The problem as caused by version 12.0.0 of ld which lived in my Anaconda virtual environment. ld 13.1.6 did not have the issue. % ld -v @(#)PROGRAM:ld PROJECT:ld64-764 BUILD 11:29:01 May 17 2022 configured to support archs: armv6 armv7 armv7s arm64 arm64e arm64_32 i386 x86_64 x86_64h armv6m armv7k armv7m armv7em LTO support using: LLVM version 13.1.6, (clang-1316.0.21.2.5) (static support for 28, runtime is 28) TAPI support using: Apple TAPI version 13.1.6 (tapi-1316.0.7.3)

I am getting this error trying to compile AI-Feynman ld: unsupported tapi file type '!tapi-tbd' in YAML file '/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/lib/libSystem.tbd' for architecture x86_64 I tried to generate a new .tbd file from libSystem.dylib with 'tapi stubify ...' but I can't locate the libSystem.B.dylib file. The other .dylibs in XCode are not the right ones. % locate libSystem.B.dylib /Applications/Xcode.app/Contents/Developer/Platforms/AppleTVOS.platform/Library/Developer/CoreSimulator/Profiles/Runtimes/tvOS.simruntime/Contents/Resources/RuntimeRoot/usr/lib/libSystem.B.dylib /Applications/Xcode.app/Contents/Developer/Platforms/WatchOS.platform/Library/Developer/CoreSimulator/Profiles/Runtimes/watchOS.simruntime/Contents/Resources/RuntimeRoot/usr/lib/libSystem.B.dylib /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Library/Developer/CoreSimulator/Profiles/Runtimes/iOS.simruntime/Contents/Resources/RuntimeRoot/usr/lib/libSystem.B.dylib Any ideas on how to generate a replacement .tbd file from a 'virtual' shared library which lives in a cache? % otool -L /Applications/Xcode.app/Contents/Developer/Platforms/AppleTVOS.platform/Library/Developer/CoreSimulator/Profiles/Runtimes/tvOS.simruntime/Contents/Resources/RuntimeRoot/usr/lib/libSystem.dylib /Applications/Xcode.app/Contents/Developer/Platforms/AppleTVOS.platform/Library/Developer/CoreSimulator/Profiles/Runtimes/tvOS.simruntime/Contents/Resources/RuntimeRoot/usr/lib/libSystem.dylib (architecture x86_64): /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 1311.100.3) /usr/lib/system/libcache.dylib (compatibility version 1.0.0, current version 85.0.0) /usr/lib/system/libcommonCrypto.dylib (compatibility version 1.0.0, current version 60191.100.1) /usr/lib/system/libcompiler_rt.dylib (compatibility version 1.0.0, current version 103.1.0) /usr/lib/system/libcopyfile.dylib (compatibility version 1.0.0, current version 1.0.0) /usr/lib/system/libcorecrypto.dylib (compatibility version 1.0.0, current version 1218.100.47) /usr/lib/system/libdispatch.dylib (compatibility version 1.0.0, current version 1325.100.36) /usr/lib/system/libdyld.dylib (compatibility version 1.0.0, current version 1.0.0) /usr/lib/system/libmacho.dylib (compatibility version 1.0.0, current version 994.0.0) /usr/lib/system/libremovefile.dylib (compatibility version 1.0.0, current version 60.0.0) /usr/lib/system/libsystem_asl.dylib (compatibility version 1.0.0, current version 392.100.2) /usr/lib/system/libsystem_blocks.dylib (compatibility version 1.0.0, current version 79.1.0) /usr/lib/system/libsystem_c.dylib (compatibility version 1.0.0, current version 1507.100.9) /usr/lib/system/libsystem_collections.dylib (compatibility version 1.0.0, current version 1507.100.9) /usr/lib/system/libsystem_configuration.dylib (compatibility version 1.0.0, current version 1163.100.19) /usr/lib/system/libsystem_containermanager.dylib (compatibility version 1.0.0, current version 1.0.0) /usr/lib/system/libsystem_coreservices.dylib (compatibility version 1.0.0, current version 133.0.0) /usr/lib/system/libsystem_darwin.dylib (compatibility version 1.0.0, current version 1.0.0) /usr/lib/system/libsystem_dnssd.dylib (compatibility version 1.0.0, current version 1557.103.1) /usr/lib/system/libsystem_featureflags.dylib (compatibility version 1.0.0, current version 56.0.0) /usr/lib/system/libsystem_info.dylib (compatibility version 1.0.0, current version 1.0.0) /usr/lib/system/libsystem_m.dylib (compatibility version 1.0.0, current version 3204.80.2) /usr/lib/system/libsystem_malloc.dylib (compatibility version 1.0.0, current version 374.100.5) /usr/lib/system/libsystem_networkextension.dylib (compatibility version 1.0.0, current version 1.0.0) /usr/lib/system/libsystem_notify.dylib (compatibility version 1.0.0, current version 301.0.0) /usr/lib/system/libsystem_product_info_filter.dylib (compatibility version 1.0.0, current version 10.0.0) /usr/lib/system/libsystem_sandbox.dylib (compatibility version 1.0.0, current version 1657.103.1) /usr/lib/system/libsystem_sim_kernel.dylib (compatibility version 1.0.0, current version 238.100.1) /usr/lib/system/libsystem_sim_platform.dylib (compatibility version 1.0.0, current version 238.100.1) /usr/lib/system/libsystem_sim_pthread.dylib (compatibility version 1.0.0, current version 238.100.1) /usr/lib/system/libsystem_trace.dylib (compatibility version 1.0.0, current version 1375.100.9) /usr/lib/system/libunwind.dylib (compatibility version 1.0.0, current version 202.2.0) ... (base) davidlaxer@x86_64-apple-darwin13 iot-inspector-client % ls -l /usr/lib/system total 1720 drwxr-xr-x 4 root wheel 128 May 9 14:30 introspection -rwxr-xr-x 1 root wheel 1617536 May 9 14:30 libsystem_kernel.dylib -rwxr-xr-x 1 root wheel 512560 May 9 14:30 libsystem_platform.dylib -rwxr-xr-x 1 root wheel 656656 May 9 14:30 libsystem_pthread.dylib -rwxr-xr-x 1 root wheel 150080 May 9 14:30 wordexp-helper Any ideas on what the linker doesn't like about file type '!tapi-tbd' in YAML file '/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/lib/libSystem.tbd' for architecture x86_64

I have the same issue. https://developer.apple.com/forums/thread/707071?page=1#715262022

I tried uninstalling and reinstalling CommandLineTools % ls -ag /Library/Developer total 0 drwxr-xr-x 4 wheel 128 May 31 18:18 . drwxr-xr-x 72 wheel 2304 May 17 12:01 .. drwxr-xr-x 6 wheel 192 May 31 18:17 CommandLineTools drwxr-xr-x 8 admin 256 May 17 01:44 PrivateFrameworks % xcrun --show-sdk-platform-path xcrun: error: unable to lookup item 'PlatformPath' from command line tools installation xcrun: error: unable to lookup item 'PlatformPath' in SDK '/Library/Developer/CommandLineTools/SDKs/MacOSX12.3.sdk' (AI-Feynman) davidlaxer@x86_64-apple-darwin13 AI-Feynman % xcode-select -p /Library/Developer/CommandLineTools (AI-Feynman) davidlaxer@x86_64-apple-darwin13 AI-Feynman % xcrun --show-sdk-path --sdk macosx /Library/Developer/CommandLineTools/SDKs/MacOSX12.3.sdk (AI-Feynman) davidlaxer@x86_64-apple-darwin13 AI-Feynman % xcrun --sdk macosx10.13 --show-sdk-path xcrun: error: SDK "macosx10.13" cannot be located xcrun: error: SDK "macosx10.13" cannot be located xcrun: error: unable to lookup item 'Path' in SDK 'macosx10.13' Why the reference to macosx10.13? How do I delete the old SDK reference?

On Google Colab with a GPU each epoch is ~383 seconds (not 32417seconds): `--2022-03-29 16:58:04-- https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.132.216 Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.132.216|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 13565642 (13M) [application/zip] Saving to: ‘jena_climate_2009_2016.csv.zip’ jena_climate_2009_2 100%[===================>] 12.94M 61.8MB/s in 0.2s 2022-03-29 16:58:04 (61.8 MB/s) - ‘jena_climate_2009_2016.csv.zip’ saved [13565642/13565642] Archive: jena_climate_2009_2016.csv.zip inflating: jena_climate_2009_2016.csv inflating: __MACOSX/._jena_climate_2009_2016.csv ['"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 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 1/50 819/819 [==============================] - 383s 461ms/step - loss: 147.6203 - mae: 10.0559 - val_loss: 137.3602 - val_mae: 9.6686 Epoch 2/50 118/819 [===>....`

Are you using the 'Adam' optimizer?

Which optimizer are you using? If 'Adam' try 'SGD'.

The exception is generated building a list of document vectors from input documents not in model training: E.g. - document_vectors.append(self.embed(train_corpus[current:current + batch_size])) The python 3.8 process grows in memory to 100GB and then generates the OOM exception. def _embed_documents(self, train_corpus): self._check_import_status() self._check_model_status() # embed documents batch_size = 5 document_vectors = [] current = 0 batches = int(len(train_corpus) / batch_size) extra = len(train_corpus) % batch_size for ind in range(0, batches): try: __**document_vectors.append(self.embed(train_corpus[current:current + batch_size]))**__ except Exception as e: print (e.__doc__) print (e.message) current += batch_size if extra > 0: document_vectors.append(self.embed(train_corpus[current:current + extra])) document_vectors = self._l2_normalize(np.array(np.vstack(document_vectors))) return document_vectors

The AdamOptimizer is still causing crashes with: tensorflow-metal 0.3.0 tensorflow-macos 2.7.0

This code crashes with the 'adam' optimzer. It does work with 'SGD'. I am running Monterey 12.1 beta, and the latest versions of tensorflow-macos and tensorflow-metal from pypi. import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10) ]) predictions = model(x_train[:1]).numpy() tf.nn.softmax(predictions).numpy() loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) loss_fn(y_train[:1], predictions).numpy() model.compile(optimizer = 'adam', loss = loss_fn) model.fit(x_train, y_train, epochs=100)

Try changing optimizer='adam' to optimizer='SGD'

Hi, Your example runs for me on Monterey 12.0.1 with Python 3.8 ... if I replace the ADAM optimizer with SGD. model.compile( loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.SGD(0.001), metrics=['accuracy'], ) I've noticed ADAM crash the session. Metal device set to: AMD Radeon Pro 5700 XT 2021-10-25 12:01:51.733970: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2021-10-25 12:01:51.734526: 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. 2021-10-25 12:01:51.734764: 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>) 2021-10-25 12:01:51.902618: 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. 2021-10-25 12:01:51.902647: 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>) 2021-10-25 12:01:52.021880: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.035650: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.081019: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.099696: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.211089: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.229341: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.237014: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.261855: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.279544: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) 2021-10-25 12:01:52.304527: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-25 12:01:52.324218: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. Train on 469 steps, validate on 79 steps Epoch 1/12 469/469 [==============================] - ETA: 0s - batch: 234.0000 - size: 1.0000 - loss: 2.2622 - accuracy: 0.1993 /Users/davidlaxer/tensorflow-metal/lib/python3.8/site-packages/keras/engine/training.py:2470: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically. warnings.warn('`Model.state_updates` will be removed in a future version. ' 2021-10-25 12:02:06.665054: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 469/469 [==============================] - 15s 19ms/step - batch: 234.0000 - size: 1.0000 - loss: 2.2622 - accuracy: 0.1993 - val_loss: 2.2074 - val_accuracy: 0.4665 Epoch 2/12 469/469 [==============================] - 11s 20ms/step - batch: 234.0000 - size: 1.0000 - loss: 2.1208 - accuracy: 0.3812 - val_loss: 1.9072 - val_accuracy: 0.6792 Epoch 3/12 469/469 [==============================] - 11s 21ms/step - batch: 234.0000 - size: 1.0000 - loss: 1.6169 - accuracy: 0.5601 - val_loss: 1.0289 - val_accuracy: 0.8151 Epoch 4/12 469/469 [==============================] - 12s 22ms/step - batch: 234.0000 - size: 1.0000 - loss: 1.0248 - accuracy: 0.6935 - val_loss: 0.5984 - val_accuracy: 0.8613 Epoch 5/12 469/469 [==============================] - 12s 23ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.7831 - accuracy: 0.7570 - val_loss: 0.4718 - val_accuracy: 0.8799 Epoch 6/12 469/469 [==============================] - 13s 24ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.6629 - accuracy: 0.7937 - val_loss: 0.4055 - val_accuracy: 0.8929 Epoch 7/12 469/469 [==============================] - 13s 24ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.6024 - accuracy: 0.8123 - val_loss: 0.3660 - val_accuracy: 0.9007 Epoch 8/12 469/469 [==============================] - 13s 25ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.5541 - accuracy: 0.8301 - val_loss: 0.3380 - val_accuracy: 0.9073 Epoch 9/12 469/469 [==============================] - 13s 25ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.5244 - accuracy: 0.8397 - val_loss: 0.3181 - val_accuracy: 0.9121 Epoch 10/12 469/469 [==============================] - 13s 24ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.4910 - accuracy: 0.8500 - val_loss: 0.2988 - val_accuracy: 0.9161 Epoch 11/12 469/469 [==============================] - 13s 25ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.4683 - accuracy: 0.8570 - val_loss: 0.2857 - val_accuracy: 0.9186 Epoch 12/12 469/469 [==============================] - 14s 25ms/step - batch: 234.0000 - size: 1.0000 - loss: 0.4562 - accuracy: 0.8600 - val_loss: 0.2736 - val_accuracy: 0.9207 [1]: <keras.callbacks.History at 0x7f8758fd9310> [ ]: ​

On my iMac 27" with Monterey 12.0.1 it crashes with the GPU in tensorflow-metal: % python muzero.py 2021-10-21 08:36:21.088556: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. Metal device set to: AMD Radeon Pro 5700 XT systemMemory: 128.00 GB maxCacheSize: 7.99 GB 2021-10-21 08:36:21.089347: 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. 2021-10-21 08:36:21.089966: 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>) 2021-10-21 08:36:21.753689: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) 2021-10-21 08:36:21.759239: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-21 08:36:34.888 python[14296:730686] -[MPSGraph adamUpdateWithLearningRateTensor:beta1Tensor:beta2Tensor:epsilonTensor:beta1PowerTensor:beta2PowerTensor:valuesTensor:momentumTensor:velocityTensor:gradientTensor:name:]: unrecognized selector sent to instance 0x600001b26220 zsh: segmentation fault python muzero.py It runs with the CPU. % python --version Python 3.8.5 % pip freeze absl-py==0.12.0 anyio==3.3.2 appnope==0.1.2 argon2-cffi==21.1.0 asttokens==2.0.5 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 colorama==0.4.4 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 dotmap==1.3.24 entrypoints==0.3 executing==0.8.2 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 gviz-api==1.9.0 gym==0.21.0 h5py==3.1.0 hdbscan==0.8.27 icecream==2.1.1 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 memory-profiler==0.58.0 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 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 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-profile==2.5.0 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 widgetsnbextension==3.5.1 wordcloud==1.8.1 wrapt==1.12.1 zipp==3.6.0

Virtual Environment % 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 dotmap 1.3.24 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 memory-profiler 0.58.0 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 (tensorflow-metal) (base) davidlaxer@x86_64-apple-darwin13 ~ %

This code reproduces the crash: test.txt Also, running WITH OUT metal (just CPU) is 4X faster with 'SDG' optimizer. I can't compare the ADAM optimizer since it crashed. In [2]: import tensorflow as tf ...: ...: mnist = tf.keras.datasets.mnist ...: ...: (x_train, y_train), (x_test, y_test) = mnist.load_data() ...: x_train, x_test = x_train / 255.0, x_test / 255.0 ...: ...: model = tf.keras.models.Sequential([ ...: tf.keras.layers.Flatten(input_shape=(28, 28)), ...: tf.keras.layers.Dense(128, activation='relu'), ...: tf.keras.layers.Dropout(0.2), ...: tf.keras.layers.Dense(10) ...: ]) ...: ...: predictions = model(x_train[:1]).numpy() ...: tf.nn.softmax(predictions).numpy() ...: ...: loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True ...: ) ...: ...: loss_fn(y_train[:1], predictions).numpy() ...: ...: model.compile(optimizer = 'adam', loss = loss_fn) ...: model.fit(x_train, y_train, epochs=100) Epoch 1/100 2021-10-10 10:50:53.503460: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 2021-10-10 10:50:53.527 python[25080:3485800] -[MPSGraph adamUpdateWithLearningRateTensor:beta1Tensor:beta2Tensor:epsilonTensor:beta1PowerTensor:beta2PowerTensor:valuesTensor:momentumTensor:velocityTensor:gradientTensor:name:]: unrecognized selector sent to instance 0x6000037975a0 zsh: segmentation fault ipython tensorflow_metal (GPU): % time python test.py 2021-10-10 11:34:34.602604: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. Metal device set to: AMD Radeon Pro 5700 XT systemMemory: 128.00 GB maxCacheSize: 7.99 GB 2021-10-10 11:34:34.603850: 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. 2021-10-10 11:34:34.604642: 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>) 2021-10-10 11:34:35.779610: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) Epoch 1/100 2021-10-10 11:34:35.929611: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 1875/1875 [==============================] - 7s 3ms/step - loss: 0.7213 Epoch 2/100 1875/1875 [==============================] - 6s 3ms/step - loss: 0.38653ms/step - loss: 0.0474 ... Epoch 100/100 1875/1875 [==============================] - 6s 3ms/step - loss: 0.0473 python test.py 721.48s user 375.56s system 173% cpu 10:31.28 total (tensorflow-metal) (base) davidlaxer@x86_64-apple-darwin13 ~ % tensorflow (CPU): % time python ~/test.py 2021-10-10 11:45:44.111971: I tensorflow/core/platform/cpu_feature_guard.cc:142] 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. 2021-10-10 11:45:44.487763: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2) Epoch 1/100 1875/1875 [==============================] - 1s 460us/step - loss: 0.7210 Epoch 2/100 1875/1875 [==============================] - 1s 459us/step - loss: 0.3874 Epoch 3/100 1875/1875 [==============================] - 1s 459us/step - loss: 0.3233 Epoch 4/100 1875/1875 [==============================] - 1s 460us/step - loss: 0.2884 Epoch 5/100 1875/1875 [==============================] - 1s 471us/step - loss: 0.2608 Epoch 6/100 1875/1875 [==============================] - 1s 462us/step - loss: 0.2400 Epoch 7/100 ... Epoch 99/100 1875/1875 [==============================] - 1s 468us/step - loss: 0.0455 Epoch 100/100 1875/1875 [==============================] - 1s 469us/step - loss: 0.0463 python ~/test.py 181.09s user 48.20s system 246% cpu 1:32.86 total (ai) davidlaxer@x86_64-apple-darwin13 text %