TensorFlow Lite for Mobile Deployment — Shrink, Convert, and Run

import tensorflow as tf
import numpy as np

# io.thecodeforge: Standard Mobile Conversion Pipeline
# Load your trained Keras model
model = tf.keras.models.load_model('forge_vision_v1')

# Initialize the converter from a SavedModel directory
converter = tf.lite.TFLiteConverter.from_saved_model('forge_vision_v1')

# Post-Training Integer Quantization with representative dataset
# This reduces model size by ~75% and speeds up inference 2x-3x
converter.optimizations = [tf.lite.Optimize.DEFAULT]

def representative_data_gen():
    """Provide 100-200 diverse samples for Int8 calibration."""
    for sample in calibration_dataset.take(200):
        yield [tf.cast(sample, tf.float32)]

converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8

# Convert to FlatBuffer format
tflite_model = converter.convert()

# Write the binary file to disk
with open('model_int8.tflite', 'wb') as f:
    f.write(tflite_model)

print(f"Float32 model: {os.path.getsize('forge_vision_v1/saved_model.pb') / 1e6:.1f} MB")
print(f"Int8 TFLite model: {len(tflite_model) / 1e6:.1f} MB")

import numpy as np
import tensorflow as tf

# io.thecodeforge: Low-latency Python Inference (Testing)
# Load the TFLite model and allocate tensors
interpreter = tf.lite.Interpreter(model_path="model_int8.tflite")
interpreter.allocate_tensors()

# Get input/output details for mapping data correctly
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

print(f"Input dtype: {input_details[0]['dtype']}")
print(f"Input shape: {input_details[0]['shape']}")

# Prepare input data — dtype must match model expectation (int8 or float32)
# For Int8 models: scale and zero_point from input_details[0]['quantization']
scale, zero_point = input_details[0]['quantization']
input_data = np.array([[1.0]], dtype=np.float32)
input_data_quantized = (input_data / scale + zero_point).astype(np.int8)

interpreter.set_tensor(input_details[0]['index'], input_data_quantized)

# Execute the computation graph on the mobile runtime
interpreter.invoke()

# Extract and dequantize the result
output_data = interpreter.get_tensor(output_details[0]['index'])
out_scale, out_zp = output_details[0]['quantization']
prediction = (output_data.astype(np.float32) - out_zp) * out_scale
print(f"Mobile Prediction: {prediction}")

package io.thecodeforge.ml;

import org.tensorflow.lite.Interpreter;
import java.nio.MappedByteBuffer;
import java.io.FileInputStream;
import java.nio.channels.FileChannel;
import android.content.res.AssetFileDescriptor;

public class MobileInferenceService {
    private Interpreter tflite;
    private static final String MODEL_ASSET = "model_int8.tflite";

    /**
     * io.thecodeforge: Loading TFLite model from Android Assets
     * Uses MappedByteBuffer for zero-copy model loading — avoids OOM on large models
     */
    public void loadModel(AssetFileDescriptor fileDescriptor) throws Exception {
        FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
        FileChannel fileChannel = inputStream.getChannel();
        long startOffset = fileDescriptor.getStartOffset();
        long declaredLength = fileDescriptor.getDeclaredLength();
        MappedByteBuffer modelBuffer = fileChannel.map(
            FileChannel.MapMode.READ_ONLY, startOffset, declaredLength
        );

        Interpreter.Options options = new Interpreter.Options();
        options.setNumThreads(4); // Tune based on device CPU core count
        this.tflite = new Interpreter(modelBuffer, options);
    }

    public float runInference(float inputVal) {
        float[][] input = {{inputVal}};
        float[][] output = new float[1][1];
        tflite.run(input, output);
        return output[0][0];
    }

    public void close() {
        if (tflite != null) tflite.close();
    }
}

# io.thecodeforge: TFLite Edge Inference Container
FROM python:3.11-slim

# Install TFLite Runtime only (no full TF for lean image)
# tflite-runtime is ~3 MB vs TensorFlow's ~500 MB
RUN pip install --no-cache-dir tflite-runtime numpy

WORKDIR /app
COPY model_int8.tflite .
COPY edge_inference.py .

# Health check to verify model loads correctly at startup
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
    CMD python -c "from tflite_runtime.interpreter import Interpreter; Interpreter('model_int8.tflite').allocate_tensors(); print('OK')"

# Run the inference script
CMD ["python", "edge_inference.py"]

-- io.thecodeforge: Telemetry for Edge Inference
INSERT INTO io.thecodeforge.edge_metrics (
    device_id,
    model_version,
    inference_latency_ms,
    battery_drain_pct,
    timestamp
)
VALUES ('GATEWAY-001', 'v1.0-int8-quantized', 12.4, 0.02, CURRENT_TIMESTAMP);