I am trying to profile a tensorflow 2.5 model with tensorflow-macos and tensorflow-metal. I am getting this error: AttributeError: module 'tensorflow.compat.v1.profiler' has no attribute 'experimental' Here's a code snippet: import tensorflow as tf import numpy as np from utils import * tf.compat.v1.enable_v2_behavior() from tensorflow.python.framework.ops import disable_eager_execution disable_eager_execution() options = tf.profiler.experimental.ProfilerOptions(host_tracer_level = 3,                                                    python_tracer_level = 1,                                                    device_tracer_level = 1) tf.profiler.experimental.start('~/logdir', options=options) ... tf.profiler.experimental.stop() % pip list Package                    Version -------------------------- ------------------- absl-py                    0.12.0 anyio                      3.2.1 appnope                    0.1.2 argon2-cffi                20.1.0 astunparse                 1.6.3 async-generator            1.10 attrs                      21.2.0 Babel                      2.9.1 backcall                   0.2.0 bleach                     3.3.1 cachetools                 4.2.2 certifi                    2021.5.30 cffi                       1.14.6 charset-normalizer         2.0.1 cloudpickle                1.6.0 cycler                     0.10.0 Cython                     0.29.24 debugpy                    1.3.0 decorator                  5.0.9 defusedxml                 0.7.1 dill                       0.3.4 dm-tree                    0.1.6 dotmap                     1.3.23 entrypoints                0.3 flatbuffers                1.12 future                     0.18.2 gast                       0.4.0 gensim                     4.0.1 google-auth                1.32.1 google-auth-oauthlib       0.4.4 google-pasta               0.2.0 googleapis-common-protos   1.53.0 grpcio                     1.34.1 gviz-api                   1.9.0 gym                        0.18.3 h5py                       3.1.0 idna                       3.2 importlib-resources        5.2.0 ipykernel                  6.0.1 ipython                    7.25.0 ipython-genutils           0.2.0 ipywidgets                 7.6.3 jedi                       0.18.0 Jinja2                     3.0.1 json5                      0.9.6 jsonschema                 3.2.0 jupyter-client             6.1.12 jupyter-core               4.7.1 jupyter-server             1.9.0 jupyterlab                 3.0.16 jupyterlab-pygments        0.1.2 jupyterlab-server          2.6.1 jupyterlab-widgets         1.0.0 keras-nightly              2.5.0.dev2021032900 Keras-Preprocessing        1.1.2 kiwisolver                 1.3.1 Markdown                   3.3.4 MarkupSafe                 2.0.1 matplotlib                 3.4.2 matplotlib-inline          0.1.2 memory-profiler            0.58.0 mistune                    0.8.4 nbclassic                  0.3.1 nbclient                   0.5.3 nbconvert                  6.1.0 nbformat                   5.1.3 nest-asyncio               1.5.1 notebook                   6.4.0 numpy                      1.19.5 oauthlib                   3.1.1 opt-einsum                 3.3.0 packaging                  21.0 pandas                     1.3.0 pandocfilters              1.4.3 parso                      0.8.2 pexpect                    4.8.0 pickleshare                0.7.5 Pillow                     8.2.0 pip                        21.2.1 prometheus-client          0.11.0 promise                    2.3 prompt-toolkit             3.0.19 protobuf                   3.17.3 psutil                     5.8.0 ptyprocess                 0.7.0 pyasn1                     0.4.8 pyasn1-modules             0.2.8 pybind11                   2.6.2 pycparser                  2.20 pyglet                     1.5.15 Pygments                   2.9.0 pyparsing                  2.4.7 pyrsistent                 0.18.0 python-dateutil            2.8.2 pytz                       2021.1 pyzmq                      22.1.0 requests                   2.26.0 requests-oauthlib          1.3.0 requests-unixsocket        0.2.0 rsa                        4.7.2 scipy                      1.7.0 Send2Trash                 1.7.1 setuptools                 41.2.0 six                        1.15.0 smart-open                 5.1.0 sniffio                    1.2.0 tensorboard                2.5.0 tensorboard-data-server    0.6.1 tensorboard-plugin-profile 2.4.0 tensorboard-plugin-wit     1.8.0 tensorflow-datasets        4.3.0 tensorflow-estimator       2.5.0 tensorflow-hub             0.12.0 tensorflow-macos           2.5.0 tensorflow-metadata        1.1.0 tensorflow-metal           0.1.1 tensorflow-probability     0.13.0 termcolor                  1.1.0 terminado                  0.10.1 testpath                   0.5.0 tornado                    6.1 tqdm                       4.61.2 traitlets                  5.0.5 typing-extensions          3.7.4.3 urllib3                    1.26.6 wcwidth                    0.2.5 webencodings               0.5.1 websocket-client           1.1.0 Werkzeug                   2.0.1 wheel                      0.36.2 widgetsnbextension         3.5.1 wrapt                      1.12.1 zipp                       3.5.0

Details here: https://stackoverflow.com/questions/68551935/why-does-my-tensorflow-model-stop-training I can run the VariationalDeepSemantic Hashing model in an Anaconda python 3.85 virtual environment with tensorflow 2.5 on CPUs. If I run the same code in the tensorflow-metal virtual environment with python 3.82, accessing my AMD Radeon Pro 5700 XT GPU, the process stops training at epoch 5 on the 5200 batch.

I am running tensorflow-macos and tensorflow-metal on Big Sur. I am getting this error: AttributeError: module 'tensorflow.keras' has no attribute 'utils_dataset_from_directory' https://github.com/keras-team/keras-io/issues/12 Can I install tf_nightly? Or does it conflict with tensorflow-macos? from tensorflow import keras from tensorflow.python.framework.ops import disable_eager_execution dataset = keras.utils_dataset_from_directory( "celeba_gan", label_mode=None, image_size=(64, 64), batch_size=32, smart_resize=True) --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/ipykernel_41859/2519466253.py in <module> 1 from tensorflow import keras 2 from tensorflow.python.framework.ops import disable_eager_execution ----> 3 dataset = keras.utils_dataset_from_directory( 4 "celeba_gan", 5 label_mode=None, AttributeError: module 'tensorflow.keras' has no attribute 'utils_dataset_from_directory'

I have installed tensorflow-macos and tensorflow-metal on Big Sur on a iMac 27" with AMD Radeon Pro 5700 XT. I am trying to run Keras code from Francios Challet's Deep Learning example: E.g Chapter 11-part04_sequence-to-Sequence https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_part04_sequence-to-sequence-learning.ipynb seq2seq_rnn.compile( optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) seq2seq_rnn.fit(train_ds, epochs=15, validation_data=val_ds) 2021-07-15 13:17:00.117869: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-07-15 13:17:01.403133: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at partitioned_function_ops.cc:114 : Invalid argument: No OpKernel was registered to support Op 'CudnnRNNV3' used by {{node cond_41/then/_0/cond/CudnnRNNV3}} with these attrs: [T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="gru", seed2=0, is_training=true, num_proj=0, time_major=false, seed=0, dropout=0] Registered devices: [CPU, GPU] Registered kernels: <no registered kernels> [[cond_41/then/_0/cond/CudnnRNNV3]] 2021-07-15 13:17:01.419061: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at partitioned_function_ops.cc:114 : Invalid argument: No OpKernel was registered to support Op 'CudnnRNNV3' used by {{node cond_41/then/_0/cond/CudnnRNNV3}} with these attrs: [time_major=false, dropout=0, seed=0, T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="gru", seed2=0, is_training=true, num_proj=0] Registered devices: [CPU, GPU] Registered kernels: <no registered kernels> [[cond_41/then/_0/cond/CudnnRNNV3]] --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) /var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/ipykernel_94493/3093225856.py in <module> 3 loss="sparse_categorical_crossentropy", 4 metrics=["accuracy"]) ----> 5 seq2seq_rnn.fit(train_ds, epochs=15, validation_data=val_ds) ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing) 1181 _r=1): 1182 callbacks.on_train_batch_begin(step) -> 1183 tmp_logs = self.train_function(iterator) 1184 if data_handler.should_sync: 1185 context.async_wait() ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds) 887 888 with OptionalXlaContext(self._jit_compile): --> 889 result = self._call(*args, **kwds) 890 891 new_tracing_count = self.experimental_get_tracing_count() ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds) 948 # Lifting succeeded, so variables are initialized and we can run the 949 # stateless function. --> 950 return self._stateless_fn(*args, **kwds) 951 else: 952 _, _, _, filtered_flat_args = \ ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs) 3021 (graph_function, 3022 filtered_flat_args) = self._maybe_define_function(args, kwargs) -> 3023 return graph_function._call_flat( 3024 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access 3025 ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager) 1958 and executing_eagerly): 1959 # No tape is watching; skip to running the function. -> 1960 return self._build_call_outputs(self._inference_function.call( 1961 ctx, args, cancellation_manager=cancellation_manager)) 1962 forward_backward = self._select_forward_and_backward_functions( ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager) 589 with _InterpolateFunctionError(self): 590 if cancellation_manager is None: --> 591 outputs = execute.execute( 592 str(self.signature.name), 593 num_outputs=self._num_outputs, ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 57 try: 58 ctx.ensure_initialized() ---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, 60 inputs, attrs, num_outputs) 61 except core._NotOkStatusException as e: InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: No OpKernel was registered to support Op 'CudnnRNNV3' used by {{node cond_41/then/_0/cond/CudnnRNNV3}} with these attrs: [T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="gru", seed2=0, is_training=true, num_proj=0, time_major=false, seed=0, dropout=0] Registered devices: [CPU, GPU] Registered kernels: <no registered kernels> [[cond_41/then/_0/cond/CudnnRNNV3]] [[model/bidirectional/backward_gru/PartitionedCall]] [[broadcast_weights_1/assert_broadcastable/is_valid_shape/else/_1/broadcast_weights_1/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/then/_53/broadcast_weights_1/assert_broadcastable/is_valid_shape/has_valid_nonscalar_shape/has_invalid_dims/concat/_66]] (1) Invalid argument: No OpKernel was registered to support Op 'CudnnRNNV3' used by {{node cond_41/then/_0/cond/CudnnRNNV3}} with these attrs: [T=DT_FLOAT, input_mode="linear_input", direction="unidirectional", rnn_mode="gru", seed2=0, is_training=true, num_proj=0, time_major=false, seed=0, dropout=0] Registered devices: [CPU, GPU] Registered kernels: <no registered kernels> [[cond_41/then/_0/cond/CudnnRNNV3]] [[model/bidirectional/backward_gru/PartitionedCall]] 0 successful operations. 0 derived errors ignored. [Op:__inference_train_function_520769] Function call stack: train_function -> train_function

I installed tensorflow-mac and tensorflow-metal on an iMac 2021 27" running Big Sur with an AMD Radeon Pro 5700 XT. I am running Python 3.8.5 (tensorflow-metal) (base) davidlaxer@x86_64-apple-darwin13 ~ % pip list Package                 Version             Location ----------------------- ------------------- ------------------------- ... tensorboard             2.5.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit  1.8.0 tensorflow              2.5.0 tensorflow-estimator    2.5.0 tensorflow-hub          0.12.0 tensorflow-macos        2.5.0 tensorflow-metal        0.1.1 tensorflow-text         2.5.0 When I try to import tensorflow I get this error:  % ipython Python 3.8.5 (default, Sep  4 2020, 02:22:02)  Type 'copyright', 'credits' or 'license' for more information IPython 7.24.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: import tensorflow --------------------------------------------------------------------------- NotFoundError                             Traceback (most recent call last) <ipython-input-1-d6579f534729> in <module> ----> 1 import tensorflow ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/__init__.py in <module>     447     _plugin_dir = _os.path.join(_s, 'tensorflow-plugins')     448     if _os.path.exists(_plugin_dir): --> 449       _ll.load_library(_plugin_dir)     450       # Load Pluggable Device Library     451       _ll.load_pluggable_device_library(_plugin_dir) ~/tensorflow-metal/lib/python3.8/site-packages/tensorflow/python/framework/load_library.py in load_library(library_location)     152      153     for lib in kernel_libraries: --> 154       py_tf.TF_LoadLibrary(lib)     155      156   else: NotFoundError: dlopen(/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 6): Symbol not found: _TF_AssignUpdateVariable   Referenced from: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/tensorflow-plugins/libmetal_plugin.dylib   Expected in: flat namespace

I am trying to get the AMD Radeon Pro 5700 XT GPU on my iMac 27" 2021 running Big Sur 11.4 to work with tensorflow-macos. If I disable eager execution I get an exception, if I don't, tensorflow-macos choses the CPU and not the GPU. Here's a simple example which shows the exception: import tensorflow as tf import tensorflow_hub as hub import tensorflow_text import numpy as np from sklearn.preprocessing import normalize from tensorflow.python.framework.ops import disable_eager_execution disable_eager_execution() m4 = hub.load("/Users/davidlaxer/Downloads/universal-sentence-encoder_4") english_sentences = ["dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog."] r4 = np.array(m4(english_sentences)) print(r4) print(m4) type(r4) type(m4) --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) <ipython-input-4-8b0ba0e4c28c> in <module> 1 m4 = hub.load("/Users/davidlaxer/Downloads/universal-sentence-encoder_4") 2 english_sentences = ["dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog."] ----> 3 r4 = np.array(m4(english_sentences)) 4 print(r4) 5 print(m4) ~/anaconda3/envs/tensorflow_mac/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in __array__(self) 850 851 def __array__(self): --> 852 raise NotImplementedError( 853 "Cannot convert a symbolic Tensor ({}) to a numpy array." 854 " This error may indicate that you're trying to pass a Tensor to" NotImplementedError: Cannot convert a symbolic Tensor (StatefulPartitionedCall_1:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported And commenting out disable_eager_execution(): from tensorflow.python.framework.ops import disable_eager_execution #disable_eager_execution() m4 = hub.load("/Users/davidlaxer/Downloads/universal-sentence-encoder_4") english_sentences = ["dog", "Puppies are nice.", "I enjoy taking long walks along the beach with my dog."] r4 = np.array(m4(english_sentences)) print(r4) print(m4) type(r4) type(m4) [-0.06334164 -0.01812314 0.03680531 ... -0.02809388 0.02786911 -0.04715428] [ 0.01975714 -0.02284616 0.04316505 ... -0.01376714 -0.00614742 -0.00124967] [-0.02169351 -0.003993 0.06716524 ... 0.05952153 0.02262796 0.03501643]] <tensorflow.python.saved_model.load.Loader._recreate_base_user_object.<locals>._UserObject object at 0x7fbb3892a9a0> [5]: tensorflow.python.saved_model.load.Loader._recreate_base_user_object.<locals>._UserObject The tensorflow group does not support this GPU and the tensorflow-mac repository is now read-only. https://github.com/tensorflow/tensorflow/issues/50353 https://github.com/apple/tensorflow_macos Any ideas?