Save AI Model's Input to NumPy Files

Introduction

When deploying AI models to edge devices, having consistent input data formats is crucial for testing, debugging, and optimization. Whether you're preparing models for mobile devices, embedded systems, or edge computing platforms, saving your input tensors as NumPy files creates a standardized format that works across all frameworks.

This is especially important when using end-to-end on-device AI infrastructure like ZETIC.MLange, which requires consistent input formats for optimal model deployment and performance optimization on edge devices. By standardizing your inputs as NumPy files, you can seamlessly transition from development to production deployment.

Save input tensors from different deep learning frameworks with just a few lines of code.

1. Pytorch

import torch
import numpy as np

# Your model and input
model = torch.nn.Linear(10, 5)
input_tensor = torch.randn(1, 10)

# Save input tensor
input_numpy = input_tensor.detach().cpu().numpy()
np.save('pytorch_input.npy', input_numpy)

print(f"Saved PyTorch input: {input_numpy.shape}")

2. TorchScript

import torch
import numpy as np

# Create TorchScript model
model = torch.nn.Linear(10, 5)
traced_model = torch.jit.trace(model, torch.randn(1, 10))

# Your input
input_tensor = torch.randn(1, 10)

# Save input tensor
input_numpy = input_tensor.detach().cpu().numpy()
np.save('torchscript_input.npy', input_numpy)

# Run inference
output = traced_model(input_tensor)
print(f"Saved TorchScript input: {input_numpy.shape}")

3. Tensorflow

import tensorflow as tf
import numpy as np

# Your model and input
model = tf.keras.Sequential([
    tf.keras.layers.Dense(5, input_shape=(10,))
])

input_data = tf.random.normal((1, 10))

# Save input tensor
input_numpy = input_data.numpy()
np.save('tensorflow_input.npy', input_numpy)

# Run inference
output = model(input_data)
print(f"Saved TensorFlow input: {input_numpy.shape}")

3. ONNX

import onnxruntime as ort
import numpy as np

# Load ONNX model (assuming you have one)
session = ort.InferenceSession('your_model.onnx')

# Create input data
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)

# Save input tensor
np.save('onnx_input.npy', input_data)

# Run inference
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: input_data})

print(f"Saved ONNX input: {input_data.shape}")

Conclusion

Saving input tensors as NumPy files is a simple but powerful practice that brings several benefits:

Why Save Your Models and Inputs?

• Reproducibility: Ensure consistent results across different environments

• Debugging: Easily compare inputs and outputs between frameworks

• Testing: Create standardized test datasets for model validation

• Edge Deployment: Prepare data in formats optimized for on-device inference

Ready for Edge Deployment?

Once you have your models and input tensors saved, you're ready to deploy them efficiently on edge devices. ZETIC.MLange provides end-to-end on-device AI infrastructure that can take your saved models and NumPy inputs to:

• Optimize models for specific hardware targets

• Deploy across multiple edge platforms seamlessly

• Monitor and manage model performance in production

• Handle the entire ML lifecycle from development to deployment

By following this simple tensor-saving workflow, you're already taking the first step toward robust, scalable edge AI deployment. The standardized NumPy format ensures your data will work smoothly with any edge AI infrastructure, making your path from development to production much smoother.