A "Best of the Best Practices" (BOBP) guide to developing in Python.
- "Build tools for others that you want to be built for you." - Kenneth Reitz
- "Simplicity is alway better than functionality." - Pieter Hintjens
# -*- coding: utf-8 -*- | |
""" | |
Upload folder to Google Drive | |
""" | |
# Enable Python3 compatibility | |
from __future__ import (unicode_literals, absolute_import, print_function, | |
division) |
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) |
Requirements: Terraform, GCP credentials with permission to create a project Tested with: Terraform v0.12.23, google cloud provider v3.26
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