Python program on a "chief" machine that holds a TF_CONFIG environment variable With ParameterServerStrategy, you will need to launch a remote cluster of machinesĬonsisting "worker" and "ps", each running a tf.distribute.Server, then run your Workers update the gradients to parameter servers asynchronously.ĭistributed training is somewhat more involved than single-machine multi-device training. Multi-worker solution, where the parameters are stored on parameter servers, and tf. implements an asynchronous CPU/GPU.Using synchronous reduction of gradients across the replicas. Multi-worker solution to work with Keras-style model building and training loop, tf.distribute.MultiWorkerMirroredStrategy implements a synchronous CPU/GPU. ![]() Multiple devices on a single machine), there are two distribution strategies youĬould use: MultiWorkerMirroredStrategy and ParameterServerStrategy: This also applies to any Keras model: justĪdd a tf.distribute distribution strategy scope enclosing the modelīuilding and compiling code, and the training will be distributed according toįor distributed training across multiple machines (as opposed to training that only leverages ( tf.distribute.Strategy) corresponding to your hardware of choice, Workers and accelerators by only adding to it a distribution strategy TensorFlow enables you to write code that is almost entirelyĪny code that can run locally can be distributed to multiple How can I distribute training across multiple machines? device_scope ( '/cpu:0' ): merged_vector = keras. device_scope ( '/gpu:1' ): encoded_b = shared_lstm ( input_b ) # Concatenate results on CPU with tf. device_scope ( '/gpu:0' ): encoded_a = shared_lstm ( input_a ) # Process the next sequence on another GPU with tf. LSTM ( 64 ) # Process the first sequence on one GPU with tf. Input ( shape = ( 140, 256 )) shared_lstm = keras. # Model where a shared LSTM is used to encode two different sequences in parallel input_a = keras. MirroredStrategy (which replicates your model on each available device and keeps the state of each model in sync): Make sure to read our guide about using () with Keras.Ī) instantiate a "distribution strategy" object, e.g. The best way to do data parallelism with Keras models is to use the tf.distribute API. In most cases, what you need is most likely data parallelism.ĭata parallelism consists in replicating the target model once on each device, and using each replica to process a different fraction of the input data. There are two ways to run a single model on multiple GPUs: data parallelism and device parallelism. General questions How can I train a Keras model on multiple GPUs (on a single machine)?
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