ML-Engine with GPUs workers errors - google-cloud-ml-engine

Question

Hi I am using ML Engine with a custom tier made up of a complex_m master, four workers each with a GPU and one complex_m as parameter server.
The model is training a CNN. However, there seem to be trouble with the workers.
This is an image of the logs https://i.stack.imgur.com/VJqE0.png.
The master still seems to be working because there are session checkpoints being saved, however, this is nowwhere near the speed it should be.
With complex_m workers, the model works. It just gives a waiting for the model to be ready in the beginning (i assume it is until the master intializes global variables, correct me if i am wrong) and then works normally. With GPUs however there seem to be a problem with the task.
I didnt' use the tf.Device() function anywhere, in the cloud i thought the device is set automatically if a GPU is available.
I followed the Census example and loaded the TF_CONFIG environment variable.
tf.logging.info('Setting up the server')
tf_config = os.environ.get('TF_CONFIG')
# If TF_CONFIG is not available run local
if not tf_config:
return run('', True, *args, **kwargs)
tf_config_json = json.loads(tf_config)
cluster = tf_config_json.get('cluster')
job_name = tf_config_json.get('task', {}).get('type')
task_index = tf_config_json.get('task', {}).get('index')
# If cluster information is empty run local
if job_name is None or task_index is None:
return run('', True, *args, **kwargs)
cluster_spec = tf.train.ClusterSpec(cluster)
server = tf.train.Server(cluster_spec,
job_name=job_name,
task_index=task_index)
# Wait for incoming connections forever
# Worker ships the graph to the ps server
# The ps server manages the parameters of the model.
if job_name == 'ps':
server.join()
return
elif job_name in ['master', 'worker']:
return run(server.target, job_name == 'master', *args, **kwargs)
Then used the tf.replica_device_setter before defining the main graph.
As a session i am using tf.train.MonitoredTrainingSession, this should handle the initialization of variables and checkpoint saving. I do not know why the workers are saying that the variables are not initialized.
Variables to be initialized are all variables: https://i.stack.imgur.com/hAHPL.png
Optimizer: AdaDelta
I appreciate the help!

Answer 1

In the comments, you seem to have answered your own question (using cluster_spec in replica_setter). Allow me to address the issue of throughput of a cluster of CPUs vs. a cluster of GPUs.
GPUs are fairly powerful. You'll typically get higher throughput by getting a single machine with many GPUs rather than having many machines each with a single GPU. That's because the communication overhead becomes a bottleneck (the bandwidth and latency to main memory on the same machine is much better than communicating with a parameter server on a remote machine).
The reason for the GPUs being slower than CPUs may be due to the extra overhead of GPUs needing to copy data from main memory to the GPU and back. If you're doing a lot of parallelizable computation, then this copy is negligible. Your model may be doing too little on the GPU and the overhead may swamp the actual computation.
For more information about building high performance models, see this guide.
In the meantime, I recommend using a single machine with more GPUs to see if that helps:
{
"scaleTier": "CUSTOM",
"masterType": "complex_model_l_gpu",
...
}
Just beware, that you'll have to modify your code to assign ops to the right GPUs, probably using towers.