Horovod-Usage

Usage

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)