Horovod在GPU集群上为深度学习模型提供了一个简单的分布式训练python程序

Question

我正在尝试运行一些示例python3代码https://docs.databricks.com/applications/deep-learning/distributed-training/horovod-runner.html在databricks GPU集群上(具有1个驱动程序和2个工作程序(。

Databricks环境：

ML 6.6, scala 2.11, Spark 2.4.5, GPU 

它用于分布式深度学习模型训练。

一开始我只是尝试了一个非常简单的例子：

from sparkdl import HorovodRunner
hr = HorovodRunner(np=2)
def train():
print('in train')
import tensorflow as tf
print('after import tf')
hvd.init()
print('done')
hr.run(train)

但是，命令一直在运行，没有任何进展。

HorovodRunner will stream all training logs to notebook cell output. If there are too many 
logs, you
can adjust the log level in your train method. Or you can set driver_log_verbosity to
'log_callback_only' and use a HorovodRunner log  callback on the first worker to get concise
progress updates.
The global names read or written to by the pickled function are {'print', 'hvd'}.
The pickled object size is 1444 bytes.
### How to enable Horovod Timeline? ###
HorovodRunner has the ability to record the timeline of its activity with Horovod  Timeline. 
To
record a Horovod Timeline, set the `HOROVOD_TIMELINE` environment variable  to the location 
of the
timeline file to be created. You can then open the timeline file  using the chrome://tracing
facility of the Chrome browser.

我是错过了什么，还是需要设置一些东西来让它发挥作用？

感谢

Answer 1

您的代码在其中没有进行实际的训练。使用更好的示例代码可能会更幸运

https://docs.databricks.com/applications/machine-learning/train-model/distributed-training/mnist-pytorch.html