{"action":"created","issue":{"url":"https://api.github.com/repos/pytorch/ignite/issues/62","repository_url":"https://api.github.com/repos/pytorch/ignite","labels_url":"https://api.github.com/repos/pytorch/ignite/issues/62/labels{/name}","comments_url":"https://api.github.com/repos/pytorch/ignite/issues/62/comments","events_url":"https://api.github.com/repos/pytorch/ignite/issues/62/events","html_url":"https://github.com/pytorch/ignite/issues/62","id":294195217,"number":62,"title":"[Feature Request] Checkpoint Helper Functions","user":{"login":"pkdogcom","id":10513430,"avatar_url":"https://avatars2.githubusercontent.com/u/10513430?v=4","gravatar_id":"","url":"https://api.github.com/users/pkdogcom","html_url":"https://github.com/pkdogcom","followers_url":"https://api.github.com/users/pkdogcom/followers","following_url":"https://api.github.com/users/pkdogcom/following{/other_user}","gists_url":"https://api.github.com/users/pkdogcom/gists{/gist_id}","starred_url":"https://api.github.com/users/pkdogcom/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pkdogcom/subscriptions","organizations_url":"https://api.github.com/users/pkdogcom/orgs","repos_url":"https://api.github.com/users/pkdogcom/repos","events_url":"https://api.github.com/users/pkdogcom/events{/privacy}","received_events_url":"https://api.github.com/users/pkdogcom/received_events","type":"User","site_admin":false},"labels":[{"id":759300265,"url":"https://api.github.com/repos/pytorch/ignite/labels/enhancement","name":"enhancement","color":"84b6eb","default":true},{"id":759300266,"url":"https://api.github.com/repos/pytorch/ignite/labels/help%20wanted","name":"help wanted","color":"33aa3f","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":10,"created_at":"2018-02-04T12:57:19Z","updated_at":"2018-02-12T11:05:38Z","closed_at":null,"author_association":"NONE","body":"When training models, I usually would like to save a checkpoint of the model every few epochs or minutes/hours, so that I can resume training from the latest checkpoint when there are errors either in the codes or hardwares, or when I find it necessary to finetune the learning rate without losing the progress. \r\n\r\nIn Tensorflow, there are some helper functions in the tf.train package to simplify checkpoint handling, like looking up for latest checkpoints As I was wondering if ignite can also include some of those features so that we don't need to write too many boilerplate codes."},"comment":{"url":"https://api.github.com/repos/pytorch/ignite/issues/comments/364891469","html_url":"https://github.com/pytorch/ignite/issues/62#issuecomment-364891469","issue_url":"https://api.github.com/repos/pytorch/ignite/issues/62","id":364891469,"user":{"login":"alykhantejani","id":687194,"avatar_url":"https://avatars1.githubusercontent.com/u/687194?v=4","gravatar_id":"","url":"https://api.github.com/users/alykhantejani","html_url":"https://github.com/alykhantejani","followers_url":"https://api.github.com/users/alykhantejani/followers","following_url":"https://api.github.com/users/alykhantejani/following{/other_user}","gists_url":"https://api.github.com/users/alykhantejani/gists{/gist_id}","starred_url":"https://api.github.com/users/alykhantejani/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/alykhantejani/subscriptions","organizations_url":"https://api.github.com/users/alykhantejani/orgs","repos_url":"https://api.github.com/users/alykhantejani/repos","events_url":"https://api.github.com/users/alykhantejani/events{/privacy}","received_events_url":"https://api.github.com/users/alykhantejani/received_events","type":"User","site_admin":false},"created_at":"2018-02-12T11:05:38Z","updated_at":"2018-02-12T11:05:38Z","author_association":"COLLABORATOR","body":"@elanmart I think saving at iterations would be preferable as sometimes epoch boundaries can be very far apart (e.g. imagenet). Resuming from mid-epoch is a bit tricky, I guess we can choose to define it as:\r\n\r\nIf an epoch is 10 batches, and the engine is saved at batch 5, when resumed only 5 batches will be consumed for the remaining epoch. However, we can't guarantee that you wont see the same data twice in that epoch, as this would also require saving the dataloader/sampler etc (which actually may be possible).\r\n\r\n"}}
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