replace bash script to docker-compose to build server

README.md

@@ -7,17 +7,18 @@ Try to use [MLflow](https://mlflow.org) platform to log PyTorch model training,
 * MacOS 12.5
 * Docker 20.10
 
-# Dir
+# Dirs
 
 * **service**
   * House MLflow service data, including MLflow artifacts, backend store and model registry
+* **env**
+  * **mlflow.yaml**
+    * conda env yaml to run this repo
 
 # Files
 
-* **conda.yaml**
+* **docker-compose.yaml**
-  * conda env yaml to run this repo
+  * a yaml to apply docker-compose to start MLflow service with basic configuration (run ```docker-compose -f docker-compose.yaml up```)
-* **start_mlflow_server.sh**
-  * a script to start MLflow server with basic configuration
 * **test_pytorch_m1.py**
   * a script to test PyTorch on Apple M1 platform with GPU acceleration
 * **train.py**

docker-compose.yaml

@@ -0,0 +1,18 @@
+version: '3.7'
+
+services:
+  mlflow_server:
+    image: ghcr.io/mlflow/mlflow:v2.1.1
+    restart: always
+    ports:
+      - 5001:5001
+    volumes:
+      - ~/python/test_mlflow/service:/home
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://0.0.0.0:5001"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 20s
+    command: bash -c "apt update && apt install -y curl && mlflow server --host 0.0.0.0 --port 5001 --backend-store-uri sqlite:////home/backend.db --registry-store-uri sqlite:////home/registry.db --artifacts-destination /home/artifacts --serve-artifacts"
+

get_registered_model_via_rest_api.py

@@ -12,7 +12,7 @@ def main():
     production_model_version = None
 
     query = {'name': registered_model_name}
-    res = requests.get('http://127.0.0.1:5000/api/2.0/mlflow/registered-models/get', params=query)
+    res = requests.get('http://127.0.0.1:5001/api/2.0/mlflow/registered-models/get', params=query)
     content = json.loads(res.text)
     print(content)
 
@@ -23,7 +23,7 @@ def main():
 
     if production_model_version is not None:
         query = {'name': registered_model_name, 'version': production_model_version}
-    res = requests.get('http://127.0.0.1:5000/api/2.0/mlflow/model-versions/get-download-uri', params=query)
+    res = requests.get('http://127.0.0.1:5001/api/2.0/mlflow/model-versions/get-download-uri', params=query)
     print(res.text)
 
 if __name__ == '__main__':

predict.py

@@ -10,7 +10,7 @@ import mlflow
 if __name__ == '__main__':
 
     # set MLflow server
-    mlflow.set_tracking_uri('http://127.0.0.1:5000')
+    mlflow.set_tracking_uri('http://127.0.0.1:5001')
 
     # load production model
     model = mlflow.pytorch.load_model('models:/fortune_predict_model/production')

start_mlflow_server.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-# start_mlflow_server.sh
-#
-# author: deng
-# date  : 20230221
-
-mlflow server --backend-store-uri sqlite:///service/backend.db --registry-store-uri sqlite:///service/registry.db --default-artifact-root ./service/artifacts --host 127.0.0.1 --port 5000 --serve-artifacts

train.py

@@ -64,7 +64,7 @@ if __name__ == '__main__':
     optimizer = SGD(model.parameters(), lr=learning_rate)
 
     # set the tracking URI to the model registry
-    mlflow.set_tracking_uri('http://127.0.0.1:5000')
+    mlflow.set_tracking_uri('http://127.0.0.1:5001')
     mlflow.set_experiment('train_fortune_predict_model')
 
     # start a new MLflow run