Removed API developer note -- will be in readme

notes.md

-## General API (Level 1)
-
-- Knob:
-  - A name -- constant
-  - kwargs (for knob parameter, for example: 25% in 25% perforation) -- constant
-  - Whether coexist with another knob or not -- method
-    - Useful when we want to support multiple knobs in an op
-
-- Application:
-  - List of knobs for each operator -- constant
-    - (We provide a knob value for "baseline" and sneak that in each layer)
-  - Argparser extra arguments -- static method
-  - How to measure QoS & performance given a configuration -- method
-    - Input? Don't care
-    - Empirical or modeled? Don't care
-    - This is a minimal interface to interact with `opentuner`, nothing ApproxTuner yet.
-
-## Predictive Tuning (Level 2)
-
-Performance model + QoS model (P1, P2)
-
-- Performance model (linear combination of operator cost * speedup of knob)
-  - Requires list of operator cost
-  - Requires table of knob speedup on layer
-
-- QoS P1 (linear in tensor output)
-  - Requires tensor output of each single knob
-
-- QoS P2 (linear in QoS)
-  - Requires QoS of each single knob
-
-- Predict-tunable application
-  - User should inherit from a number of interfaces. Inherit P1Interface -> can use P1, etc.
-  - Detect self capability using `isinstance(self, ...)` and offers argparser extra arguments
-    (or config file entries).
-
-## Predictive Tuning for PyTorch Modules (Level 3)
-
-Automate some definitions using inspection on module
-
-- PyTorch application
-  - *Provides list of operators and cost in FLOPs
-  - *Provides function for running inference on module and combining output
-  - *Implements P2
-  - *Implements P1 if output is tensor
-  - Require a PyTorch module
-  - Require an input dataset
-
-## API usage
-
-- User defines classes for knobs
-  - Perforation, sampling...
-- User defines application
-  - Most general: just `Application`. Useful for tuning a binary, for example.