Hi everyone,
I’m currently working on deploying a deep learning model for real-time inference, and I’m trying to strike the right balance between latency and accuracy. One approach I’m considering is using model quantization and pruning to reduce the model size without significantly impacting performance.
Here’s a simplified snippet of what I’m trying with TensorFlow:
import tensorflow as tf
from tensorflow_model_optimization.sparsity import keras as sparsity
model = tf.keras.models.load_model(‘model.h5’)
prune_low_magnitude = sparsity.prune_low_magnitude(model)
Fine-tune the pruned model
prune_low_magnitude.compile(optimizer=‘adam’,
loss=‘categorical_crossentropy’,
metrics=[‘accuracy’])
prune_low_magnitude.fit(train_data, train_labels, epochs=5)
Export the optimized model
final_model = sparsity.strip_pruning(prune_low_magnitude)
final_model.save(‘optimized_model.h5’)
Has anyone experimented with similar optimization techniques for deployment, such as TensorRT, ONNX, or quantization-aware training? Also, I found some interesting insights from AI development services that discuss end-to-end model optimization strategies.
I’d love to hear about the methods or frameworks you’ve used to improve model efficiency while maintaining acceptable accuracy.
Thanks in advance!