Python tf.profiler.experimental.client.trace用法及代码示例

将 gRPC 请求发送到一个或多个分析器服务器以执行 on-demand 分析。

用法

tf.profiler.experimental.client.trace(
    service_addr, logdir, duration_ms, worker_list='',
    num_tracing_attempts=3, options=None
)

此方法将阻塞调用线程，直到它收到来自所有服务器的响应或直到截止日期到期。 CPU、GPU 和 TPU 支持单主机和多主机分析。分析结果将由每个服务器保存到指定的 TensorBoard 日志目录(即您保存模型检查点的目录)。使用 TensorBoard 配置文件插件查看可视化和分析结果。

使用示例(CPU/GPU)：

# Start a profiler server before your model runs.
  tf.profiler.experimental.server.start(6009)
  # (Model code goes here).
  # Send gRPC request to the profiler server to collect a trace of your model.
  tf.profiler.experimental.client.trace('grpc://localhost:6009',
                                        '/nfs/tb_log', 2000)

示例用法(多个 GPU)：

# E.g. your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you
  # would like to schedule start of profiling 1 second from now, for a
  # duration of 2 seconds.
  options['delay_ms'] = 1000
  tf.profiler.experimental.client.trace(
      'grpc://10.0.0.2:8466,grpc://10.0.0.3:8466,grpc://10.0.0.4:8466',
      'gs://your_tb_dir',
      2000,
      options=options)

示例用法(TPU)：

# Send gRPC request to a TPU worker to collect a trace of your model. A
  # profiler service has been started in the TPU worker at port 8466.
  # E.g. your TPU IP address is 10.0.0.2 and you want to profile for 2 seconds
  # .
  tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
                                        'gs://your_tb_dir', 2000)

示例用法(多个 TPU)：

# Send gRPC request to a TPU pod to collect a trace of your model on
  # multiple TPUs. A profiler service has been started in all the TPU workers
  # at the port 8466.
  # E.g. your TPU IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you want
  # to profile for 2 seconds.
  tf.profiler.experimental.client.trace(
      'grpc://10.0.0.2:8466',
      'gs://your_tb_dir',
      2000,
      '10.0.0.2:8466,10.0.0.3:8466,10.0.0.4:8466')

启动 TensorBoard 并将其指向您提供给此 API 的同一 logdir。

# logdir can be gs://your_tb_dir as in the above examples.
  $ tensorboard --logdir=/tmp/tb_log

打开浏览器并转到 localhost:6006/#profile 以查看分析结果。