What are the GPUOptions for Go binding in TensorFlow?

In TensorFlow's Go bindings, the GPUOptions struct allows you to configure GPU-related options for your TensorFlow program. It provides flexibility to control GPU memory allocation, growth, and other settings. Here's a detailed explanation of the available options along with an example:

1. GPUOptions struct:

   • AllowGrowth (bool): Specifies whether to allocate GPU memory dynamically as needed (true) or allocate the entire memory upfront (false). If set to true, TensorFlow will initially allocate a small portion of GPU memory and then grow it as required. If set to false, TensorFlow will allocate the entire requested GPU memory at once.

   • PerProcessGPUMemoryFraction (float32): Specifies the fraction of the available GPU memory to allocate per TensorFlow process. This value should be between 0.0 and 1.0. For example, setting it to 0.4 will allocate 40% of the available GPU memory to TensorFlow.

   • VisibleDeviceList (string): Allows you to specify a comma-separated list of GPU devices that TensorFlow should use. By default, TensorFlow will use all available GPU devices. For example, setting it to "0,1" will limit TensorFlow to use only GPU devices 0 and 1.

   • PerProcessGPUMemoryLimit (int64): Sets an upper limit on the amount of GPU memory TensorFlow can allocate per process in bytes. If not specified, TensorFlow will use the system's default limit.

   • Experimental (ExperimentalGPUOptions): Provides access to experimental GPU options that are subject to change and may not be fully supported. You can explore additional experimental options in the ExperimentalGPUOptions struct.

Example:

package main
 
import (
	"fmt"
	tf "github.com/tensorflow/tensorflow/tensorflow/go"
)
 
func main() {
	// Create a new GPUOptions struct
	gpuOptions := &tf.GPUOptions{
		AllowGrowth:                 true,
		PerProcessGPUMemoryFraction: 0.4,
		VisibleDeviceList:           "0,1",
	}
 
	// Create a new Config struct and set GPUOptions
	config := &tf.Config{
		GPUOptions: gpuOptions,
	}
 
	// Create a new SessionOptions struct and set Config
	sessionOptions := &tf.SessionOptions{
		Config: *config,
	}
 
	// Create a new TensorFlow session with the specified SessionOptions
	session, err := tf.NewSession(sessionOptions)
	if err != nil {
		fmt.Printf("Failed to create session: %v", err)
		return
	}
 
	// Use the TensorFlow session for further computations
	// ...
 
	// Close the session when finished
	session.Close()
}

In the above example, we create a GPUOptions struct and customize the GPU-related options. We set AllowGrowth to true to dynamically allocate GPU memory, PerProcessGPUMemoryFraction to 0.4 to allocate 40% of the available GPU memory, and VisibleDeviceList to "0,1" to limit TensorFlow to use only GPU devices 0 and 1. These options are then used to create a Config struct, which is set in SessionOptions. Finally, we create a TensorFlow session with the specified session options.

By leveraging the GPUOptions struct, you can configure various GPU-related settings in TensorFlow's Go bindings to optimize GPU memory usage and control device allocation according to your requirements.