feat(server): Machine learning's image optimisations (#1908)

* Use multi stage build to slim down ML image size

* Use gunicorn as WSGI server in ML image

* Configure gunicorn server for ML use case

* Use requirements.txt file to install python dependencies in ML image

* Make ML listen IP configurable

machine-learning/gunicorn.conf.py

@@ -0,0 +1,29 @@
+"""
+Gunicorn configuration options.
+https://docs.gunicorn.org/en/stable/settings.html
+"""
+import os
+
+
+# Set the bind address based on the env
+port = os.getenv("MACHINE_LEARNING_PORT") or "3003"
+listen_ip = os.getenv("MACHINE_LEARNING_IP") or "0.0.0.0"
+bind = [f"{listen_ip}:{port}"]
+
+# Preload the Flask app / models etc. before starting the server
+preload_app = True
+
+# Logging settings - log to stdout and set log level
+accesslog = "-"
+loglevel = os.getenv("MACHINE_LEARNING_LOG_LEVEL") or "info"
+
+# Worker settings
+# ----------------------
+# It is important these are chosen carefully as per
+# https://pythonspeed.com/articles/gunicorn-in-docker/
+# Otherwise we get workers failing to respond to heartbeat checks,
+# especially as requests take a long time to complete.
+workers = 2
+threads = 4
+worker_tmp_dir = "/dev/shm"
+timeout = 60