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Hi team, It's nice that tensorflow can now running on my MacBook Air M1 (macOS 12). Have tried it on python 3.9 and 3.8. Comparing the performance, looks 3.8 version release by apple team is much faster. See below: Code for testing: import tensorflow as tf import time 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, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) start = time.time() model.fit(x_train, y_train, epochs=5) end = time.time() model.evaluate(x_test, y_test) print(end - start) below is result of python 3.9, tensorflow-macos released from google team. using tensorflow-metal. Init Plugin Init Graph Optimizer Init Kernel Metal device set to: Apple M1 systemMemory: 16.00 GB maxCacheSize: 5.33 GB 2021-08-26 23:05:31.742656: 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. 2021-08-26 23:05:31.742796: 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>) 2021-08-26 23:05:31.982641: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2) 2021-08-26 23:05:31.984696: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz Epoch 1/5 2021-08-26 23:05:32.121772: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 1875/1875 [==============================] - 8s 4ms/step - loss: 0.2914 - accuracy: 0.9160 Epoch 2/5 1875/1875 [==============================] - 8s 4ms/step - loss: 0.1454 - accuracy: 0.9569 Epoch 3/5 1875/1875 [==============================] - 8s 4ms/step - loss: 0.1104 - accuracy: 0.9669 Epoch 4/5 1875/1875 [==============================] - 8s 4ms/step - loss: 0.0878 - accuracy: 0.9728 Epoch 5/5 1875/1875 [==============================] - 8s 5ms/step - loss: 0.0756 - accuracy: 0.9764 2021-08-26 23:06:13.969665: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled. 313/313 [==============================] - 1s 4ms/step - loss: 0.0814 - accuracy: 0.9752 42.12474703788757 below is result of python 3.8. tensorflow-macos released from apple team. 2021-08-26 23:06:40.105911: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2) 2021-08-26 23:06:40.106407: W tensorflow/core/platform/profile_utils/cpu_utils.cc:126] Failed to get CPU frequency: 0 Hz Epoch 1/5 1875/1875 [==============================] - 1s 382us/step - loss: 0.5059 - accuracy: 0.8497 Epoch 2/5 1875/1875 [==============================] - 1s 380us/step - loss: 0.1957 - accuracy: 0.9406 Epoch 3/5 1875/1875 [==============================] - 1s 375us/step - loss: 0.1563 - accuracy: 0.9522 Epoch 4/5 1875/1875 [==============================] - 1s 375us/step - loss: 0.1336 - accuracy: 0.9596 Epoch 5/5 1875/1875 [==============================] - 1s 418us/step - loss: 0.1213 - accuracy: 0.9633 313/313 [==============================] - 0s 255us/step - loss: 0.0831 - accuracy: 0.9741 3.882438898086548 As you can see the result is quite different. Almost 14 times... python 3.9 version: 42.12s python 3.8 version: 3.88s Understand python 3.8 version is used "ML compute", is trained very faster. Is it possible to make it on python 3.9 ? or anyway I can find some reference to do it ? On the other hand, the reason needs to use python 3.9 is error occur when using "class_weight" on tensorflow classification mode. can refer to this GitHub issue 275 can you pls maybe give me a guide ? Thanks in advance.