More details can be found on this GitHub issue. I am posting here as well so that this won't go without being seen.

Is there any way to open new tab next to the current tab instead of opening it all the way to the right? I tried my best to find some setting to do this but didn't found any. Any help will be appreciated. 🙂

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?