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Makefile
121 lines (98 loc) 路 5.29 KB
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Makefile
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# ===================================================================================================
# Makefile for building and killing services in the Anomaly Detection Platform
# _PORT variables should have the same values as Port number specified in service's Dockerfile
# Set Absolute Paths for DATASET_PATH and DOCKER_COMPOSE_PATH according to the setup on your machine
# ===================================================================================================
# Jupyter Variables
JUPYTER_IMAGE_NAME = koushik/anomaly-platform-jupyter
JUPYTER_DOCKERFILE = jupyter/Dockerfile
JUPYTER_TOKEN = dummytoken
JUPYTER_PORT = 8888
# set DATASET_PATH to absolute path of directory where train.csv and test.csv are present
DATASET_PATH = D:/projects/anomaly-detection-platform/jupyter
# set absolute path of directory where docker-compose.yml file is located
DOCKER_COMPOSE_PATH = D:/projects/anomaly-detection-platform/monitoring
# Prediction API Variables
API_IMAGE_NAME = koushik/anomaly-platform-api
API_DOCKERFILE = service/Dockerfile
# Same as port in uvicorn.run() in main.py
API_PORT = 8000
# Python Virtual Environment Variables
# Windows is the default OS. Specify OS=<YOUR_OS_NAME> along with the 'make' command if you're running a different OS
OS = Windows
BIN = bin
ifeq ($(OS), Windows)
BIN = Scripts
endif
VENV = venv
PYTHON = $(VENV)/$(BIN)/python
PIP = $(VENV)/$(BIN)/pip3
.PHONY: help run-jupyter build-api run-api run-platform run-all stop-jupyter stop-api stop-platform stop-all remove-all test-platform remove-env
# Execute "run-platform" as the default target for the "make" command
.DEFAULT_GOAL := run-platform
help: ## Lists all targets in this Makefile along with their descriptions
@echo ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
@echo +' '+
@echo +' 'Targets available for this Makefile' '+
@echo +' '+
@echo ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
@fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##//'
run-jupyter: ## Build and Run a Jupyter Lab Container
docker build -t $(JUPYTER_IMAGE_NAME) --file $(JUPYTER_DOCKERFILE) .
docker run -d -p $(JUPYTER_PORT):$(JUPYTER_PORT) -e JUPYTER_TOKEN=$(JUPYTER_TOKEN) --mount type=bind,source=$(DATASET_PATH),target=/src/ $(JUPYTER_IMAGE_NAME)
@echo -------------------------------------------
@echo Jupyter Lab URL: http://localhost:$(JUPYTER_PORT)/
@echo Jupyter Lab Token: $(JUPYTER_TOKEN)
build-api: ## Build the Prediction API Docker Image
docker build -t $(API_IMAGE_NAME) --file $(API_DOCKERFILE) .
run-api: build-api ## Build and Run a Prediction API Container
docker run -d -p $(API_PORT):$(API_PORT) $(API_IMAGE_NAME)
@echo -------------------------------------------
@echo Prediction API Endpoint: http://localhost:$(API_PORT)/
run-platform: build-api ## Build and Run the entire Platform
docker-compose -f $(DOCKER_COMPOSE_PATH)/docker-compose.yml up -d
@echo ===========================================
@echo Access Services Here
@echo ===========================================
@echo Prediction API Endpoint: http://localhost:$(API_PORT)/
@echo Prometheus Console: http://localhost:9090/graph
@echo Grafana UI: http://localhost:3000/?orgId=1
run-all: run-jupyter run-platform ## Run all containers
@echo ===========================================
@echo Access Services Here
@echo ===========================================
@echo Jupyter Lab URL: http://localhost:$(JUPYTER_PORT)/
@echo Jupyter Lab Token: $(JUPYTER_TOKEN)
@echo Prediction API Endpoint: http://localhost:$(API_PORT)/
@echo Prometheus Console: http://localhost:9090/graph
@echo Grafana UI: http://localhost:3000/?orgId=1
stop-jupyter: ## Kill and Delete the Jupyter Lab Container
docker stop $$(docker ps -q --filter ancestor=$(JUPYTER_IMAGE_NAME))
@echo Stopped Jupyter Lab Container
docker rm $$(docker ps -a -q --filter "ancestor=$(JUPYTER_IMAGE_NAME)")
@echo Deleted Jupyter Lab Container
stop-api: ## Kill and Delete the Prediction API Container
docker stop $$(docker ps -q --filter ancestor=$(API_IMAGE_NAME))
@echo Stopped Prediction API Container
docker rm $$(docker ps -a -q --filter "ancestor=$(API_IMAGE_NAME)")
@echo Deleted Prediction API Container
stop-platform: # Kill and Delete the Entire Platform
docker-compose -f $(DOCKER_COMPOSE_PATH)/docker-compose.yml stop
@echo Stopped Anomaly Detection Platform
stop-all: stop-jupyter stop-platform ## Kill and Delete all containers
@echo ========== All Containers Killed ==========
remove-all: ## Remove all Docker images
docker rmi -f $(JUPYTER_IMAGE_NAME):latest
docker rmi -f $(API_IMAGE_NAME):latest
docker rmi -f prom/prometheus:v2.20.0
docker rmi -f grafana/grafana:7.1.1
docker rmi -f stefanprodan/caddy
@echo ========== All Images Removed ==========
$(VENV)/$(BIN)/activate: ./tester/requirements.txt
python -m venv $(VENV)
$(PIP) install -r ./tester/requirements.txt
test-platform: $(VENV)/$(BIN)/activate ## Create a Python Virtual Environment, run tester.py and visualize output in Grafana
$(PYTHON) ./tester/tester.py
remove-env: ## Remove the Python Virtual Environment created for testing
rm -rf __pycache__
rm -rf $(VENV)