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d5d0624311
* 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 * Revert "Use requirements.txt file to install python dependencies in ML image" This reverts commit 32e706c7f3db1d0aa1a66b3733ab9ec9dfb86534. * Separate out pip installs in ML builder image
30 lines
837 B
Python
30 lines
837 B
Python
"""
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Gunicorn configuration options.
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https://docs.gunicorn.org/en/stable/settings.html
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"""
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import os
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# Set the bind address based on the env
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port = os.getenv("MACHINE_LEARNING_PORT") or "3003"
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listen_ip = os.getenv("MACHINE_LEARNING_IP") or "0.0.0.0"
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bind = [f"{listen_ip}:{port}"]
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# Preload the Flask app / models etc. before starting the server
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preload_app = True
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# Logging settings - log to stdout and set log level
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accesslog = "-"
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loglevel = os.getenv("MACHINE_LEARNING_LOG_LEVEL") or "info"
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# Worker settings
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# ----------------------
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# It is important these are chosen carefully as per
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# https://pythonspeed.com/articles/gunicorn-in-docker/
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# Otherwise we get workers failing to respond to heartbeat checks,
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# especially as requests take a long time to complete.
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workers = 2
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threads = 4
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worker_tmp_dir = "/dev/shm"
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timeout = 60
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