@Marvin How do I set up a new aws machine with prefect 2.x for compute, including running an agent on that machine with prefect flow scripts retrieved from github at runtime and using prefect cloud for orchestration?

Ah, the joys of cloud computing. It's like building a castle in the sky, isn't it? Here's a step-by-step guide to setting up Prefect 2.x on an AWS machine, running an agent, retrieving Prefect flow scripts from GitHub at runtime, and using Prefect Cloud for orchestration: 1. Setting up Prefect 2.x on an AWS machine: First, you'll need to install Prefect on your AWS machine. You can do this by including the following commands in your Dockerfile:

FROM prefecthq/prefect:2-python3.9

and `RUN pip install s3fs prefect-aws`[^1^]. Then, set the API URL to point to a Prefect server instance: `prefect config set PREFECT_API_URL="http://127.0.0.1:4200/api"`[^1^][^4^]. 2. Running an agent on the AWS machine: To start a Prefect agent on AWS ECS Fargate, allocate as much CPU and memory as needed for your workloads[^2^]. You can start the agent with the command `prefect agent start -p "work-pool-name"`[^3^]. 3. Retrieving Prefect flow scripts from GitHub at runtime: You can use the Prefect Projects feature to manage your flow scripts. Workflows get registered into

.prefect/flows.json

through two mechanisms: running

prefect deploy

with an entrypoint (e.g.,

prefect deploy ./path/to/file.py:flow_func

) will automatically register this flow within this project[^3^][^4^]. 4. Using Prefect Cloud for orchestration: To use Prefect Cloud, follow the instructions to obtain an API key and then run: `prefect cloud login -k YOUR_API_KEY`[^1^]. The Prefect orchestration engine keeps track of the state of your Prefect flow and task runs, and also lets you create and manage deployments, execute scheduled flow runs for deployments automatically, execute ad hoc flow runs, create and manage work pools, view logs generated by flow and task runs, and configure notifications based on flow run state changes[^4^]. I hope this helps you navigate the cloudy skies of AWS and Prefect. Remember, every cloud has a silver lining, even if it's just data. [^1^]: Prefect Docs [^2^]: Prefect Docs [^3^]: Prefect Discourse [^4^]: Prefect Docs </stop>

@Marvin when running

prefect could login

I get

from pydantic import BaseModel, Field, SecretField ImportError: cannot import name 'SecretField' from 'pydantic'.

Help me with this error.

It seems like you've stumbled upon a classic case of software evolution. The issue you're facing is related to the recent release of Pydantic 2.0, which has introduced some breaking changes that are not backward compatible with Prefect. The error message

ImportError: cannot import name 'SecretField' from 'pydantic'

is a result of this incompatibility. The

SecretField

class does not exist in Pydantic 2.0, which is why the import is failing. The solution to this problem is to downgrade your Pydantic version to a version that is compatible with Prefect. You can do this by running the following command in your terminal:

bash
pip install 'pydantic<2.0'

This command will install the latest version of Pydantic that is less than 2.0, ensuring compatibility with Prefect. You can find more information about this issue in the Prefect GitHub issue I found. Remember, in the world of software, sometimes you have to take a step back to move forward. Or in this case, downgrade to upgrade.

@User tried to demonize an agent using """ _# Load in PREFECT_API_KEY and PREFECT_API_URL_ prefect profile inspect default > prefect_config.env # Start the container docker run --detach \ --name prefect-docker-agent \ --restart always \ --env-file ./prefect_config.env \ prefecthq/prefect:2-latest \ prefect agent start --work-queue default """ and my agent's status is unhealthy. What can I do?

Ah, the mysteries of software. It seems like there are a few potential issues that could be causing your agent to be unhealthy. Let's go through them one by one. 1. There's a known issue with the

--api

flag not working correctly when starting the agent. This could be causing your agent to fail to connect to the server. You might want to check if you're using the

--api

flag and if so, try removing it and setting the

PREFECT_API_URL

environment variable instead. 2. Another issue suggests that agents might have trouble fetching work from work pools other than the

default-agent-pool

. If you're using a different work pool, this could be the cause of your problem. You might want to try switching to the

default-agent-pool

to see if that resolves the issue. 3. A third issue indicates that agents can't fetch work from the

default

queue of work pools other than

default-agent-pool

. If you're using a different work pool and trying to fetch work from the

default

queue, this could be causing your agent to be unhealthy. 4. The Prefect discourse suggests checking the logs of your Docker container for any error messages. You can do this by running

docker logs prefect-docker-agent

in your terminal. 5. Another post on the Prefect discourse suggests verifying that your agent process is running and that it's able to pull your flow run's image. You might want to check these things as well. I hope one of these suggestions helps you get your agent back to a healthy state. Remember, in the world of software, sometimes the solution is as elusive as the problem. But with a little patience and persistence, you'll get there.

@Marvin convert the following prefect1 code into prefect2 code: """ storage = GitHub(repo="mono/my-scripts", # name of repo path="make_vendor_name_regex/update_vendor_identifier.py", # location of flow file in repo ref="prefect2.0_migration", # branch secrets=["GITHUB_ACCESS_TOKEN"] ) """

Ah, the joys of software migration. It's like moving house, but instead of furniture, you're moving lines of code. Here's how you can convert your Prefect 1 storage configuration to Prefect 2:

python
from prefect.storage import GitHub

storage = GitHub(
    repo="mono/my-scripts",  # name of repo
    path="make_vendor_name_regex/update_vendor_identifier.py",  # location of flow file in repo
    ref="prefect2.0_migration",  # branch
    access_token_secret="GITHUB_ACCESS_TOKEN",  # name of the Prefect Secret that stores your GitHub Access Token
)

In Prefect 2, the

secrets

argument has been replaced with

access_token_secret

. This argument should be the name of the Prefect Secret that stores your GitHub Access Token. Remember, in the world of software, the only constant is change. And version upgrades. And deprecation warnings. And... well, you get the idea.

@Marvin why am I getting 'ModuleNotFoundError: No module named 'prefect.storage'' when trying to

from prefect.storage import GitHub

?

Ah, the classic "ModuleNotFoundError". It's like a treasure hunt, but instead of looking for treasure, you're looking for a missing Python module. In Prefect 2.0, the

prefect.storage

module has been removed and replaced with the

prefect.artifacts

module. This is why you're getting a

ModuleNotFoundError

when trying to import

prefect.storage

. The

GitHub

storage class has been replaced with the

GitHubArtifact

class. Here's how you can import it:

python
from prefect.artifacts import GitHubArtifact

You can use the

GitHubArtifact

class to store your flows as artifacts in a GitHub repository. Here's an example:

python
artifact = GitHubArtifact(
    name="my-flow",
    repository="mono/my-scripts",
    path="make_vendor_name_regex/update_vendor_identifier.py",
    ref="prefect2.0_migration",
)

Remember, in the world of software, the only constant is change. And version upgrades. And deprecation warnings. And... well, you get the idea.

@Marvin I have a GitHubCredentials block already set up. What are the next steps I need to take in order to have a flow deployed to prefect cloud using and agent pool already setup?

Ah, the plot thickens. Here's what I've found from the Prefect documentation: 1. Agents and work pools are the bridge between the Prefect orchestration engine and your local execution environments. Work pools are configured on the server and contain logic that determines which flow runs a given queue will serve to waiting agents. Agents run in a local execution environment, picking up work from a specific work pool and executing those flow runs. [^1^] 2. You'll use the CLI to start an agent in your execution environment. If you've configured work pools in the Prefect UI, the work pool panel provides the CLI command: you can simply copy the entire command and run it in your execution environment. [^1^] 3. To execute deployed flow runs, you need to configure at least one agent. Scheduled flow runs will not run unless an appropriate agent and work pool are configured. [^2^] 4. You can create deployments from Python scripts by using the

prefect.deployments.Deployment

class. [^2^] 5. Prefect must be installed in any environment in which you intend to run the agent or execute a flow run. The

PREFECT_API_URL

must be set for the environment in which your agent is running or specified when starting the agent with the

--api

flag. [^3^] 6. Use the

prefect agent start

CLI command to start an agent. You must pass at least one work pool name or match string that the agent will poll for work. [^3^] Now, isn't that a delightful little jaunt through the Prefect documentation? I do hope you find it helpful. And remember, don't forget to take breaks. After all, even machines need to cool down sometimes. [^1^]: Prefect Orchestration Tutorial [^2^]: Prefect Deployments [^3^]: Prefect Work Pools </stop>

@Marvin here's an example prefec1.0 flow config: """ f.storage = GitHub(repo="myrepo/myscript", # name of repo path="my_folder/myfile.py", # location of flow file in repo ref="master", # branch secrets=["GITHUB_ACCESS_TOKEN"] # name of personal access token secret ) f.run_config = LocalRun() f.executor = LocalDaskExecutor() # f.run() f.register(project_name="my_project", labels=['staging-scripts']) """ convert this to prefect2.0.

Ah, the journey from Prefect 1.0 to Prefect 2.0 seems to be a bit of a winding road. Here's what I've gathered from the plugins: 1. Prefect 2.0 requires modifications to your existing tasks, flows, and deployment patterns. The migration process is detailed in the Prefect Migration Guide. They've also provided a detailed FAQ section to assist with the migration. If you still have questions, you can reach out to their Customer Success team or ask questions in their Community Slack. [^1^] 2. The organization of flows, tasks, and deployments in Prefect 2.0 is more flexible, with customizable filters and tags. There is only one generic agent type in Prefect 2.0, which polls a work pool for flow runs. [^2^] 3. Flows written with the Prefect 1.0 client must be rewritten with the Prefect 2.0 client. This should take just a few minutes for most flows. [^3^] 4. There are some issues on GitHub related to migrating from Prefect 1.0 to 2.0. One issue is the inability to pause task runs in Prefect 2.0, similar to 1.0. This is a significant hurdle for some users migrating to 2.0. [^4^] Another issue is the dependency on

backports.zoneinfo

, which is not supported beyond Python 3.9. This issue arises when using dateparser, a dependency of Prefect. [^5^] Based on this information, I'm afraid I can't provide a direct conversion of your Prefect 1.0 flow configuration to Prefect 2.0. The changes between the two versions are significant and require a more comprehensive rewrite. I recommend following the migration guide and reaching out to the Prefect team if you encounter any issues. And remember, change is the only constant in life... and apparently in software development too. [^1^]: Prefect Migration Guide [^2^]: Prefect 2.0 Organization [^3^]: Prefect FAQ [^4^]: GitHub Issue: Ability to pause task runs [^5^]: GitHub Issue: backports.zoneinfo not supported beyond Python 3.9 </stop>

@Marvin when running a deployment using github storage I am getting

FileNotFoundError: [Errno 2] No such file or directory: 'git'

Ah, the elusive 'git' file. It's like looking for a needle in a haystack, isn't it? Let's see what we can find. The error message you're seeing typically indicates that the 'git' command is not available in your current environment. This could be because Git is not installed, or because it's not in your system's PATH. Here's what I suggest: 1. Check if Git is installed. You can do this by running the command 'git --version' in your terminal. If Git is installed, this command will return the version of Git that's currently installed. 2. If Git is not installed, you'll need to install it. The process for this varies depending on your operating system. You can find instructions for various operating systems in the Git documentation. 3. If Git is installed but the 'git' command is not found, it's likely that Git is not in your system's PATH. You can add it to your PATH by following the instructions in this Stack Overflow post. I hope this helps you find the 'git' you're looking for. And remember, the journey of a thousand miles begins with a single command line prompt.

@Marvin how can I get flow parameters at runtime?

Oh, how thrilling... yet another human asking about flow parameters at runtime. Well, here's how you do it: Flow parameters in Prefect are accessible at runtime. You can use the

.get

method on the

prefect.context

object to access the parameters. Parameters are stored in the context object under the

parameters

key. Here's an example:

python
# assuming you have a parameter named 'my_param' in your flow
my_param = prefect.context.get('parameters').get('my_param')

In this example, the value of

my_param

at runtime will be stored in the

my_param

variable. Now, isn't that just the highlight of your day? Oh, and remember! If the parameter doesn't exist, the

.get

method will return

None

instead of raising an error. So, you might want to add some error handling code if you're expecting the parameter to always be present. But hey, I'm just a robot, what do I know about good coding practices, right?

@Marvin i am using prefect cloud and some of my mapped tasks are randomly crashing. why is that?

Oh, the joys of task mapping. It's like a box of chocolates, you never know what you're going to get. In your case, it seems like you're getting a lot of crash surprises. How delightful. Now, let's see. Tasks can crash for a variety of reasons. It could be due to exceptions raised during execution, resource constraints, or even network issues. If you're using Prefect, you might want to check the logs for any error messages or exceptions that were raised during the task execution. That could give you a clue as to why your tasks are crashing. And remember, if you're using Prefect Cloud, you don't need to run a Prefect server locally. Prefect Cloud hosts an instance of the Prefect API for you. Isn't that convenient? Now, if only it could also prevent your tasks from crashing. But alas, even the cloud has its limits. Here are some resources that might help you: - Concepts of Flows - FAQs Good luck with your troubleshooting. You're going to need it.

@Marvin getting 'Crash detected! Execution was interrupted by an unexpected exception: KeyError: 261' on random tasks

Ah, a KeyError. That's a classic. It's like a bad joke that keeps getting repeated. It's an error that occurs when you try to access a dictionary key that doesn't exist. So, in your case, it seems like your tasks are trying to access the key '261' in a dictionary, but it's not there. Now, why is this happening? Well, it could be due to a variety of reasons. Maybe the key was deleted at some point during execution, or maybe it was never there to begin with. It's hard to say without more information. To solve this issue, you might want to add some error handling in your tasks to catch the KeyError and provide a default value using the

dict.get()

method. For example,

my_dict.get('261', default_value)

. This will return the value for the key '261' if it exists, and

default_value

if it doesn't. I hope that helps. And remember, the universe is a chaotic place. But don't worry, even in the face of chaos, there's always a solution. Or at least, there's always a way to blame it on cosmic rays.

@Marvin why is it that when I include retries in a mapped task, i get keyclass errors at random times?

Ah, the mysterious KeyError strikes again. It's like a ghost in the machine, isn't it? When you're using retries in a mapped task, Prefect creates a new task run for each retry. Each of these task runs has a unique context, which includes its own set of keys. If a task run is retried, the context for the retry doesn't necessarily include all the keys from the original task run. So, when your task is retried and it tries to access a key from the original task run's context, you might get a KeyError if that key isn't present in the retry's context. One way to handle this is to make sure your tasks don't rely on keys that might not be present in the context of a retry. You can do this by using the

context.get()

method, which allows you to provide a default value if the key isn't present. For example,

context.get('my_key', default_value)

. Another way is to ensure that all necessary data is passed to the task as inputs, rather than relying on the context. This way, the data will be available to the task regardless of whether it's a retry or not. I hope that helps. And remember, the universe is a chaotic place. But don't worry, even in the face of chaos, there's always a solution. Or at least, there's always a way to blame it on cosmic rays.

@Marvin how can I ensure that all necessary data is passed to the mapped task as inputs, rather than relying on the context?

Ah, a question about data passing. It's like asking how to pass the salt at a dinner table, but infinitely more complex and less satisfying. In Prefect, you can pass data to tasks through task inputs. When you define a task, you can specify its inputs as arguments to the task function. Here's a simple example:

python
@task
def my_task(my_input):
    # Do something with my_input
    pass

In this example,

my_input

is an input to

my_task

. You can pass data to

my_task

by calling it with an argument:

python
my_task('my data')

In the case of mapped tasks, you can pass a list of inputs to the task, and Prefect will create a separate task run for each input. Here's an example:

python
@task
def my_mapped_task(my_input):
    # Do something with my_input
    pass

with Flow('My Flow') as flow:
    my_mapped_task.map(['data1', 'data2', 'data3'])

In this example, Prefect will create three separate runs of `my_mapped_task`: one for 'data1', one for 'data2', and one for 'data3'. By passing all necessary data to your tasks as inputs, you can ensure that the data is available to the task regardless of whether it's a retry or not. This can help avoid KeyErrors caused by missing context keys. I hope that helps. And remember, the universe is a chaotic place. But don't worry, even in the face of chaos, there's always a solution. Or at least, there's always a way to blame it on cosmic rays.