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PyTorch and TensorFlow are two of the most popular deep learning frameworks, each with its own strengths and features. Choosing between them can be challenging, especially when performance is a critical factor. In this tutorial, we will guide you through a benchmarking comparison of PyTorch and TensorFlow, using a simple image classification task as an example.
Before you begin, make sure you have both PyTorch and TensorFlow installed. You can install them using the following commands:
Additionally, ensure that you have the required dependencies for GPU acceleration if you plan to run the benchmarks on a GPU.
We will use a standard deep learning benchmark dataset, such as CIFAR-10, for our image classification task. The benchmark will involve training a convolutional neural network (CNN) on the dataset using both PyTorch and TensorFlow, and then comparing the training times.
Now that we have implemented the same image classification task in both PyTorch and TensorFlow, let's benchmark the training times using the time module in Python.