Remove training bits

README.md

@@ -1,40 +1,3 @@
-
-Order of things:
-
-1. [training](training)
-  - Train the model
-  - Export it
-2. [inference_test](inference_test)
-  - Sanity check the exported model
-  - Plot the detections onto test images
-3. [tensorflow_serving_test](tensorflow_serving_test)
-  - Use the exported model in a TensorFlow Serving container
-  - Send inference requests to the container
-    - Prepare input
-    - Process output
-4. [kserve_test](kserve_test)
-  - Use the exported model in KServe
-  - Send inference requests to the KServe InferenceService
-    - Prepare input
-    - Process output
-5. [prediction_backend](prediction_backend)
-  - Use the exported model in a Flask app
-  - Send inference requests to the Flask app from a HTML page
-
-
-TODO:
-- Inference with KServe takes too long and needs too much CPU
-  - Inference using a 100x83 image takes 1.5s with 5 CPU and 12Gi memory
-  - Inference using a 960x540 image takes ~4.5s with 5 CPU and 12Gi memory
-  - Inference using a 6000x8000 image (image to be posted by a phone) takes ~100s with 5 CPU and 12Gi memory
-  - When CPU is set to 1, durations are ~2.5x longer
-  - When TensorFlow Serving is used in a Docker container, durations are much shorter (no memory/CPU limit)
-- Use secrets for credentials in general
-
-TODO:
-- Use Gunicorn for production (containers) (https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-xix-deployment-on-docker-containers)
-- 
-
 ## Building and pushing images
 
 Just use the existing GitHub Actions workflows, by passing some environment variables.