Install TensorFlow on Apple M1 (M1, Pro, Max) with GPU (Metal)
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-depsto install Apple's TensorFlow dependencies - Run:
pip install tensorflow-metalto install Apple's Metal GPU APIs for TensorFlow - Execute:
pip install tensorflow-macosto install MacOS arm64 version of TensorFlow - Execute:
pip install tensorflow-datasets pandas jupyterlabto install relevant dependencies to run sample code.
Verifying Installation
- Execute:
jupyter-labto 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')))Checking GPU and 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,
)MNSIT