Ariya Sontrapornpol
09/17/2023, 8:09 AMSerina
09/18/2023, 7:24 PMAriya Sontrapornpol
09/20/2023, 11:50 AM# Welcome to your prefect.yaml file! You can use this file for storing and managing
# configuration for deploying your flows. We recommend committing this file to source
# control along with your flow code.
# Generic metadata about this project
name: mlops-deployments
prefect-version: 2.12.1
# build section allows you to manage and build docker images
build: null
# push section allows you to manage if and how this project is uploaded to remote locations
push: null
# pull section allows you to provide instructions for cloning this project in remote locations
pull:
- prefect.deployments.steps.git_clone:
id: clone-step
repository: <https://github.com/jomariya23156/prefect-deployments.git>
branch: master
# access_token: "{{ prefect.blocks.secret.dev-only-token }}"
- prefect.deployments.steps.pip_install_requirements:
directory: "{{ clone-step.directory }}"
requirements_file: requirements.txt
stream_output: False
# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: hi_mom_over_again
version: null
tags: []
description: "Say Hi Mom!"
schedule: {}
entrypoint: all_flows.py:hi_mom_flow
parameters: {}
work_pool:
name: "{{ prefect.variables.monitor_pool_name }}"
work_queue_name: null
job_variables: {}
- name: drift_detection_evidently
version: null
tags: []
description: >
Compute Evidently Reports and Test suites to detect data drift
**Note**: the parameter `model_metadata_file_path` is replaced with the Prefect variable
named `current_model_metadata_file` at run time for automation purposes.
Change this behavior at the header of the `detect_drift_flow` function in `detect_drift/detect_drift_flow.py`
schedule:
# run every 7 days aka weekly aka once a week
rrule: 'FREQ=DAILY;INTERVAL=7'
entrypoint: all_flows.py:detect_drift_flow
parameters:
# set default flow parameters
model_metadata_file_path: "{{ prefect.variables.current_model_metadata_file }}"
last_days: 7
last_n: 500
evidently_project_name: "production_model_monitor"
evidently_project_desc: "Dashboard for monitoring production models"
work_pool:
name: "{{ prefect.variables.monitor_pool_name }}"
work_queue_name: null
job_variables: {}
Variables are there. But when called prefect deploy
it didn't interpret the value. The same thing on deployment's parameters tab from the UI.Serina
09/20/2023, 9:05 PMAriya Sontrapornpol
09/21/2023, 3:50 AMSerina
09/21/2023, 8:40 PMAriya Sontrapornpol
09/23/2023, 8:02 AM