I obtain the following error when running tensorflow (before the model training).
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation cond_1/then/_10/cond_1/Adam/add: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=-1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='' resource_device_name_='' supported_device_types_=[CPU] possible_devices_=[]
AddV2: CPU
Colocation members, user-requested devices, and framework assigned devices, if any:
cond_1/then/_10/cond_1/Adam/add (AddV2) /job:localhost/replica:0/task:0/device:GPU:0
Op: AddV2
Node attrs: T=DT_INT64
Registered kernels:
device='XLA_CPU_JIT'; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BFLOAT16, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64]
device='GPU'; T in [DT_FLOAT]
device='DEFAULT'; T in [DT_INT32]
device='CPU'; T in [DT_FLOAT]
device='CPU'; T in [DT_HALF]
device='CPU'; T in [DT_DOUBLE]
device='CPU'; T in [DT_INT32]
device='CPU'; T in [DT_INT64]
device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_INT8]
device='CPU'; T in [DT_INT16]
device='CPU'; T in [DT_COMPLEX64]
device='CPU'; T in [DT_UINT8]
device='CPU'; T in [DT_UINT16]
device='CPU'; T in [DT_UINT32]
device='CPU'; T in [DT_UINT64]
device='CPU'; T in [DT_COMPLEX128]
[[{{node cond_1/Adam/add}}]] [Op:__inference_train_112314]
My questions are: What is the meaning of this error and how can it be fixed?
I'm working on Apples M1 with MacOs Monterey. Tensorflow 2.5.0. I do not quite understand the origin of the error since running tf.config.list_physical_devices() showes me:
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Those three questions are related but also remained without a solution: first, second, third
Enabling sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) mentioned here did not resolve the problem for me.
