tf.test.is_gpu_available

Returns whether TensorFlow can access a GPU. (deprecated)

tf.test.is_gpu_available(
    cuda_only: bool = False,
    min_cuda_compute_capability: Optional[tuple[int, int]] = None
) -> bool

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))

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"

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