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View Code? Open in Web Editor NEWImplementing Google's DistBelief paper
License: GNU General Public License v3.0
Implementing Google's DistBelief paper
License: GNU General Public License v3.0
Planning on implementing a decentralized version of asyncSGD.
Current approach is just a ring-based network, but ill investigate different structures.
curious how much time is spent is send/recv for tensors vs training
i.e. different clients can train different models
So that clients and servers can use different optimization techniques. Right now it looks like -lr*grad is being sent over, which is sgd only
Hey, I am trying to implement distbelief on a single machine with multiple-GPUs but neither GLOO nor NCCL support send or recv, so is there a workaround this? In general is point to point communication possible between GPUs using any of these backend?
traces:
[1] 98051 segmentation fault /opt/homebrew/bin/python3.5 first_node.py
libc++abi.dylib: terminating with uncaught exception of type std::__1::system_error: mutex lock failed: Invalid argument [1] 98060 abort /opt/homebrew/bin/python3.5 second_node.py
its not going to be faster :( but i hope it will be
for example for parameter_request
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