tf.test.is_gpu_available

Returns whether TensorFlow can access a GPU. (deprecated)

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.test.is_gpu_available

tf.test.is_gpu_available(
    cuda_only=False, min_cuda_compute_capability=None
)

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.config.list_physical_devices('GPU') instead.

Warning: if a non-GPU version of the package is installed, the function would also return False. Use tf.test.is_built_with_cuda to validate if TensorFlow was build with CUDA support.

For example,

>>> gpu_available = tf.test.is_gpu_available()
>>> is_cuda_gpu_available = tf.test.is_gpu_available(cuda_only=True)
>>> is_cuda_gpu_min_3 = tf.test.is_gpu_available(True, (3,0))

Args
cuda_only	limit the search to CUDA GPUs.
min_cuda_compute_capability	a (major,minor) pair that indicates the minimum CUDA compute capability required, or None if no requirement.

Note that the keyword arg name "cuda_only" is misleading (since routine will return true when a GPU device is available irrespective of whether TF was built with CUDA support or ROCm support. However no changes here because

++ Changing the name "cuda_only" to something more generic would break backward compatibility

++ Adding an equivalent "rocm_only" would require the implementation check the build type. This in turn would require doing the same for CUDA and thus potentially break backward compatibility

++ Adding a new "cuda_or_rocm_only" would not break backward compatibility, but would require most (if not all) callers to update the call to use "cuda_or_rocm_only" instead of "cuda_only"

Returns
True if a GPU device of the requested kind is available.