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This article mainly explains the "Tensorflow data parallel multi-GPU processing method", the content of the article is simple and clear, easy to learn and understand, the following please follow the editor's ideas slowly in depth, together to study and learn "Tensorflow data parallel multi-GPU processing method" bar!

If we write programs in C++ that can only be used on a single CPU core, when we need to run in parallel on multiple GPU, we need to rewrite the program from scratch. But this is not the case with Tensorflow. Because of its symbolic nature, Tensorflow can hide all these complexities and easily extend the program to multiple CPU and GPU.

For example, add two vectors on CPU.

It can also be done on GPU.

Let's rewrite it in a more general form.

Above is to use 2 blocks of GPU parallel training to fit the unary quadratic function. Note: when using multiple GPU, the weight parameters of the model are shared by each GPU at the same time, so we need to use tf.get_variable () when defining, which is different from other definition methods, which I have explained in the previous article, so I won't say much here. Try it for yourselves.

Thank you for reading, the above is the content of "Tensorflow data parallel multi-GPU processing method". After the study of this article, I believe you have a deeper understanding of the problem of Tensorflow data parallel multi-GPU processing method, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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