CUDA Operations in TensorFlow QUIZ (MCQ QUESTIONS AND ANSWERS)

tf.device()

tf.place()

tf.allocate_device()

tf.assign_device()

To compare TensorFlow performance with other frameworks

To measure GPU temperature

To identify bottlenecks and optimize performance

To allocate more GPU memory

Increasing the GPU memory limit

Reducing the number of TensorFlow operations

Using dynamic GPU memory allocation

Rebooting the GPU

tf.gpu_options

tf.config.gpu_options

tf.GPUOptions

tf.gpu_config

tf.release_gpu_memory()

tf.clear_gpu_memory()

tf.free_gpu_memory()

tf.gpu_memory_release()

By disabling GPU support during TensorFlow installation

By setting the environment variable TF_GPU=False

By using tf.device() to specify CPU as the device

By setting the GPU option allow_soft_placement=True

To optimize GPU memory usage

To identify bottlenecks and optimize performance

To measure GPU temperature

To check GPU compatibility

To configure TensorFlow sessions for distributed computing

To set GPU options for TensorFlow sessions

To initialize TensorFlow sessions

To clear TensorFlow session resources

tf.set_device_placement()

tf.device()

tf.set_placement()

tf.operation_device()

To profile GPU operations

To configure GPU memory usage

To check GPU compatibility

To force GPU operations

tf.init_gpu()

tf.initialize_gpu()

tf.initialize_all_variables()

tf.config.experimental.initialize_gpu()

Using tf.device()

Using tf.check_execution_device()

Using tf.get_device()

Using tf.execution_device()

GPU hardware settings

GPU memory allocation

GPU profiling options

GPU driver versions

tf.run_on_cpu()

tf.place_on_cpu()

tf.cpu()

tf.device()

tf.measure_time()

tf.execution_time()

tf.timer()

tf.timestamp()

tf.benchmark_gpu()

tf.profiler.benchmark()

tf.profile_gpu()

tf.profiler.profile()

TensorFlow pre-allocates all available GPU memory

TensorFlow allocates GPU memory dynamically as needed

TensorFlow does not support GPU memory allocation

TensorFlow requires manual allocation of GPU memory

tf.set_memory_growth()

tf.gpu_memory_growth()

tf.config.gpu_options.allow_growth

tf.allow_gpu_memory_growth()

Depletion of GPU memory due to excessive operations

Inefficient allocation of memory blocks in GPU

Lack of compatibility between TensorFlow and GPU

Overloading of GPU resources

tf.is_gpu_available()

tf.detect_gpu()

tf.gpu_available()

tf.check_gpu()

tf.clear_graph()

tf.reset_default_graph()

tf.graph_clear()

tf.reset_graph()

tf.profile

tf.benchmark

tf.gpu

tf.profiler

tf.check_gpu()

tf.ensure_gpu()

tf.test.is_gpu_available()

tf.gpu_check()

Using tf.clear_memory()

Using tf.clear_gpu_memory()

Using tf.reset_gpu()

Using tf.reset_default_graph()

tf.benchmark

tf.profiler

tf.gpu_benchmark

tf.gpu_profiler

tf.execute()

tf.run()

tf.evaluate()

tf.Session().run()

tf.list_gpus()

tf.available_gpus()

tf.get_available_gpus()

tf.gpu_list()

tf.place_on_device()

tf.device_placement()

tf.assign_device()

tf.device()

To configure GPU settings for training

To disable GPU operations in TensorFlow

To clear GPU memory

To profile GPU operations

tf.release_session()

tf.close_session()

tf.end_session()

tf.clear_session()