text: import os
  import logging
  import tensorflow as tf
  import horovod.tensorflow as hvd
  
  
  def hvd_init():
      hvd.init()
      print("I am worker {}/{}".format(hvd.rank(), hvd.size()))
  
      # 设置显示的日志级别，用于控制输出哪些信息
      tf.get_logger().setLevel(logging.INFO)
  
      # 设置gpu信息
      gpus = os.environ.get('CUDA_VISIBLE_DEVICES')
      gpus = tf.config.list_physical_devices('GPU')
      
      # 为程序指定gpu
      tf.config.set_visible_devices(gpus[hvd.local_rank() % len(gpus)], 'GPU')
      # TensorFlow 自动选择一个现有且受支持的设备来运行操作，gpu优先级高于cpu
      tf.config.set_soft_device_placement(True)
      
      # 设置cpu运算的核数
      tf.config.threading.set_inter_op_parallelism_threads(8)
  
  
  class HorovodModel(tf.keras.Model):
  
      def __init__(self, ):
          super().__init__()
          self.iterations = None
          self.clip_grad_fn = None
  
      def compile(self,
                  optimizer,
                  *,
                  dynamic_loss_scale,
                  run_eagerly=False,
                  clip_grad_fn=None):
          # 读取optimizer的迭代次数
          self.iterations = optimizer.iterations
          self.clip_grad_fn = clip_grad_fn
  
          if dynamic_loss_scale:
              optimizer = tf.keras.mixed_precision.LossScaleOptimizer(optimizer)
          else:
              optimizer = tf.keras.mixed_precision.LossScaleOptimizer(
                  optimizer, dynamic=False, initial_scale=1.0)
  
          super(HorovodModel, self).compile(optimizer=optimizer,
                                            run_eagerly=run_eagerly)
  
      # tensorflow默认执行动态图，使用tf.function装饰器后，可将这个函数转化为静态图结点进行加速
      @tf.function
      def hvd_broadcast(self, iterations):
          if iterations == 0:
              hvd.broadcast_variables(self.variables, root_rank=0)
              hvd.broadcast_variables(self.optimizer.variables(), root_rank=0)
  
      def train_step(self, *, tape, loss):
          iterations = self.iterations.read_value()
  
          with tape:
              scaled_loss = self.optimizer.get_scaled_loss(loss)
  
          # 用于在tensorboard中显示标量信息
          tf.summary.scalar('loss_scale',
                            tf.math.divide_no_nan(scaled_loss, loss))
          
          # 多机多卡计算梯度
          tape = hvd.DistributedGradientTape(tape)
          vars = self.trainable_variables
          scaled_grads = tape.gradient(scaled_loss, vars)
          grads = self.optimizer.get_unscaled_gradients(scaled_grads)
          
          # 梯度裁减
          if self.clip_grad_fn is not None:
              grads = self.clip_grad_fn(grads, vars)
              
          # 更新参数
          self.optimizer.apply_gradients(zip(grads, vars))
  
          # 更新iterations的值
          self.iterations.assign(iterations + 1, read_value=False)
          
          # 如果是第0步则需要广播模型参数和optimizer的参数
          self.hvd_broadcast(iterations)
  
          return {'loss': loss}
  
      def fit(
          self,
          dataset,
          epochs,
          logdir,
          summary_freq = 100,
          validation_data = None,
      ):
          # 新建一个CheckpointManager用于checkpoint储存，只在主进程上保存
          ckpt = tf.train.Checkpoint(net=self, optimizer=self.optimizer)
          ckpt_manager = tf.train.CheckpointManager(ckpt, logdir, max_to_keep=3)
  
          def save_ckpt():
              ckpt_manager.save(checkpoint_number=self.iterations)
  
          cpkt_callback = tf.keras.callbacks.LambdaCallback(
              on_epoch_end=lambda epoch, logs: save_ckpt())
  
          # 打印log
          def print_log(logs):
              print('iter {} loss: {:.6f}'.format(self.iterations.read_value(),
                                                  logs['loss']))
  
          log_callback = tf.keras.callbacks.LambdaCallback(
              on_batch_end=lambda batch, logs: print_log(logs))
  
          # 启动tensorboard
          tb_callback = tf.keras.callbacks.TensorBoard(
              log_dir=logdir if hvd.rank() == 0 else '/tmp',
              write_graph=True,
              update_freq=summary_freq)
  
          if hvd.rank() == 0:
              callbacks = [cpkt_callback, log_callback, tb_callback]
          else:
              callbacks = [log_callback, tb_callback]
  
          # verbose：日志显示
          # verbose = 0 为不在标准输出流输出日志信息
          # verbose = 1 为输出进度条记录
          # verbose = 2 为每个epoch输出一行记录
          super(HorovodModel, self).fit(
              dataset,
              epochs = epochs,
              initial_epoch = self.iterations // len(dataset),
              verbose = 2,
              callbacks=callbacks,
              validation_data = validation_data,
          )
  
      # 重新加载logdir中的模型checkpoint
      # Checkpoint只保存模型的参数，不保存模型的计算过程，因此一般用于在具有模型源代码的时候恢复之前训练好的模型参数
      # 如果需要导出模型（无需源代码也能运行模型）请使用SavedModel
      def restore_or_initialize(self, logdir):
          # 初始化的参数是一系列的键值对，键名可以随意取，值为需要保存的对象
          ckpt = tf.train.Checkpoint(net=self, optimizer=self.optimizer)
          # 定义一个checkpointmanager来管理模型的checkpoint
          ckpt_manager = tf.train.CheckpointManager(
              ckpt,
              logdir,
              max_to_keep=None,
          )
          # 如果有checkpoint加载，否则_init_fn
          latest = ckpt_manager.restore_or_initialize()
          if latest:
              print('Restore checkpoint from {}'.format(latest))
  