A simple distributed training python program for deep learning models by Horovod on GPU cluster

Question

I am trying to run some example python3 code https://docs.databricks.com/applications/deep-learning/distributed-training/horovod-runner.html on databricks GPU cluster (with 1 driver and 2 workers).

Databricks environment:

 ML 6.6, scala 2.11, Spark 2.4.5, GPU 

It is for distributed deep learning model training.

I just tried a very simple example at first:

 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)

But, the command is alway running without any progress.

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.

Do I miss something or need to set up something to make it work ?

Thanks

Answer 1

your code does no actual training within it.. you might have better luck with the better example code

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