pip install -quiet -upgrade -pre tensorflow tensorflow-datasets The final section briefly describes the interaction between tf.saved_model and tf.experimental.dtensor as of TensorFlow 2.9.ĭTensor is part of TensorFlow 2.9.0 release. Then continue with Model Parallel Training and Spatial Parallel Training. The model constructor takes additional Layout arguments to control the sharding of variables.įor training, you will first use data parallel training together with tf.experimental.dtensor's checkpoint feature. Use a tf.Module to track the inference variables. Next build an MLP model with custom Dense and BatchNorm layers. This tutorial will walk through the following steps:įirst start with some data cleaning to obtain a tf.data.Dataset of tokenized sentences and their polarity. To learn about the complete training and evaluation workflow (without DTensor), refer to that notebook.
The training portion of this tutorial is inspired A Kaggle guide on Sentiment Analysis notebook. Spatial Parallel training, where the features of input data are sharded to devices.Model Parallel training, where the model variables are sharded to devices.Data Parallel training, where the training samples are sharded (partitioned) to devices.Three distributed training schemes are demonstrated with this example: In this tutorial, you will train a Sentiment Analysis model with DTensor. For more details on DTensor concepts, see The DTensor Programming Guide. DTensor provides a way for you to distribute the training of your model across devices to improve efficiency, reliability and scalability.
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