代码中要包含以下6步：

1. 初始化

   Run hvd.init() to initialize Horovod.

2. 将每个GPU固定到单个进程以避免资源争用。

   一个线程一个GPU，设置到 local rank ，第一个线程将分配给第一个GPU。第二个线程将分配给第二个GPU 向每个 TensorFlow 进程分配一个 GPU

   config = tf.ConfigProto()
   config.gpu_options.visible_device_list = str(hvd.local_rank())

3. 根据worker的数量，来确定学习率

   loss = …
   opt = tf.train.AdagradOptimizer(0.01 * hvd.size())

4. 使用 Horovod 优化器包裹每一个常规 TensorFlow 优化器，Horovod 优化器使用 ring-allreduce 平均梯度

   opt = hvd.DistributedOptimizer(opt)

5. 将变量从第一个流程向其他流程传播，以实现一致性初始化. 从 rank 0 广播到所有的线程

   hooks = [hvd.BroadcastGlobalVariablesHook(0)]

6. 将checkpoints 保存在worker0上

   with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir,
   config=config,
   hooks=hooks) as mon_sess:

   import tensorflow as tf
   import horovod.tensorflow as hvd

   Initialize Horovod

   hvd.init()

   Pin GPU to be used to process local rank (one GPU per process)

   config = tf.ConfigProto()
   config.gpu_options.visible_device_list = str(hvd.local_rank())

   Build model…

   loss = …
   opt = tf.train.AdagradOptimizer(0.01 * hvd.size())

   Add Horovod Distributed Optimizer

   opt = hvd.DistributedOptimizer(opt)

   Add hook to broadcast variables from rank 0 to all other processes during

   initialization.

   hooks = [hvd.BroadcastGlobalVariablesHook(0)]

   Make training operation

   train_op = opt.minimize(loss)

   Save checkpoints only on worker 0 to prevent other workers from corrupting them.

   checkpoint_dir = '/tmp/train_logs' if hvd.rank() == 0 else None

   The MonitoredTrainingSession takes care of session initialization,

   restoring from a checkpoint, saving to a checkpoint, and closing when done

   or an error occurs.

   with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir,
   config=config,
   hooks=hooks) as mon_sess:
   while not mon_sess.should_stop():
   # Perform synchronous training.
   mon_sess.run(train_op)