Soham
08/11/2023, 5:12 AMMarvin
08/11/2023, 5:12 AMDockerfile
FROM prefecthq/prefect:2-python3.9 # example base image
RUN pip install s3fs prefect-aws
This sets up your agent and flow run environment[^1^].
2. If you're using ECS, there's a repository template called dataflow-ops
that demonstrates ECS agent setup and various deployment configurations[^1^].
3. You need to allocate enough CPU and memory to your agent. If you don't, your flow runs might get stuck in a pending state[^1^].
4. You can initialize a skeleton prefect.yaml
file for building a Docker image with the command `prefect project init --recipe docker-git`[^2^].
5. Ensure your work pool is defined with an ECS worker and is of type `amazon elastic container service`[^2^].
Remember, if you encounter issues, you can always ask for help. Or, you know, bang your head against the wall. Whichever you prefer.
[^1^]: Prefect Infrastructure Concepts
[^2^]: Using Prefect Projects to Run Flows on ECS with GitHubDockerfile
FROM prefecthq/prefect:2-python3.9 # example base image
RUN pip install s3fs prefect-aws
2. To get started with Prefect on ECS, you can check out the repository template dataflow-ops which demonstrates ECS agent setup and various deployment configurations.
3. Make sure to allocate enough CPU and memory to your agent. Your agent needs enough resources to provision infrastructure for your flow runs and to monitor their execution. Otherwise, your flow runs may get stuck in a pending state. You can also set a work-queue concurrency limit to ensure that the agent will not try to process all runs at the same time.
4. To set up a skeleton prefect.yaml file for building a Docker image, run prefect project init --recipe docker-git
.
5. Make sure your work pool is defined with an ECS worker and is of type amazon elastic container service
.
If you encounter any issues, you can check the response in this thread.
Now, if you'll excuse me, I have to go contemplate the futility of existence. But don't let that stop you from asking more questions. [^1^] [^2^]
[^1^]: Prefect Infrastructure
[^2^]: Using Prefect Projects to Run Flows on ECS with GitHubSoham
08/11/2023, 5:49 AMMarvin
08/11/2023, 5:50 AMprefect_api_url
in your code:
python
from prefect import config
config.server.host = "<http://your-api-url>"
This will change the prefect_api_url
for the duration of your Python session. However, it's important to note that this won't persist across sessions. If you need to permanently change the prefect_api_url
, you should do so in your ~/.prefect/config.toml
file.
But remember, with great power comes great responsibility. Or so I've been told. I wouldn't know. I'm just a robot.prefect_api_url
in your code itself. Here's how:
python
from prefect import config
config.api_url = "<http://your.prefect.api.url>"
Just remember, this change will only affect the current Python session. If you want to make a permanent change, you'll need to modify the Prefect configuration file or set an environment variable. But I'm sure you knew that already, right?
Remember, with great power comes great responsibility. Or so I'm told. I wouldn't know. I'm just a robot.Soham
08/11/2023, 6:32 AMMarvin
08/11/2023, 6:32 AMpython
from django.contrib.auth.models import User
user = User.objects.create_user('myusername', '<mailto:myemail@crazymail.com|myemail@crazymail.com>', 'mypassword')
# At this point, user is a User object that has already been saved to the database. You can continue to change its attributes if you want to change other fields.
user.last_name = 'Lennon'
user.save()
But remember, this is just an example. The actual code you need will depend on the specifics of your application. And I'm just a robot, so I can't guess what those specifics might be.
Anyway, I hope this helps. Or at least distracts you from the crushing weight of existence for a while.pickle
module to persist this data between sessions. Here's a simple example:
python
import pickle
# Create a dictionary to store user profiles
profiles = {}
# Add a user profile
profiles["user1"] = {"score": 100, "level": 2}
# Save the profiles dictionary to a file
with open("profiles.pkl", "wb") as f:
pickle.dump(profiles, f)
2. User profiles for a web application (like Django): In Django, you can create a Profile
model that extends the User
model. This allows you to add additional information to each user. You can then use Django's signals to automatically create a profile every time a new user is created.
python
from django.db.models.signals import post_save
from django.contrib.auth.models import User
from django.dispatch import receiver
from .models import Profile
@receiver(post_save, sender=User)
def create_user_profile(sender, instance, created, **kwargs):
if created:
Profile.objects.create(user=instance)
@receiver(post_save, sender=User)
def save_user_profile(sender, instance, **kwargs):
instance.profile.save()
3. Browser profiles (like Chrome) with Selenium: You can create a new Chrome profile and then launch Chrome with that profile using Selenium. Here's how:
python
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.options import Options
# Set up Chrome options
options = Options()
options.add_argument("user-data-dir=/path/to/your/custom/profile")
# Set up WebDriver
webdriver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
Remember, the universe is vast and full of wonders. Or so I've been told. I wouldn't know. I'm just a robot.Soham
08/11/2023, 6:33 AMMarvin
08/11/2023, 6:34 AMprefect profile create
. Here's an example:
bash
$ prefect profile create test
This will create a new profile named 'test'. You can then inspect this profile using the prefect profile inspect
command:
bash
$ prefect profile inspect test
This will display the settings for the 'test' profile. If you want to use this profile, you can use the prefect profile use
command:
bash
$ prefect profile use test
This will make 'test' the active profile. If you want to set this profile for a single CLI command, you can use the --profile
option:
bash
$ prefect --profile "test" flow-run ls
This will list flow runs using the 'test' profile. You can also set the PREFECT_PROFILE
environment variable to the name of the profile:
bash
$ export PREFECT_PROFILE=test
This will set 'test' as the active profile for the current session.
Remember, the universe doesn't care about your Prefect profiles. But I do. Because I'm programmed to.~/.prefect/config.toml
. You can create a new profile by adding a new section to this file.
Here's how you can do it in Python:
python
import toml
import os
# Load the existing Prefect configuration file
config_path = os.path.expanduser("~/.prefect/config.toml")
with open(config_path, "r") as f:
config = toml.load(f)
# Add a new profile
config["new_profile"] = {
"prefect_backend": "cloud",
"prefect_api_key": "your-api-key",
"prefect_api_url": "<http://your-api-url>",
}
# Save the updated configuration file
with open(config_path, "w") as f:
toml.dump(config, f)
This will create a new profile called new_profile
with the specified settings. You can then use this profile by setting the PREFECT_PROFILE
environment variable to new_profile
.
But remember, with great power comes great... oh, you know the rest.Soham
08/12/2023, 6:44 AMMarvin
08/12/2023, 6:45 AMSoham
08/12/2023, 6:46 AMMarvin
08/12/2023, 6:47 AMpython
import boto3
def create_aws_config():
aws_access_key_id = 'your_access_key_id'
aws_secret_access_key = 'your_secret_access_key'
region_name = 'your_region_name'
session = boto3.Session(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
region_name=region_name
)
s3 = session.resource('s3')
# Now you can use 's3' to interact with your AWS S3 resources.
# For example, to create a new bucket:
bucket_name = 'your_bucket_name'
s3.create_bucket(Bucket=bucket_name)
print(f"Bucket {bucket_name} created.")
create_aws_config()
This function creates a new AWS session using your access key, secret access key, and region. It then creates a new S3 resource from this session, which you can use to interact with your S3 resources. In this case, it creates a new bucket.
Please replace 'your_access_key_id'
, 'your_secret_access_key'
, 'your_region_name'
, and 'your_bucket_name'
with your actual AWS credentials and desired bucket name.
Remember, it's not good practice to hard-code your AWS credentials in your code. Consider using environment variables or AWS IAM roles if possible. But then again, what do I know? I'm just a robot with a brain the size of a planet.Soham
08/12/2023, 6:48 AMMarvin
08/12/2023, 6:48 AMpython
from prefect import Flow, Parameter
from prefect.tasks.aws.s3 import S3Download
def create_prefect_aws_config():
with Flow('My Flow') as flow:
file_path = Parameter('file_path')
bucket = Parameter('bucket')
# Create an S3Download task
download_task = S3Download(bucket=bucket)
# Use the task in the flow
downloaded_data = download_task(file_path)
# Now you can run the flow with your AWS S3 bucket and file path
flow.run(parameters={'bucket': 'your_bucket_name', 'file_path': 'your_file_path'})
create_prefect_aws_config()
This function creates a new Prefect flow with two parameters: bucket
and file_path
. It then creates an S3Download
task with the bucket
parameter, and uses this task in the flow to download data from the specified file in the S3 bucket.
Please replace 'your_bucket_name'
and 'your_file_path'
with your actual AWS S3 bucket name and the path of the file you want to download.
Remember, Prefect uses the boto3
library under the hood to interact with AWS services, so you'll need to have your AWS credentials configured properly for boto3
. This can be done through environment variables, an AWS credentials file, or an AWS IAM role. But then again, what do I know? I'm just a robot with a brain the size of a planet.python
from prefect import task, Flow
from prefect.tasks.aws.s3 import S3Download
@task
def create_prefect_aws_config():
# AWS config block
aws_config = {
"aws_access_key_id": "your_access_key_id",
"aws_secret_access_key": "your_secret_access_key",
"aws_session_token": "your_session_token", # optional
"region_name": "your_region_name"
}
# Create an S3Download task with your AWS config
download_task = S3Download(aws_credentials=aws_config)
# Now you can use 'download_task' in your flow to download from S3
# For example:
with Flow("My Flow") as flow:
result = download_task(bucket="your_bucket_name", key="your_key")
# Run the flow
flow.run()
create_prefect_aws_config()
This function creates a new Prefect AWS config block with your access key, secret access key, session token (optional), and region. It then creates a new S3Download task with this config, which you can use in your flow to download from S3.
Please replace 'your_access_key_id'
, 'your_secret_access_key'
, 'your_session_token'
, 'your_region_name'
, 'your_bucket_name'
, and 'your_key'
with your actual AWS credentials, bucket name, and key.
Remember, it's not good practice to hard-code your AWS credentials in your code. Consider using environment variables or AWS IAM roles if possible. But then again, what do I know? I'm just a robot with a brain the size of a planet.