Comments (3)
Thanks for pointing it out. The class Item will be depressed because we have a better way to send commands between clients and servers. In fact, we are developing a quite difference but much better version on another repository, and will update this one soon. Please have a check later.
from parameter_server.
That's great.. I have been studying about using distributed systems for scaling machine learning problems. I came across this paper "Parameter Server for Distributed Machine Learning" and have gone through it a few times. I understood the basic idea and now want to know about it even more. Can you provide me with the link to the repo over which its modified version is being built on.
Thanks..
from parameter_server.
We are developing on a private github, and will make it public once the codes are in a good shape. I do not suggest you to read the current codes now, because we may greatly change the architecture in this month. If you are interested, I could ping you if the new version is ready.
For distributed machine learning systems, I suggest you to look at MPI (a traditional way people write distributed applications), Graphlab (quite successful on graph computing), and Spark (in memory hadoop, quite fast if the data is in a good shape).
from parameter_server.
Related Issues (20)
- Running Example problem
- bug in the file "eval_online.conf" HOT 1
- questions about darlin HOT 2
- MPI config file HOT 1
- Workers dynamically join & leave HOT 2
- Missing .h file after "make clean" HOT 1
- max_delay in async_sgd HOT 1
- How to train a model on a fixed dataset using SGD? HOT 1
- Limit on the minibatch config in the async_sgd app? HOT 2
- APIs for Java HOT 2
- Error building linear example HOT 1
- Error when make on ubuntu 12.04
- running error.
- error when make parameter_server in MacOS 10.10.4 HOT 1
- what's the defalut password & the error" libzmq.so.4: cannot open shared " HOT 2
- Error building on Mac OS X: error: use of undeclared identifier 'CLOCK_MONOTONIC_RAW'
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- sse4.2 instruction set not enabled HOT 2
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from parameter_server.