Chainer - Integration With Other Frameworks

Chainer can be integrated with various other deep learning and machine learning frameworks, libraries and tools to enhance its capabilities. These integrations allow developers to combine the flexibility and power of Chainers define-by-run architecture with the advantages of other systems.

In this tutorial we will discuss different common ways to integrate chainer with other frameworks −

Integration with NumPy and CuPy

Chainer's Integration with NumPy and CuPy enables smooth transitions between CPU and GPU computations by optimizing the efficiency of neural network training. NumPy is a core library for CPU-based numerical calculations while CuPy is its GPU equivalent which is designed for accelerated performance using CUDA.

NumPy Integration (CPU-Based Operations)

Chainer fully supports NumPy arrays by making it simple to use these arrays for computations on the CPU. We can easily pass NumPy arrays to Chainer's models and perform various deep learning tasks such as forward propagation, backward propagation and more. Chainer will treat these arrays as tensors and seamlessly execute operations.

Following is the example in which chainer operates directly on NumPy arrays for CPU-based calculations by showcasing how NumPy arrays can be incorporated easily −

import numpy as np
import chainer
from chainer import Variable

# Create a NumPy array
x_cpu = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)

# Convert NumPy array to a Chainer variable
x_var = Variable(x_cpu)

# Perform an operation using Chainer (CPU-based)
y_var = x_var + 2  # Add 2 to every element

print("Result (NumPy):", y_var.data)

Here is the outout of integrating the NumPy with Chainer −

Result (NumPy): [[3. 4.]
 [5. 6.]]

CuPy Integration (GPU-Based Operations)

Chainer provides seamless GPU acceleration through its integration with CuPy which is a library that mimics the NumPy API but runs on CUDA-enabled GPUs.

This means that we can switch between CPU and GPU simply by converting NumPy arrays to CuPy arrays and vice versa. Following is the example which integrates CuPy with the Chainer −

import cupy as cp
import chainer
from chainer import Variable

# Create a CuPy array (GPU-based)
x_gpu = cp.array([[1.0, 2.0], [3.0, 4.0]], dtype=cp.float32)

# Create a Chainer Variable from the CuPy array
x_var_gpu = Variable(x_gpu)

# Perform operations using Chainer (GPU-based)
y_var_gpu = x_var_gpu * 2  # Element-wise multiplication

print("Output (CuPy):", y_var_gpu.data)

Switching Between NumPy and CuPy

Chainer allows easy switching between NumPy and CuPy which is especially useful when we want to move data between CPU and GPU.

import numpy as np
import cupy as cp
import chainer
from chainer import Variable

# Create a NumPy array and move it to GPU
x_cpu = np.array([1.0, 2.0, 3.0], dtype=np.float32)
x_var = Variable(x_cpu)
x_var.to_gpu()  # Move the Variable to GPU

# Perform a computation on the GPU
y_var = x_var * 2

# Move the result back to CPU
y_var.to_cpu()

print("Output after moving back to CPU:", y_var.data)

Exporting Models to ONNX

ONNX is abbreviated as Open Neural Network Exchange which is an open-source format designed to facilitate integrating between various deep learning frameworks.

This is developed by Microsoft and Facebook which allows models to be trained in one framework and deployed in another by bridging gaps between tools such as PyTorch, TensorFlow, Chainer and more.

ONNX defines a standard set of operators and model representations that can be universally understood by making it easier for developers to share and deploy models across different platforms and hardware environments.

Steps for Exporting a Chainer Model to ONNX

Following are the steps to be used for exporting a chainer model to ONNX −

• Install ONNX Chainer Exporter: To export models from Chainer to ONNX format, we need to install the onnx-chainer package with the help of below code −

  pip install onnx-chainer
  

• Define a Chainer Model: First we need to create or load a trained Chainer model.
• Export the Model to ONNX Format: With the help of onnx_chainer.export function we can export the model.

Example

Here is the example of the model saved as simple_model.onnx which can be used in other frameworks or for deployment in ONNX-compatible environments.

import chainer
import chainer.links as L
import chainer.functions as F
from chainer import Chain, Variable
import numpy as np
import onnx_chainer

# Define a simple Chainer model
class SimpleModel(Chain):
    def __init__(self):
        super(SimpleModel, self).__init__()
        with self.init_scope():
            self.l1 = L.Linear(None, 3)  # Input to hidden layer

    def forward(self, x):
        return F.relu(self.l1(x))

# Instantiate the model
model = SimpleModel()

# Create dummy input data
x = np.random.rand(1, 5).astype(np.float32)

# Convert to Chainer variable
x_var = Variable(x)

# Forward pass
y = model.forward(x_var)

# Export the model to ONNX format
onnx_chainer.export(model, x, filename="simple_model.onnx")