Xcode

从 App Store 安装 Xcode。

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Command Line Tools

从 Apple Developer 下载安装 Xcode Command Line Tools 或者执行以下命令。

$ xcode-select --install

Homebrew

$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Miniforge

Anaconda 无法在 M1 上运行, Miniforge 是用来替代它的。

从 https://github.com/conda-forge/miniforge 下载 Miniforge3-MacOSX-arm64。

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执行以下命令，安装 Miniforge

$ bash Miniforge3-MacOSX-arm64.sh

重启终端并检查 Python 安装情况。

$ which python
/Users/catchzeng/miniforge3/bin/python
$ which pip
/Users/catchzeng/miniforge3/bin/pip

下载 Apple TensorFlow

从 https://github.com/apple/tensorflow_macos/releases 下载 TensorFlow 并解压，然后进入 arm64 目录下。

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创建虚拟环境

创建一个 conda 创建虚拟环境，这里使用 python 3.8 (ATF 2.4 需要)。

$ conda create -n tensorflow python=3.8
$ conda activate tensorflow

安装必须的包

$ brew install libjpeg
$ conda install -y pandas matplotlib scikit-learn jupyterlab

注意: libjpeg 是 matplotlib 需要依赖的库。

安装特殊版本的 pip 和其他包

$ pip install --force pip==20.2.4 wheel setuptools cached-property six packaging

注意: Apple TensorFlow 特殊版本的 pip。

安装 Apple 提供的包(numpy, grpcio, h5py)

$ pip install --upgrade --no-dependencies --force numpy-1.18.5-cp38-cp38-macosx_11_0_arm64.whl grpcio-1.33.2-cp38-cp38-macosx_11_0_arm64.whl h5py-2.10.0-cp38-cp38-macosx_11_0_arm64.whl

安装额外的包

$ pip install absl-py astunparse flatbuffers gast google_pasta keras_preprocessing opt_einsum protobuf tensorflow_estimator termcolor typing_extensions wrapt wheel tensorboard typeguard

安装 TensorFlow

$ pip install --upgrade --no-dependencies --force tensorflow_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl
$ pip install --upgrade --no-dependencies --force
tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl

最后，升级 pip 到正确的版本。

$ pip install --upgrade pip

测试

TensorFlow

$ python
Python 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:57)
[Clang 11.0.1 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
2.4.0-rc0
>>>

JupyterLab

$ jupyter lab

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from tensorflow.keras import layers
from tensorflow.keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

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from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)
test_acc