TylerWanner

Provisioning Prefect Server on GCP using Terraform

Requirements: Terraform, GCP credentials with permission to create a project Tested with: Terraform v0.12.23, google cloud provider v3.26

To run:

You can use an environment variable to set which service account key to use during provisioning export GOOGLE_APPLICATION_CREDENTIALS=/path/to/key The service account you use must belong to a GCP project that has the necessary APIs enabled (such as billing and resource manager)--if it does not, you may have to enable these APIs manually along the way in those projects In this directory, run terraform init then terraform apply -- you will need to provide a billing_id and organization_id for your GCP project

Once successful, be sure to add the following to your ~/.prefect/config.toml

liorshk

def log_run(gridsearch: sklearn.GridSearchCV, experiment_name: str, model_name: str, run_index: int, conda_env, tags={}):
    """Logging of cross validation results to mlflow tracking server
    
    Args:
        experiment_name (str): experiment name
        model_name (str): Name of the model
        run_index (int): Index of the run (in Gridsearch)
        conda_env (str): A dictionary that describes the conda environment (MLFlow Format)
        tags (dict): Dictionary of extra data and tags (usually features)

jmlrt

# -*- coding: utf-8 -*-

"""
    Upload folder to Google Drive
"""

# Enable Python3 compatibility
from __future__ import (unicode_literals, absolute_import, print_function,
                        division)

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens