Merge pull request #25 from Toumash/pr/simplify-images

feat: simplify images and e2e test so that we know if it works

README.md

@@ -28,7 +28,7 @@
 
 1. Configure your client-side
 
-For running mlflow files you need various environment variables set on the client side. To generate them user the convienience script `./bashrc_install.sh`, which installs it on your system or `./bashrc_generate.sh`, which just displays the config to copy & paste.
+For running mlflow files you need various environment variables set on the client side. To generate them use the convienience script `./bashrc_install.sh`, which installs it on your system or `./bashrc_generate.sh`, which just displays the config to copy & paste.
 
 > $ ./bashrc_install.sh   
 > [ OK ] Successfully installed environment variables into your .bashrc!

docker-compose.yml

@@ -31,12 +31,9 @@ services:
     networks:
       - internal
   mlflow:
+    image: ubuntu/mlflow:2.1.1_1.0-22.04
     container_name: tracker_mlflow
-    image: tracker_ml
     restart: unless-stopped
-    build:
-      context: ./mlflow
-      dockerfile: Dockerfile
     ports:
       - "5000:5000"
     environment:
@@ -70,6 +67,25 @@ services:
     command: tcp db:3306 -t 90s -i 250ms
     networks:
       - internal
+  run_test_experiment:
+    build:
+      context: ./test_experiment
+      dockerfile: Dockerfile
+    depends_on:
+      - "mlflow"
+    environment:
+      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
+      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
+      - AWS_DEFAULT_REGION=${AWS_REGION}
+      - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
+      - MLFLOW_TRACKING_URI=http://mlflow:5000
+    entrypoint: >
+      /bin/sh -c "
+      python3 mlflow_tracking.py;
+      exit 0;
+      "
+    networks:
+      - internal
 networks:
   internal:
   public:

quickstart/mlflow_tracking.py

@@ -1,22 +1,22 @@
 import os
 from random import random, randint
 
-from mlflow import mlflow,log_metric, log_param, log_artifacts
+import mlflow
 
 if __name__ == "__main__":
     with mlflow.start_run() as run:
         mlflow.set_tracking_uri('http://localhost:5000')
         print("Running mlflow_tracking.py")
 
-        log_param("param1", randint(0, 100))
+        mlflow.log_param("param1", randint(0, 100))
 
-        log_metric("foo", random())
-        log_metric("foo", random() + 1)
-        log_metric("foo", random() + 2)
+        mlflow.log_metric("foo", random())
+        mlflow.log_metric("foo", random() + 1)
+        mlflow.log_metric("foo", random() + 2)
 
         if not os.path.exists("outputs"):
             os.makedirs("outputs")
         with open("outputs/test.txt", "w") as f:
             f.write("hello world!")
 
-        log_artifacts("outputs")
+        mlflow.log_artifacts("outputs")

mlflow/Dockerfile → test_experiment/Dockerfile

@@ -1,7 +1,6 @@
 FROM continuumio/miniconda3:latest
 
-RUN pip install mlflow boto3 pymysql
+RUN pip install mlflow boto3
 
-ADD . /app
 WORKDIR /app
-
+COPY . .

test_experiment/mlflow_tracking.py

@@ -0,0 +1,22 @@
+import os
+from random import random, randint
+
+import mlflow
+
+if __name__ == "__main__":
+    with mlflow.start_run() as run:
+        mlflow.set_tracking_uri('http://mlflow:5000')
+        print("Running mlflow_tracking.py")
+
+        mlflow.log_param("param1", randint(0, 100))
+
+        mlflow.log_metric("foo", random())
+        mlflow.log_metric("foo", random() + 1)
+        mlflow.log_metric("foo", random() + 2)
+
+        if not os.path.exists("outputs"):
+            os.makedirs("outputs")
+        with open("outputs/test.txt", "w") as f:
+            f.write("hello world!")
+
+        mlflow.log_artifacts("outputs")