Distributed Erlang node with multiple names

Distributed Erlang node with multiple names

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Distributed Erlang node with multiple names
Tag : erlang , By : Bharath
Date : November 25 2020, 01:01 AM

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Erlang: What's a good way to automatically assign node names?

Tag : erlang , By : snapshooter
Date : March 29 2020, 07:55 AM
To fix this issue Use uuidgen, or the equivalent API if your shell scripting language isn't (ba)sh. If you don't want to see single quotes around node names, then replace - with _ and add a prefix, just in case the first character is a decimal digit.
(Sorry, if I'm stating the obvious, but...) If you want the node to be discoverable, get the initialisation code to publish the node to some kind of directory service node.

Can I set multiple cookies for a given Erlang node?

Tag : erlang , By : Vitalik
Date : March 29 2020, 07:55 AM
may help you . As explained here and as mentioned by @legoscia in the comments:

How can I run an Erlang Service in Windows with long node names?

Tag : windows , By : Ben
Date : March 29 2020, 07:55 AM
Hope that helps It turns out that the vm.args file specifies a name also, which can conflict with the name specified on the erlsrv command. I fixed it by making a new win_vm.args without the -name parameter, and changing the start_erl.cmd to look for the windows version of the file. I also changed all the -sname to -name options in the application cmd script.

How to start distributed Erlang app without starting dependencies at every node?

Tag : erlang , By : lili
Date : March 29 2020, 07:55 AM
will help you I tried to run a simple app in a distributed manner to test failover-takeover features but failed. , Now this gets a bit confusing because you say:
  [{distributed, [{myapp_api,
    ['n1@myhost', {'n2@myhost', 'n3@myhost'}]}]},
    {sync_nodes_optional, ['n2@myhost', 'n3@myhost']},
    {sync_nodes_timeout, 5000}
  [{distributed, [
     {myapp_api, 1000, ['n1@myhost', {'n2@myhost', 'n3@myhost'}]},
     {myapp, 1000, ['n1@myhost', {'n2@myhost', 'n3@myhost'}]},
   {sync_nodes_optional, ['n2@myhost', 'n3@myhost']},
   {sync_nodes_timeout, 5000}
  [{distributed, [
     {myapp, 1000, ['n1@myhost', {'n2@myhost', 'n3@myhost'}]},
   {sync_nodes_optional, ['n2@myhost', 'n3@myhost']},
   {sync_nodes_timeout, 5000}

Does distributed training produce NN that is average of NNs trained within each distributed node?

Tag : python , By : user119413
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further The question in the title is different that the question in the body :) I'll answer both:
Title question: "Does distributed training produce NN that is average of NNs trained within each distributed node?"
# create Parameter dict storing model parameters
p1 = net1.collect_params()
p2 = net2.collect_params()
p3 = net3.collect_params()

for k1, k2, k3 in zip(p1, p2, p3):
    p3[k3].set_data(0.5*(p1[k1].data() + p2[k2].data()))
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