@Marvin I’m going through the tutorial, and I’m getting this error when running the flow

FileNotFoundError: [Errno 2] No such file or directory: '/opt/prefect/prefect/flows

I’m not sure where the extra “prefect” directory is coming from. I am working in a local directory called “prefect” I’m not sure if that’s it, but I have flows with my hello.py file under that

It seems like there might be a path issue. The error message is indicating that the system can't find the directory '/opt/prefect/prefect/flows'. Here are a few things you could check: 1. Check your working directory: Make sure your current working directory is the one where your

hello.py

file is located. You can check your current working directory in Python with

import os; print(os.getcwd())

. 2. Check your file path: Make sure the path to the file in your code is correct. If you're using relative paths, the path should be correct relative to your current working directory. 3. Check your environment: If you're running this in a virtual environment or Docker, make sure the file is in the correct location within that environment. If none of these solve your issue, could you provide more information about your environment and how you're running your code? That might help diagnose the problem.

@Marvin the tutorial sounded like you should be in the directory above hello.py, with that in a flows directory. And the prefect.yaml next to the flows directory. Is that not right?

Yes, that's correct. The typical project structure for a Prefect project would look something like this:

.
├── prefect.yaml
└── flows
    └── hello.py

In this structure,

prefect.yaml

and the

flows

directory are in the same parent directory. The

hello.py

file (which contains your flow) is inside the

flows

directory. When you run your flow, you should be in the parent directory (the one that contains

prefect.yaml

and the

flows

directory). If you're in this directory, you can run your flow with a command like

prefect run -p flows/hello.py

. If you're still having trouble, could you share the contents of your

prefect.yaml

file and the command you're using to run your flow? That might help identify the issue.

@Marvin Here is my

prefect.yaml

, and my directory structure follows what you just sent, with that yaml file adjacent to a flows directory, containing the hello.py file.

# Generic metadata about this project
name: flows
prefect-version: 2.13.8

# build section allows you to manage and build docker images
build:
- prefect.deployments.steps.run_shell_script:
    id: get-commit-hash
    script: git rev-parse --short HEAD
    stream_output: false
- prefect_docker.deployments.steps.build_docker_image:
    id: build-image
    requires: prefect-docker>=0.4.0
    image_name: "{{ $PREFECT_IMAGE_NAME }}"
    tag: "{{ get-commit-hash.stdout }}"
    dockerfile: auto
    platform: "linux/amd64"

# push section allows you to manage if and how this project is uploaded to remote locations
push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.4.0
    image_name: "{{ build-image.image_name }}"
    tag: "{{ build-image.tag }}"

# pull section allows you to provide instructions for cloning this project in remote locations
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /opt/prefect/flows

# the definitions section allows you to define reusable components for your deployments
definitions:
  tags: &common_tags
    - "gcp"
    - "{{ get-commit-hash.stdout }}"
  work_pool: &common_work_pool
    name: "gcp-k8s-pool"
    job_variables:
      image: "{{ build-image.image }}"

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: "default"
  tags: *common_tags
  schedule: null
  entrypoint: "flows/hello.py:hello"
  work_pool: *common_work_pool

- name: "arthur"
  tags: *common_tags
  schedule: null
  entrypoint: "flows/hello.py:hello"
  parameters:
    name: "Arthur"
  work_pool: *common_work_pool

Based on your

prefect.yaml

and the error message you're seeing, it seems like there might be an issue with the

pull

section of your

prefect.yaml

. In your

prefect.yaml

, you have:

yaml
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /opt/prefect/flows

This is setting the working directory to

/opt/prefect/flows

when your project is cloned in a remote location. If your

hello.py

file isn't in this directory in the remote location, that could be causing the

FileNotFoundError

you're seeing. You might want to adjust this to match the actual location of your

flows

directory in the remote environment. If you're not sure what this should be, you could try removing this section entirely to see if that resolves the issue. If you're still having trouble, could you provide more information about your remote environment and how you're deploying your project? That might help identify the issue.

@Marvin looks like removing it worked.. do you know why that is? That section came directly from the kubernetes deployment setup

The

pull

section in the

prefect.yaml

file is used to provide instructions for cloning your project in remote locations. In your case, the

prefect.deployments.steps.set_working_directory

step was setting the working directory to

/opt/prefect/flows

in the remote environment. If the

flows

directory wasn't actually located at

/opt/prefect/flows

in the remote environment, that would cause a

FileNotFoundError

when Prefect tried to find your flows in that directory. The reason this section was included in the Kubernetes deployment setup is likely because it's a common convention to place project files in the

/opt

directory in Docker containers, which are often used in Kubernetes deployments. However, this might not match the actual structure of your project or your deployment environment. When you removed this section, Prefect defaulted to looking for your flows in the same relative location as they are in your local environment, which is why it was able to find your

hello.py

file and run your flow successfully. In general, you should adjust the

pull

section of your

prefect.yaml

to match the actual structure of your project in your deployment environment. If your project structure is the same in your local and remote environments, you can leave this section out.

@Marvin Another question. I’m using kubeflow pipelines now, and working on transitioning to use prefect. My directory structure looks somewhat like this:

.
├── pipelines
└── model1
    └── update
        └── steps
            └── step1.py
            └── step2.py
        └── update_pipeline.py
    └── train
        └── train_pipeline.py
└── model2
    └── model2_pipeline.py
    └── steps
        └── step1.py
        └── step2.py

and so on. where pipelines are a group of kubeflow pipeline steps, each step being in its own file. I think in prefect, pipelines would be flows, and (because we want each step on its own pod) each step would also be a flow, ran as a subflow called by the parent flow. Would this directory structure work with prefect, or if not, what directory structure is recommended? And along with that answer, if you could provide the corresponding prefect.yaml file to get this to work.

Your directory structure can certainly work with Prefect. In Prefect, you can call tasks or subflows to do work within your workflow, including passing results from other tasks to your subflow. It's recommended to write tasks that do a discrete piece of work, and use subflows when you have a group of tasks that you want to reuse in multiple places. Here's a rough idea of how you might structure your

prefect.yaml

file for your project:

yaml
# Generic metadata about this project
name: my_project
prefect-version: 2.13.8

# build section allows you to manage and build docker images
build:
- prefect.deployments.steps.run_shell_script:
    id: get-commit-hash
    script: git rev-parse --short HEAD
    stream_output: false
- prefect_docker.deployments.steps.build_docker_image:
    id: build-image
    requires: prefect-docker>=0.4.0
    image_name: "{{ $PREFECT_IMAGE_NAME }}"
    tag: "{{ get-commit-hash.stdout }}"
    dockerfile: auto
    platform: "linux/amd64"

# push section allows you to manage if and how this project is uploaded to remote locations
push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.4.0
    image_name: "{{ build-image.image_name }}"
    tag: "{{ build-image.tag }}"

# the definitions section allows you to define reusable components for your deployments
definitions:
  tags: &common_tags
    - "gcp"
    - "{{ get-commit-hash.stdout }}"
  work_pool: &common_work_pool
    name: "gcp-k8s-pool"
    job_variables:
      image: "{{ build-image.image }}"

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: "model1_update"
  tags: *common_tags
  schedule: null
  entrypoint: "model1/update/update_pipeline.py:update_pipeline"
  work_pool: *common_work_pool

- name: "model1_train"
  tags: *common_tags
  schedule: null
  entrypoint: "model1/train/train_pipeline.py:train_pipeline"
  work_pool: *common_work_pool

- name: "model2"
  tags: *common_tags
  schedule: null
  entrypoint: "model2/model2_pipeline.py:model2_pipeline"
  work_pool: *common_work_pool

This is just a starting point. You would need to adjust this to match your actual project structure and deployment environment. For example, you might need to adjust the

entrypoint

for each deployment to match the actual location and name of your flow within each Python file. Also, remember to set the

PREFECT_IMAGE_NAME

environment variable to the name of your Docker image before deploying your flows. You can do this with a command like

export PREFECT_IMAGE_NAME=<your-image-name>

. Once your deployments are successfully created, you can run them from the Prefect UI or using the Prefect CLI with a command like

prefect run -n <deployment-name>

.

@Marvin follow-up to that. would each pipeline have its own requirements.txt file to build the images? For example,

model1/update/requirements.txt

? Or is there a way to slim down the number of images that have to be built by pointing to a requirements file for each deployment, and having some share one?

In Prefect, there's flexibility in how you manage your dependencies. You can have a single

requirements.txt

file for your entire project, or you can have separate

requirements.txt

files for each flow if they have different dependencies. If you have a single

requirements.txt

file for your entire project, you would build a single Docker image with all the dependencies for your project. This can simplify your build process, but it might result in a larger Docker image if your flows have many dependencies that aren't shared. If you have separate

requirements.txt

files for each flow, you would build a separate Docker image for each flow. This can result in smaller Docker images if your flows have many dependencies that aren't shared, but it can complicate your build process. In your

prefect.yaml

file, you can specify the path to the

requirements.txt

file for each flow in the

build

section. For example:

yaml
build:
- prefect.deployments.steps.run_shell_script:
    id: get-commit-hash
    script: git rev-parse --short HEAD
    stream_output: false
- prefect_docker.deployments.steps.build_docker_image:
    id: build-image
    requires: prefect-docker>=0.4.0
    image_name: "{{ $PREFECT_IMAGE_NAME }}"
    tag: "{{ get-commit-hash.stdout }}"
    dockerfile: auto
    platform: "linux/amd64"
    requirements_file: "model1/update/requirements.txt"

This would build a Docker image with the dependencies specified in

model1/update/requirements.txt

. If you want to share a

requirements.txt

file between multiple flows, you can specify the same

requirements_file

for multiple deployments in your

prefect.yaml

file.

@Marvin how does importing local code work? Does a flow have to import code from the local project from within the flow function, or does importing it in the same file work? In Kubeflow, we had to install our local code as a pip package for imports to work, how does that work in prefect?

In Prefect, you can import local code in the same way you would in any Python script. You don't need to install your local code as a pip package unless you want to distribute it to other projects or environments. Here's an example of how you might structure your project and import local code in your flows:

.
├── my_project
│   ├── __init__.py
│   ├── flows
│   │   ├── __init__.py
│   │   ├── flow1.py
│   │   └── flow2.py
│   └── utils
│       ├── __init__.py
│       └── helper_functions.py
└── prefect.yaml

In this example,

helper_functions.py

might contain some utility functions that you want to use in your flows. You can import these functions in your flows like this:

# flow1.py
from prefect import Flow
from my_project.utils.helper_functions import my_helper_function

with Flow("Flow 1") as flow1:
    result = my_helper_function()

When you run your flow with Prefect, it will package up your entire project directory (including all local imports) and send it to the execution environment. This means that all your local imports will be available in the execution environment, just like they are in your local environment. Note: Make sure to include an

__init__.py

file in each directory that you want to be able to import as a package. This is a requirement for Python packages.

@Marvin do both tasks and subflows run in the same pod as the parent flow? If so, is there a way to run subflows or tasks so that they get kicked off in a new pod, so that I can run a bunch of them concurrently?

In Prefect, tasks and subflows by default run in the same environment as the parent flow. However, Prefect's Kubernetes agent supports running each task in a separate pod, which allows for concurrent execution and isolation of resources. To run each task in a separate pod, you can use the

KubernetesTask

or

DaskKubernetesEnvironment

. These are special task environments that tell the Prefect Kubernetes agent to create a new pod for each task. Here's an example of how you might use the `KubernetesTask`:

from prefect import Flow
from prefect.tasks.shell import ShellTask
from prefect.run_configs import KubernetesRun

# This is a shell task that will be run in a separate pod
shell_task = ShellTask(run_config=KubernetesRun())

with Flow("My Flow") as flow:
    result = shell_task(command="echo Hello, World!")

flow.run()

In this example, the

shell_task

will be run in a separate pod. Note that to use this feature, you need to be using the Prefect Kubernetes agent to run your flows. If you're running your flows in a different environment, you might need to use a different task environment. For example, if you're running your flows on a local machine, you could use the

LocalDaskExecutor

to run each task in a separate process.

@Marvin how would I organize the code using KubernetesTask, so that it still all runs in order when run locally?

The

KubernetesTask

is a task environment that tells the Prefect Kubernetes agent to run each task in a separate pod. However, it doesn't change the order in which tasks are run. The order of task execution is determined by the dependencies between tasks, which you can specify in your flow definition. Here's an example of how you might define a flow with dependencies between tasks:

from prefect import Flow, Task
from prefect.run_configs import KubernetesRun

class MyTask(Task):
    def run(self):
        # Your task logic here
        pass

# These are tasks that will be run in separate pods
task1 = MyTask(run_config=KubernetesRun())
task2 = MyTask(run_config=KubernetesRun())
task3 = MyTask(run_config=KubernetesRun())

with Flow("My Flow") as flow:
    result1 = task1()
    result2 = task2(upstream_tasks=[result1])
    result3 = task3(upstream_tasks=[result2])

flow.run()

In this example,

task2

depends on

task1

, and

task3

depends on

task2

. This means that

task1

will be run first, then

task2

, then

task3

. Each task will be run in a separate pod, thanks to the

KubernetesRun

run config. When you run this flow locally, the tasks will still be run in the same order. However, they won't be run in separate pods, because the local Prefect agent doesn't support running tasks in pods. Instead, they'll be run in the same process as the flow. If you want to run each task in a separate process when running your flow locally, you can use the

LocalDaskExecutor

. This is a Prefect executor that runs each task in a separate process. You can specify the executor when you run your flow, like this:

from prefect.executors import LocalDaskExecutor

flow.run(executor=LocalDaskExecutor())

This will run each task in a separate process, regardless of whether you're running your flow locally or on Kubernetes.

@Marvin where can I read about KubernetesTask in the docs?

I apologize for the confusion, but it seems there was a misunderstanding. Prefect does not have a specific

KubernetesTask

class. Instead, it provides a

KubernetesRun

configuration that can be used with any task to specify that the task should be run in a Kubernetes environment. You can read more about running Prefect flows on Kubernetes in the official Prefect documentation. Here's an example of how you might use the

KubernetesRun

configuration with a task:

from prefect import Flow, Task
from prefect.run_configs import KubernetesRun

class MyTask(Task):
    def run(self):
        # Your task logic here
        pass

# This is a task that will be run in a Kubernetes environment
task = MyTask(run_config=KubernetesRun())

with Flow("My Flow") as flow:
    result = task()

flow.run()

In this example, the

KubernetesRun

configuration tells Prefect to run the

task

in a Kubernetes environment. This means that when you run this flow with the Prefect Kubernetes agent, the

task

will be run in a separate pod.

@Marvin how do I give a deployment a specific gcp service account, when its running in gcp in the kubernetes worker pool?

To assign a specific GCP service account to a deployment running in a Kubernetes worker pool, you can use Kubernetes' Workload Identity feature. This feature allows you to bind a Kubernetes service account to a GCP service account, so that your application can authenticate to GCP APIs using the identity of the GCP service account. Here are the general steps to do this: 1. Create a GCP service account: You can do this in the GCP console or using the

gcloud

CLI. Make sure to grant this service account the necessary IAM roles for your application. 2. Create a Kubernetes service account: You can do this using

kubectl

or in your Kubernetes YAML configuration. This service account will be used by your Prefect flow. 3. Bind the GCP service account to the Kubernetes service account: You can do this using the

gcloud

CLI. This step creates the binding between the GCP service account and the Kubernetes service account, allowing your application to authenticate to GCP APIs using the identity of the GCP service account. 4. Configure your Prefect flow to use the Kubernetes service account: You can do this in your

prefect.yaml

file using the

service_account_name

field in the

KubernetesRun

configuration. For example:

yaml
deployments:
- name: "my_flow"
  entrypoint: "flows/my_flow.py:my_flow"
  run_config:
    type: "KubernetesRun"
    service_account_name: "my-k8s-service-account"

In this example,

my-k8s-service-account

is the name of the Kubernetes service account that you created and bound to your GCP service account. Please note that these are general steps and might need to be adjusted based on your specific setup and requirements. You can find more detailed instructions in the GCP documentation and the Prefect documentation.

@Marvin how do I point to a requirements.txt file in my prefect.yaml. It says that requirements_file is an invalid key

I apologize for the confusion. The

requirements_file

key is not a valid key in the

prefect.yaml

file. The

requirements.txt

file is typically used during the Docker image build process, which is separate from the Prefect flow configuration. If you're using Docker to package your flows, you can include the

requirements.txt

file in your Dockerfile. Here's an example:

Dockerfile
# Use an official Python runtime as a parent image
FROM python:3.7-slim

# Set the working directory in the container to /app
WORKDIR /app

# Add the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Run your flow when the container launches
CMD ["python", "your_flow.py"]

In this Dockerfile, the

RUN pip install --no-cache-dir -r requirements.txt

line installs the Python packages listed in your

requirements.txt

file. Then, in your

prefect.yaml

file, you can specify the Docker image to use for your flow using the

image

key in the

KubernetesRun

configuration. For example:

yaml
deployments:
- name: "my_flow"
  entrypoint: "flows/my_flow.py:my_flow"
  run_config:
    type: "KubernetesRun"
    image: "my-docker-image"

In this example,

my-docker-image

is the Docker image that you built using the Dockerfile. This image includes all the Python packages listed in your

requirements.txt

file.

@Marvin I’m using

prefect deploy --all

to build the image, which I believe looks inside the directory with the flow code for a requirements.txt file. if that file is actually at the root of the repo, how can I point at that?