I am running tensorflow-macos and tensorflow-metal version 2.6 on Monterey Beta (21A5543b) on an iMac 27" 2021 with an AMD Radeon GPU. I got the following error training the model VariationalDeepSemanticHashing e.g. 2021-10-09 13:05:14.521286: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-09 13:05:27.092823: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-09 13:05:27.153 python[6315:1459657] -[MPSGraph adamUpdateWithLearningRateTensor:beta1Tensor:beta2Tensor:epsilonTensor:beta1PowerTensor:beta2PowerTensor:valuesTensor:momentumTensor:velocityTensor:gradientTensor:name:]: unrecognized selector sent to instance 0x600000eede10 [I 2021-10-09 13:05:28.157 ServerApp] AsyncIOLoopKernelRestarter: restarting kernel (1/5), keep random ports kernel d25e6066-74f7-4b4a-b5e7-b2911e7501d9 restarted https://github.com/unsuthee/VariationalDeepSemanticHashing/blob/master/Run_Experiment_Unsupervised.ipynb Here's the repository: https://github.com/unsuthee/VariationalDeepSemanticHashing

[I am running Top2vec on Big Sur 11.6 with tensorflow-macos and tensorflow-metal. Python crashed ... linkText Crashed Thread: 0 Dispatch queue: metal gpu stream Exception Type: EXC_CRASH (SIGABRT) Exception Codes: 0x0000000000000000, 0x0000000000000000 Exception Note: EXC_CORPSE_NOTIFY Application Specific Information: /System/Volumes/Data/SWE/macOS/BuildRoots/38cf1d983f/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MetalPerformanceShaders-124.6.1/MPSCore/Utility/MPSCommandBufferImageCache.mm:1386: failed assertion `Failed to allocate private MTLBuffer for size 421888000 Crash Log

https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac Does this feature support AMD GPUs with Metal or only M1 support? Does v1.12 nightly build support the Apple Metal only with source?

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?

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

Will tensorflow_text be supported with Tensorflow_macos & Tensorflow_metal? E.g. https://github.com/tensorflow/text/issues/654

In a tensorflow-metal virtual environment on OS X 12.1: tensorboard 2.6.0 tensorboard-data-server 0.6.1 tensorboard-plugin-profile 2.5.0 tensorboard-plugin-wit 1.8.0 tensorflow 2.6.0 tensorflow-addons 0.14.0 tensorflow-consciousness 0.1 tensorflow-datasets 4.4.0 tensorflow-estimator 2.7.0 tensorflow-gan 2.1.0 tensorflow-hub 0.12.0 tensorflow-io-gcs-filesystem 0.22.0 tensorflow-macos 2.7.0 tensorflow-metadata 1.2.0 tensorflow-metal 0.3.0 tensorflow-probability 0.14.1 tensorflow-similarity 0.13.45 tensorflow-text 2.7.3 Running the Top2vec model: https://github.com/ddangelov/Top2Vec import numpy as np import pandas as pd import json import os import ipywidgets as widgets from IPython.display import clear_output, display from top2vec import Top2Vec papers_prepared_df = pd.read_feather("/Users/davidlaxer/Downloads/archive/covid19_papers_processed.feather") top2vec_trained = Top2Vec(documents=papers_prepared_df.text.tolist(), embedding_model="universal-sentence-encoder", use_embedding_model_tokenizer=True, embedding_model_path="/Users/davidlaxer/Downloads/universal-sentence-encoder_4/", workers=4) 2021-12-20 06:30:52,188 - top2vec - INFO - Pre-processing documents for training /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead. warnings.warn(msg, category=FutureWarning) 2021-12-20 06:31:57,351 - top2vec - INFO - Loading universal-sentence-encoder model at /Users/davidlaxer/Downloads/universal-sentence-encoder_4 2021-12-20 06:31:57.488459: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-12-20 06:31:57.489288: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2021-12-20 06:31:57.489490: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) Metal device set to: AMD Radeon Pro 5700 XT 2021-12-20 06:31:59.447260: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-12-20 06:32:00,841 - top2vec - INFO - Creating joint document/word embedding 2021-12-20 06:32:00.923838: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. Some resource has been exhausted. For example, this error might be raised if a per-user quota is exhausted, or perhaps the entire file system is out of space. @@__init__ 2 root error(s) found. (0) RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[114389,320] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator Simple allocator [[{{node EncoderDNN/EmbeddingLookup/EmbeddingLookupUnique/GatherV2}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode. [[StatefulPartitionedCall/StatefulPartitionedCall/EncoderDNN/EmbeddingLookup/EmbeddingLookupUnique/Reshape_1/_188]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode. (1) RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[114389,320] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator Simple allocator [[{{node EncoderDNN/EmbeddingLookup/EmbeddingLookupUnique/GatherV2}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode. ... I tried adjusting the batchsize (e.g - 500, 100, 50, 10, 5).

I am running a recurrent neural network example on an iMac 27" with an AMD Radeon Pro 5700 XT on OS X 12.3. The code runs, the GPU was initialized the GPU is not active. Each epoch takes: 819/819 [==============================] - 32417s 40s/step - loss: 121.7538 - mae: 8.9641 - val_loss: 100.3145 - val_mae: 8.0313 % python chapter-10.py ['"Date Time"', '"p (mbar)"', '"T (degC)"', '"Tpot (K)"', '"Tdew (degC)"', '"rh (%)"', '"VPmax (mbar)"', '"VPact (mbar)"', '"VPdef (mbar)"', '"sh (g/kg)"', '"H2OC (mmol/mol)"', '"rho (g/m**3)"', '"wv (m/s)"', '"max. wv (m/s)"', '"wd (deg)"'] 420451 num_train_samples: 210225 num_val_samples: 105112 num_test_samples: 105114 2022-03-28 18:28:59.988516: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Metal device set to: AMD Radeon Pro 5700 XT systemMemory: 128.00 GB maxCacheSize: 7.99 GB 2022-03-28 18:28:59.989242: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2022-03-28 18:28:59.989616: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>) WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU. Epoch 1/50 2022-03-28 18:29:02.342296: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 819/819 [==============================] - ETA: 0s - loss: 121.7538 - mae: 8.9641 2022-03-29 03:21:02.092397: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled. 819/819 [==============================] - 32417s 40s/step - loss: 121.7538 - mae: 8.9641 - val_loss: 100.3145 - val_mae: 8.0313 Epoch 2/50 381/819 [============>.................] - ETA: 4:50:31 - loss: 93.7597 - mae: 7.6880 from tensorflow import keras from tensorflow.keras import layers num_features = 14 steps = 120 import os fname = os.path.join("jena_climate_2009_2016.csv") with open(fname) as f: data = f.read() lines = data.split("\n") header = lines[0].split(",") lines = lines[1:] print(header) print(len(lines)) import numpy as np temperature = np.zeros((len(lines),)) raw_data = np.zeros((len(lines), len(header) - 1)) for i, line in enumerate(lines): values = [float(x) for x in line.split(",")[1:]] temperature[i] = values[1] raw_data[i, :] = values[:] sampling_rate = 6 sequence_length = 120 delay = sampling_rate * (sequence_length + 24 - 1) batch_size = 256 num_train_samples = int(0.5 * len(raw_data)) num_val_samples = int(0.25 * len(raw_data)) num_test_samples = len(raw_data) - num_train_samples - num_val_samples print("num_train_samples:", num_train_samples) print("num_val_samples:", num_val_samples) print("num_test_samples:", num_test_samples) train_dataset = keras.utils.timeseries_dataset_from_array( raw_data[:-delay], targets=temperature[delay:], sampling_rate=sampling_rate, sequence_length=sequence_length, shuffle=True, batch_size=batch_size, start_index=0, end_index=num_train_samples) val_dataset = keras.utils.timeseries_dataset_from_array( raw_data[:-delay], targets=temperature[delay:], sampling_rate=sampling_rate, sequence_length=sequence_length, shuffle=True, batch_size=batch_size, start_index=num_train_samples, end_index=num_train_samples + num_val_samples) test_dataset = keras.utils.timeseries_dataset_from_array( raw_data[:-delay], targets=temperature[delay:], sampling_rate=sampling_rate, sequence_length=sequence_length, shuffle=True, batch_size=batch_size, start_index=num_train_samples + num_val_samples) inputs = keras.Input(shape=(steps, num_features)) x = layers.SimpleRNN(16, return_sequences=True)(inputs) x = layers.SimpleRNN(16, return_sequences=True)(x) outputs = layers.SimpleRNN(16)(x) inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1])) x = layers.LSTM(32, recurrent_dropout=0.25)(inputs) x = layers.Dropout(0.5)(x) outputs = layers.Dense(1)(x) model = keras.Model(inputs, outputs) callbacks = [ keras.callbacks.ModelCheckpoint("jena_lstm_dropout.keras", save_best_only=True) ] model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"]) history = model.fit(train_dataset, epochs=50, validation_data=val_dataset, callbacks=callbacks) Any idea why the GPU is not active? How does this code example run on an M1 Ultra?

Training Top2vec with embedding_batch_size=256 crashed OS X 12.3.1 tensorflow_macos 2.8.0, tensorflow_metal 0.4.0 Anaconda Python 3.8.5 % pip show tensorflow_macos WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages) Name: tensorflow-macos Version: 2.8.0 Summary: TensorFlow is an open source machine learning framework for everyone. Home-page: https://www.tensorflow.org/ Author: Google Inc. Author-email: packages@tensorflow.org License: Apache 2.0 Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, keras-preprocessing, libclang, numpy, opt-einsum, protobuf, setuptools, six, tensorboard, termcolor, tf-estimator-nightly, typing-extensions, wrapt Required-by: (tensorflow-metal) (base) davidlaxer@x86_64-apple-darwin13 top2vec % pip show tensorflow_metal WARNING: Ignoring invalid distribution -umpy (/Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages) Name: tensorflow-metal Version: 0.4.0 Summary: TensorFlow acceleration for Mac GPUs. Home-page: https://developer.apple.com/metal/tensorflow-plugin/ Author: Author-email: License: MIT License. Copyright © 2020-2021 Apple Inc. All rights reserved. Location: /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages Requires: six, wheel Required-by: To train the model with embedding_model="universal-sentence-encoder", you'll have to download universal-sentence-encoder_4. top2vec_trained = Top2Vec(documents=papers_filtered_df.text.tolist(), split_documents=True, **embedding_batch_size=256,** embedding_model="universal-sentence-encoder", use_embedding_model_tokenizer=True, embedding_model_path="/Users/davidlaxer/Downloads/universal-sentence-encoder_4", workers=8) Here's the project: https://github.com/ddangelov/Top2Vec Here's the Jupyter notebook: https://github.com/ddangelov/Top2Vec/blob/master/notebooks/CORD-19_top2vec.ipynb You'll have to load the COVID-19 data set from Kaggle here: https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge I set filter size to 1,000: def filter_short(papers_df): papers_df["token_counts"] = papers_df["text"].str.split().map(len) papers_df = **papers_df[papers_df.token_counts>1000].reset_index(drop=True)** papers_df.drop('token_counts', axis=1, inplace=True) return papers_df Trace panic(cpu 8 caller 0xffffff8020d449ad): userspace watchdog timeout: no successful checkins from WindowServer in 120 seconds service: logd, total successful checkins since wake (127621 seconds ago): 12763, last successful checkin: 0 seconds ago service: WindowServer, total successful checkins since wake (127621 seconds ago): 12751, last successful checkin: 120 seconds ago service: remoted, total successful checkins since wake (127621 seconds ago): 12763, last successful checkin: 0 [Trace](https://developer.apple.com/forums/content/attachment/d17c2c9b-569b-4c1a-9c61-892ced7f785b)

Will Metal Performance Shaders Framework ('MPS') support Float64? https://developer.apple.com/documentation/metalperformanceshaders

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 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 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'

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 just recreated my tensorflow-metal virtual environment on OS X 11.6. Now, when I import tensorflow, I am getting: No supported GPU was found. My AMD GPU worked in the prior versions of OS X 11.6. Do the latest versions require OS X 12? % ipython Python 3.8.5 (default, Sep 4 2020, 02:22:02) Type 'copyright', 'credits' or 'license' for more information IPython 7.28.0 -- An enhanced Interactive Python. Type '?' for help. In [1]: import tensorflow No supported GPU was found. % pip list Package Version ------------------------ --------- absl-py 0.12.0 anyio 3.3.2 appnope 0.1.2 argon2-cffi 21.1.0 astunparse 1.6.3 attrs 21.2.0 Babel 2.9.1 backcall 0.2.0 bleach 4.1.0 bokeh 2.3.3 cachetools 4.2.4 certifi 2021.5.30 cffi 1.14.6 charset-normalizer 2.0.6 clang 5.0 cloudpickle 2.0.0 cycler 0.10.0 Cython 0.29.24 debugpy 1.5.0 decorator 5.1.0 defusedxml 0.7.1 dill 0.3.4 distinctipy 1.1.5 dm-tree 0.1.6 entrypoints 0.3 flatbuffers 1.12 future 0.18.2 gast 0.4.0 gensim 3.8.3 google-auth 1.35.0 google-auth-oauthlib 0.4.6 google-pasta 0.2.0 googleapis-common-protos 1.53.0 grpcio 1.41.0 h5py 3.1.0 hdbscan 0.8.27 idna 3.2 importlib-resources 5.2.2 ipykernel 6.4.1 ipython 7.28.0 ipython-genutils 0.2.0 ipywidgets 7.6.5 jedi 0.18.0 Jinja2 3.0.2 joblib 1.1.0 json5 0.9.6 jsonschema 4.0.1 jupyter-client 7.0.6 jupyter-core 4.8.1 jupyter-server 1.11.1 jupyterlab 3.1.18 jupyterlab-pygments 0.1.2 jupyterlab-server 2.8.2 jupyterlab-widgets 1.0.2 keras 2.6.0 Keras-Preprocessing 1.1.2 kiwisolver 1.3.2 llvmlite 0.37.0 Markdown 3.3.4 MarkupSafe 2.0.1 matplotlib 3.4.3 matplotlib-inline 0.1.3 mistune 0.8.4 nbclassic 0.3.2 nbclient 0.5.4 nbconvert 6.2.0 nbformat 5.1.3 nest-asyncio 1.5.1 nmslib 2.1.1 notebook 6.4.4 numba 0.54.0 numpy 1.20.3 oauthlib 3.1.1 opt-einsum 3.3.0 packaging 21.0 pandas 1.3.3 pandocfilters 1.5.0 parso 0.8.2 pexpect 4.8.0 pickleshare 0.7.5 Pillow 8.3.2 pip 21.2.4 prometheus-client 0.11.0 promise 2.3 prompt-toolkit 3.0.20 protobuf 3.18.1 psutil 5.8.0 ptyprocess 0.7.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 pybind11 2.6.1 pycparser 2.20 Pygments 2.10.0 pynndescent 0.5.4 pyparsing 2.4.7 pyrsistent 0.18.0 python-dateutil 2.8.2 pytz 2021.3 PyYAML 5.4.1 pyzmq 22.3.0 requests 2.26.0 requests-oauthlib 1.3.0 requests-unixsocket 0.2.0 rsa 4.7.2 scikit-learn 1.0 scipy 1.7.1 Send2Trash 1.8.0 setuptools 47.1.0 six 1.15.0 smart-open 5.2.1 sniffio 1.2.0 tabulate 0.8.9 tensorboard 2.6.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.0 tensorflow 2.6.0 tensorflow-consciousness 0.1 tensorflow-datasets 4.4.0 tensorflow-estimator 2.6.0 tensorflow-gan 2.1.0 tensorflow-hub 0.12.0 tensorflow-macos 2.6.0 tensorflow-metadata 1.2.0 tensorflow-metal 0.2.0 tensorflow-probability 0.14.1 tensorflow-similarity 0.13.45 tensorflow-text 2.6.0 termcolor 1.1.0 terminado 0.12.1 testpath 0.5.0 threadpoolctl 3.0.0 top2vec 1.0.26 tornado 6.1 tqdm 4.62.3 traitlets 5.1.0 typing-extensions 3.7.4.3 umap-learn 0.5.1 urllib3 1.26.7 wcwidth 0.2.5 webencodings 0.5.1 websocket-client 1.2.1 Werkzeug 2.0.2 wheel 0.37.0 widgetsnbextension 3.5.1 wordcloud 1.8.1 wrapt 1.12.1 zipp 3.6.0