Makefile

# ----------------------------------
#          INSTALL & TEST
# ----------------------------------
install_requirements:
	@pip install -r requirements.txt

check_code:
	@flake8 scripts/* TaxiFareModel/*.py

black:
	@black scripts/* TaxiFareModel/*.py

test:
	@coverage run -m pytest tests/*.py
	@coverage report -m --omit="${VIRTUAL_ENV}/lib/python*"

ftest:
	@Write me

clean:
	@rm -fr */__pycache__
	@rm -fr __init__.py
	@rm -fr build
	@rm -fr dist
	@rm -fr TaxiFareModel-*.dist-info
	@rm -fr TaxiFareModel.egg-info
	-@rm model.joblib

install:
	@pip install . -U

all: clean install test black check_code


uninstal:
	@python setup.py install --record files.txt
	@cat files.txt | xargs rm -rf
	@rm -f files.txt

count_lines:
	@find ./ -name '*.py' -exec  wc -l {} \; | sort -n| awk \
        '{printf "%4s %s\n", $$1, $$2}{s+=$$0}END{print s}'
	@echo ''
	@find ./scripts -name '*-*' -exec  wc -l {} \; | sort -n| awk \
		        '{printf "%4s %s\n", $$1, $$2}{s+=$$0}END{print s}'
	@echo ''
	@find ./tests -name '*.py' -exec  wc -l {} \; | sort -n| awk \
        '{printf "%4s %s\n", $$1, $$2}{s+=$$0}END{print s}'
	@echo ''

# ----------------------------------
#      UPLOAD PACKAGE TO PYPI
# ----------------------------------
PYPI_USERNAME=<AUTHOR>
build:
	@python setup.py sdist bdist_wheel

pypi_test:
	@twine upload -r testpypi dist/* -u $(PYPI_USERNAME)

pypi:
	@twine upload dist/* -u $(PYPI_USERNAME)

default: pylint

pylint:
	find . -iname "*.py" -not -path "./tests/*" | xargs -n1 -I {}  pylint --output-format=colorized {}; true
# ----------------------------------
#         LOCAL SET UP
# ----------------------------------

run_locally:
	@python -W ignore -m TaxiFareModel.main

# ----------------------------------
#            GCP Setup
# ----------------------------------
# project id - replace with your Project's ID
PROJECT_ID=fed-up-taxi-challenge

# bucket name - follow the convention of wagon-ml-[YOUR_LAST_NAME]-[BATCH_NUMBER]
BUCKET_NAME=wagon-ml-zastrow-566

# Choose your region https://cloud.google.com/storage/docs/locations#available_locations
REGION=europe-west1

# path of the file to upload to gcp (the path of the file should be absolute or should match the directory where the make command is run)
LOCAL_PATH="raw_data/train.csv" # Replace with your local path to the `train_1k.csv` and make sure to put it between quotes

# bucket directory in which to store the uploaded file (we choose to name this data as a convention)
BUCKET_FOLDER=data

# will store the packages uploaded to GCP for the training
BUCKET_TRAINING_FOLDER = 'trainings'

# name for the uploaded file inside the bucket folder (here we choose to keep the name of the uploaded file)
# BUCKET_FILE_NAME=another_file_name_if_I_so_desire.csv
BUCKET_FILE_NAME=$(shell basename ${LOCAL_PATH})

### GCP AI Platform - - - - - - - - - - - - - - - - - - - -

##### Machine configuration - - - - - - - - - - - - - - - -

REGION=europe-west1

PYTHON_VERSION=3.7
FRAMEWORK=scikit-learn
RUNTIME_VERSION=1.15

##### Package params  - - - - - - - - - - - - - - - - - - -

PACKAGE_NAME=TaxiFareModel
FILENAME=main

##### Job - - - - - - - - - - - - - - - - - - - - - - - - -

JOB_NAME=taxi_fare_training_pipeline_$(shell date +'%Y%m%d_%H%M%S')

###### machine type
MACHINE-TYPE=n1-standard-16

set_project:
	@gcloud config set project ${PROJECT_ID}

create_bucket:
	@gsutil mb -l ${REGION} -p ${PROJECT_ID} gs://${BUCKET_NAME}

upload_data:
	# @gsutil cp train_1k.csv gs://wagon-ml-my-bucket-name/data/train_1k.csv
	@gsutil cp ${LOCAL_PATH} gs://${BUCKET_NAME}/${BUCKET_FOLDER}/${BUCKET_FILE_NAME}

gcp_submit_training:
	gcloud ai-platform jobs submit training ${JOB_NAME} \
		--job-dir gs://${BUCKET_NAME}/${BUCKET_TRAINING_FOLDER} \
		--package-path ${PACKAGE_NAME} \
		--module-name ${PACKAGE_NAME}.${FILENAME} \
		--python-version=${PYTHON_VERSION} \
		--runtime-version=${RUNTIME_VERSION} \
		--region ${REGION} \
		--stream-logs \
		--master-machine-type=${MACHINE_TYPE}

run_api:
	uvicorn api.fast:app --reload  # load web server with code autoreload