Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Implementation | HackerNoon
Briefly

from Hackernoon4 months ago
Apparate leverages TensorFlowServing and Clockwork to optimize performance. By utilizing ONNX format for models, it enhances the efficiency of ramp training with a focused 10% dataset.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-implementation
The ramp training method integrates the first 10% of each dataset to create a balanced approach for training and validation, maintaining a 1:9 split for efficiency.
Hackernoonhttps://hackernoon.com/apparate-early-exit-models-for-ml-latency-and-throughput-optimization-implementation
Read at Hackernoon
#apparate #tensorflowserving #onnx #model-optimization #ramp-training
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