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Hi <@ULVA73B9P> we are using `from prefect.infrast...
# ask-marvin
s
Hi @Marvin we are using 
from prefect.infrastructure import KubernetesJob
and are upgrading to use 
from prefect_kubernetes import KubernetesJob
. The current KubernetesJob method signature is
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KubernetesJob(
   namespace
    service_account_name
    image,
    env,
    finished_job_ttl,
    customizations,
    stream_output
)
How do we upgrade this to use use the new method signature including our customizations?
m
To upgrade from the old 
KubernetesJob
to the new one in 
prefect_kubernetes
, you'll need to adjust the method signature accordingly. Here are the main points to consider based on the information retrieved: 1. Import Path: - Previous: 
from prefect.infrastructure import KubernetesJob
- New: 
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob
2. Customizations: The new 
KubernetesJob
still supports customizations, but the way to specify them might differ slightly. Ensure they are provided as a list. 3. Other Parameters: Mapping remains straightforward for most parameters such as 
namespace
, 
service_account_name
, 
image
, 
env
, 
finished_job_ttl
, 
stream_output
. Here is an example conversion based on your parameters:
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python
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

customizations = [
    {
        "op": "add",
        "path": "/spec/template/spec/containers/0/resources",
        "value": {
            "requests": {
                "cpu": "2000m",
                "memory": "4gi"
            },
            "limits": {
                "cpu": "4000m",
                "memory": "8Gi",
                "<http://nvidia.com/gpu|nvidia.com/gpu>": "1"
            }
        }
    }
]

k8s_job = KubernetesJob(
    namespace="your-namespace",
    service_account_name="your-service-account",
    image="your-image",
    env={"ENV_VAR": "value"},
    finished_job_ttl=300,
    customizations=customizations,
    stream_output=True
)

k8s_job.save("devk8s")
For further details, refer to the documentation. This should provide you with a good starting point to migrate while retaining your customizations and other configurations. If you encounter any specific issues, feel free to provide more details!
s
@Marvin 
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob
the method signature seems different to what you have described and doesn't take parameters
@Marvin
m
It seems like there might have been a misunderstanding regarding the new 
KubernetesJob
class in 
prefect_kubernetes
. The new 
KubernetesJob
does not take the same parameters directly in its constructor. Instead, it appears to be more orientated around configuration through methods and possibly YAML files. Here is what you need to know: 1. New Signature and Class Usage: The 
KubernetesJob
class in 
prefect_kubernetes
is structured differently, focusing more on method calls for triggering and YAML-based configurations. 2. YAML Configuration: You can create a 
KubernetesJob
using a YAML file with 
from_yaml_file
method. ### Example using YAML First, create a YAML file (e.g., 
job_config.yaml
) with your job configuration:
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: example-job
spec:
  template:
    spec:
      containers:
      - name: example
        image: your-image
        env:
        - name: ENV_VAR
          value: "value"
        resources:
          requests:
            cpu: "2000m"
            memory: "4Gi"
          limits:
            cpu: "4000m"
            memory: "8Gi"
      restartPolicy: Never
      serviceAccountName: your-service-account
  backoffLimit: 4
Then, you can load and use this configuration in your Python script:
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python
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

k8s_job = KubernetesJob.from_yaml_file(manifest_path="job_config.yaml")
k8s_job.save("devk8s_config")
### Direct Configuration Alternatively, if you need to set configurations directly in Python, you might use methods like 
trigger
to execute the job:
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python
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

k8s_job = KubernetesJob(
    namespace="your-namespace",
    # other configurations can be added using the job's methods
)

# Save the configuration to a block
k8s_job.save("devk8s_config")

# Trigger the Kubernetes job
await k8s_job.trigger()
For more detailed configurations and method usages, you can check out the Prefect Kubernetes documentation. If more specific functionality is required, please ensure to refer to the source code and documentation as it gets updated frequently.
s
@Marvin so values for TTL, safe-to-evict would need to be added to the manifest and then added to the KubernetesJob using from_yaml_file, is my understanding correct?
m
Yes, your understanding is correct. To configure settings like TTL and 
safe-to-evict
for Kubernetes jobs, you will need to add these specifications to your manifest file and use the 
from_yaml_file
method to create the 
KubernetesJob
. Here's an updated example YAML manifest with the 
ttlSecondsAfterFinished
and 
safe-to-evict
annotations:
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: example-job
  annotations:
    <http://cluster-autoscaler.kubernetes.io/safe-to-evict|cluster-autoscaler.kubernetes.io/safe-to-evict>: "true"
spec:
  ttlSecondsAfterFinished: 300  # This sets the TTL
  template:
    spec:
      containers:
      - name: example
        image: your-image
        env:
        - name: ENV_VAR
          value: "value"
        resources:
          requests:
            cpu: "2000m"
            memory: "4Gi"
          limits:
            cpu: "4000m"
            memory: "8Gi"
      restartPolicy: Never
      serviceAccountName: your-service-account
  backoffLimit: 4
Then, in your Python script, you can load this configuration:
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python
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

k8s_job = KubernetesJob.from_yaml_file(manifest_path="job_config.yaml")
k8s_job.save("devk8s_config")
Referencing the documentation will ensure you have the latest and most accurate configurations: Prefect Kubernetes Documentation. If you have any further questions or need more specific functionality, feel free to ask!
s
@Marvin can we template the yaml so values can be injected for items like namespace, stream_output, etc?
m
Yes, you can template the YAML file and use Python to inject values dynamically. One approach is to define placeholders in your YAML file and then use Python to replace those placeholders with actual values. Here's how you can do it: 1. Create a template YAML file with placeholders:
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: example-job
  namespace: {{ namespace }}
  annotations:
    <http://cluster-autoscaler.kubernetes.io/safe-to-evict|cluster-autoscaler.kubernetes.io/safe-to-evict>: "true"
spec:
  ttlSecondsAfterFinished: 300  # This sets the TTL
  template:
    spec:
      containers:
      - name: example
        image: {{ image }}
        env:
        - name: ENV_VAR
          value: "{{ env_var_value }}"
        resources:
          requests:
            cpu: "2000m"
            memory: "4Gi"
          limits:
            cpu: "4000m"
            memory: "8Gi"
      restartPolicy: Never
      serviceAccountName: {{ service_account_name }}
  backoffLimit: 4
2. Use Python to replace the placeholders with actual values before loading the YAML file:
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python
import yaml

# Define the template values
template_values = {
    "namespace": "your-namespace",
    "image": "your-image",
    "env_var_value": "value",
    "service_account_name": "your-service-account"
}

# Read the template YAML file
with open("job_template.yaml", "r") as file:
    job_template = file.read()

# Replace placeholders with actual values
for key, value in template_values.items():
    job_template = job_template.replace(f"{{{{ {key} }}}}", value)

# Load the YAML content as a dictionary
job_config = yaml.safe_load(job_template)

# Assuming 'KubernetesJob' can accept a dictionary directly
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

k8s_job = KubernetesJob.from_dict(job_config)
k8s_job.save("devk8s_config")
Note: Make sure to adjust the 
from_dict
part to the actual method if it directly doesn’t support this. Template processing libraries like Jinja2 can be used for more complex templating needs. This way, you can keep your YAML file flexible and inject runtime values as needed. If there are additional configurations required, you can always adjust the templates and Python code accordingly.
s
@Marvin I think I will need to use 
KubernetesJob.from_yaml_file(job_config)
m
You can indeed use 
KubernetesJob.from_yaml_file(job_config)
. To do this while templating the YAML, you can save the templated content to a temporary file and then load it using 
from_yaml_file
. Here's how you can achieve that: 1. Create a template YAML file with placeholders:
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: example-job
  namespace: {{ namespace }}
  annotations:
    <http://cluster-autoscaler.kubernetes.io/safe-to-evict|cluster-autoscaler.kubernetes.io/safe-to-evict>: "true"
spec:
  ttlSecondsAfterFinished: 300  # This sets the TTL
  template:
    spec:
      containers:
      - name: example
        image: {{ image }}
        env:
        - name: ENV_VAR
          value: "{{ env_var_value }}"
        resources:
          requests:
            cpu: "2000m"
            memory: "4Gi"
          limits:
            cpu: "4000m"
            memory: "8Gi"
      restartPolicy: Never
      serviceAccountName: {{ service_account_name }}
  backoffLimit: 4
2. Use Python to replace placeholders with actual values and save to a temporary file:
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python
import tempfile
import yaml
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

# Define the template values
template_values = {
    "namespace": "your-namespace",
    "image": "your-image",
    "env_var_value": "value",
    "service_account_name": "your-service-account"
}

# Read the template YAML file
with open("job_template.yaml", "r") as file:
    job_template = file.read()

# Replace placeholders with actual values
for key, value in template_values.items():
    job_template = job_template.replace(f"{{{{ {key} }}}}", value)

# Write the rendered template to a temporary file
with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.yaml') as temp_file:
    temp_file_path = temp_file.name
    temp_file.write(job_template)

# Load the KubernetesJob from the YAML file
k8s_job = KubernetesJob.from_yaml_file(manifest_path=temp_file_path)
k8s_job.save("devk8s_config")
By using this approach, you keep your YAML configuration flexible and can inject specific values at runtime. If you need further customization or encounter any issues, please refer to the Prefect Kubernetes documentation, or let me know!
s
Hi @Marvin, how do I extend the base job template adding Kubernetes cpu and memory limits?
m
To extend the base job template in your 
KubernetesJob
to add CPU and memory limits, you can modify the 
customizations
parameter. Here's a step-by-step guide on how to do it: 1. Create a YAML Template: Include placeholders for CPU and memory resources:
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yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: example-job
spec:
  template:
    spec:
      containers:
      - name: example
        image: your-image
        env:
        - name: ENV_VAR
          value: "{{ env_var_value }}"
        resources:
          requests:
            cpu: "{{ cpu_request }}"
            memory: "{{ memory_request }}"
          limits:
            cpu: "{{ cpu_limit }}"
            memory: "{{ memory_limit }}"
      restartPolicy: Never
      serviceAccountName: {{ service_account_name }}
  backoffLimit: 4
2. Use Python to Inject Values: Replace placeholders with actual values and load the YAML file using `KubernetesJob.from_yaml_file`:
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python
import tempfile
from <http://prefect_kubernetes.jobs|prefect_kubernetes.jobs> import KubernetesJob

# Define the template values
template_values = {
    "namespace": "your-namespace",
    "cpu_request": "500m",
    "memory_request": "1Gi",
    "cpu_limit": "1000m",
    "memory_limit": "2Gi",
    "env_var_value": "value",
    "service_account_name": "your-service-account"
}

# Read the template YAML file
with open("job_template.yaml", "r") as file:
    job_template = file.read()

# Replace placeholders with actual values
for key, value in template_values.items():
    job_template = job_template.replace(f"{{{{ {key} }}}}", value)

# Write the rendered template to a temporary file
with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.yaml') as temp_file:
    temp_file_path = temp_file.name
    temp_file.write(job_template)

# Load the KubernetesJob from the YAML file
k8s_job = KubernetesJob.from_yaml_file(manifest_path=temp_file_path)
k8s_job.save("devk8s_config")
By following these steps, you can ensure that your CPU and memory limits are dynamically set within the 
KubernetesJob
configuration. For more detailed information on customizing and extending the job templates, you can refer to the Kubernetes Deployment Guide.
s
@Marvin KubernetesJob is deprecated from my understanding and is used for Blocks that apply to agents. Is there a way to do this using Workers?
m
To use custom configurations for Kubernetes jobs with the new Prefect worker setup, you can define custom job templates within a work pool for workers. The use of 
KubernetesJob
is indeed deprecated in favor of this approach. Here’s how you can extend the base job template using the new worker model: ### Steps to Extend the Base Job Template for Kubernetes Workers 1. Create Custom Job Template: You can define a custom job template JSON with your specific requirements (e.g., CPU and memory limits). 2. Create and Manage a Work Pool: Use Prefect to create a work pool, where you can upload your customized job template. 3. Start the Kubernetes Worker: Ensure that the worker uses this custom job template for executing the tasks. ### Example 1. Create a Custom Job Template JSON: Create a file named `custom_job_template.json`:
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json
{
  "containers": [
    {
      "name": "my-container",
      "image": "{{ image }}",
      "resources": {
        "requests": {
          "cpu": "{{ cpu_request }}",
          "memory": "{{ memory_request }}"
        },
        "limits": {
          "cpu": "{{ cpu_limit }}",
          "memory": "{{ memory_limit }}"
        }
      },
      "env": [
        {
          "name": "ENV_VAR",
          "value": "{{ env_var_value }}"
        }
      ]
    }
  ],
  "restartPolicy": "Never",
  "serviceAccountName": "{{ service_account_name }}"
}
2. Define the Template Values in Your Deployment Configuration: In your Prefect deployment script or configuration, you can specify these variables:
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python
from prefect.deployments import DeploymentSpec
from prefect.infrastructure.kubernetes import KubernetesJob

DeploymentSpec(
    name="my-flow",
    flow_location="path/to/your/flow.py",
    infrastructure=KubernetesJob(
        customizations={
            "image": "your-image",
            "cpu_request": "500m",
            "memory_request": "1Gi",
            "cpu_limit": "1000m",
            "memory_limit": "2Gi",
            "env_var_value": "value",
            "service_account_name": "your-service-account"
        },
        job_template_path="path/to/your/custom_job_template.json"
    )
)
3. Start a Kubernetes Worker: Use the Prefect CLI to start a Kubernetes worker that uses your custom work pool:
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sh
export PREFECT_API_URL="<https://api.prefect.io>"
export PREFECT_API_KEY="your-prefect-api-key"

prefect worker start --pool 'my-work-pool' --type kubernetes
### References: - Prefect Kubernetes Worker Documentation - Prefect Work Pools Guide By following these steps, you can extend your Kubernetes job templates and manage resources such as CPU and memory limits efficiently with the new Prefect worker model. If you need further customization or encounter specific issues, referring to the docs or reaching out for more detailed support may be beneficial.
s
@Marvin once I have called 
my_flow.deploy()
and the flow has been successfully deployed, how do I run the flow from within Python code?
m
To run a deployed flow from within Python code, you can use Prefect's 
run_deployment
function. Here is how you can do it: ### Step-by-Step Guide 1. Import the 
run_deployment
Function: Use the 
run_deployment
function from 
prefect.deployments
. 2. Call the Deployment: You need to provide the name of the deployment which typically follows the pattern 
<flow_name>/<deployment_name>
. ### Example Here’s an example of how you can run an already deployed flow:
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python
from prefect.deployments import run_deployment

def main():
    # Run the deployment by name
    flow_run = run_deployment(name="my_flow_name/my_deployment_name")
    print(flow_run)  # This will print out details of the flow run

if __name__ == "__main__":
    main()
### Blocking vs Non-Blocking By default, 
run_deployment
will block until the scheduled flow run finishes executing. To run it without blocking, you can pass `timeout=0`:
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python
from prefect.deployments import run_deployment

def main():
    # Run the deployment without blocking
    flow_run = run_deployment(name="my_flow_name/my_deployment_name", timeout=0)
    print(flow_run)

if __name__ == "__main__":
    main()
### Running as Subflow If you call 
run_deployment
from within a flow or task, the scheduled flow run will be linked to the calling flow run as a subflow run by default. If you prefer not to link it, you can disable this behavior by setting `as_subflow=False`:
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python
from prefect import flow
from prefect.deployments import run_deployment

@flow
def main():
    # Run the deployment as an independent flow, not a subflow
    flow_run = run_deployment(name="my_flow_name/my_deployment_name", as_subflow=False)
    print(flow_run)

if __name__ == "__main__":
    main()
### References For more detailed information, you can refer to the Prefect documentation on deploying and running flows. This should help you run your deployed flows programmatically from within your Python code. If you have any further questions or need specific examples, feel free to ask!
s
@Marvin can you please include the 
Flow.from_source().deploy()
in the example as well?
m
Certainly! Below is a complete example that includes fetching a flow instance using 
Flow.from_source()
, deploying it using 
deploy()
, and then running the deployed flow using 
run_deployment
. ### Complete Example 1. Fetch Flow and Deploy: Use 
Flow.from_source()
to retrieve the flow and 
deploy()
to deploy it. 2. Run the Deployed Flow: Use 
run_deployment
to trigger the deployed flow. ### Example Code
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python
from prefect import Flow
from prefect.deployments import run_deployment

# Define a source location for your flow (this could be a file path or a module path)
flow_source = "path/to/your/flow.py"

def main():
    # Load flow from source
    flow = Flow.from_source(flow_source)
    
    # Deploy the flow
    deployment = flow.deploy(
        name="my_deployment_name",
        work_pool_name="my_work_pool",
        # You can add more deployment configurations here
        path=flow_source,
    )
    
    print(f"Deployment created: {deployment}")

    # Run the deployment by name
    flow_run = run_deployment(name=f"{flow.name}/my_deployment_name")
    print(f"Flow run: {flow_run}")

if __name__ == "__main__":
    main()
### Explanation 1. Load Flow from Source: 
Flow.from_source(flow_source)
loads the flow from the specified source path. 2. Deploy the Flow: 
flow.deploy()
is used to deploy the flow. You provide the deployment name, work pool name, and other configurations. 3. Run the Deployed Flow: 
run_deployment(name=f"{flow.name}/my_deployment_name")
triggers the flow deployment by name. ### Adjustments - Path to Flow: Ensure 
flow_source
accurately points to your flow's source file or module path. - Deployment Configuration: Customize 
deploy()
with any additional configurations you need. - Error Handling: Consider adding error handling for robustness. For more details, refer to the Prefect documentation which provides comprehensive guides on deployments and running flows. This setup should help you load, deploy, and run your Prefect flows programmatically! If you face any issues or need further assistance, feel free to reach out.
s
@Marvin we used to start the flow run using the following code is this now not how it should be done?
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flow_run_id = asyncio.run(prefect_client.create_flow_run_from_deployment(
    deployment_id=deployment_id,
    name=flow_run_name)).id
m
Yes, the code you provided for starting a flow run using the deployment ID with 
create_flow_run_from_deployment
is still valid in Prefect 2.0, even though you can use the 
run_deployment
function for convenience. Here's how you can do it using the 
create_flow_run_from_deployment
method: ### Using 
create_flow_run_from_deployment
1. Deploy the Flow: Deploy the flow and obtain the deployment ID. 2. Run the Deployment Using the Deployment ID: Use 
create_flow_run_from_deployment
to start the flow run using the deployment ID. ### Example Code Here is your complete example including both deployment and running the flow using the deployment ID:
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python
import asyncio
from prefect import Flow
from prefect.client import get_client

# Define a source location for your flow (this could be a file path or a module path)
flow_source = "path/to/your/flow.py"

async def main():
    # Load flow from source
    flow = Flow.from_source(flow_source)
    
    # Deploy the flow
    deployment = await flow.deploy(
        name="my_deployment_name",
        work_pool_name="my_work_pool",
        path=flow_source,
    )
    
    # Print deployment details
    print(f"Deployment created: {deployment}")
    
    # Retrieve the deployment ID
    deployment_id = deployment.id
    print(f"Deployment ID: {deployment_id}")
    
    # Run the deployment using the deployment ID
    async with get_client() as client:
        flow_run = await client.create_flow_run_from_deployment(deployment_id=deployment_id, name="my_flow_run_name")
        print(f"Flow run ID: {flow_run.id}")
        return flow_run.id

# Use an async entry point for Python 3.7+
if __name__ == "__main__":
    flow_run_id = asyncio.run(main())
    print(f"Flow run has been started with ID: {flow_run_id}")
### Explanation 1. Deploy the Flow: Deploy the flow using 
flow.deploy()
and capture the 
deployment.id
. 2. Run the Deployment Using the Deployment ID: Use 
create_flow_run_from_deployment
method to run the deployment using the 
deployment_id
and capture the 
flow_run.id
. ### References The approach you are familiar with (using 
create_flow_run_from_deployment
) remains valid and useful, particularly in scenarios where you have a deployment ID and prefer explicit control. For further reading and more details, refer to the Prefect 2.0 documentation on deployments. If you have any further questions or need additional examples, feel free to ask!
s
@Marvin what does this message mean as I have declared an S3Block and it has the correct bucket_name and bucket_path inside my Python code? No class found for dispatch key 's3-bucket' in registry for type 'Block'
m
The error message "No class found for dispatch key 's3-bucket' in registry for type 'Block'" indicates that the 
S3Bucket
block has not been registered properly or there's an issue with the import paths or installation. Here's how you can troubleshoot and resolve this issue: ### Steps to Resolve 1. Ensure 
prefect_aws
is Installed: Ensure that the 
prefect_aws
package is installed in your environment.
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sh
   pip install prefect_aws
2. Register the S3 Block: Ensure the 
S3Bucket
block has been registered. You can do this using the Prefect CLI:
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sh
   prefect block register -m prefect_aws
3. Check Environment Variables: Ensure the 
PREFECT_EXTRA_ENTRYPOINTS
environment variable is set properly if you are using custom entry points. 4. Sample Code to Check Registration: Here’s a simple script to test if the 
S3Bucket
block is registered correctly:
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python
   from prefect_aws.s3 import S3Bucket

   def check_s3block_registration():
       try:
           s3_block = S3Bucket(bucket_name="your-bucket-name", bucket_folder="your-folder")
           print("S3Bucket block creation successful:", s3_block)
       except Exception as e:
           print("Error creating S3Bucket block:", e)

   if __name__ == "__main__":
       check_s3block_registration()
5. Deploying and Running a Flow: Deploy your flow and then run it using 
create_flow_run_from_deployment
method:
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python
   import asyncio
   from prefect import Flow
   from prefect.client import get_client
   from prefect.deployments import run_deployment
   from prefect_aws.s3 import S3Bucket

   # Define a source location for your flow (this could be a file path or a module path)
   flow_source = "path/to/your/flow.py"

   async def main():
       # Load flow from source
       flow = Flow.from_source(flow_source)
       
       # Deploy the flow
       deployment = await flow.deploy(
           name="my_deployment_name",
           work_pool_name="my_work_pool",
           path=flow_source,
       )
       
       # Print deployment details
       print(f"Deployment created: {deployment}")
       
       # Retrieve the deployment ID
       deployment_id = deployment.id
       print(f"Deployment ID: {deployment_id}")
       
       # Run the deployment using the deployment ID
       async with get_client() as client:
           flow_run = await client.create_flow_run_from_deployment(deployment_id=deployment_id, name="my_flow_run_name")
           print(f"Flow run ID: {flow_run.id}")
           return flow_run.id

   # Use an async entry point for Python 3.7+
   if __name__ == "__main__":
       flow_run_id = asyncio.run(main())
       print(f"Flow run has been started with ID: {flow_run_id}")
### Caveat with 
PREFECT_EXTRA_ENTRYPOINTS
Make sure that if you're setting 
PREFECT_EXTRA_ENTRYPOINTS
, it is pointing to the correct module paths. This might look something like 
my_module
or 
my_module.my_example_block
where your custom blocks are defined. ### References For more information on block registration and the latest updates: - Prefect AWS Documentation - Prefect Deployment Concepts By following these troubleshooting steps, you should be able to resolve the issue and ensure your S3 block is correctly registered and used. If you need further assistance, feel free to ask!
s
@Marvin the S3Block has been created via the Prefect UI do I still need to register it? The code I am using is 
s3_storage = S3Bucket.load('flow_storage_block')
m
If the 
S3Bucket
block has been created via the Prefect UI and you are loading it with 
S3Bucket.load('flow_storage_block')
, you typically should not need to register it again. However, the error message you're seeing ("No class found for dispatch key 's3-bucket' in registry for type 'Block'") indicates that there might be an issue with the block registration on your current setup or environment. ### Possible Issues and Solutions 1. Ensure 
prefect_aws
is installed in your environment: Make sure your environment where the flow is running has the 
prefect_aws
package installed.
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sh
   pip install prefect_aws
2. Check Environment and Entry Points: If you are setting the 
PREFECT_EXTRA_ENTRYPOINTS
environment variable, ensure it includes the path to the module where 
prefect_aws
is located. 3. Verify Block Loading in Code: Here’s a minimal script to check if the block can be loaded correctly:
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python
   from prefect_aws.s3 import S3Bucket

   def main():
       try:
           s3_storage = S3Bucket.load('flow_storage_block')
           print("S3Bucket block loaded successfully:", s3_storage)
       except Exception as e:
           print("Error loading S3Bucket block:", e)

   if __name__ == "__main__":
       main()
4. Ensure Correct Registration of Prefect AWS Blocks: It might be helpful to re-register the Prefect AWS blocks just to ensure the registry is updated if you’ve made any changes.
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sh
   prefect block register -m prefect_aws
### Example Within Your Flow Deployment Code Below is the updated code that includes loading the S3 block and using it within the deployment process:
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python
import asyncio
from prefect import Flow
from prefect.client import get_client
from prefect.deployments import run_deployment
from prefect_aws.s3 import S3Bucket

# Define a source location for your flow (this could be a file path or a module path)
flow_source = "path/to/your/flow.py"

async def main():
    # Load flow from source
    flow = Flow.from_source(flow_source)
    
    # Load the S3 storage block
    try:
        s3_storage = S3Bucket.load('flow_storage_block')
        print("S3Bucket block loaded successfully:", s3_storage)
    except Exception as e:
        print("Error loading S3Bucket block:", e)
        return

    # Deploy the flow
    deployment = await flow.deploy(
        name="my_deployment_name",
        work_pool_name="my_work_pool",
        path=flow_source,
        storage=s3_storage,
    )
    
    # Print deployment details
    print(f"Deployment created: {deployment}")
    
    # Retrieve the deployment ID
    deployment_id = deployment.id
    print(f"Deployment ID: {deployment_id}")
    
    # Run the deployment using the deployment ID
    async with get_client() as client:
        flow_run = await client.create_flow_run_from_deployment(deployment_id=deployment_id, name="my_flow_run_name")
        print(f"Flow run ID: {flow_run.id}")
        return flow_run.id

# Use an async entry point for Python 3.7+
if __name__ == "__main__":
    flow_run_id = asyncio.run(main())
    print(f"Flow run has been started with ID: {flow_run_id}")
### If Issue Persists If the issue persists: - Ensure there's no mismatch in Prefect versions across your local environment and the runtime environment. - Restart the Prefect agent or worker to ensure it picks up any updated configurations. ### References For more detailed information: - Prefect AWS Documentation - Prefect Block Guide By ensuring that the 
prefect_aws
package is correctly installed and the environment is configured properly, you should be able to load and use the 
S3Bucket
block without issues. If you need further assistance, feel free to reach out!
s
@Marvin using an S3Bucket block how is flow run code saved and pulled as part of a job execution using Flow.from_storage().deploy()?
m
To use an 
S3Bucket
block for saving and pulling flow run code as part of a job execution using Prefect, follow the steps below: ### Steps to Deploy and Run Flow with S3 Storage 1. Create and Save the S3Bucket Block: Define and save the 
S3Bucket
block using appropriate AWS credentials and bucket details. 2. Deploy the Flow: Use 
Flow.from_source()
and 
deploy()
to save and deploy your flow code to the S3 bucket. 3. Run the Flow Using Deployment: Use 
run_deployment
or similar methods to start the flow run. ### Example Code Here’s a step-by-step guide with example code: #### 1. Define and Save the S3Bucket Block
Copy code
python
from prefect_aws import S3Bucket
from prefect_aws.credentials import AWSCredentials

# Define AWS credentials (ensure these credentials have access to the S3 bucket)
aws_credentials = AWSCredentials(
    aws_access_key_id="your_access_key",
    aws_secret_access_key="your_secret_key",
    region_name="your_region"
)

# Create and save the S3Bucket block
s3_bucket = S3Bucket(
    bucket_name="your_s3_bucket",
    credentials=aws_credentials
)
s3_bucket.save(name="my_s3_bucket_block", overwrite=True)
#### 2. Deploy the Flow Using S3Bucket Storage Create a Python script to define, deploy, and run your flow using the S3Bucket block:
Copy code
python
import asyncio
from prefect import Flow
from prefect.client import get_client
from prefect.deployments import run_deployment
from prefect_aws.s3 import S3Bucket

# Define a source location for your flow (this could be a file path or a module path)
flow_source = "path/to/your/flow.py"

async def main():
    try:
        # Load flow from source
        flow = Flow.from_source(
            source=flow_source,
            entrypoint="main_flow_function"  # Update with your flow's entry point
        )
        
        # Load the S3 storage block
        s3_storage = S3Bucket.load('my_s3_bucket_block')
        print("S3Bucket block loaded successfully:", s3_storage)

        # Deploy the flow
        deployment = await flow.deploy(
            name="my_deployment_name",
            work_pool_name="my_work_pool",
            storage=s3_storage
        )
        
        # Print deployment details
        print(f"Deployment created: {deployment}")
        
        # Retrieve the deployment ID
        deployment_id = deployment.id
        print(f"Deployment ID: {deployment_id}")
        
        # Run the deployment using the deployment ID
        async with get_client() as client:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id=deployment_id,
                name="my_flow_run_name"
            )
            print(f"Flow run ID: {flow_run.id}")
            return flow_run.id
    
    except Exception as e:
        print(f"An error occurred: {e}")

# Use an async entry point for Python 3.7+
if __name__ == "__main__":
    flow_run_id = asyncio.run(main())
    print(f"Flow run has been started with ID: {flow_run_id}")
### Explanation 1. Create and Save S3Bucket Block: An S3 bucket block is created using AWS credentials and saved with a specified name for later use. 2. Deploy the Flow with S3 Storage: - `Flow.from_source()`: Loads the flow from a specified source. - `s3_storage = S3Bucket.load('my_s3_bucket_block')`: Loads the previously created S3 bucket block. - `deploy()`: Deploys the flow using the S3 bucket for storage. 3. Run the Deployed Flow: - `create_flow_run_from_deployment`: Runs the flow deployment using its deployment ID. ### Additional Notes - Environment Setup: Ensure that your execution environment has access to the same AWS credentials and Prefect configuration. - Monitoring and Debugging: Use Prefect UI or CLI to monitor the deployment and flow run status.
This setup should allow you to deploy and run flows using S3Bucket storage in Prefect. If you encounter issues or need further customization, referring to the Prefect documentation can provide additional insights and examples.
s
@Marvin what is the purpose of 
s3_bucket.save(name="my_s3_bucket_block", overwrite=True)
if I created my S3Bucket block using the Prefect UI?
m
The 
s3_bucket.save(name="my_s3_bucket_block", overwrite=True)
command is used to programmatically create and save a new S3Bucket block or update an existing one within your Prefect project. This is particularly useful for automating the setup process and ensuring consistency across different environments. However, if you have already created the S3Bucket block using the Prefect UI, you do not need to save it again programmatically. You can directly use the block by loading it with 
S3Bucket.load('my_s3_bucket_block')
. ### Using S3Bucket Block Created via Prefect UI If your S3Bucket block has been created using the Prefect UI, follow these steps to use the block for deploying and running your flow: 1. Define Your Flow: Ensure you have a flow definition ready. 2. Load the S3Bucket Block: Load the existing S3Bucket block using its name. 3. Deploy the Flow: Use the loaded S3Bucket block to deploy the flow. 4. Run the Deployment: Run the deployed flow using the 
run_deployment
function. ### Example Code Here is a streamlined version of your code that assumes the S3Bucket block has been created via the Prefect UI:
Copy code
python
import asyncio
from prefect import Flow
from prefect.client import get_client
from prefect.deployments import run_deployment
from prefect_aws.s3 import S3Bucket

# Define a source location for your flow (this could be a file path or a module path)
flow_source = "path/to/your/flow.py"

async def main():
    try:
        # Load flow from source
        flow = Flow.from_source(
            source=flow_source,
            entrypoint="main_flow_function"  # Update with your flow's entry point
        )
        
        # Load the existing S3 storage block created via Prefect UI
        s3_storage = S3Bucket.load('flow_storage_block')
        print("S3Bucket block loaded successfully:", s3_storage)

        # Deploy the flow
        deployment = await flow.deploy(
            name="my_deployment_name",
            work_pool_name="my_work_pool",
            storage=s3_storage
        )
        
        # Print deployment details
        print(f"Deployment created: {deployment}")
        
        # Retrieve the deployment ID
        deployment_id = deployment.id
        print(f"Deployment ID: {deployment_id}")
        
        # Run the deployment using the deployment ID
        async with get_client() as client:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id=deployment_id,
                name="my_flow_run_name"
            )
            print(f"Flow run ID: {flow_run.id}")
            return flow_run.id
    
    except Exception as e:
        print(f"An error occurred: {e}")

# Use an async entry point for Python 3.7+
if __name__ == "__main__":
    flow_run_id = asyncio.run(main())
    print(f"Flow run has been started with ID: {flow_run_id}")
### Key Changes - Skip 
save
Step: The 
s3_bucket.save()
step is omitted because the block has already been created using the Prefect UI. - Directly Loading the Block: The block is loaded using 
S3Bucket.load('flow_storage_block')
. ### Considerations 1. Block Name: Ensure the block name used in 
S3Bucket.load()
matches the name used in the Prefect UI. 2. Environment Setup: Verify that your execution environment (where this script runs) has network access and appropriate permissions to interact with AWS services. ### Conclusion By directly loading an S3Bucket block created via the Prefect UI, you simplify your code and avoid unnecessary steps. This ensures your deployment process is efficient and utilizes pre-configured settings. If you have additional configurations or encounter specific issues, the Prefect documentation can provide more detailed guidance. If you need further assistance, feel free to ask!
s
@Marvin how do I write flow run code that has been updated back to S3Bucket storage?
m
To write updated flow run code back to an 
S3Bucket
storage and deploy it, follow these steps: 1. Define Your Flow: Make sure your flow definition is updated. 2. Use 
S3Bucket
for Storage: Load the existing 
S3Bucket
block. 3. Deploy and Update Flow Code: Use 
Flow.from_source()
and 
deploy()
to save the updated flow code back to the S3 bucket. ### Example Code Here’s how you can achieve this: #### 1. Define Your Flow Update your flow definition as needed.
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python
from prefect import flow

@flow(name="my_updated_flow")
def my_updated_flow():
    print("This is my updated flow code.")
#### 2. Load S3Bucket Block and Deploy Use the existing 
S3Bucket
block for storage and save the updated flow code.
Copy code
python
import asyncio
from prefect import Flow
from prefect_aws.s3 import S3Bucket
from prefect.deployments import run_deployment

# Define the path to your flow source
flow_source = "path/to/your/updated_flow.py"

async def main():
    try:
        # Load flow from source
        flow = Flow.from_source(
            source=flow_source,
            entrypoint="my_updated_flow"  # Update with your flow's entry point
        )
        
        # Load the existing S3 storage block created via Prefect UI
        s3_storage = S3Bucket.load('flow_storage_block')
        print("S3Bucket block loaded successfully:", s3_storage)

        # Deploy the updated flow
        deployment = await flow.deploy(
            name="my_updated_deployment_name",
            work_pool_name="my_work_pool",
            storage=s3_storage
        )
        
        # Print deployment details
        print(f"Deployment created: {deployment}")
        
        # Retrieve the deployment ID
        deployment_id = deployment.id
        print(f"Deployment ID: {deployment_id}")

        # Run the deployment using the deployment ID
        async with get_client() as client:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id=deployment_id,
                name="my_flow_run_name"
            )
            print(f"Flow run ID: {flow_run.id}")
            return flow_run.id
    
    except Exception as e:
        print(f"An error occurred: {e}")

# Use an async entry point for Python 3.7+
if __name__ == "__main__":
    flow_run_id = asyncio.run(main())
    print(f"Flow run has been started with ID: {flow_run_id}")
### Explanation 1. Define Your Flow: Create or update your flow definition. 2. Load S3Bucket Block: Load the S3 bucket block that you've previously created using the Prefect UI. 3. Deploy the Updated Flow: - Use 
Flow.from_source()
to load the updated flow from the source. - Deploy the flow using 
flow.deploy()
which stores the flow's code in the S3 bucket. 4. Run the Deployment: - Use 
run_deployment
or 
create_flow_run_from_deployment
to execute the flow. ### Additional Considerations - Block Name: Ensure the block name used in 
S3Bucket.load()
is accurate. - Permissions: Verify that the AWS credentials have the appropriate permissions to read/write from the specified S3 bucket. - Environment Setup: Ensure that the environment where this script runs has access to Prefect, AWS, and network requirements. This approach ensures that any changes to your flow code are uploaded back to the S3 bucket upon deployment, keeping your flow codebase synchronized. If you have further questions or need additional customization, refer to the Prefect documentation for more detailed information.
s
@Marvin using 
s3_storage
that is returned from 
S3Bucket.load()
can I use the object to get the bucket name and bucket folder?
m
Yes, you can use the 
s3_storage
object returned from 
S3Bucket.load()
to access its properties such as 
bucket_name
and 
bucket_folder
. The 
S3Bucket
class in Prefect AWS exposes these attributes, allowing you to retrieve the configured values. Here's how you can do it: ### Example Code
Copy code
python
from prefect_aws.s3 import S3Bucket

# Load the existing S3 storage block created via Prefect UI
s3_storage = S3Bucket.load('flow_storage_block')

# Access the bucket name and bucket folder
bucket_name = s3_storage.bucket_name
bucket_folder = s3_storage.bucket_folder

# Print the bucket name and bucket folder
print(f"Bucket Name: {bucket_name}")
print(f"Bucket Folder: {bucket_folder}")
### Full Example in a Deployment Script Here’s how you can incorporate this into a full script to deploy and run your flow:
Copy code
python
import asyncio
from prefect import Flow
from prefect.client import get_client
from prefect.deployments import run_deployment
from prefect_aws.s3 import S3Bucket

# Define the path to your flow source
flow_source = "path/to/your/updated_flow.py"

async def main():
    try:
        # Load flow from source
        flow = Flow.from_source(
            source=flow_source,
            entrypoint="my_updated_flow"  # Update with your flow's entry point
        )
        
        # Load the existing S3 storage block created via Prefect UI
        s3_storage = S3Bucket.load('flow_storage_block')
        print("S3Bucket block loaded successfully:", s3_storage)

        # Access bucket details
        bucket_name = s3_storage.bucket_name
        bucket_folder = s3_storage.bucket_folder
        print(f"Bucket Name: {bucket_name}")
        print(f"Bucket Folder: {bucket_folder}")

        # Deploy the updated flow
        deployment = await flow.deploy(
            name="my_updated_deployment_name",
            work_pool_name="my_work_pool",
            storage=s3_storage
        )
        
        # Print deployment details
        print(f"Deployment created: {deployment}")
        
        # Retrieve the deployment ID
        deployment_id = deployment.id
        print(f"Deployment ID: {deployment_id}")

        # Run the deployment using the deployment ID
        async with get_client() as client:
            flow_run = await client.create_flow_run_from_deployment(
                deployment_id=deployment_id,
                name="my_flow_run_name"
            )
            print(f"Flow run ID: {flow_run.id}")
            return flow_run.id
    
    except Exception as e:
        print(f"An error occurred: {e}")

# Use an async entry point for Python 3.7+
if __name__ == "__main__":
    flow_run_id = asyncio.run(main())
    print(f"Flow run has been started with ID: {flow_run_id}")
### Explanation 1. Load S3Bucket Block: The 
S3Bucket.load('flow_storage_block')
method loads the existing S3 bucket block. 2. Access Bucket Properties: You can access 
bucket_name
and 
bucket_folder
directly from the 
s3_storage
object. 3. Print Bucket Details: Print the bucket name and folder to verify their values. ### Additional Considerations - Ensure Correct Setup: Make sure the block name used in 
S3Bucket.load()
matches the name used in the Prefect UI. - Consistent and Secure Access: Ensure that your environment has the necessary AWS credentials and network access to interact with the S3 bucket. This approach allows you to dynamically access and use the S3 bucket details within your deployment and flow execution scripts. If you encounter any issues or need further assistance, feel free to ask!
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