This message was deleted.

Sure, let's gather information relevant to the

create_docker_container

function within the Prefect context. I'll look up relevant documentation and any related GitHub issues to give you the most accurate and up-to-date information. Let's proceed with the following steps: 1. Search the Prefect documentation for "create_docker_container". 2. Search GitHub issues in the "prefecthq/prefect" repository for any mentions of "create_docker_container". I'll start by running these queries. ### Documentation Details: The

create_docker_container

function is used to create a Docker container without starting it. It is similar to the

docker create

command. Here is some detailed information: - Parameters: - image (str): The image to run. (required) - command (Optional[Union[str, List[str]]): The command(s) to run in the container. (default: None) - name (Optional[str]): The name for this container. (default: None) - detach (Optional[bool]): Run container in the background. (default: None) - docker_host (Optional[DockerHost]): Settings for interacting with a Docker host. (default: None) - entrypoint (Optional[Union[str, List[str]]): The entrypoint for the container. (default: None) - environment (Optional[Union[Dict[str, str], List[str]]]): Environment variables to set inside the container, as a dictionary or a list of strings in the format

["SOMEVARIABLE=xxx"]

. (default: None) - create_kwargs (Dict[str, Any]): Additional keyword arguments to pass. (default: {}) - Returns: - Container: A Docker Container object. For more detailed examples and a comprehensive guide, you can visit the Prefect documentation. ### GitHub Issues: Surprisingly, there were no relevant issues found on GitHub concerning

create_docker_container

. If you have any more specific questions or need further assistance on this topic, feel free to ask!

@Marvin so here: @task async def create_container(): logger = get_run_logger() # Create a container with the specified flags logger.info("Creating a container with the specified flags...") container = await create_docker_container( image="seedooinsights/build:latest", command=["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/data.pkl"], environment=[ "MKL_NUM_THREADS=$MKL_NUM_THREADS", "NUMBA_NUM_THREADS=$NUMBA_NUM_THREADS", "NUMBA_THREADING_LAYER=$NUMBA_THREADING_LAYER", "OMP_NUM_THREADS=$OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS=$OPENBLAS_NUM_THREADS", "SEEDOO_DB_IP=$SEEDOO_DB_IP", "SEEDOO_DB_PORT=$SEEDOO_DB_PORT", "SEEDOO_PRINT_SQL_DEBUG=$SEEDOO_PRINT_SQL_DEBUG", ], I want to be using parameter class to adjust the inputs of command and environment in each flow run

Understood. Let's apply the

Parameter

class in this context to make the

command

and

environment

inputs adjustable for each flow run. Here’s how you can modify your code to incorporate the

Parameter

class: 1. Define the parameters at the flow level. 2. Pass these parameters to the

create_container

task. Here's the revised code:

python
from prefect import flow, task, Parameter, get_run_logger
from prefect_docker.containers import create_docker_container

@task
async def create_container(image: str, command: list, environment: list):
    logger = get_run_logger()
    <http://logger.info|logger.info>("Creating a container with the specified flags...")
    container = await create_docker_container(
        image=image,
        command=command,
        environment=environment
    )
    container.start()

@flow
def create_docker_container_flow():
    image = Parameter("image", default="seedooinsights/build:latest")
    command = Parameter("command", default=["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/data.pkl"])
    environment = Parameter("environment", default=[
        "MKL_NUM_THREADS=$MKL_NUM_THREADS",
        "NUMBA_NUM_THREADS=$NUMBA_NUM_THREADS",
        "NUMBA_THREADING_LAYER=$NUMBA_THREADING_LAYER",
        "OMP_NUM_THREADS=$OMP_NUM_THREADS",
        "OPENBLAS_NUM_THREADS=$OPENBLAS_NUM_THREADS",
        "SEEDOO_DB_IP=$SEEDOO_DB_IP",
        "SEEDOO_DB_PORT=$SEEDOO_DB_PORT",
        "SEEDOO_PRINT_SQL_DEBUG=$SEEDOO_PRINT_SQL_DEBUG",
    ])

    create_container(image, command, environment)

if __name__ == "__main__":
    create_docker_container_flow()

### How to Run: To run the flow with different parameters, you can use the Prefect CLI or script it:

bash
prefect deployment build flow.py:create_docker_container_flow --name my-flow-deployment
prefect deployment apply create_docker_container_flow-deployment.yaml
prefect deployment run "create_docker_container_flow/my-flow-deployment" -p '{"command": ["bash", "-c", "echo Different Command"], "environment": ["ENV_VAR1=value1", "ENV_VAR2=value2"]}'

### Key Points: 1. Defining Parameters:

image

,

command

, and

environment

are defined as parameters at the flow level. 2. Passing Parameters: These parameters are then passed to the

create_container

task. 3. Running with Different Parameters: Use the Prefect CLI to provide different values for

command

and

environment

. References: - Prefect Parameters Documentation - Prefect CLI Documentation By following this approach, you can flexibly adjust the

command

and

environment

inputs for each flow run.

@Marvin what if I already have deployment already pushed. If want to run an existing deployment and override these parameters how to do that?

If you have an existing deployment and you'd like to run it with overridden parameters, you can use the

prefect deployment run

command with the

-p

flag to specify the new parameter values. Here's the step-by-step process: 1. Identify the deployment name: Ensure you know the name of your existing deployment. 2. Run the deployment with overridden parameters: Use the Prefect CLI to run the deployment and pass the new parameter values. Here's an example:

bash
prefect deployment run "create_docker_container_flow/my-existing-deployment" -p '{"command": ["bash", "-c", "new_command"], "environment": ["NEW_ENV_VAR=new_value"]}'

### Breakdown: - Deployment Name:

"create_docker_container_flow/my-existing-deployment"

is the full name of your deployment. - Overridden Parameters: Use the

-p

flag followed by a JSON string representing the new parameter values. For your specific case, you might do something like:

bash
prefect deployment run "create_docker_container_flow/my-existing-deployment" -p '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/new_data.pkl"], "environment": ["NEW_VAR1=value1", "NEW_VAR2=value2"]}'

### Example of the Original Flow and Task Code

python
from prefect import flow, task, Parameter, get_run_logger
from prefect_docker.containers import create_docker_container

@task
async def create_container(image: str, command: list, environment: list):
    logger = get_run_logger()
    <http://logger.info|logger.info>("Creating a container with the specified flags...")
    container = await create_docker_container(
        image=image,
        command=command,
        environment=environment
    )
    container.start()

@flow
def create_docker_container_flow():
    image = Parameter("image", default="seedooinsights/build:latest")
    command = Parameter("command", default=["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/data.pkl"])
    environment = Parameter("environment", default=[
        "MKL_NUM_THREADS=$MKL_NUM_THREADS",
        "NUMBA_NUM_THREADS=$NUMBA_NUM_THREADS",
        "NUMBA_THREADING_LAYER=$NUMBA_THREADING_LAYER",
        "OMP_NUM_THREADS=$OMP_NUM_THREADS",
        "OPENBLAS_NUM_THREADS=$OPENBLAS_NUM_THREADS",
        "SEEDOO_DB_IP=$SEEDOO_DB_IP",
        "SEEDOO_DB_PORT=$SEEDOO_DB_PORT",
        "SEEDOO_PRINT_SQL_DEBUG=$SEEDOO_PRINT_SQL_DEBUG",
    ])

    create_container(image, command, environment)

if __name__ == "__main__":
    create_docker_container_flow()

### Key Points 1. JSON String: Ensure the parameters passed via the

-p

flag are correctly formatted JSON. 2. Prefect CLI: Use

prefect deployment run

to execute the deployment with the new parameters. For more details on running deployments and overriding parameters, you can refer to the Prefect CLI documentation: - Prefect CLI Documentation By using the CLI in this manner, you can override parameters for any existing deployment seamlessly.

@Marvin update the trigger-flow.py so that I can make these parameters injected on run-time: from prefect.client.orchestration import get_client from prefect.client.schemas.filters import FlowRunFilter, LogFilter import asyncio async def trigger_deployment(): async with get_client() as client: # Fetch the last deployment deployments = await client.read_deployments() last_deployment = deployments[-1] # Assuming this is the correct logic to fetch the last one print(f"Last Deployment ID: {last_deployment.id}") flow_run = await client.create_flow_run_from_deployment( deployment_id=last_deployment.id ) print(f"Flow Run ID: {flow_run.id}") while True: flow_run_state = (await client.read_flow_run(flow_run.id)).state.name if flow_run_state in {"Completed", "Failed", "Cancelled"}: break print(f"Current state: {flow_run_state}. Waiting to complete...") await asyncio.sleep(10) logs_filter = LogFilter(flow_run_id={"any_": [str(flow_run.id)]}) logs = await client.read_logs(log_filter=logs_filter) for log in logs: print(f"{log.timestamp} - {log.level}: {log.message}") # Run the asyncio event loop asyncio.run(trigger_deployment())

Your message was too long by 11 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

update the trigger-flow.py so that I can make these parameters injected on run-time:

from prefect.client.orchestration import get_client from prefect.client.schemas.filters import FlowRunFilter, LogFilter import asyncio async def trigger_deployment(): async with get_client() as client: # Fetch the last deployment deployments = await client.read_deployments() last_deployment = deployments[-1] # Assuming this is the correct logic to fetch the last one print(f"Last Deployment ID: {last_deployment.id}") # Run the last deployment flow_run = await client.create_flow_run_from_deployment( deployment_id=last_deployment.id ) print(f"Flow Run ID: {flow_run.id}") # Wait for flow run to complete (simple polling logic) while True: flow_run_state = (await client.read_flow_run(flow_run.id)).state.name if flow_run_state in {"Completed", "Failed", "Cancelled"}: break print(f"Current state: {flow_run_state}. Waiting to complete...") await asyncio.sleep(10) # Retrieve logs from the flow run logs_filter = LogFilter(flow_run_id={"any_": [str(flow_run.id)]}) logs = await client.read_logs(log_filter=logs_filter) for log in logs: print(f"{log.timestamp} - {log.level}: {log.message}") # Run the

@Marvin python3 trigger-flow.py --params '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/data.pkl"], "environment": ["MKL_NUM_THREADS=5"]}' Traceback (most recent call last): File "trigger-flow.py", line 1, in <module> from prefect.client.orchestration import get_client ModuleNotFoundError: No module named 'prefect'

It looks like you're encountering a

ModuleNotFoundError

because the Prefect module isn't installed in your environment. Here are the steps to resolve this issue: 1. Ensure Prefect is Installed: Install Prefect using pip. 2. Run the Script: Retry running your script after installation. ### Step 1: Install Prefect You can install Prefect using pip:

bash
pip install prefect

### Step 2: Check Installation Ensure Prefect is installed correctly by importing it in a Python shell:

python
python3 -c "import prefect"

If you don't get any errors, Prefect is installed correctly. ### Step 3: Retry Running Your Script After installing Prefect, retry running your

trigger-flow.py

script:

bash
python3 trigger-flow.py --params '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/data.pkl"], "environment": ["MKL_NUM_THREADS=5"]}'

### Additional Tips: - Virtual Environment: It's a good practice to use a virtual environment to manage dependencies. You can create and activate a virtual environment as follows:

bash
  python3 -m venv venv
  source venv/bin/activate  # On Windows: venv\Scripts\activate
  pip install prefect

- Check Python Path: Ensure that you're running the script with the correct Python interpreter where Prefect is installed. By following these steps, you'll ensure that Prefect is installed and can be imported, which should resolve the

ModuleNotFoundError

.

@Marvinfix AttributeError: module 'lib' has no attribute 'X509_V_FLAG_NOTIFY_POLICY' Original exception was: Traceback (most recent call last): File "trigger-flow.py", line 41, in <module> asyncio.run(trigger_deployment(params)) File "/usr/lib/python3.8/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/usr/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete return future.result() File "trigger-flow.py", line 11, in trigger_deployment last_deployment = deployments[-1] # Assuming this is the correct logic to fetch the last one IndexError: list index out of range

It looks like you're facing two different issues: an

AttributeError

related to

lib.X509_V_FLAG_NOTIFY_POLICY

and an

IndexError

because the

deployments

list is empty. ### Issue 1: AttributeError The

AttributeError

related to

lib.X509_V_FLAG_NOTIFY_POLICY

often occurs due to compatibility issues between your SSL library and its bindings in Python. This could be caused by outdated versions or a corrupted installation. #### Potential Fixes: 1. Update SSL Library: - Ensure your OpenSSL library is up-to-date. For example, on a Debian-based system:

bash
     sudo apt-get update
     sudo apt-get install --only-upgrade openssl

2. Update pip and its dependencies:

bash
   pip install --upgrade pip
   pip install --upgrade cryptography

3. Reinstall Prefect:

bash
   pip uninstall prefect
   pip install prefect

4. Virtual Environment: If the issue persists, create a virtual environment to isolate dependencies and avoid system-level conflicts:

bash
   python3 -m venv venv
   source venv/bin/activate
   pip install prefect

### Issue 2: IndexError The

IndexError

suggests that your list of deployments is empty, meaning no deployments are found. You should handle this case gracefully. #### Fix: Add a check to see if the

deployments

list is empty before accessing the last element. Here is the updated

trigger-flow.py

script with these modifications:

python
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import FlowRunFilter, LogFilter
import asyncio
import argparse
import json

async def trigger_deployment(params):
    async with get_client() as client:
        # Fetch the last deployment
        deployments = await client.read_deployments()
        
        if not deployments:
            print("No deployments found.")
            return

        last_deployment = deployments[-1]  # Assuming this is the correct logic to fetch the last one
        print(f"Last Deployment ID: {last_deployment.id}")

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=last_deployment.id,
            parameters=params
        )
        print(f"Flow Run ID: {flow_run.id}")

        while True:
            flow_run_state = (await client.read_flow_run(flow_run.id)).state.name
            if flow_run_state in {"Completed", "Failed", "Cancelled"}:
                break
            print(f"Current state: {flow_run_state}. Waiting to complete...")
            await asyncio.sleep(10)

        logs_filter = LogFilter(flow_run_id={"any_": [str(flow_run.id)]})
        logs = await client.read_logs(log_filter=logs_filter)

        for log in logs:
            print(f"{log.timestamp} - {log.level}: {log.message}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Trigger a deployment with dynamic parameters.')
    parser.add_argument('-p', '--params', type=str, required=True, help='JSON string of the parameters to override.')

    args = parser.parse_args()
    params = json.loads(args.params)  # Ensure the JSON string is converted to a dictionary

    # Run the asyncio event loop
    asyncio.run(trigger_deployment(params))

### Key Points: 1. Check for Empty Deployments List: Added a check to ensure the

deployments

list is not empty. 2. Exception Handling: Gracefully exit if no deployments are found. Try running the updated script after ensuring your environment is correctly set up, and if the SSL issue persists, consider the suggested fixes for updating your SSL library and dependencies.

@Marvin python3 trigger-flow.py --params '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/data.pkl"], "environment": ["MKL_NUM_THREADS=5"]}' No deployments found. but there is a deployment: Deployment: train_flow • Prefect Server

It appears that although there are deployments visible in the Prefect UI, your script isn't able to fetch them. This discrepancy could be due to several factors, such as connectivity issues, API permissions, or incorrect filters. ### Steps to Diagnose and Resolve: 1. Verify Prefect API Connection: Ensure your script can connect to the Prefect server. 2. Adjust Filters: Check if the filters applied while fetching deployments are restricting the results. 3. Debugging Information: Add more debugging statements to understand what

read_deployments()

returns. Here's an updated version of the script with additional logging and handling for troubleshooting purposes:

python
from prefect.client.orchestration import get_client
from prefect.client.schemas.filters import FlowRunFilter, LogFilter
import asyncio
import argparse
import json

async def trigger_deployment(params):
    async with get_client() as client:
        # Fetch the deployments
        deployments = await client.read_deployments()
        
        # Debugging
        print(f"Deployments fetched: {deployments}")

        if not deployments:
            print("No deployments found.")
            return

        # Assuming fetching the last one is what you want
        last_deployment = deployments[-1]
        print(f"Last Deployment ID: {last_deployment.id}")

        flow_run = await client.create_flow_run_from_deployment(
            deployment_id=last_deployment.id,
            parameters=params
        )
        print(f"Flow Run ID: {flow_run.id}")

        while True:
            flow_run_state = (await client.read_flow_run(flow_run.id)).state.name
            if flow_run_state in {"Completed", "Failed", "Cancelled"}:
                break
            print(f"Current state: {flow_run_state}. Waiting to complete...")
            await asyncio.sleep(10)

        logs_filter = LogFilter(flow_run_id={"any_": [str(flow_run.id)]})
        logs = await client.read_logs(log_filter=logs_filter)

        for log in logs:
            print(f"{log.timestamp} - {log.level}: {log.message}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Trigger a deployment with dynamic parameters.')
    parser.add_argument('-p', '--params', type=str, required=True, help='JSON string of the parameters to override.')

    args = parser.parse_args()
    params = json.loads(args.params)  # Ensure the JSON string is converted to a dictionary

    # Run the asyncio event loop
    asyncio.run(trigger_deployment(params))

### Points to Check: 1. API Connection: - Ensure your script is connecting to the correct Prefect API server. Double-check your

.prefect/config.toml

file or environment variables if you have special configurations. 2. Permissions: - Ensure the API client has the necessary permissions to fetch deployments. 3. Logs and Debugging: - The additional print statements will help verify what

client.read_deployments()

is returning. 4. Deployment Filters: - If you need to apply specific filters, adjust them accordingly to narrow down the search. ### Direct Deployment Run: If necessary, you could also try bypassing the dynamic fetching and directly use the deployment ID from the UI for testing purposes: ```python async def trigger_deployment(params): async with get_client() as client: deployment_id = "2eb6ef1b-76c4-4c36-9333-b1a17dc9c8d1" # Your known deployment ID flow_run = await client.create_flow_run_from_deployment( deployment_id=deployment_id, parameters=params ) print(f"Flow Run ID: {flow_run.id}") while True: flow_run_state = (await client.read_flow_run(flow_run.id)).state.name if flow_run_state in {"Completed", "Failed", "Cancelled"}: break print(f"Current state: {flow_run_state}. Waiting to complete...") await asyncio.sleep(10)

@Marvin I tried it direct from the prefect cli : prefect deployment run 'pull-and-run-image/train_flow' -p '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/new_data.pkl"], "environment": ["MKL_NUM_THREADS=5", "NUMBA_NUM_THREADS=3"]}' Failed to parse JSON for parameter '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/new_data.pkl"], "environment": ["MKL_NUM_THREADS' with value 5", "NUMBA_NUM_THREADS=3"]} JSON Error: Extra data: line 1 column 2 (char 1)

It looks like there is an issue with how the JSON string for parameters is being parsed. This can be due to various factors, such as improper escaping of quotes. Let's improve the formatting to ensure proper JSON parsing. Here’s how you can correctly pass a JSON string on the command line: 1. Correct JSON Formatting: The JSON string should be properly escaped to avoid parsing errors. 2. Command using Prefect CLI: Use single quotes around the JSON string and escape double quotes inside the string. Here’s the corrected command:

bash
prefect deployment run 'pull-and-run-image/train_flow' -p '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/new_data.pkl"], "environment": ["MKL_NUM_THREADS=5", "NUMBA_NUM_THREADS=3"]}'

Ensure your terminal or shell supports this formatting. If not, you might need to adjust the escaping based on your shell. Below are different examples for various shells: ### Bash/Zsh

bash
prefect deployment run 'pull-and-run-image/train_flow' -p '{"command": ["bash", "-c", "./train.sh", "/seedoodata/datasets/data/traffic_lights/new_data.pkl"], "environment": ["MKL_NUM_THREADS=5", "NUMBA_NUM_THREADS=3"]}'

### Windows Command Prompt For the Windows Command Prompt, you might have to use double quotes and escape double quotes inside the JSON string:

cmd
prefect deployment run "pull-and-run-image/train_flow" -p "{\"command\": [\"bash\", \"-c\", \"./train.sh\", \"/seedoodata/datasets/data/traffic_lights/new_data.pkl\"], \"environment\": [\"MKL_NUM_THREADS=5\", \"NUMBA_NUM_THREADS=3\"]}"

### PowerShell In PowerShell, you might need to use double quotes and escape the inner quotes with a backslash:

powershell
prefect deployment run "pull-and-run-image/train_flow" -p '{\"command\": [\"bash\", \"-c\", \"./train.sh\", \"/seedoodata/datasets/data/traffic_lights/new_data.pkl\"], \"environment\": [\"MKL_NUM_THREADS=5\", \"NUMBA_NUM_THREADS=3\"]}'

By ensuring that the JSON string is correctly formatted and properly escaped, you should be able to pass parameters to your deployment without parsing errors. If the JSON parsing issues persist, you could also consider using a JSON file as an argument or another method to pass complex data structures. Would you like more assistance with this, or did this solve your issue?