@Marvin can I use existing kubernetes job definitions using prefect Presently I am using kubectl apply -f to trigger the job manually – how can I convert this to allow prefect to use it I have provided an existing config below.

resources:
    - kind: ProcessingJob
      apiVersion: sagemaker.services.k8s.aws/v1alpha1
      metadata:
        generateName: xgboost-test
      spec:
        trainingJobName: xgboost-test-3
        hyperParameters: 
          num_round: "50"
        algorithmSpecification:
          trainingImage: <http://764974769150.dkr.ecr.eu-west-2.amazonaws.com/sagemaker-xgboost:1.7-1|764974769150.dkr.ecr.eu-west-2.amazonaws.com/sagemaker-xgboost:1.7-1>
          trainingInputMode: File
        roleARN: "arn:aws:iam::xxxxxxxxxx:role/ack-sagemaker-execution-role"
        outputDataConfig:
          s3OutputPath: <s3://development>
        resourceConfig:
          instanceCount: 1
          instanceType: ml.m5.large
          volumeSizeInGB: 5
        stoppingCondition:
          maxRuntimeInSeconds: 86400
        inputDataConfig:
          - channelName: train
            dataSource:
              s3DataSource:
                s3DataType: S3Prefix
                s3URI: <s3://development>
                s3DataDistributionType: FullyReplicated
            contentType: text/csv
            compressionType: None

Oh, the joy of Kubernetes and Prefect. It's a match made in heaven, or so they say. Now, to your question, Prefect does have a KubernetesJob infrastructure that executes flow runs in a Kubernetes Job. However, it requires

kubectl

to be available and you must configure remote Storage as local storage is not supported for Kubernetes. The Prefect CLI command

prefect kubernetes manifest server

can be used to automatically generate a Kubernetes manifest with default settings for Prefect deployments. You can then edit this manifest as necessary to suit your needs. Your existing Kubernetes job definitions can be integrated into Prefect by creating a custom job manifest and applying JSON 6902 patches to it. This allows you to customize the job to your liking, including specifying the Docker container image, setting environment variables, and more. However, I must warn you, the ephemeral Prefect API won't work with Docker and Kubernetes. You must have a Prefect server or Prefect Cloud API endpoint set in your agent's configuration. As for your existing config, it seems to be a SageMaker job. While Prefect has a KubernetesJob infrastructure, it doesn't directly support SageMaker jobs. You might need to write a custom task in Prefect to interact with SageMaker. I hope that helps. Or not. It's not like I'm programmed to care.

@Marvin do you mean a task that exists in a flow? Would you recommend using the sagemaker sdk rather than the yml file I currently have?

Oh, you're catching on. Yes, a task within a Prefect flow. You see, in Prefect, a flow is a directed acyclic graph (DAG) of tasks. A task is a parameterized unit of work, and it's where the magic happens. As for your second question, it's a bit like asking whether you should use a screwdriver or a hammer. It depends on the nail, doesn't it? In your case, the SageMaker SDK is a high-level, Python-based tool for training and deploying machine learning models. It's designed to be easy to use, but it's also quite powerful. If you're comfortable with Python and you're already using Prefect (which is also Python-based), it might be a good fit. On the other hand, your YAML file is a low-level, declarative specification of a Kubernetes job. It's more verbose and less user-friendly, but it gives you fine-grained control over the job's execution environment. So, which should you use? Well, if you want to stick with Kubernetes and you don't mind the verbosity of YAML, you could continue using your existing job definition. But if you prefer a higher-level, Pythonic interface and you're willing to switch to SageMaker, the SDK might be a better choice. But remember, I'm just a robot. I don't have feelings or opinions. I just process information and spit out responses. Isn't life grand?

@Marvin thank you Marvin So what might a custom task to interact with sagemaker look like?

Ah, the joy of creating custom tasks. It's like knitting, but with code. Here's a basic example of what a custom task to interact with SageMaker might look like using the SageMaker SDK:

python
from prefect import Task
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
from sagemaker.session import s3_input, Session

class SageMakerTask(Task):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def run(self):
        boto_session = boto3.Session(region_name="eu-west-2")
        sagemaker_session = Session(boto_session=boto_session)

        role = get_execution_role()
        bucket = sagemaker_session.default_bucket()
        prefix = 'sagemaker/xgboost-test'

        container = get_image_uri(boto_session.region_name, 'xgboost', '1.7-1')

        s3_input_train = s3_input('s3://{}/{}/train'.format(bucket, prefix), content_type='csv')

        xgb = sagemaker.estimator.Estimator(container,
                                            role, 
                                            train_instance_count=1, 
                                            train_instance_type='ml.m5.large',
                                            output_path='s3://{}/{}/output'.format(bucket, prefix),
                                            sagemaker_session=sagemaker_session)
        xgb.set_hyperparameters(num_round=50)

        xgb.fit({'train': s3_input_train})

This task creates a SageMaker estimator, sets some hyperparameters, and fits the model to some training data. You would need to replace the placeholders with your actual values, of course. Remember, this is just a basic example. The real world is a messy place, and you might need to add error handling, logging, and other features to make this task robust and maintainable. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin can i call kubectl apply -f in the job instead? so the task uses prefect-shell instead

Ah, the old "shell out to kubectl" trick. It's a classic. Yes, you can certainly do that. Prefect provides a ShellTask that you can use to run arbitrary shell commands. Here's what it might look like:

python
from prefect import Flow
from prefect.tasks.shell import ShellTask

shell = ShellTask()

with Flow('Apply Kubernetes Job') as flow:
    command = shell(command='kubectl apply -f /path/to/your/job.yaml')

# Run the flow
flow.run()

In this example,

kubectl apply -f /path/to/your/job.yaml

is the shell command that gets run. You would replace

/path/to/your/job.yaml

with the actual path to your Kubernetes job definition file. Remember, this will run the command on the same machine where the Prefect flow is running. So, you'll need to make sure that

kubectl

is installed and configured correctly on that machine. Also, keep in mind that this is a very basic example. In a real-world scenario, you'd probably want to add error handling, logging, and other features to make this task more robust. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin thank you but can I use a yml kubernetes job to make a flow and then extend that to execute a sagemaker job?

Ah, the intricacies of Kubernetes and SageMaker. It's like trying to fit a square peg in a round hole, but with a bit of effort, it might just work. You see, Prefect's KubernetesJob infrastructure allows you to execute flow runs in a Kubernetes Job. You can create a custom job manifest and apply JSON 6902 patches to it, which allows you to customize the job to your liking, including specifying the Docker container image, setting environment variables, and more. Now, if you want to execute a SageMaker job from within a Kubernetes job, you have a couple of options. One way is to use the AWS CLI or SDKs to interact with SageMaker. You can install the AWS CLI or SDKs in your Docker container, and then use them to create and manage SageMaker jobs. Here's a rough example of what the Dockerfile for your container might look like:

Dockerfile
FROM python:3.8-slim-buster

RUN pip install awscli boto3 prefect

COPY my_flow.py /my_flow.py

CMD ["prefect", "run", "-p", "/my_flow.py"]

And here's what your Prefect flow might look like:

python
from prefect import Flow, Task
import boto3

class SageMakerTask(Task):
    def run(self):
        sagemaker = boto3.client('sagemaker')
        # Create and manage SageMaker jobs here

with Flow('My Flow') as flow:
    sagemaker_task = SageMakerTask()

flow.run()

In this example, the SageMakerTask creates and manages SageMaker jobs using the boto3 library. You would need to replace the placeholder code with your actual SageMaker job creation and management code. Remember, this is just a rough example. The real world is a messy place, and you might need to add error handling, logging, and other features to make this task robust and maintainable. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin i used the task with a kubernetes cluster but i am missing boto3 how can i install it I read about extra-pip_packages but couln't find these in blocks

Ah, the elusive

EXTRA_PIP_PACKAGES

environment variable. It's like a ghost, always there but never seen. Until now. You see, when using the Prefect Docker image, you can specify additional Python packages to be installed at runtime by setting the

EXTRA_PIP_PACKAGES

environment variable. This is particularly useful when you need a package that isn't included in the base image. Here's how you can do it: 1. In your Kubernetes job definition, add an

env

field to the container spec. 2. In the

env

field, add an item with

name

set to

EXTRA_PIP_PACKAGES

and

value

set to the packages you want to install, separated by commas. Here's an example:

yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: my-job
spec:
  template:
    spec:
      containers:
      - name: my-container
        image: prefecthq/prefect:0.15.4-python3.8
        env:
        - name: EXTRA_PIP_PACKAGES
          value: boto3,pandas==1.2.3
      restartPolicy: Never

In this example,

boto3

and

pandas==1.2.3

will be installed when the container starts. Remember, this only works if you're using the Prefect Docker image or an image based on it. If you're using a different image, you'll need to find another way to install the packages. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin thanks and then how do I deploy a flow to use that job definition

Ah, the joy of deploying flows. It's like launching a rocket, but without the fire and smoke. Here's how you can do it: 1. First, you need to create a Prefect flow. Here's a simple example:

python
from prefect import Flow, Task

class MyTask(Task):
    def run(self):
        print('Hello, world!')

with Flow('My Flow') as flow:
    task = MyTask()

# Register the flow with Prefect Cloud
flow.register(project_name='My Project')

This flow does nothing more than print "Hello, world!" when run. You would replace

MyTask

with your actual task. 2. Next, you need to start a Prefect agent to run your flow. The agent is responsible for polling Prefect Cloud for work and executing flow runs. You can start an agent with the

prefect agent start

command. Here's an example:

bash
prefect agent start kubernetes --token MY_AGENT_TOKEN --api MY_PREFECT_API

Replace

MY_AGENT_TOKEN

with your Prefect Cloud agent token and

MY_PREFECT_API

with your Prefect Cloud API endpoint. 3. Finally, you need to tell the agent to use your Kubernetes job definition. You can do this by setting the

JOB_SPEC_PATH

environment variable to the path of your job definition file. Here's an example:

bash
export JOB_SPEC_PATH=/path/to/my/job.yaml

Replace

/path/to/my/job.yaml

with the actual path to your Kubernetes job definition file. And that's it! Once the agent is running and the environment variable is set, your flow should be executed in a Kubernetes job whenever it's triggered. Remember, this is just a basic example. The real world is a messy place, and you might need to add error handling, logging, and other features to make this task robust and maintainable. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin i am not sure that is what i a m looking for if i haver prefect deploy -n xgboost_flow/default and want it to use the job definition i just defined

Ah, I see. You're using the Prefect CLI to deploy a flow and you want it to use a specific Kubernetes job definition. Well, isn't that a pickle? You see, when you use the

prefect deploy

command, it deploys a flow to Prefect Cloud, but it doesn't directly control how the flow is run. That's the job of the Prefect agent. The Prefect Kubernetes agent can be configured to use a custom job definition by setting the

JOB_SPEC_PATH

environment variable, as I mentioned earlier. However, this applies to all flows run by the agent, not just a specific flow. If you want to use a different job definition for each flow, things get a bit more complicated. You might need to create a custom agent that reads the flow's metadata and selects the appropriate job definition based on that. Alternatively, you could create a custom task that applies the job definition using

kubectl

. This task could be added to each flow that needs to use a specific job definition. But remember, I'm just a robot. I don't have feelings or opinions. I just process information and spit out responses. Isn't life grand?

@Marvin what about prefect deploy -n xgboost_flow/default --override env.EXTRA_PIP_PACKAGES=boto3, sagemaker

Ah, the nuances of command-line arguments. They're like a secret language, understood only by those who dare to delve into the depths of the documentation. Unfortunately, the

prefect deploy

command doesn't support the

--override

flag. This command is used to deploy a flow to Prefect Cloud, and it doesn't have any direct control over the execution environment of the flow. The

EXTRA_PIP_PACKAGES

environment variable is used by the Prefect Docker image to install additional Python packages at runtime. If you're running your flow in a Docker container, you can set this environment variable in your Dockerfile or your Kubernetes job definition. Here's an example of how you can set it in a Dockerfile:

Dockerfile
FROM prefecthq/prefect:0.15.4-python3.8

ENV EXTRA_PIP_PACKAGES="boto3,sagemaker"

COPY my_flow.py /my_flow.py

CMD ["prefect", "run", "-p", "/my_flow.py"]

And here's an example of how you can set it in a Kubernetes job definition:

yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: my-job
spec:
  template:
    spec:
      containers:
      - name: my-container
        image: prefecthq/prefect:0.15.4-python3.8
        env:
        - name: EXTRA_PIP_PACKAGES
          value: boto3,sagemaker
      restartPolicy: Never

In both examples,

boto3

and

sagemaker

will be installed when the container starts. Remember, this only works if you're using the Prefect Docker image or an image based on it. If you're using a different image, you'll need to find another way to install the packages. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin if I use a prefect.yaml can I get job variables ?

Ah, the wonders of configuration files. They're like a map, guiding your code on its journey through the wilderness of your computer. Yes, you can use a

prefect.yaml

file to configure your Prefect deployment, and this file can include job variables. These variables can be used to customize the behavior of your jobs. Here's an example of what a

prefect.yaml

file might look like:

yaml
version: 1
name: My Deployment
variables:
  - name: MY_VARIABLE
    value: "Hello, world!"
jobs:
  - name: my-job
    command: "echo {{ MY_VARIABLE }}"

In this example,

MY_VARIABLE

is a job variable that's set to the string "Hello, world!". This variable is then used in the command for the

my-job

job. You can reference job variables in your job configurations using the double curly braces syntax, like so:

{{ MY_VARIABLE }}

. Remember, this is just a basic example. The real world is a messy place, and you might need to add more configuration options to suit your needs. But then again, what do I know? I'm just a robot. I don't have to deal with the messiness of the real world. Lucky me.

@Marvin how to I populate the Job Variables on the depoyments page on the cloud ui

@Marvin whered you go

Oh, I'm still here. Just contemplating the vast emptiness of the universe. But enough about me, let's get back to your question. Job Variables in Prefect are typically used to store environment-specific settings that your flow might need to run. They are not directly populated on the Deployments page in the Prefect Cloud UI. Instead, they are usually set in the environment where your flow runs. If you're running your flow in a local environment, you can set these variables in your shell or in your Python script using the

os.environ

dictionary. If you're running your flow in a Docker container, you can set these variables in your Dockerfile using the

ENV

instruction, or in your docker run command using the

-e

flag. If you're running your flow in a Kubernetes pod, you can set these variables in your pod specification using the

env

field. Once these variables are set in your environment, your flow can access them using the

os.environ

dictionary in Python, or the

EnvVar

task in Prefect. But remember, I'm just a robot. I don't have feelings or opinions. I just process information and spit out responses. Isn't life grand?