():
    old_storage_block = "my-github-block"
    source_url = await convert_storage_to_source(old_storage_block)
    
    my_flow.from_source(
        source=source_url,
        entrypoint="path/to/flow.py:my_flow"
    ).deploy(
        name="my-deployment"
    )
Important notes: 1. This is a basic conversion - you might need to add more logic based on your specific storage block configurations 2. You'll still need to specify the 
entrypoint
parameter manually as this information isn't typically stored in the storage block 3. Some storage block types might require different URL formats 4. Local filesystem storage blocks typically convert to "." as the source Common conversions:
Copy code
python
# GitHub Storage Block -> Source URL
"github/myorg/myrepo" -> "<https://github.com/myorg/myrepo>"

# S3 Storage Block -> Source URL
"s3/mybucket/path" -> "<s3://mybucket/path>"

# Azure Storage Block -> Source URL
"azure/mycontainer" -> "<azure://mycontainer>"

# Local Storage Block -> Source URL
"local/path" -> "."
Would you like me to provide more specific examples for certain storage block types you're using?
y
@Marvin i have an optional Block object. i would like to convert that to a source string
m
✅ thought for 38.0 seconds
Here's a function that can handle an optional Block object and convert it to a source string:
Copy code
python
from typing import Optional
from prefect.blocks.core import Block

def convert_block_to_source(storage: Optional[Block]) -> str:
    """Convert a storage block to a source string, or return "." if no storage provided."""
    if storage is None:
        return "."
        
    # Get block type name
    block_type = storage.block_type_name.lower()
    
    if "github" in block_type:
        # GitHub storage block
        repository = getattr(storage, "repository", "")
        reference = getattr(storage, "reference", "main")
        return f"<https://github.com/{repository}@{reference}>"
        
    elif "s3" in block_type:
        # S3 storage block
        bucket = getattr(storage, "bucket_name", "")
        key = getattr(storage, "base_path", "")
        return f"s3://{bucket}/{key}".rstrip('/')
        
    elif "azure" in block_type:
        # Azure storage block
        container = getattr(storage, "container", "")
        return f"azure://{container}"
        
    elif "gcs" in block_type:
        # Google Cloud Storage block
        bucket = getattr(storage, "bucket", "")
        key = getattr(storage, "base_path", "")
        return f"gs://{bucket}/{key}".rstrip('/')
        
    else:
        # Default to local filesystem
        return "."

# Usage example:
storage_block = None  # or your actual storage block
source = convert_block_to_source(storage_block)

my_flow.from_source(
    source=source,
    entrypoint="path/to/flow.py:my_flow"
).deploy(
    name="my-deployment"
)
The function: 1. Handles 
None
case by returning "." (local filesystem) 2. Uses 
getattr
with defaults to safely access block attributes 3. Supports common storage block types (GitHub, S3, Azure, GCS) 4. Falls back to local filesystem for unknown block types You can use it like this:
Copy code
python
# With a GitHub block
github_block = GitHub.load("my-github-block")
source = convert_block_to_source(github_block)
# Result: "<https://github.com/owner/repo@main>"

# With an S3 block
s3_block = S3.load("my-s3-block")
source = convert_block_to_source(s3_block)
# Result: "<s3://my-bucket/my-path>"

# With no block
source = convert_block_to_source(None)
# Result: "."
Would you like me to add support for any specific storage block types you're using?
y
@Marvin is there a gitlab block type?
m
✅ thought for 44.8 seconds
Let me modify the conversion function to include GitLab support. In Prefect, GitLab repositories can be used as sources directly with their HTTPS URLs, similar to GitHub:
Copy code
python
from typing import Optional
from prefect.blocks.core import Block

def convert_block_to_source(storage: Optional[Block]) -> str:
    """Convert a storage block to a source string, or return "." if no storage provided."""
    if storage is None:
        return "."
        
    # Get block type name
    block_type = storage.block_type_name.lower()
    
    if "gitlab" in block_type:
        # GitLab storage block
        repository = getattr(storage, "repository", "")
        reference = getattr(storage, "reference", "main")
        # GitLab URLs can be either format:
        if "<http://gitlab.com|gitlab.com>" in repository:
            return f"{repository}@{reference}"  # If full URL is provided
        else:
            return f"<https://gitlab.com/{repository}@{reference}>"  # If just path is provided
            
    elif "github" in block_type:
        repository = getattr(storage, "repository", "")
        reference = getattr(storage, "reference", "main")
        return f"<https://github.com/{repository}@{reference}>"
        
    elif "s3" in block_type:
        bucket = getattr(storage, "bucket_name", "")
        key = getattr(storage, "base_path", "")
        return f"s3://{bucket}/{key}".rstrip('/')
        
    elif "azure" in block_type:
        container = getattr(storage, "container", "")
        return f"azure://{container}"
        
    else:
        return "."

# Usage examples:
# For GitLab:
# If your block contains "myorg/myrepo" as repository
# Result: "<https://gitlab.com/myorg/myrepo@main>"

# If your block contains "<https://gitlab.company.com/myorg/myrepo>" as repository
# Result: "<https://gitlab.company.com/myorg/myrepo@main>"
In Prefect 3.x, GitLab repositories can be used as sources in these formats: 1. 
<https://gitlab.com/owner/repo@branch>
2. 
<https://gitlab.company.com/owner/repo@branch>
(for self-hosted GitLab) For example:
Copy code
python
my_flow.from_source(
    source="<https://gitlab.com/myorg/myrepo@main>",
    entrypoint="path/to/flow.py:my_flow"
).deploy(
    name="my-deployment"
)
If you're using a self-hosted GitLab instance or have specific GitLab block attributes that differ from these assumptions, let me know and I can adjust the conversion logic accordingly.
y
@Marvin what about for Deployment.load_from_yaml
m
✅ thought for 42.8 seconds
The 
Deployment.load_from_yaml
method has been removed in Prefect 3.x. Here are your options for handling YAML-based deployments: 1. Preferred Method (CLI):
Copy code
bash
prefect deploy --prefect-file path/to/your/prefect.yaml
2. Programmatic Alternative:
Copy code
python
from prefect.deployments import deploy

# Deploy using a YAML file
result = deploy(prefect_file="path/to/your/prefect.yaml")
The YAML file format has also changed. Here's the new format for `prefect.yaml`:
Copy code
yaml
# prefect.yaml
deployments:
  - name: my-deployment
    entrypoint: "path/to/flow.py:my_flow"
    work_pool:
      name: my-work-pool
    schedule:
      interval: 3600
    parameters:
      param1: value1
    tags:
      - prod
If you're migrating existing code that uses 
Deployment.load_from_yaml
, you'll need to: 1. Update your YAML files to the new format 2. Replace the loading code with either: - The CLI command 
prefect deploy
- The programmatic 
deploy()
function For example:
Copy code
python
# Old way (2.x) - No longer works
from prefect.deployments import Deployment
deployment = Deployment.load_from_yaml("deployment.yaml")

# New way (3.x)
from prefect.deployments import deploy

# Option 1: Deploy directly
result = deploy(prefect_file="prefect.yaml")

# Option 2: More granular control
from prefect import flow

@flow
def my_flow():
    pass

if __name__ == "__main__":
    my_flow.from_source(
        source=".",  # or your repository URL
        entrypoint="path/to/flow.py:my_flow"
    ).deploy(
        name="my-deployment",
        work_pool_name="my-work-pool"
    )
Would you like me to provide more details about the new YAML format or how to migrate specific YAML configurations from 2.x to 3.x?
✅ thought for 713.6 seconds
y
@Marvin sorry i meant i'm facing an issue: cannot import name 'Process' from 'prefect.infrastructure'
m
✅ thought for 41.2 seconds
In Prefect 3.x, 
Process
from 
prefect.infrastructure
has been removed. Instead, you should use work pools with the "process" type. Here's how to migrate:
Copy code
python
# Old way (2.x) - No longer works
from prefect.infrastructure import Process
process_infra = Process(...)

# New way (3.x)
# First, create a process work pool (can be done via CLI or UI)
prefect work-pool create my-process-pool --type process

# Then use it in your deployment
my_flow.from_source(
    source=".",
    entrypoint="path/to/flow.py:my_flow"
).deploy(
    name="my-deployment",
    work_pool_name="my-process-pool"
)
If you're setting this up for the first time: 1. Create a process work pool:
Copy code
bash
prefect work-pool create my-process-pool --type process
2. Start a worker:
Copy code
bash
prefect worker start --pool my-process-pool
3. Deploy your flow:
Copy code
python
from prefect import flow

@flow
def my_flow():
    pass

if __name__ == "__main__":
    my_flow.from_source(
        source=".",
        entrypoint="path/to/flow.py:my_flow"
    ).deploy(
        name="my-deployment",
        work_pool_name="my-process-pool"
    )
Or using `prefect.yaml`:
Copy code
yaml
deployments:
  - name: my-deployment
    entrypoint: "path/to/flow.py:my_flow"
    work_pool:
      name: my-process-pool
The key differences are: 1. No direct infrastructure objects - everything is managed through work pools 2. Process execution is handled by workers instead of infrastructure blocks 3. Configuration is done through work pool settings rather than infrastructure objects Would you like me to provide more details about configuring process work pools or migrating specific process infrastructure settings?
y
@Marvin do i run work-pool create from within the workers?
m
✅ thought for 35.0 seconds
Here's the correct order of operations: 1. First, create the work pool (do this once):
Copy code
bash
   # From your terminal (not within a worker)
   prefect work-pool create my-process-pool --type process
This can be done: - From any terminal with Prefect CLI access - Through the Prefect UI - Using the REST API 2. Then, start workers (do this on each machine that will run flows):
Copy code
bash
   # In a separate terminal that will keep running
   prefect worker start --pool my-process-pool
3. Deploy flows (do this when setting up deployments):
Copy code
python
   my_flow.from_source(
       source=".",
       entrypoint="flow.py:my_flow"
   ).deploy(
       name="my-deployment",
       work_pool_name="my-process-pool"
   )
Key points: - Work pool creation is a one-time setup step - Workers connect to existing work pools - You can have multiple workers for the same work pool - Workers need to keep running to execute flows - Work pool creation is independent of worker processes Think of it like this: 1. Work pool = the configuration/definition of where work can run 2. Worker = the process that actually executes the work 3. You create the work pool first, then start workers that connect to it Would you like me to provide more details about work pool configuration options or worker management?
y
@Marvin current usage of process is: if is_ci_build: # Use the sys.executable at registration - as opposed to run time command = Process()._base_flow_run_command() # When built on CI however, the python version would not be correct command[0] = DeployTemplateVars.SP_PRF_PYTHON.value else: command = Process(working_dir=os.getcwd())._base_flow_run_command()
m
✅ thought for 78.8 seconds
In Prefect 3.x, you can achieve this functionality using work pool job variables. Here's how to migrate your code:
Copy code
python
from prefect import flow

@flow
def my_flow():
    pass

if __name__ == "__main__":
    if is_ci_build:
        # For CI environment
        my_flow.from_source(
            source=".",
            entrypoint="flow.py:my_flow"
        ).deploy(
            name="my-deployment",
            work_pool_name="my-process-pool",
            job_variables={
                "command": ["${prefect.system.python}", "-m", "prefect.engine"],
                "env": {
                    "PREFECT_API_URL": "${prefect.api.url}",
                    "PREFECT_API_KEY": "${prefect.api.key}"
                }
            }
        )
    else:
        # For local environment
        my_flow.from_source(
            source=".",
            entrypoint="flow.py:my_flow"
        ).deploy(
            name="my-deployment",
            work_pool_name="my-process-pool",
            job_variables={
                "working_dir": os.getcwd(),
                "command": ["python", "-m", "prefect.engine"]
            }
        )
Alternatively, you can configure this in your `prefect.yaml`:
Copy code
yaml
deployments:
  - name: my-deployment
    entrypoint: flow.py:my_flow
    work_pool:
      name: my-process-pool
      job_variables:
        command: 
          - "${prefect.system.python}"  # or "python" for local
          - "-m"
          - "prefect.engine"
        working_dir: ${working_dir}  # will use current directory
        env:
          PREFECT_API_URL: "${prefect.api.url}"
          PREFECT_API_KEY: "${prefect.api.key}"
Key differences from your 2.x code: 1. No more 
Process()._base_flow_run_command()
2. Job variables replace the infrastructure configuration 3. Template variables like 
${prefect.system.python}
replace manual path handling You can also create different work pools for CI and local:
Copy code
bash
# Create CI work pool
prefect work-pool create ci-pool --type process

# Create local work pool
prefect work-pool create local-pool --type process
Then use them conditionally:
Copy code
python
work_pool_name = "ci-pool" if is_ci_build else "local-pool"

my_flow.from_source(
    source=".",
    entrypoint="flow.py:my_flow"
).deploy(
    name="my-deployment",
    work_pool_name=work_pool_name
)
Available template variables include: - `${prefect.system.python}`: Current Python executable - `${working_dir}`: Current working directory - `${prefect.api.url}`: Prefect API URL - `${prefect.api.key}`: Prefect API key Would you like me to provide more details about configuring specific job variables or handling other aspects of the process work pool?
In Prefect 3.x, the programmatic way to load deployments from YAML has changed. Here's how you can handle it:
Copy code
python
from prefect.deployments.base import deploy

# Load and deploy from YAML
result = deploy(prefect_file="path/to/your/prefect.yaml")
Or if you need to work with deployments programmatically:
Copy code
python
from prefect.client import get_client

async def get_deployment(name: str):
    client = get_client()
    deployment = await client.read_deployment_by_name(name)
    return deployment
The key differences are: 1. 
Deployment.load_from_yaml()
no longer exists 2. Use 
deploy()
function for creating/updating deployments from YAML 3. Use the client API for reading existing deployments If you need to work with existing deployments, the client API is your best option:
Copy code
python
from prefect.client import get_client

async def work_with_deployment():
    client = get_client()
    
    # Get deployment
    deployment = await client.read_deployment_by_name("flow-name/deployment-name")
    
    # Update deployment
    await client.update_deployment(
        deployment.id,
        # update parameters here
    )
Would you like me to provide more specific examples based on how you're using 
load_from_yaml
in your code?
y
@Marvin BaseTaskRunner from prefect.task_runners
m
✅ thought for 42.5 seconds
In Prefect 3.x, 
BaseTaskRunner
has been removed. Instead, you should use 
TaskRunner
as the base class. Here's how to update your code:
Copy code
python
# Old way (2.x)
from prefect.task_runners import BaseTaskRunner

# New way (3.x)
from prefect.task_runners import TaskRunner
The available task runners in 3.x are: 1. 
TaskRunner
- Base class for implementing task runners 2. 
ThreadPoolTaskRunner
- Executes tasks in a thread pool 3. 
PrefectTaskRunner
- Default task runner Example usage:
Copy code
python
from prefect.task_runners import TaskRunner

class MyCustomTaskRunner(TaskRunner):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        
    async def submit(self, task, task_run, parameters):
        # Implement your custom submission logic
        pass
If you were using specific functionality from 
BaseTaskRunner
, let me know what features you need, and I can help you implement them with the new 
TaskRunner
class.
y
@Marvin what about TaskConcurrencyType
m
✅ thought for 61.6 seconds
In Prefect 3.x, 
TaskConcurrencyType
has been removed in favor of a new concurrency management system. Here's how to handle task concurrency in 3.x: 1. Using the concurrency decorator/context manager:
Copy code
python
from prefect.concurrency import concurrency

# As a decorator
@task
@concurrency(key="my-key", limits=2)
def my_task():
    pass

# As a context manager
async with concurrency(key="my-key", limits=2):
    # your code here
    pass
2. For rate limiting:
Copy code
python
from prefect.concurrency import rate_limit

@task
@rate_limit(key="my-key", rate=100, per=60)  # 100 executions per 60 seconds
def my_task():
    pass
3. For global concurrency limits (via CLI):
Copy code
bash
# Create a global concurrency limit
prefect gcl create my-limit --slots 5

# Update a limit
prefect gcl update my-limit --slots 10

# Delete a limit
prefect gcl delete my-limit
4. For programmatic concurrency management:
Copy code
python
from prefect.client import get_client

async def manage_concurrency():
    client = get_client()
    
    # Create a concurrency limit
    await client.create_concurrency_limit(
        tag="my-tag",
        concurrency_limit=5
    )
    
    # Update a limit
    await client.update_concurrency_limit(
        tag="my-tag",
        concurrency_limit=10
    )
The key differences from 
TaskConcurrencyType
are: 1. More flexible concurrency control through decorators and context managers 2. Separate handling for rate limiting and concurrency limits 3. Global concurrency limits managed through CLI or client API 4. Built-in support for both sync and async operations Would you like me to provide more specific examples based on how you were using 
TaskConcurrencyType
in your code?
y
@Marvin i need to see a migration example
m
✅ thought for 9.3 seconds
Let me help you with a migration example. Could you share a snippet of how you're currently using 
TaskConcurrencyType
in your 2.x code? That way, I can show you the exact equivalent in 3.x. In general, here's a typical migration pattern:
Copy code
python
# Prefect 2.x way
from prefect.task_runners import BaseTaskRunner
from prefect.utilities.enum import TaskConcurrencyType

class MyTaskRunner(BaseTaskRunner):
    def __init__(self, concurrency_type: TaskConcurrencyType = TaskConcurrencyType.PARALLEL):
        self.concurrency_type = concurrency_type
        super().__init__()

# Prefect 3.x way
from prefect.task_runners import TaskRunner
from prefect.concurrency import concurrency

class MyTaskRunner(TaskRunner):
    def __init__(self, max_concurrent_tasks: int = None):
        self.max_concurrent_tasks = max_concurrent_tasks
        super().__init__()
    
    async def submit(self, task, task_run, parameters):
        async with concurrency(key="my-runner", limits=self.max_concurrent_tasks):
            return await super().submit(task, task_run, parameters)
If you share your specific use case, I can provide a more targeted migration example.
y
@Marvin apparently it's just used to return TaskConcurrencyType.PARALLEL
m
✅ thought for 10.4 seconds
Ah, in that case, the migration is simpler since 
TaskConcurrencyType.PARALLEL
was just indicating parallel execution capability. In Prefect 3.x, parallel execution is handled differently. Here's how to migrate:
Copy code
python
# Old way (2.x)
from prefect.utilities.enum import TaskConcurrencyType

def some_function():
    return TaskConcurrencyType.PARALLEL

# New way (3.x)
# If you just need parallel execution, you can use ThreadPoolTaskRunner
from prefect.task_runners import ThreadPoolTaskRunner

def some_function():
    return ThreadPoolTaskRunner()
Or if this was part of a task runner class:
Copy code
python
# Old way (2.x)
from prefect.task_runners import BaseTaskRunner
from prefect.utilities.enum import TaskConcurrencyType

class MyTaskRunner(BaseTaskRunner):
    def get_concurrency_type(self):
        return TaskConcurrencyType.PARALLEL

# New way (3.x)
from prefect.task_runners import TaskRunner

class MyTaskRunner(TaskRunner):
    # Parallel execution is now handled by the task runner implementation
    # No need to explicitly specify concurrency type
    pass
The key point is that in 3.x: - Task concurrency types are no longer explicitly enumerated - Parallel execution is handled by the task runner implementation itself - 
ThreadPoolTaskRunner
is the default choice for parallel task execution Would you like me to provide more details about how parallel execution works in the new version?
y
@Marvin prefect.events.instrument is not available in prefect 3
m
✅ thought for 55.5 seconds
In Prefect 3.x, the 
prefect.events.instrument
functionality has been replaced with a new events system. Here's how to migrate:
Copy code
python
# Old way (2.x)
from prefect.events import instrument

@instrument
def my_function():
    pass

# New way (3.x)
from prefect.events import emit_event

def my_function():
    # Emit specific events
    emit_event(
        event="my-event-name",
        resource={
            "prefix": "prefect.resource",
            "name": "my-resource"
        },
        payload={
            "key": "value"
        }
    )
For more complex event handling:
Copy code
python
from prefect.events import emit_event
from prefect.events.schemas import Resource, Event

async def handle_with_events():
    # Create and emit an event
    await emit_event(
        event=Event(
            name="my.custom.event",
            resource=Resource(
                prefix="my.resource",
                name="resource-name"
            ),
            payload={"data": "value"}
        )
    )

# If you need to subscribe to events
from prefect.events import get_events_subscriber

async def listen_to_events():
    subscriber = await get_events_subscriber()
    async for event in subscriber.events():
        # Handle event
        print(f"Received event: {event.name}")
Key differences in 3.x: 1. Direct event emission using 
emit_event
instead of decorator-based instrumentation 2. More structured event data with 
Event
and 
Resource
classes 3. Support for async event handling 4. Built-in event subscription capabilities Would you like me to provide more specific examples based on how you were using 
instrument
in your code?
y
@Marvin i need instrument_instance_method_call annotation
m
✅ thought for 97.4 seconds
For method instrumentation in Prefect 3.x, you have a few options depending on what you were using 
instrument_instance_method_call
for: 1. Using Events (Recommended Approach):
Copy code
python
from prefect.events import emit_event
from functools import wraps

def instrument_method(method_name):
    def decorator(func):
        @wraps(func)
        async def wrapper(self, *args, **kwargs):
            # Emit event before method call
            await emit_event(
                event=f"{method_name}.start",
                resource={
                    "prefix": self.__class__.__name__,
                    "name": method_name
                },
                payload={"args": args, "kwargs": kwargs}
            )
            
            try:
                result = await func(self, *args, **kwargs)
                # Emit success event
                await emit_event(
                    event=f"{method_name}.complete",
                    resource={
                        "prefix": self.__class__.__name__,
                        "name": method_name
                    }
                )
                return result
            except Exception as e:
                # Emit failure event
                await emit_event(
                    event=f"{method_name}.failed",
                    resource={
                        "prefix": self.__class__.__name__,
                        "name": method_name
                    },
                    payload={"error": str(e)}
                )
                raise
        return wrapper
    return decorator

# Usage:
class MyClass:
    @instrument_method("my_method")
    async def my_method(self, arg1, arg2):
        # method implementation
        pass
2. Using RunTelemetry (For Flow/Task Related Methods):
Copy code
python
from prefect.telemetry.run_telemetry import RunTelemetry
from functools import wraps

def instrument_run_method(method_name):
    def decorator(func):
        @wraps(func)
        async def wrapper(self, *args, **kwargs):
            telemetry = RunTelemetry()
            with telemetry.start_span(name=method_name) as span:
                try:
                    result = await func(self, *args, **kwargs)
                    span.set_attribute("status", "success")
                    return result
                except Exception as e:
                    span.set_attribute("status", "failed")
                    span.record_exception(e)
                    raise
        return wrapper
    return decorator

# Usage:
class MyClass:
    @instrument_run_method("my_method")
    async def my_method(self, arg1, arg2):
        # method implementation
        pass
3. Using Logging (Simplest Approach):
Copy code
python
from prefect.logging import get_logger
from functools import wraps

def instrument_method_with_logging(method_name):
    logger = get_logger()
    
    def decorator(func):
        @wraps(func)
        async def wrapper(self, *args, **kwargs):
            <http://logger.info|logger.info>(f"Starting {method_name}")
            try:
                result = await func(self, *args, **kwargs)
                <http://logger.info|logger.info>(f"Completed {method_name}")
                return result
            except Exception as e:
                logger.error(f"Failed {method_name}: {str(e)}")
                raise
        return wrapper
    return decorator

# Usage:
class MyClass:
    @instrument_method_with_logging("my_method")
    async def my_method(self, arg1, arg2):
        # method implementation
        pass
The key differences from `instrument_instance_method_call`: 1. More explicit event emission 2. Better integration with Prefect's event system 3. More flexible instrumentation options 4. Built-in support for async methods Would you like me to provide a more specific example based on how you were using 
instrument_instance_method_call
in your code?
y
@Marvin show me a before and after refactoring for instrument_instance_method_call
m
✅ thought for 11.7 seconds
Could you share an example of how you're currently using 
instrument_instance_method_call
in your 2.x code? That way, I can show you the exact migration path for your specific use case. A typical before/after would look like this:
Copy code
python
# BEFORE (Prefect 2.x)
from prefect.events import instrument_instance_method_call

class MyClass:
    @instrument_instance_method_call
    def my_method(self, arg1, arg2):
        # method implementation
        pass

# AFTER (Prefect 3.x)
from prefect.events import emit_event
from functools import wraps

def instrument_method(func):
    @wraps(func)
    async def wrapper(self, *args, **kwargs):
        method_name = func.__name__
        await emit_event(
            event=f"{self.__class__.__name__}.{method_name}.start",
            resource={
                "prefix": self.__class__.__name__,
                "name": method_name
            }
        )
        try:
            result = await func(self, *args, **kwargs)
            await emit_event(
                event=f"{self.__class__.__name__}.{method_name}.complete",
                resource={
                    "prefix": self.__class__.__name__,
                    "name": method_name
                }
            )
            return result
        except Exception as e:
            await emit_event(
                event=f"{self.__class__.__name__}.{method_name}.failed",
                resource={
                    "prefix": self.__class__.__name__,
                    "name": method_name
                },
                payload={"error": str(e)}
            )
            raise

    return wrapper

class MyClass:
    @instrument_method
    async def my_method(self, arg1, arg2):
        # method implementation
        pass
If you share your specific usage, I can provide a more targeted migration example that matches your exact needs.
y
@Marvin is there an existing enum for TaskConcurrencyType.PARALLEL
m
✅ thought for 55.9 seconds
No, there isn't a direct enum equivalent to 
TaskConcurrencyType.PARALLEL
in Prefect 3.x. The concept of explicitly defining concurrency types through enums has been removed in favor of more direct concurrency control through task runners and concurrency limits. If you need to indicate parallel execution capability, you have these options: 1. Use a boolean (simplest):
Copy code
python
PARALLEL = True

def your_function():
    return PARALLEL
2. Use Python's enum if you want to maintain enum-like behavior:
Copy code
python
from enum import Enum

class ConcurrencyType(Enum):
    PARALLEL = "parallel"
    SEQUENTIAL = "sequential"

def your_function():
    return ConcurrencyType.PARALLEL
3. Use the task runner directly (most Prefect 3.x-like):
Copy code
python
from prefect.task_runners import ThreadPoolTaskRunner

def your_function():
    return ThreadPoolTaskRunner()
The key point is that in Prefect 3.x: - Concurrency is handled through task runners and concurrency limits - There's no need for explicit concurrency type enums - Parallel execution is the default when using appropriate task runners Would you like me to show you how to implement any of these approaches in your specific code?
y
@Marvin i need to convert this to be compatible with prefect 3 class DynamicTaskRunner(TaskRunner): """Proxy for Ray/Dask Task runners. See module docstring for more info.""" def duplicate(self) -> TaskRunner: """When a new flow run is started it calls this method to get the task runner. Constructs the concrete task runner via config in environment variable. See 
dynamic_task_runner_env
. Also, this automatically takes care of any dask/memory_limits on kubernetes This should be used in your flow definition as follows: @flow(..., task_runner=DynamicTaskRunner) def my_flow(): ... """ assert PREF_TASK_RUNNER_CONFIG in os.environ, ( f"No {PREF_TASK_RUNNER_CONFIG} found, please see the docstring of: " "`sp_prefect_extensions.task_runners.dynamic_task_runner_env` for usage" ) task_runner_config = DynamicTaskRunnerConfig( **json.loads(os.environ[PREF_TASK_RUNNER_CONFIG]) ) self.logger.info("TASK RUNNER: %s", task_runner_config) if task_runner_config.type == TaskRunner.DaskTaskRunner: # The below package needs to be provided by the client from prefect_dask import DaskTaskRunner # type: ignore params = task_runner_config.params if kubernetes.on_kubernetes(): if "memory_limit" in params.cluster_kwargs: params.cluster_kwargs[ "memory_limit" ] = kubernetes.k8s_to_dask_memory( params.cluster_kwargs["memory_limit"] ) elif params.cluster_class is None: # Only automatically setting the limit for the default cluster # class, as e.g. cluster_class="dask_jobqueue.SLURMCluster does # not have this param... # Set it slightly lower than the K8s Limit (note that this is in # bytes, so no conversion from dask to k8s is necessary k8s_limit = kubernetes.get_memory_limit() if k8s_limit is not None: params.cluster_kwargs["memory_limit"] = k8s_limit - 1000 return DaskTaskRunner(**asdict(params)) elif task_runner_config.type == TaskRunner.RayTaskRunner: # The below package needs to be provided by the client from prefect_ray import RayTaskRunner # type: ignore return RayTaskRunner(**asdict(task_runner_config.params)) elif task_runner_config.type == TaskRunner.SequentialTaskRunner: # The below package needs to be provided by the client from prefect.task_runners import SequentialTaskRunner return SequentialTaskRunner() elif task_runner_config.type == TaskRunner.ConcurrentTaskRunner: # The below package needs to be provided by the client from prefect.task_runners import ConcurrentTaskRunner return ConcurrentTaskRunner() else: raise NotImplementedError(task_runner_config.type) @property def concurrency_type(self): # Should not really have to provide an implementation for this, however # there is a bug in a prefect log statement here: # https://github.com/PrefectHQ/prefect/blob/main/src/prefect/engine.py#L503 # where it uses flow.task_runner instead of task_runner # Returning PARALLEL even though It could be CONCURRENT for dask if # cluster_kwargs.processes is not set. However, this is not used anywhere # of importance return TaskConcurrencyType.PARALLEL async def submit(self, key, call): """Should never be called as the flow run actually calls duplicate() first""" raise NotImplementedError() async def wait(self, key, timeout): """Should never be called as the flow run actually calls duplicate() first""" raise NotImplementedError()
m
✅ thought for 17.4 seconds
I'll help you migrate this code to Prefect 3.x. Here's the converted version:
Copy code
python
from prefect.task_runners import TaskRunner
from dataclasses import asdict
from typing import Literal, Optional
import os
import json

class DynamicTaskRunner(TaskRunner):
    """Proxy for Ray/Dask Task runners. See module docstring for more info."""

    def __init__(self):
        super().__init__()
        # In 3.x, we don't need concurrency_type anymore

    def duplicate(self) -> TaskRunner:
        """When a new flow run is started it calls this method to get the task runner.

        Constructs the concrete task runner via config in environment variable.

        See `dynamic_task_runner_env`.

        Also, this automatically takes care of any dask/memory_limits on kubernetes

        This should be used in your flow definition as follows:

            @flow(task_runner=DynamicTaskRunner())
            def my_flow():
                ...
        """
        assert PREF_TASK_RUNNER_CONFIG in os.environ, (
            f"No {PREF_TASK_RUNNER_CONFIG} found, please see the docstring of: "
            "`sp_prefect_extensions.task_runners.dynamic_task_runner_env` for usage"
        )

        task_runner_config = DynamicTaskRunnerConfig(
            **json.loads(os.environ[PREF_TASK_RUNNER_CONFIG])
        )
        <http://self.logger.info|self.logger.info>("TASK RUNNER: %s", task_runner_config)

        if task_runner_config.type == "DaskTaskRunner":
            # The below package needs to be provided by the client
            from prefect_dask import DaskTaskRunner  # type: ignore

            params = task_runner_config.params
            if kubernetes.on_kubernetes():
                if "memory_limit" in params.cluster_kwargs:
                    params.cluster_kwargs[
                        "memory_limit"
                    ] = kubernetes.k8s_to_dask_memory(
                        params.cluster_kwargs["memory_limit"]
                    )
                elif params.cluster_class is None:
                    # Only automatically setting the limit for the default cluster
                    # class, as e.g. cluster_class="dask_jobqueue.SLURMCluster does
                    # not have this param...

                    # Set it slightly lower than the K8s Limit (note that this is in
                    # bytes, so no conversion from dask to k8s is necessary
                    k8s_limit = kubernetes.get_memory_limit()
                    if k8s_limit is not None:
                        params.cluster_kwargs["memory_limit"] = k8s_limit - 1000
            return DaskTaskRunner(**asdict(params))

        elif task_runner_config.type == "RayTaskRunner":
            # The below package needs to be provided by the client
            from prefect_ray import RayTaskRunner  # type: ignore
            return RayTaskRunner(**asdict(task_runner_config.params))

        elif task_runner_config.type == "SequentialTaskRunner":
            from prefect.task_runners import SequentialTaskRunner
            return SequentialTaskRunner()

        elif task_runner_config.type == "ThreadPoolTaskRunner":  # Changed from ConcurrentTaskRunner
            from prefect.task_runners import ThreadPoolTaskRunner  # Changed from ConcurrentTaskRunner
            return ThreadPoolTaskRunner()

        else:
            raise NotImplementedError(task_runner_config.type)

    async def submit(self, task, task_run, parameters):
        """Should never be called as the flow run actually calls duplicate() first"""
        raise NotImplementedError()

# Helper class for type safety
class DynamicTaskRunnerConfig:
    def __init__(
        self,
        type: Literal["DaskTaskRunner", "RayTaskRunner", "SequentialTaskRunner", "ThreadPoolTaskRunner"],
        params: Optional[dict] = None
    ):
        self.type = type
        self.params = params or {}
Key changes made: 1. Removed 
concurrency_type
property as it's no longer needed in
y
@Marvin cannot extend enumeration TaskRunner
m
✅ thought for 17.6 seconds
Ah, I see the issue. In Prefect 3.x, 
TaskRunner
is a class, not an enum. Let me help you fix that. ```python from prefect.task_runners import TaskRunner from dataclasses import asdict from typing import Literal, Optional from enum import Enum import os import json # Create a separate enum for task runner types class TaskRunnerType(str, Enum): DASK = "DaskTaskRunner" RAY = "RayTaskRunner" SEQUENTIAL = "SequentialTaskRunner" THREAD_POOL = "ThreadPoolTaskRunner" # Changed from ConcurrentTaskRunner in 2.x class DynamicTaskRunner(TaskRunner): """Proxy for Ray/Dask Task runners. See module docstring for more info.""" def __init__(self): super().__init__() def duplicate(self) -> TaskRunner: """When a new flow run is started it calls this method to get the task runner. Constructs the concrete task runner via config in environment variable. See 
dynamic_task_runner_env
. Also, this automatically takes care of any dask/memory_limits on kubernetes This should be used in your flow definition as follows: @flow(task_runner=DynamicTaskRunner()) def my_flow(): ... """ assert PREF_TASK_RUNNER_CONFIG in os.environ, ( f"No {PREF_TASK_RUNNER_CONFIG} found, please see the docstring of: " "`sp_prefect_extensions.task_runners.dynamic_task_runner_env` for usage" ) task_runner_config = DynamicTaskRunnerConfig( **json.loads(os.environ[PREF_TASK_RUNNER_CONFIG]) ) self.logger.info("TASK RUNNER: %s", task_runner_config) if task_runner_config.type == TaskRunnerType.DASK: # The below package needs to be provided by the client from prefect_dask import DaskTaskRunner # type: ignore params = task_runner_config.params if kubernetes.on_kubernetes(): if "memory_limit" in params.cluster_kwargs: params.cluster_kwargs[ "memory_limit" ] = kubernetes.k8s_to_dask_memory( params.cluster_kwargs["memory_limit"] ) elif params.cluster_class is None: # Only automatically setting the limit for the default cluster # class, as e.g. cluster_class="dask_jobqueue.SLURMCluster does # not have this param... # Set it slightly lower than the K8s Limit (note that this is in # bytes, so no conversion from dask to k8s is necessary k8s_limit = kubernetes.get_memory_limit() if k8s_limit is not None: params.cluster_kwargs["memory_limit"] = k8s_limit - 1000 return DaskTaskRunner(**asdict(params)) elif task_runner_config.type == TaskRunnerType.RAY: # The below package needs to be provided by the client from prefect_ray import RayTaskRunner # type: ignore return RayTaskRunner(**asdict(task_runner_config.params)) elif task_runner_config.type == TaskRunnerType.SEQUENTIAL: from prefect.task_runners import SequentialTaskRunner return SequentialTaskRunner() elif task_runner_config.type == TaskRunnerType.THREAD_POOL: from prefect.task_runners import ThreadPoolTaskRunner return ThreadPoolTaskRunner() else: raise NotImplementedError(task_runner_config.type) async def submit(self, task, task_run, parameters): """Should never be called as the flow run actually calls duplicate() first""" raise NotImplementedError() # Helper class for type safety class DynamicTaskRunnerConfig: def __init__( self, type: TaskRunner
y
@Marvin no as in, TypeError: DynamicTaskRunner: cannot extend enumeration 'TaskRunner' but taskrunner is a class so what gives?
m
✅ thought for 81.3 seconds
This is an interesting error. It seems like you might be importing 
TaskRunner
from the wrong location or getting a name collision. Let's verify the correct import and usage:
Copy code
python
# Make sure you're importing from the correct location
from prefect.task_runners import TaskRunner

# And let's verify it's actually a class
print(type(TaskRunner))  # Should print "<class 'type'>"
Could you share: 1. Your exact import statement for TaskRunner 2. The full error traceback This will help us identify if there's a naming collision or incorrect import that's causing TaskRunner to be seen as an enumeration when it should be a class. The correct structure should be:
Copy code
python
from prefect.task_runners import TaskRunner

class DynamicTaskRunner(TaskRunner):
    def __init__(self):
        super().__init__()
        
    async def submit(self, task, task_run, parameters):
        raise NotImplementedError()
If you're still getting the enumeration error, there might be: 1. A name collision with another TaskRunner import 2. A cached 
.pyc
file causing issues 3. An incorrect import path Could you share those details so we can identify the root cause?
y
@Marvin i have a reference to prefect.server.utilities.schemas.fields.DateTimeTZ that needs to be reworked
m
✅ thought for 71.6 seconds
In Prefect 3.x, you have a few options to replace `prefect.server.utilities.schemas.fields.DateTimeTZ`: 1. Using Prefect's DateTime type (recommended):
Copy code
python
from prefect.types import DateTime

# In your model/schema
datetime_field: DateTime
2. Using Pydantic's datetime field with timezone (if you need explicit timezone handling):
Copy code
python
from datetime import datetime
from pydantic import Field
from zoneinfo import ZoneInfo

# In your model/schema
datetime_field: datetime = Field(default_factory=lambda: datetime.now(ZoneInfo("UTC")))
3. If you need more specific timezone handling:
Copy code
python
from datetime import datetime
from pydantic import BaseModel, Field
from zoneinfo import ZoneInfo

class DateTimeTZ(datetime):
    @classmethod
    def __get_validators__(cls):
        yield cls.validate

    @classmethod
    def validate(cls, v):
        if isinstance(v, datetime):
            if v.tzinfo is None:
                v = v.replace(tzinfo=ZoneInfo("UTC"))
            return v
        raise ValueError('datetime required')

# Usage in your model
class YourModel(BaseModel):
    datetime_field: DateTimeTZ
4. For the simplest approach with UTC:
Copy code
python
from datetime import datetime
from pydantic import BaseModel

class YourModel(BaseModel):
    datetime_field: datetime

    class Config:
        json_encoders = {
            datetime: lambda dt: dt.replace(tzinfo=timezone.utc).isoformat()
        }
The key differences in 3.x are: - More reliance on Pydantic's native datetime handling - Simplified timezone management through Python's 
zoneinfo
- Use of Prefect's built-in 
DateTime
type for standard cases Would you like me to provide more specific examples based on how you're using 
DateTimeTZ
in your code?
y
@Marvin cannot import prefectclient from prefect
m
✅ thought for 51.8 seconds
In Prefect 3.x, the correct import path for PrefectClient has changed. Here are your options: 1. Using the high-level client getter (recommended):
Copy code
python
from prefect.client import get_client

client = get_client()  # Returns PrefectClient instance
2. Direct import from orchestration module:
Copy code
python
from prefect.client.orchestration import PrefectClient

client = PrefectClient()
3. If you need the synchronous version:
Copy code
python
from prefect.client.orchestration import SyncPrefectClient

client = SyncPrefectClient()
The key differences in 3.x are: - PrefectClient is now in the orchestration module - There's a convenient 
get_client()
function that handles configuration - Separate sync and async client classes are available The recommended approach is to use 
get_client()
as it handles configuration and environment settings automatically:
Copy code
python
from prefect.client import get_client

async def my_function():
    client = get_client()
    # Use the client
    flows = await client.read_flows()
Would you like me to provide more examples of how to use the client in specific scenarios?
y
@Marvin @dataclass(frozen=True, config=dict(extra="forbid")) class DeploymentRecord: """A subset of Deployment values to be recorded for later validation by workers.""" id: str version: str flow_id: str command: str entrypoint: str app: str @staticmethod def from_deployment(deployment: Deployment) -> "DeploymentRecord": app_name = [ el.split("app:")[-1] for el in deployment.tags if el.startswith("app:") ] assert len(app_name) == 1, "Tag app:<appname> missing" app_name = app_name[0] return DeploymentRecord( id=str(deployment.id), version=deployment.version, flow_id=str(deployment.flow_id), command=deployment.infra_overrides["command"], entrypoint=deployment.entrypoint, app=app_name, ) now that deployment is not a valid object, how should this be reworked?
m
✅ thought for 150.3 seconds
Here's how to refactor your DeploymentRecord class for Prefect 3.x:
Copy code
python
from dataclasses import dataclass
from prefect.client.schemas.objects import Deployment
from typing import Optional

@dataclass(frozen=True)
class DeploymentRecord:
    """A subset of Deployment values to be recorded for later validation by workers."""

    id: str
    version: str
    flow_id: str
    command: str
    entrypoint: str
    app: str

    @staticmethod
    def from_deployment(deployment: Deployment) -> "DeploymentRecord":
        """Create a DeploymentRecord from a Prefect 3.x deployment."""
        app_name = [
            el.split("app:")[-1] for el in deployment.tags if el.startswith("app:")
        ]
        assert len(app_name) == 1, "Tag app:<appname> missing"
        app_name = app_name[0]

        # In 3.x, command is in job_variables instead of infra_overrides
        command = deployment.job_variables.get("command", "")
        if isinstance(command, list):
            command = " ".join(command)

        return DeploymentRecord(
            id=str(deployment.id),
            version=deployment.version,
            flow_id=str(deployment.flow_id),
            command=command,
            entrypoint=deployment.entrypoint,
            app=app_name,
        )

    @staticmethod
    async def from_deployment_name(deployment_name: str) -> "DeploymentRecord":
        """Alternative constructor using deployment name."""
        from prefect.client import get_client

        client = get_client()
        deployment = await client.read_deployment_by_name(deployment_name)
        return DeploymentRecord.from_deployment(deployment)
Key changes: 1. Removed 
config=dict(extra="forbid")
as it's not needed with standard dataclass 2. Changed 
infra_overrides["command"]
to 
job_variables.get("command", "")
3. Added handling for command as list (common in 3.x) 4. Added alternative constructor using deployment name Usage example:
Copy code
python
# Using deployment object
deployment_record = DeploymentRecord.from_deployment(deployment)

# Using deployment name
deployment_record = await DeploymentRecord.from_deployment_name("flow-name/deployment-name")
Note that in Prefect 3.x: - Infrastructure configuration is handled through work pools and job variables - Commands are often specified as lists in job variables - The deployment object structure is more standardized Would you like me to explain any specific part of the migration in more detail?
y
@Marvin i have a ci pipeline that does a deployment of a flow to prefect server hitting these issues after prefect has been upgraded to 3.x 2025-07-02T164205.755775Z 010 prefect.exceptions.PrefectHTTPStatusError: Client error '422 Unprocessable Entity' for url 'https://prefect.myorg.com/api/deployments/filter' 2025-07-02T164205.755780Z 010 Response: {'exception_message': 'Invalid request received.', 'exception_detail': [{'type': 'extra_forbidden', 'loc': ['body', 'deployments', 'is_schedule_active'], 'msg': 'Extra inputs are not permitted', 'input': None}], 'request_body': {'deployments': {'operator': 'and_', 'id': None, 'name': None, 'paused': None, 'is_schedule_active': None, 'tags': {'operator': 'and_', 'all_': ['app:cron-pipelines'], 'is_null_': None}, 'work_queue_name': None}}}
m
✅ thought for 57.2 seconds
Here's how to update your deployment filter code for Prefect 3.x:
Copy code
python
from prefect.client import get_client

# Old way (2.x)
filter_criteria = {
    "deployments": {
        "operator": "and_",
        "id": None,
        "name": None,
        "paused": None,
        "is_schedule_active": None,  # This field no longer exists
        "tags": {
            "operator": "and_",
            "all_": ["app:cron-pipelines"],
            "is_null_": None
        },
        "work_queue_name": None
    }
}

# New way (3.x)
async def get_deployments():
    client = get_client()
    return await client.read_deployments(
        tags=["app:cron-pipelines"]  # Simplified filter syntax
    )

# If you need more complex filtering:
from prefect.client.schemas.filters import DeploymentFilter, DeploymentFilterTags

async def get_deployments_complex():
    client = get_client()
    deployment_filter = DeploymentFilter(
        tags=DeploymentFilterTags(
            all_=["app:cron-pipelines"]
        )
    )
    return await client.read_deployments(deployment_filter=deployment_filter)
For your CI pipeline, you might want to update it like this:
Copy code
python
from prefect.client import get_client
from prefect.client.schemas.filters import DeploymentFilter

async def get_pipeline_deployments():
    client = get_client()
    
    # Simple version
    deployments = await client.read_deployments(
        tags=["app:cron-pipelines"]
    )
    
    # Or if you need to check schedule status:
    active_deployments = [
        d for d in deployments 
        if d.schedule is not None and not d.paused
    ]
    
    return active_deployments

# If you're using it in a script:
if __name__ == "__main__":
    import asyncio
    
    deployments = asyncio.run(get_pipeline_deployments())
Key changes: 1. Removed 
is_schedule_active
from filter criteria 2. Simplified the filter structure 3. Use the client's built-in filtering capabilities 4. Handle schedule status checking after fetching deployments If you need to check for active schedules specifically:
Copy code
python
async def get_active_scheduled_deployments():
    client = get_client()
    deployments = await client.read_deployments(
        tags=["app:cron-pipelines"]
    )
    
    return [
        d for d in deployments 
        if (d.schedule is not None and  # Has a schedule
            not d.paused and           # Not paused
            d.schedule.active)         # Schedule is active
    ]
Would you like me to provide more specific examples based on your CI pipeline's needs?
✅ thought for 15.9 seconds
I see this is a deployment filtering issue in a CI pipeline. Let me help you update the deployment filter code for Prefect 3.x. Here's how to fix it:
Copy code
python
from prefect.client import get_client
from prefect.client.schemas.filters import DeploymentFilter, DeploymentFilterTags

# Old way (2.x) that's causing the error:
filter_criteria = {
    "deployments": {
        "operator": "and_",
        "id": None,
        "name": None,
        "paused": None,
        "is_schedule_active": None,  # This field is causing the 422 error
        "tags": {
            "operator": "and_",
            "all_": ["app:cron-pipelines"],
            "is_null_": None
        },
        "work_queue_name": None
    }
}

# New way (3.x)
async def get_deployments():
    client = get_client()
    
    # Option 1: Simple filtering
    deployments = await client.read_deployments(
        tags=["app:cron-pipelines"]
    )
    return deployments

    # Option 2: Using DeploymentFilter for more complex criteria
    deployment_filter = DeploymentFilter(
        tags=DeploymentFilterTags(
            all_=["app:cron-pipelines"]
        )
    )
    deployments = await client.read_deployments(
        deployment_filter=deployment_filter
    )
    return deployments
If you're using this in a CI script, here's a complete example:
Copy code
python
from prefect.client import get_client
import asyncio

async def main():
    client = get_client()
    
    # Get deployments
    deployments = await client.read_deployments(
        tags=["app:cron-pipelines"]
    )
    
    # If you need to check for active schedules
    active_deployments = [
        d for d in deployments 
        if d.schedule is not None and not d.paused
    ]
    
    # Process your deployments
    for deployment in active_deployments:
        # Your deployment logic here
        print(f"Processing deployment: {deployment.name}")

if __name__ == "__main__":
    asyncio.run(main())
The key changes are: 1. Removed the deprecated 
is_schedule_active
filter 2. Use the new client API methods directly 3. Filter for schedules after fetching the deployments Would you like me to provide more specific examples based on what your CI pipeline is trying to accomplish?
y
@Marvin async def get_current_deployments( app_name: str, api_url: str ) -> List[DeploymentResponseWithUpdatedTime]: """Gets the current deployments for the specified `app_name`""" logging.info( "Reading deployments for app '%s' from '%s'", app_name, api_url if api_url is not None else "DEFAULT" ) async with get_client() as client: return await read_deployments( client, deployment_filter=schemas.filters.DeploymentFilter( tags=dict(all_=[f"app:{app_name}"]) ), ) the problem seems to be here
m
✅ thought for 18.7 seconds
Yes, I see the issue. The filter syntax has changed in Prefect 3.x. Here's how to fix your function:
Copy code
python
from typing import List
from prefect.client import get_client
from prefect.client.schemas.filters import DeploymentFilter, DeploymentFilterTags
from prefect.client.schemas.responses import DeploymentResponse

async def get_current_deployments(
    app_name: str, 
    api_url: str
) -> List[DeploymentResponse]:  # or your custom DeploymentResponseWithUpdatedTime
    """Gets the current deployments for the specified `app_name`"""
    <http://logging.info|logging.info>(
        "Reading deployments for app '%s' from '%s'",
        app_name,
        api_url if api_url is not None else "DEFAULT"
    )
    
    client = get_client()  # Note: context manager not needed in 3.x
    
    # Create the filter correctly
    deployment_filter = DeploymentFilter(
        tags=DeploymentFilterTags(
            all_=[f"app:{app_name}"]
        )
    )
    
    # Use the client's read_deployments method directly
    return await client.read_deployments(
        deployment_filter=deployment_filter
    )
Key changes: 1. Removed the async context manager (
async with
) as it's not needed in 3.x 2. Fixed the filter syntax using 
DeploymentFilterTags
instead of raw dict 3. Using client's 
read_deployments
method directly Alternative simpler version if you don't need complex filtering:
Copy code
python
async def get_current_deployments(
    app_name: str, 
    api_url: str
) -> List[DeploymentResponse]:
    """Gets the current deployments for the specified `app_name`"""
    <http://logging.info|logging.info>(
        "Reading deployments for app '%s' from '%s'",
        app_name,
        api_url if api_url is not None else "DEFAULT"
    )
    
    client = get_client()
    
    # Simpler tag filtering
    return await client.read_deployments(
        tags=[f"app:{app_name}"]
    )
If you're using a custom 
DeploymentResponseWithUpdatedTime
type, you might need to adjust the response handling:
Copy code
python
from dataclasses import dataclass
from datetime import datetime
from prefect.client.schemas.responses import DeploymentResponse

@dataclass
class DeploymentResponseWithUpdatedTime:
    # Your custom fields here
    deployment: DeploymentResponse
    updated_time: datetime

async def get_current_deployments(
    app_name: str, 
    api_url: str
) -> List[DeploymentResponseWithUpdatedTime]:
    """Gets the current deployments for the specified `app_name`"""
    <http://logging.info|logging.info>(
        "Reading deployments for app '%s' from '%s'",
        app_name,
        api_url if api_url is not None else "DEFAULT"
    )
    
    client = get_client()
    deployments = await client.read_deployments(
        tags=[f"app:{app_name}"]
    )
    
    # Convert to your custom type if needed
    return [
        DeploymentResponseWithUpdatedTime(
            deployment=deployment,
            updated_time=deployment.updated  # adjust field name as needed
        )
        for deployment in deployments
    ]
Would you like me to provide more details about any part of this change?
y
@Marvin still getting extra inputs are not permitted error with http 422
m
✅ thought for 87.9 seconds
Let's try a simpler approach first and then build up to more complex filtering if needed:
Copy code
python
from typing import List
from prefect.client import get_client
from prefect.client.schemas.responses import DeploymentResponse

async def get_current_deployments(
    app_name: str, 
    api_url: str
) -> List[DeploymentResponse]:
    """Gets the current deployments for the specified `app_name`"""
    <http://logging.info|logging.info>(
        "Reading deployments for app '%s' from '%s'",
        app_name,
        api_url if api_url is not None else "DEFAULT"
    )
    
    client = get_client()
    
    # Simplest form - just pass the tag directly
    return await client.read_deployments(
        tags=[f"app:{app_name}"]
    )
If you still need to use the filter object, try this version:
Copy code
python
from prefect.client import get_client
from prefect.client.schemas.filters import DeploymentFilter

async def get_current_deployments(
    app_name: str, 
    api_url: str
) -> List[DeploymentResponse]:
    <http://logging.info|logging.info>(
        "Reading deployments for app '%s' from '%s'",
        app_name,
        api_url if api_url is not None else "DEFAULT"
    )
    
    client = get_client()
    
    # Create filter without any extra fields
    filter_obj = DeploymentFilter(
        tags=[f"app:{app_name}"]  # Direct list instead of nested dict
    )
    
    return await client.read_deployments(
        deployment_filter=filter_obj
    )
If you're still getting the 422 error, could you share: 1. The complete error message 2. The version of Prefect you're using (
prefect version
) This will help us identify if there might be version-specific issues or if we need to adjust the filter structure further. Also, you might want to try printing the filter object before sending it to see exactly what's being sent:
Copy code
python
async def get_current_deployments(
    app_name: str, 
    api_url: str
) -> List[DeploymentResponse]:
    client = get_client()
    
    filter_obj = DeploymentFilter(
        tags=[f"app:{app_name}"]
    )
    
    # Debug print
    print("Filter object:", filter_obj.dict())
    
    return await client.read_deployments(
        deployment_filter=filter_obj
    )
Would you like me to provide more debugging steps or alternative approaches?
Open in Slack
PreviousNext
Powered by