Note: Uninstall Anaconda/Anaconda Navigator and other related previously installed version of conda-based installations. Anaconda and Miniforge cannot co-exist together.

Installing Miniforge

• Install miniforge from brew: brew install miniforge
• Create an anaconda environment: conda create -n tf
• Activate the environment: conda activate tf
• Install Python: conda install python

Installing TensorFlow

• Run: conda install -c apple tensorflow-deps to install Apple's TensorFlow dependencies
• Run: pip install tensorflow-metal to install Apple's Metal GPU APIs for TensorFlow
• Execute: pip install tensorflow-macos to install MacOS arm64 version of TensorFlow
• Execute: pip install tensorflow-datasets pandas jupyterlab to install relevant dependencies to run sample code.

Verifying Installation

• Execute: jupyter-lab to open a Jupyter Notebook and run the following code:

    import tensorflow as tf
    import tensorflow_datasets as tfds
    print("TensorFlow version:", tf.__version__)
    print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
    print("Num CPUs Available: ", len(tf.config.experimental.list_physical_devices('CPU')))

Running A Sample Code (MNIST)

    (ds_train, ds_test), ds_info = tfds.load(
        'mnist',
        split=['train', 'test'],
        shuffle_files=True,
        as_supervised=True,
        with_info=True,
    )

    def normalize_img(image, label):
      """Normalizes images: `uint8` -> `float32`."""
      return tf.cast(image, tf.float32) / 255., label

    batch_size = 128
    ds_train = ds_train.map(
        normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
    ds_train = ds_train.cache()
    ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
    ds_train = ds_train.batch(batch_size)
    ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
    ds_test = ds_test.map(
        normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)

    ds_test = ds_test.batch(batch_size)
    ds_test = ds_test.cache()
    ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)

    model = tf.keras.models.Sequential([
      tf.keras.layers.Conv2D(32, kernel_size=(3, 3),
                     activation='relu'),
      tf.keras.layers.Conv2D(64, kernel_size=(3, 3),
                     activation='relu'),
      tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(128, activation='relu'),
      tf.keras.layers.Dense(10, activation='softmax')
    ])

    model.compile(
        loss='sparse_categorical_crossentropy',
        optimizer=tf.keras.optimizers.Adam(0.001),
        metrics=['accuracy'],
    )

    model.fit(
        ds_train,
        epochs=12,
        validation_data=ds_test,
    )

Credits

• Nachiket Sirsikar