TensorFlow Docker
使用 TensorFlow 创建生产级机器学习模型
Docker
docker run [-it] [--rm] [-p hostPort:containerPort] tensorflow/tensorflow[:tag] [command]
使用仅支持 CPU 的映像的示例
docker run -it --rm tensorflow/tensorflow \
python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
docker run -it --rm tensorflow/tensorflow bash
docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-jupyter
GPU 支持
lspci | grep -i nvidia
docker run --gpus all --rm nvidia/cuda nvidia-smi
使用支持 GPU 的映像的示例
docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu \
python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
docker run --gpus all -it tensorflow/tensorflow:latest-gpu bash
jupyter/tensorflow-notebook
Single Machine Mode
import tensorflow as tf
hello = tf.Variable("Hello World!")
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
sess.run(hello)
Distributed Mode
import tensorflow as tf
hello = tf.Variable("Hello Distributed World!")
server = tf.train.Server.create_local_server()
sess = tf.Session(server.target)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(hello)