{"action":"closed","issue":{"url":"https://api.github.com/repos/pytorch/ignite/issues/181","repository_url":"https://api.github.com/repos/pytorch/ignite","labels_url":"https://api.github.com/repos/pytorch/ignite/issues/181/labels{/name}","comments_url":"https://api.github.com/repos/pytorch/ignite/issues/181/comments","events_url":"https://api.github.com/repos/pytorch/ignite/issues/181/events","html_url":"https://github.com/pytorch/ignite/issues/181","id":322513913,"number":181,"title":"[Feature Request] Hyper-parameter optimization in ignite","user":{"login":"walkacross","id":22115338,"avatar_url":"https://avatars1.githubusercontent.com/u/22115338?v=4","gravatar_id":"","url":"https://api.github.com/users/walkacross","html_url":"https://github.com/walkacross","followers_url":"https://api.github.com/users/walkacross/followers","following_url":"https://api.github.com/users/walkacross/following{/other_user}","gists_url":"https://api.github.com/users/walkacross/gists{/gist_id}","starred_url":"https://api.github.com/users/walkacross/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/walkacross/subscriptions","organizations_url":"https://api.github.com/users/walkacross/orgs","repos_url":"https://api.github.com/users/walkacross/repos","events_url":"https://api.github.com/users/walkacross/events{/privacy}","received_events_url":"https://api.github.com/users/walkacross/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":12,"created_at":"2018-05-12T14:03:13Z","updated_at":"2018-05-17T14:53:11Z","closed_at":"2018-05-17T14:53:11Z","author_association":"NONE","body":"Hey guys, do you think it's a good idea that ignite supports hyper-parameter optimization/ finetune? \r\nI am wondering what's the best practice for finetune in ignite?\r\n\r\n1 Do hyper-paramter grid search in run function(like in mnist.py example)? \r\nthis is simple and straight by creating different trainers with for-loop, but may be lack of efficiency."}}
{"actor":{"display_login":"vfdev-5"}}