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Can a keras model be trained on multiple GPUs?
Synchronicity keeps the model convergence behavior identical to what you would see for single-device training. Specifically, this guide teaches you how to use the tf.distribute API to train Keras models on multiple GPUs, with minimal changes to your code, in the following two setups:
How is distributed training mechanism used in keras?
This API keeps an abstraction for training distribution among multiple processing units. This API copies model’s parameters to each and every GPU. Then it combines the gradients from all the GPUs, gets a combined value, and applies it to all copies of the model.
How to train a deep learning network with Keras?
Let’s go ahead and get started training a deep learning network using Keras and multiple GPUs. If you’re using a headless server, you’ll want to configure the matplotlib backend on Lines 3 and 4 by uncommenting the lines. This will enable your matplotlib plots to be saved to disk.
How to do single host synchronous training with Keras?
To do single-host, multi-device synchronous training with a Keras model, you would use the tf.distribute.MirroredStrategy API . Here’s how it works:
How much memory does keras use in a network?
I first create a fairly deep network, and use model.summary () to get the total number of parameters needed for the network (in this case 206538153, which corresponds to about 826 MB). I then use nvidia-smi to see how much GPU memory Keras has allocated, and I can see that it makes perfect sense (849 MB).
What kind of parallelism is used in keras?
Model parallelism, where different parts of a single model run on different devices, processing a single batch of data together. This works best with models that have a naturally-parallel architecture, such as models that feature multiple branches.
Why is Keras so slow on single core?
Workarounds that allow Python users to benefit from multi-core machines, e.g. multiprocessing, tend to be slower than a single-process, multi-threaded application, because data has to be copied between processes rather than shared by threads in a single process. As expected, doing just image decoding via Python and Keras is very slow.