NVIDIA Jetson Nano Dev Kit B01に TensorFlow をインストールする

$ sudo apt-get install libhdf5-serial-dev hdf5-tools

$ sudo apt-get install python3-pip

$ sudo apt-get install zlib1g-dev zip libjpeg8-dev libhdf5-dev 

$ sudo pip3 install -U numpy grpcio absl-py py-cpuinfo psutil portpicker grpcio six mock requests gast h5py astor termcolor

$ sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v42 tensorflow-gpu

$ python3
Python 3.6.9 (default, Apr 18 2020, 01:56:04) 
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
1.13.1
>>> quit()

mkdir logs
gedit mnist.py

import tensorflow as tf
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(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

log_filepath = "./logs/"
tb_cb = tf.keras.callbacks.TensorBoard(log_dir=log_filepath, histogram_freq=1)

model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test), callbacks=[tb_cb])
model.evaluate(x_test, y_test)

model.save('mnist.hdf5')

$ tensorboard --logdir=./logs