I get the following error when running this command in a Jupyter notebook:
v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1))) v[0, 0].assign(3.)
Environment: python == 3.11.14 tensorflow==2.19.1 tensorflow-metal==1.2.0
{ "name": "InvalidArgumentError", "message": "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.\nColocation Debug Info:\nColocation group had the following types and supported devices: \nRoot 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_=[]\nResourceStridedSliceAssign: CPU \n_Arg: GPU CPU \n\nColocation members, user-requested devices, and framework assigned devices, if any:\n ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0\n ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0\n\nOp: ResourceStridedSliceAssign\n
[...]
[[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign" }
It seems like the ResourceStridedSliceAssign operation is not implemented for the GPU