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Tf.t_log_device_placement(True) as the first statement of your To find out which devices your operations and tensors are assigned to, put For example, since tf.cast only has a CPU kernel, on a system with devices CPU:0 and GPU:0, the CPU:0 device is selected to run tf.cast, even if requested to run on the GPU:0 device. If a TensorFlow operation has no corresponding GPU implementation, then the operation falls back to the CPU device. For example, tf.matmul has both CPU and GPU kernels and on a system with devices CPU:0 and GPU:0, the GPU:0 device is selected to run tf.matmul unless you explicitly request to run it on another device. If a TensorFlow operation has both CPU and GPU implementations, by default, the GPU device is prioritized when the operation is assigned.

"/job:localhost/replica:0/task:0/device:GPU:1": Fully qualified name of the second GPU of your machine that is visible to TensorFlow.

"/GPU:0": Short-hand notation for the first GPU of your machine that is visible to TensorFlow.

"/device:CPU:0": The CPU of your machine.

They are represented with string identifiers for example: TensorFlow supports running computations on a variety of types of devices, including CPU and GPU. Print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) SetupĮnsure you have the latest TensorFlow gpu release installed. To learn how to debug performance issues for single and multi-GPU scenarios, see the Optimize TensorFlow GPU Performance guide.

This guide is for users who have tried these approaches and found that they need fine-grained control of how TensorFlow uses the GPU.

The simplest way to run on multiple GPUs, on one or many machines, is using Distribution Strategies. Note: Use tf.config.list_physical_devices('GPU') to confirm that TensorFlow is using the GPU. TensorFlow code, and tf.keras models will transparently run on a single GPU with no code changes required.