pip list output ----------------------- --------- absl-py         1.0.0 astunparse       1.6.3 brotlipy        0.7.0 cached-property     1.5.2 cachetools       5.0.0 certifi         2021.10.8 cffi          1.15.0 charset-normalizer   2.0.9 colorama        0.4.4 conda          4.11.0 conda-package-handling 1.7.3 cryptography      36.0.0 cycler         0.11.0 flatbuffers       2.0 fonttools        4.29.1 gast          0.4.0 google-auth       2.6.0 google-auth-oauthlib  0.4.6 google-pasta      0.2.0 grpcio         1.43.0 h5py          3.1.0 idna          3.1 importlib-metadata   4.10.1 keras          2.7.0 Keras-Preprocessing   1.1.2 kiwisolver       1.3.2 libclang        13.0.0 Markdown        3.3.6 matplotlib       3.5.1 munkres         1.1.4 numpy          1.19.5 oauthlib        3.2.0 opt-einsum       3.3.0 packaging        21.3 Pillow         9.0.1 pip           21.3.1 protobuf        3.19.4 pyasn1         0.4.8 pyasn1-modules     0.2.8 pycosat         0.6.3 pycparser        2.21 pyOpenSSL        21.0.0 pyparsing        3.0.7 PySocks         1.7.1 python-dateutil     2.8.2 requests        2.26.0 requests-oauthlib    1.3.1 rsa           4.8 ruamel-yaml-conda    0.15.80 scipy          1.8.0 setuptools       59.4.0 six           1.15.0 tensorboard       2.8.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.1 tensorflow-estimator  2.7.0 tensorflow-macos    2.7.0 tensorflow-metal    0.3.0 termcolor        1.1.0 tornado         6.1 tqdm          4.62.3 typing_extensions    4.0.1 unicodedata2      14.0.0 urllib3         1.26.7 Werkzeug        2.0.2 wheel          0.35.1 wrapt          1.13.3 zipp          3.7.0```

Thank you very much for your answer. At this point I am getting the same issue. I though creating an env with conda and install tensorflow whithin it should work. Unfortunately I'm not able to install tensorflow-macos and tensorflow-metal in my env. I got this error : (tf) saltyrainbow@MacBook-Pro ~ % python -m pip install tensorflow-macos ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none) ERROR: No matching distribution found for tensorflow-macos I'm very new to datascience, so maybe I didn't follow correctly the installation steps.

I reinstalled everything and tried to run a code, and I didn't notice that I get also this : 2022-02-09 12:40:41.585013: 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.

Sorry for that. In fact, I assumed that it didn't work because I got really weird accuracy results. I've found something interesting. Without changing any parameters, sometimes i got a really good model : Metal device set to: Apple M1 systemMemory: 16.00 GB maxCacheSize: 5.33 GB 2022-02-09 13:18:04.110848: 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. 2022-02-09 13:18:04.110957: 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>) 2022-02-09 13:18:04.304541: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz Epoch 1/20 2022-02-09 13:18:04.469609: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 12/16 [=====================>........] - ETA: 0s - loss: 0.0000e+00 - accuracy: 0.29442022-02-09 13:18:04.824160: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 16/16 [==============================] - 1s 18ms/step - loss: 3.1066e-04 - accuracy: 0.2627 - val_loss: 0.0000e+00 - val_accuracy: 0.2002 Epoch 2/20 16/16 [==============================] - 0s 11ms/step - loss: 2.7158e-04 - accuracy: 0.4070 - val_loss: 0.0000e+00 - val_accuracy: 0.0189 Epoch 3/20 16/16 [==============================] - 0s 11ms/step - loss: 2.7422e-04 - accuracy: 0.5366 - val_loss: 0.0000e+00 - val_accuracy: 0.0300 Epoch 4/20 16/16 [==============================] - 0s 11ms/step - loss: 2.7614e-04 - accuracy: 0.4945 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 5/20 16/16 [==============================] - 0s 11ms/step - loss: 2.7924e-04 - accuracy: 0.4931 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 6/20 16/16 [==============================] - 0s 11ms/step - loss: 2.4457e-04 - accuracy: 0.4933 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 7/20 16/16 [==============================] - 0s 11ms/step - loss: 3.3170e-04 - accuracy: 0.4931 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 8/20 16/16 [==============================] - 0s 11ms/step - loss: 3.5839e-04 - accuracy: 0.4931 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 9/20 16/16 [==============================] - 0s 11ms/step - loss: 3.7550e-04 - accuracy: 0.4931 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 10/20 16/16 [==============================] - 0s 11ms/step - loss: 3.7018e-04 - accuracy: 0.4933 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 11/20 16/16 [==============================] - 0s 13ms/step - loss: 5.4659e-04 - accuracy: 0.4933 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 12/20 16/16 [==============================] - 0s 11ms/step - loss: 6.4239e-04 - accuracy: 0.4931 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 13/20 16/16 [==============================] - 0s 11ms/step - loss: 7.3958e-04 - accuracy: 0.4931 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 14/20 16/16 [==============================] - 0s 11ms/step - loss: 0.0013 - accuracy: 0.4434 - val_loss: 0.0000e+00 - val_accuracy: 1.0000 Epoch 15/20 16/16 [==============================] - 0s 11ms/step - loss: 6.7687e-04 - accuracy: 0.4407 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 16/20 16/16 [==============================] - 0s 11ms/step - loss: 5.9926e-04 - accuracy: 0.3167 - val_loss: 0.0000e+00 - val_accuracy: 1.0000 Epoch 17/20 16/16 [==============================] - 0s 11ms/step - loss: 0.0022 - accuracy: 0.3664 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00 Epoch 18/20 16/16 [==============================] - 0s 11ms/step - loss: 0.0017 - accuracy: 0.2534 - val_loss: 0.0000e+00 - val_accuracy: 1.0000 Epoch 19/20 16/16 [==============================] - 0s 11ms/step - loss: 0.0011 - accuracy: 0.2604 - val_loss: 0.0000e+00 - val_accuracy: 1.0000 Epoch 20/20 16/16 [==============================] - 0s 11ms/step - loss: 0.0019 - accuracy: 0.2534 - val_loss: 0.0000e+00 - val_accuracy: 1.0000 71/71 [==============================] - 0s 6ms/step - loss: 0.0064 - accuracy: 0.9996 [0.00637961458414793, 0.9995548129081726] And sometimes I got this weird evaluation (without any changes in my code) : [0.0023984466679394245, 0.0]