x = tf.Variable(tf.ones(3))
x[1].assign(5)
Above code results in:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device for operation ResourceStridedSliceAssign: 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_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[]
ResourceStridedSliceAssign: CPU
_Arg: GPU CPU
Colocation members, user-requested devices, and framework assigned devices, if any:
ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0
ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0
Op: ResourceStridedSliceAssign
Node attrs: ellipsis_mask=0, Index=DT_INT32, T=DT_FLOAT, shrink_axis_mask=1, end_mask=0, begin_mask=0, new_axis_mask=0
Registered kernels:
device='XLA_CPU_JIT'; Index in [DT_INT32, DT_INT64]; T in [DT_FLOAT, DT_DOUBLE, DT_INT32, DT_UINT8, DT_INT16, DT_INT8, DT_COMPLEX64, DT_INT64, DT_BOOL, DT_QINT8, DT_QUINT8, DT_QINT32, DT_BFLOAT16, DT_UINT16, DT_COMPLEX128, DT_HALF, DT_UINT32, DT_UINT64, DT_FLOAT8_E5M2, DT_FLOAT8_E4M3FN, DT_INT4, DT_UINT4]
device='DEFAULT'; T in [DT_INT32]
device='CPU'; T in [DT_UINT64]
device='CPU'; T in [DT_INT64]
device='CPU'; T in [DT_UINT32]
device='CPU'; T in [DT_UINT16]
device='CPU'; T in [DT_INT16]
device='CPU'; T in [DT_UINT8]
device='CPU'; T in [DT_INT8]
device='CPU'; T in [DT_INT32]
device='CPU'; T in [DT_HALF]
device='CPU'; T in [DT_BFLOAT16]
device='CPU'; T in [DT_FLOAT]
device='CPU'; T in [DT_DOUBLE]
device='CPU'; T in [DT_COMPLEX64]
device='CPU'; T in [DT_COMPLEX128]
device='CPU'; T in [DT_BOOL]
device='CPU'; T in [DT_STRING]
device='CPU'; T in [DT_RESOURCE]
device='CPU'; T in [DT_VARIANT]
device='CPU'; T in [DT_QINT8]
device='CPU'; T in [DT_QUINT8]
device='CPU'; T in [DT_QINT32]
device='CPU'; T in [DT_FLOAT8_E5M2]
device='CPU'; T in [DT_FLOAT8_E4M3FN]
[[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign
I am starting to regret my Macbook purchase. There are so many issues with tensorflow-metal:
- ADAM is slow
- Inconsistent values with CPU
And now this, I saw a post regarding this but that was one year old. So, Macbooks are not even good for learning anymore?
