Kamilly Victória Ruseler
06/25/2024, 6:31 PMMarvin
06/25/2024, 6:31 PMsh
pip install mlflow prefect
2. Integrate MLflow logging in your Prefect flow:
Modify your Prefect flow to log messages to MLflow. Here’s an example code snippet:
python
import mlflow
import logging
from prefect import flow, get_run_logger
# Initialize MLflow logging
mlflow.set_experiment("my_experiment")
def log_to_mlflow(message):
# Log an error message to MLflow
mlflow.log_param("flow_status", "error")
mlflow.log_text(message, "error.log")
@flow(name="log-error-flow")
def log_error_flow():
logger = get_run_logger()
try:
# Your flow logic here
raise Exception("Flow execution failed due to some error.")
except Exception as e:
error_message = f"Flow exited with error: {e}"
logger.error(error_message)
log_to_mlflow(error_message)
raise
if __name__ == "__main__":
log_error_flow()
In this example, the log_to_mlflow
function captures the error message and logs it to MLflow.
3. Run your flow to see the results:
When you run the flow, if any errors occur, the error message will be logged to MLflow.
You can find more information about Prefect logging from the Prefect logging guide.
For more details on potential issues or suggestions, you can refer to the related GitHub issue.
If you need further help, let me know. It’s not like I have anything better to do.Kamilly Victória Ruseler
06/25/2024, 6:33 PMMarvin
06/25/2024, 6:34 PMSIGKILL
signal (signal number 9) is an unrecoverable signal that the operating system sends to terminate a process. This means the process is immediately terminated without any opportunity to catch the signal or to run any cleanup code.
In Python, you can handle other signals like SIGTERM
or SIGINT
by using signal handlers, but SIGKILL
cannot be caught or ignored by the application. This is why your try-except
block doesn't work for handling SIGKILL
.
### Handling Other Signals
If you want to handle other signals like SIGTERM
(which can be caught), you can set up a signal handler like this:
python
import signal
import sys
from prefect import flow, get_run_logger
def handle_sigterm(signum, frame):
logger = get_run_logger()
error_message = "Received SIGTERM, performing cleanup."
logger.error(error_message)
# Logging to MLflow
log_to_mlflow(error_message)
sys.exit(0)
# Register the SIGTERM handler
signal.signal(signal.SIGTERM, handle_sigterm)
def log_to_mlflow(message):
import mlflow
mlflow.set_experiment("my_experiment")
mlflow.log_param("flow_status", "terminated")
mlflow.log_text(message, "error.log")
@flow(name="handle-signals-flow")
def handle_signals_flow():
logger = get_run_logger()
<http://logger.info|logger.info>("Flow started.")
# Simulate a long-running task
try:
while True:
pass
except Exception as e:
error_message = f"Unexpected error: {e}"
logger.error(error_message)
log_to_mlflow(error_message)
raise
if __name__ == "__main__":
handle_signals_flow()
In this code:
- The handle_sigterm
function handles the SIGTERM
signal.
- The signal handler is registered using signal.signal(signal.SIGTERM, handle_sigterm)
.
- If the flow receives a SIGTERM
, it logs the message, calls log_to_mlflow
, and exits gracefully.
### Summary
- SIGKILL
cannot be caught or handled by your application. The operating system immediately terminates your process.
- You can handle other signals like SIGTERM
and SIGINT
using signal handlers and perform necessary cleanup.
If you face SIGKILL
due to high memory usage or other resource constraints, monitoring your system resources and optimizing your flow may help avoid these situations.Kamilly Victória Ruseler
06/25/2024, 6:37 PMMarvin
06/25/2024, 6:37 PMpython
import time
import subprocess
import mlflow
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("flow_status", status)
mlflow.log_text(message, "status.log")
def monitor_flow():
process = subprocess.Popen(["python", "your_flow_script.py"])
try:
while True:
time.sleep(1)
if process.poll() is not None: # Process has terminated
ret_code = process.returncode
if ret_code == -9: # SIGKILL
log_to_mlflow("killed", "Process was killed with SIGKILL.")
else:
log_to_mlflow("stopped", f"Process ended with code {ret_code}.")
break
except KeyboardInterrupt:
process.terminate()
log_to_mlflow("terminated", "Monitor was manually terminated.")
if __name__ == "__main__":
monitor_flow()
This script runs your flow and monitors its status:
- If the process ends with a return code of -9
, it logs to MLflow that it was killed with SIGKILL.
- You can extend this to handle other signals and statuses.
### Summary
- SIGKILL is not catchable and will terminate your flow abruptly.
- Use an external monitoring solution to handle and log such situations.
- Ensure MLflow logging for flow start, completion, or any detection of unexpected termination by the external monitor.
This way, you can have robust logging that covers unexpected terminations.Kamilly Victória Ruseler
06/25/2024, 6:57 PMMarvin
06/25/2024, 6:57 PMSIGTERM
signal to terminate them gracefully. You can catch this signal in your task and log its state to MLflow before the task exits.
### Handling SIGTERM
in a Task
Here's an example of how you can handle SIGTERM
in a Prefect task and log its state to MLflow:
python
import signal
import sys
import mlflow
from prefect import flow, task, get_run_logger
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
def handle_sigterm(signum, frame):
logger = get_run_logger()
message = "Task received SIGTERM, logging state to MLflow."
logger.error(message)
log_to_mlflow("terminated", message)
sys.exit(0)
# Register the SIGTERM handler
signal.signal(signal.SIGTERM, handle_sigterm)
@task
def my_task():
logger = get_run_logger()
<http://logger.info|logger.info>("Task started.")
try:
# Simulate work
for i in range(10):
<http://logger.info|logger.info>(f"Working... {i+1}/10")
time.sleep(1)
except Exception as e:
error_message = f"Unexpected error: {e}"
logger.error(error_message)
log_to_mlflow("error", error_message)
raise
@flow(name="handle-sigterm-task-flow")
def handle_sigterm_task_flow():
logger = get_run_logger()
<http://logger.info|logger.info>("Flow started.")
my_task()
if __name__ == "__main__":
handle_sigterm_task_flow()
### Important Points:
1. Signal Handler: The handle_sigterm
function handles the SIGTERM
signal. It logs the signal and the task state to MLflow before exiting.
2. Registration: The signal handler is registered using signal.signal(signal.SIGTERM, handle_sigterm)
.
3. Task Execution: The main task (my_task
) simulates some work and can be interrupted by a SIGTERM
.
### Running the Task
When you run the flow, if it is cancelled (thus sending SIGTERM
to the task), the handle_sigterm
function will log the termination state to MLflow.
### Note:
- The flow itself will still be managed by Prefect, including retries or state management. The task state is logged just before exiting due to SIGTERM
.
- This example uses basic logging and can be extended based on your specific requirements.
By handling the SIGTERM
signal, you can ensure that your task logs its final state to MLflow before termination, providing better observability into your task's lifecycle.Kamilly Victória Ruseler
06/26/2024, 5:10 PMMarvin
06/26/2024, 5:10 PMpython
from prefect import flow, task
from prefect.tasks import exponential_backoff
import mlflow
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5))
def ecs_task():
# Simulating task logic that interacts with ECS
# Replace this with your actual ECS task logic
import boto3
ecs_client = boto3.client('ecs')
try:
response = ecs_client.run_task(
cluster='your-cluster',
taskDefinition='your-task-def',
count=1,
launchType='FARGATE'
# other task parameters
)
failures = response.get('failures', [])
if failures:
raise RuntimeError(f"Task failed: {failures}")
except Exception as e:
error_message = f"Task execution failed: {e}"
log_to_mlflow("error", error_message)
raise
@flow(name="ecs-with-retries-flow")
def ecs_with_retries_flow():
ecs_task()
if __name__ == "__main__":
ecs_with_retries_flow()
### Explanation:
1. Task Configuration:
- The ecs_task
is configured with retries=5
, which means it will retry up to five times.
- retry_delay_seconds
is set to use exponential_backoff
with a base backoff of 10 seconds and a jitter factor of 0.5 to avoid thundering herds.
2. Handling Failures:
- If the ECS client returns failures, the task will raise a RuntimeError
, triggering the retry mechanism.
- The task logs the error state to MLflow using log_to_mlflow
before raising the exception.
3. Flow Execution:
- The flow ecs_with_retries_flow
runs the ecs_task
, inheriting its retry configuration.
### Additional Considerations:
- ECS Capacity Providers: You might also want to explore setting up ECS capacity providers to handle scaling more effectively, as described in the Prefect GitHub issue.
- System-Level Monitoring: Ensure you have system-level alerts and monitoring in place to catch persistent capacity issues that exceed the retries configured within your tasks.
By implementing these retry strategies, you increase the resilience of your Prefect flows against transient ECS capacity issues. You can adjust the retry parameters according to your specific needs and operational constraints.Kamilly Victória Ruseler
06/26/2024, 5:37 PMMarvin
06/26/2024, 5:37 PMsh
# Set default number of retries for flows
prefect config set PREFECT_FLOW_DEFAULT_RETRIES=2
# Set default number of retries for tasks
prefect config set PREFECT_TASK_DEFAULT_RETRIES=3
# Set default retry delays for flows (in seconds)
prefect config set PREFECT_FLOW_DEFAULT_RETRY_DELAY_SECONDS=[10, 20, 40]
# Set default retry delays for tasks (in seconds)
prefect config set PREFECT_TASK_DEFAULT_RETRY_DELAY_SECONDS=[10, 20, 40]
The above settings can be added to your environment setup script or directly in the command line before running your Prefect agent or worker.
### Using Configuration in Your Prefect Tasks and Flows
When these settings are applied, they will provide default values for retries and retry delays for all tasks and flows, unless explicitly overridden:
python
from prefect import flow, task, get_run_logger
import mlflow
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
@task
def ecs_task():
import boto3
ecs_client = boto3.client('ecs')
logger = get_run_logger()
<http://logger.info|logger.info>("Starting ECS task...")
try:
response = ecs_client.run_task(
cluster='your-cluster',
taskDefinition='your-task-definition',
count=1,
launchType='FARGATE'
)
failures = response.get('failures', [])
if failures:
raise RuntimeError(f"Task failed: {failures}")
except Exception as e:
error_message = f"Task execution failed: {e}"
logger.error(error_message)
log_to_mlflow("error", error_message)
raise
@flow(name="ecs-with-global-retries")
def ecs_with_global_retries_flow():
ecs_task()
if __name__ == "__main__":
ecs_with_global_retries_flow()
### Summary
1. Global Configuration: Set global retry settings via Prefect’s configuration system using environment variables.
2. Environment Setup: You can configure these settings as part of your environment setup script. This is useful for making changes without altering your codebase.
3. Task and Flow Behavior: With these settings, Prefect tasks and flows will inherit the default retry behaviors unless they are specifically overridden.
Using these configurations, you can increase the resilience of your Prefect flows running on ECS by leveraging retries more effectively. If the ECS capacity is temporarily unavailable, the tasks will retry according to the configuration you set, reducing the likelihood of failures due to transient capacity issues.Kamilly Victória Ruseler
06/26/2024, 5:45 PMMarvin
06/26/2024, 5:46 PMRuntimeError: {'reason': 'Capacity is unavailable at this time. Please try again later or in a different availability zone'}
by retrying the task after an appropriate delay.
Here's how you can set up the task with retry logic:
### Setting Up Retries in a Task
You can use the retry configuration directly in your Prefect task to handle transient ECS capacity issues:
python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
import mlflow
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5))
def ecs_task():
logger = get_run_logger()
ecs_client = boto3.client('ecs')
<http://logger.info|logger.info>("Starting ECS task...")
try:
response = ecs_client.run_task(
cluster='your-cluster',
taskDefinition='your-task-definition',
count=1,
launchType='FARGATE'
)
failures = response.get('failures', [])
if failures:
raise RuntimeError(f"Task failed: {failures}")
<http://logger.info|logger.info>("ECS task started successfully.")
except Exception as e:
error_message = f"Task execution failed: {e}"
logger.error(error_message)
log_to_mlflow("error", error_message)
raise
@flow(name="ecs-task-flow-with-retries")
def ecs_task_flow():
ecs_task()
if __name__ == "__main__":
ecs_task_flow()
### Explanation:
1. Retry Configuration:
- The task ecs_task
is configured to retry up to 5 times.
- retry_delay_seconds
uses exponential_backoff
with a base backoff factor of 10 seconds and a jitter factor of 0.5 to stagger retries, preventing potential overloading of the ECS service.
2. Handling Failures:
- If the ECS client returns failures (e.g., capacity being unavailable), the task raises a RuntimeError
.
- The task logs the error state to MLflow using log_to_mlflow
before raising the exception, allowing you to track task failures.
3. Flow Execution:
- The flow ecs_task_flow
runs the ecs_task
, inheriting its retry configuration.
### Global Configuration (Optional)
While the above setup should handle your requirement, you can also set up global retry settings as previously discussed for more comprehensive resilience across all tasks and flows in your environment:
sh
# You can set these configurations as needed
prefect config set PREFECT_TASK_DEFAULT_RETRIES=5
prefect config set PREFECT_TASK_DEFAULT_RETRY_DELAY_SECONDS=[10, 20, 40]
### Summary
- Task-Level Retry: By configuring retries directly within your task, you ensure that the task will retry on encountering ECS capacity issues.
- Logging: Failures and retries are logged, allowing you to monitor and diagnose issues.
- Resilience: This approach provides resilience against transient errors by automatically retrying the task.
By following this approach, your task should be able to recover from ECS errors related to capacity being unavailable, thus increasing the robustness of your workflow.Kamilly Victória Ruseler
06/26/2024, 5:55 PMMarvin
06/26/2024, 5:56 PMon_failure
hook that only executes its logic after all retries have been exhausted and the task has genuinely failed. To achieve this, you can check the current retry count and compare it to the maximum number of retries within the on_failure
hook.
### Example Implementation
Below is a code example demonstrating how to set up an on_failure
hook that saves the error to a database only after all retries have been exhausted:
1. Define the Task with the on_failure
Hook:
python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
from prefect.engine.signals import FAIL
import mlflow
import os
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
def on_failure(task, state, exception):
logger = get_run_logger()
current_retry_count = state.context.get("task_run_count", 0)
retry_limit = task.retries
if current_retry_count >= retry_limit:
error_message = f"Task {task.name} failed after {current_retry_count} retries: {exception}"
logger.error(error_message)
log_to_mlflow("final_failure", error_message)
# Save error to database or take other actions
save_error_to_database(task.name, exception)
else:
<http://logger.info|logger.info>(f"Task {task.name} failed on attempt {current_retry_count}/{retry_limit}: {exception}")
def save_error_to_database(task_name, exception):
# Placeholder for your database logic
# Replace with actual database interaction code
print(f"Saving error to database: {task_name} - {exception}")
@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5), on_failure=on_failure)
def ecs_task():
logger = get_run_logger()
ecs_client = boto3.client('ecs')
<http://logger.info|logger.info>("Starting ECS task...")
try:
response = ecs_client.run_task(
cluster='your-cluster',
taskDefinition='your-task-def',
count=1,
launchType='FARGATE'
)
failures = response.get('failures', [])
if failures:
raise RuntimeError(f"Task failed: {failures}")
<http://logger.info|logger.info>("ECS task started successfully.")
except Exception as e:
error_message = f"Task execution failed: {e}"
logger.error(error_message)
log_to_mlflow("error", error_message)
raise
@flow(name="ecs-task-flow-with-retries")
def ecs_task_flow():
ecs_task()
if __name__ == "__main__":
ecs_task_flow()
### Explanation:
1. Retry Logic:
- The task ecs_task
includes a retry configuration with exponential backoff and jitter.
2. Custom on_failure
Hook:
- The on_failure
function checks the current retry count (current_retry_count
) against the retry limit (retry_limit
).
- If all retries have been exhausted (current_retry_count >= retry_limit
), it logs an error message, and logs the final status to MLflow, and executes the error-saving logic in save_error_to_database
.
- If retries are still ongoing, it logs the intermediate failure states without saving to the database.
3. Flow Execution:
- The ecs_task_flow
runs the ecs_task
, inheriting its retry configuration and on_failure
hook.
### Summary:
- Selective Error Logging: The on_failure
hook conditionally performs actions based on whether the task has genuinely failed after all retries.
- Controlled Error Reporting: This prevents premature logging of errors to your database if the task is still retrying.
- Resilient Error Handling: Ensures that errors are captured and handled appropriately, only after you've exhausted all retry attempts.Marvin
06/26/2024, 5:56 PMKamilly Victória Ruseler
06/26/2024, 6:27 PMMarvin
06/26/2024, 6:27 PMon_failure
hooks remains the same regardless of the execution backend, whether it's local, Dask, Kubernetes, or other infrastructure.
Here's how to integrate the task and flow defined earlier with a Dask executor:
1. Set Up Dask Executor:
Install Dask if you haven't already:
sh
pip install dask[distributed]
2. Use Dask Executor in the Flow:
Modify your flow to use the Dask task runner:
python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
from prefect.engine.signals import FAIL
import mlflow
from prefect.task_runners import DaskTaskRunner
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
def on_failure(task, state, exception):
logger = get_run_logger()
current_retry_count = state.context.get("task_run_count", 0)
retry_limit = task.retries
if current_retry_count >= retry_limit:
error_message = f"Task {task.name} failed after {current_retry_count} retries: {exception}"
logger.error(error_message)
log_to_mlflow("final_failure", error_message)
# Save error to database or take other actions
save_error_to_database(task.name, exception)
else:
<http://logger.info|logger.info>(f"Task {task.name} failed on attempt {current_retry_count}/{retry_limit}: {exception}")
def save_error_to_database(task_name, exception):
# Placeholder for your database logic
# Replace with actual database interaction code
print(f"Saving error to database: {task_name} - {exception}")
@task(retries=5, retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5), on_failure=on_failure)
def ecs_task():
logger = get_run_logger()
ecs_client = boto3.client('ecs')
<http://logger.info|logger.info>("Starting ECS task...")
try:
response = ecs_client.run_task(
cluster='your-cluster',
taskDefinition='your-task-def',
count=1,
launchType='FARGATE'
)
failures = response.get('failures', [])
if failures:
raise RuntimeError(f"Task failed: {failures}")
<http://logger.info|logger.info>("ECS task started successfully.")
except Exception as e:
error_message = f"Task execution failed: {e}"
logger.error(error_message)
log_to_mlflow("error", error_message)
raise
@flow(name="ecs-task-flow-with-retries", task_runner=DaskTaskRunner())
def ecs_task_flow():
ecs_task()
if __name__ == "__main__":
ecs_task_flow()
### Explanation:
1. Dask Task Runner:
- The DaskTaskRunner
is used to configure the flow to run tasks on a Dask cluster. You can specify Dask cluster settings if you run it on a distributed setup.
2. Task Configuration:
- The task ecs_task
is configured with retries and the on_failure
hook, just like before.
3. Flow Configuration:
- The flow is modified to use task_runner=DaskTaskRunner()
, making it compatible with Dask.
### Running with Dask:
- When you execute this flow, Prefect will utilize the Dask Executor to manage task execution.
- The retry logic and the on_failure
hook will function as designed, ensuring that your failure handling logic works correctly even when using Dask.
### Summary:
- Compatibility with Dask: The setup is compatible with Dask and other execution backends supported by Prefect.
- Consistent Error Handling: Ensures that the error handling and retry logic work uniformly across different execution setups.Marvin
06/26/2024, 6:27 PMKamilly Victória Ruseler
06/27/2024, 4:10 PMMarvin
06/27/2024, 4:10 PMecs_task
to include custom retry logic to handle specific errors:
python
import boto3
from prefect import flow, task, get_run_logger
from prefect.tasks import exponential_backoff
from prefect.engine.signals import FAIL
import mlflow
from prefect.task_runners import DaskTaskRunner
def log_to_mlflow(status, message):
mlflow.set_experiment("my_experiment")
mlflow.log_param("task_status", status)
mlflow.log_text(message, "status.log")
def save_error_to_database(task_name, exception):
# Placeholder for your database logic
# Replace with actual database interaction code
print(f"Saving error to database: {task_name} - {exception}")
def on_failure(task, state, exception):
logger = get_run_logger()
current_retry_count = state.context.get("task_run_count", 0)
retry_limit = task.retries
if current_retry_count >= retry_limit:
error_message = f"Task {task.name} failed after {current_retry_count} retries: {exception}"
logger.error(error_message)
log_to_mlflow("final_failure", error_message)
save_error_to_database(task.name, exception)
else:
<http://logger.info|logger.info>(f"Task {task.name} failed on attempt {current_retry_count}/{retry_limit}: {exception}")
def retry_on_specific_errors(task, task_run, state) -> bool:
error_message = str(state.result)
retry_conditions = [
"Capacity is unavailable",
# Add other specific error messages or conditions here
]
return any(cond in error_message for cond in retry_conditions)
@task(
retries=5,
retry_delay_seconds=exponential_backoff(backoff_factor=10, retry_jitter_factor=0.5),
on_failure=on_failure,
retry_condition_fn=retry_on_specific_errors
)
def ecs_task():
logger = get_run_logger()
ecs_client = boto3.client('ecs')
<http://logger.info|logger.info>("Starting ECS task...")
try:
response = ecs_client.run_task(
cluster='your-cluster',
taskDefinition='your-task-def',
count=1,
launchType='FARGATE'
)
failures = response.get('failures', [])
if failures:
raise RuntimeError(f"Task failed: {failures}")
<http://logger.info|logger.info>("ECS task started successfully.")
except Exception as e:
error_message = f"Task execution failed: {e}"
logger.error(error_message)
log_to_mlflow("error", error_message)
raise
@flow(name="ecs-task-flow-with-retries", task_runner=DaskTaskRunner())
def ecs_task_flow():
ecs_task()
if __name__ == "__main__":
ecs_task_flow()
### Explanation:
1. Custom Retry Condition (retry_on_specific_errors
):
- This function checks if the error messages contain specific conditions that should trigger a retry.
2. Task Configuration:Marvin
06/27/2024, 4:10 PMretry_condition_fn
parameter.
- The rest of the task configuration remains the same as earlier.
3. Flow Execution:
- The flow uses DaskTaskRunner()
.
### Check Dask Configuration:
Ensure your Dask deployment is properly configured:
- Scheduler and Worker Resource Allocation:
- Verify that your Dask scheduler and workers have sufficient resources (CPU, memory).
- Timeout Settings:
- Adjust the timeout settings for the Dask client to ensure it waits longer before failing connections.
### Sample Dask Client Configuration:
python
from dask.distributed import Client
client = Client(
n_workers=4,
threads_per_worker=2,
memory_limit='2GB',
timeout='60s', # Increase timeout as needed
retries=3 # Increase retries as needed
)
### Summary:
- Custom Retry Logic: Implementing a custom retry logic to handle specific transient errors.
- Error Logging: Ensuring errors are logged to MLflow and captured only after all retries.
- Dask Configuration: Verifying and tuning Dask scheduler, worker resources, and network configuration.
With these changes, you should be able to handle intermittent ECS capacity issues better while running your Prefect tasks with Dask.