TensorFlow with GPU QUIZ (MCQ QUESTIONS AND ANSWERS)

tf.multi_gpu_strategy()

tf.distribute.MirroredStrategy()

tf.parallelize()

tf.gpu_distribution()

Manually assigning operations to each GPU.

Using tf.config.experimental.set_visible_devices().

Employing tf.distribute.MirroredStrategy.

Utilizing tf.multi_gpu().

tf.config.experimental.set_memory_growth('GPU')

tf.config.experimental.set_memory_growth(True)

tf.config.experimental.set_memory_growth(None)

tf.config.experimental.set_memory_growth(tf.list_physical_devices('GPU'))

It distributes computations across GPUs and aggregates results.

It allocates memory separately for each GPU.

It updates variables sequentially on each GPU.

It disables GPU communication.

It configures GPU kernel settings.

It restricts the visible GPUs to a subset.

It allocates memory on GPUs.

It initializes GPU drivers.

Each GPU has a copy of all variables.

Variables are randomly distributed across GPUs.

Variables are updated on a single GPU.

Variables are shared across GPUs.

To configure physical GPU devices.

To specify the number of logical GPUs.

To manage CPU resources.

To set up distributed computing.

By setting the CUDA_VISIBLE_DEVICES environment variable.

By using tf.device() with the GPU ID.

By passing the GPU ID as an argument to tf.config.experimental.set_visible_devices().

By using tf.set_gpu() with the GPU ID.

It configures physical GPUs.

It restricts memory usage.

It creates virtual GPUs.

It initializes GPU drivers.

Each GPU maintains its own set of variables.

Variables are averaged across all GPUs.

Variables are updated sequentially on each GPU.

Only one GPU updates the variables.

tf.parallel()

tf.distribute.MirroredStrategy()

tf.device_distribution()

tf.multi_gpu()

To allocate a fixed amount of memory for each GPU.

To dynamically allocate memory on GPUs as needed.

To restrict memory usage on GPUs.

To share memory between GPUs.

tf.config.experimental.set_gpu_device()

tf.device()

tf.assign_gpu()

tf.gpu()

The tensor remains on the original device.

The tensor is moved to the target device.

An error is raised.

The tensor is copied to the target device.

It provides information about available GPUs.

It allocates memory on GPUs.

It configures GPU kernel settings.

It initializes GPU drivers.

tf.device('/cpu')

tf.assign_cpu()

tf.cpu_device()

tf.set_cpu()

To specify the device for model training.

To declare a custom device.

To control resource allocation.

To assign operations to specific devices.

To dynamically allocate memory on GPUs as needed.

To restrict memory usage on GPUs.

To enable memory sharing between GPUs.

To allocate a fixed amount of memory for each GPU.

Each GPU maintains its own set of variables.

Variables are averaged across all GPUs.

Variables are updated sequentially on each GPU.

Only one GPU updates the variables.

tf.parallelize()

tf.distribute.MirroredStrategy()

tf.multi_gpu()

tf.distribute.GPUStrategy()

Creates a copy of the tensor.

Moves the tensor without creating a copy.

Deletes the tensor from the source device.

Converts the tensor to a different data type.

tf.device()

tf.assign_device()

tf.set_device()

tf.allocate_device()

To specify the number of GPUs to use.

To restrict the visible GPUs to a subset.

To enable GPU debugging.

To initialize GPU resources.

tf.parallelize()

tf.distribute.MirroredStrategy()

tf.multi_gpu()

tf.parallel()

tf.transfer()

tf.gpu_to_cpu()

tf.device()

tf.identity()

tf.device()

tf.config.experimental.set_visible_devices()

tf.gpu_config()

tf.experimental.GPU()

To dynamically allocate memory on GPUs as needed.

To restrict memory usage on GPUs.

To enable memory sharing between GPUs.

To allocate a fixed amount of memory for each GPU.

tf.multi_machine()

tf.distribute.MultiWorkerMirroredStrategy()

tf.cluster()

tf.distribute.RemoteStrategy()

tf.list_physical_devices('GPU')

tf.available_gpus()

tf.config.experimental.get_available_gpus()

tf.gpu_devices()

Better memory utilization.

Higher computational efficiency.

Reduced programming complexity.

Increased model accuracy.