Multi-GPU Computing in TensorFlow

In TensorFlow, the tf.distribute.Strategy API can be used to enable parallel computing across multiple GPUs. The API is designed for distributed training across multiple devices, allowing for parallel computation on multiple GPUs to speed up model training.

The specific implementation steps are as follows:

1. Create a tf.distribute.MirroredStrategy object to perform operations across multiple GPUs. MirroredStrategy will create a replica on each GPU and synchronize the updates of these replicas’ weights.
2. Define the model building process within the scope of MirroredStrategy object, by defining the model, loss function, and optimizer under strategy.scope(), so that TensorFlow will automatically replicate them on each GPU for parallel computation.
3. During the training process, use the strategy.run() method to execute each step of the model training. When this method is called, TensorFlow automatically runs the same operation on all GPUs and aggregates the gradients onto the primary device.

Performing parallel computing on multiple GPUs, following the steps above, can accelerate the process of model training and improve training efficiency.