如何确定seq2seq张量流RNN训练模型的最大批处理大小 [英] How to determine maximum batch size for a seq2seq tensorflow RNN training model

问题描述

当前，我正在使用默认的64作为seq2seq张量流模型的批处理大小.最大批处理大小，层大小等是多少，我可以使用具有12 GB RAM和Haswell-E xeon 128 GB RAM的单个Titan X GPU.输入数据将转换为嵌入.以下是我正在使用的一些有用的参数，似乎单元格的输入大小为1024:

Currently, I am using the default 64 as the batch size for the seq2seq tensorflow model. What is the maximum batch size , layer size etc I can go with a single Titan X GPU with 12 GB RAM with Haswell-E xeon 128GB RAM. The input data is converted to embeddings. Following are some helpful parameters I am using , it seems the cell input size is 1024:

encoder_inputs: a list of 2D Tensors [batch_size x cell.input_size].
 decoder_inputs: a list of 2D Tensors [batch_size x cell.input_size].
 tf.app.flags.DEFINE_integer("size", 1024, "Size of each model layer.")

那么根据我的硬件，我可以进行的最大批处理大小，层数和输入大小是多少?目前，GPU显示99％的内存已被占用.

So based on my hardware what is the maximum batch size , layers, input size I can go? Currently the GPU shows that 99% memory is occupied.

推荐答案

默认情况下，Tensorflow占用所有可用的GPU内存.但是，有一种方法可以改变这一点.在我的模型中，我这样做:

By default, Tensorflow occupies all GPU memory available. However, there is a way to change this. In my model, I do this:

config = tf.ConfigProto()
config.gpu_options.allow_growth = True

然后，您可以在开始会话时使用此配置:

Then you can use this config when you start your session:

with tf.Session(config=config) as sess:

现在，模型将仅使用所需的内存，然后您可以尝试使用不同的批处理大小，并查看何时内存用完.

Now, the model will only use as much memory as it needs, and then you can try with different batch sizes and see when it runs out of memory.

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