{"action":"opened","issue":{"url":"https://api.github.com/repos/pytorch/fairseq/issues/266","repository_url":"https://api.github.com/repos/pytorch/fairseq","labels_url":"https://api.github.com/repos/pytorch/fairseq/issues/266/labels{/name}","comments_url":"https://api.github.com/repos/pytorch/fairseq/issues/266/comments","events_url":"https://api.github.com/repos/pytorch/fairseq/issues/266/events","html_url":"https://github.com/pytorch/fairseq/issues/266","id":356203445,"node_id":"MDU6SXNzdWUzNTYyMDM0NDU=","number":266,"title":"Multiple copies of data caused OOM","user":{"login":"zxw866","id":12081085,"node_id":"MDQ6VXNlcjEyMDgxMDg1","avatar_url":"https://avatars1.githubusercontent.com/u/12081085?v=4","gravatar_id":"","url":"https://api.github.com/users/zxw866","html_url":"https://github.com/zxw866","followers_url":"https://api.github.com/users/zxw866/followers","following_url":"https://api.github.com/users/zxw866/following{/other_user}","gists_url":"https://api.github.com/users/zxw866/gists{/gist_id}","starred_url":"https://api.github.com/users/zxw866/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zxw866/subscriptions","organizations_url":"https://api.github.com/users/zxw866/orgs","repos_url":"https://api.github.com/users/zxw866/repos","events_url":"https://api.github.com/users/zxw866/events{/privacy}","received_events_url":"https://api.github.com/users/zxw866/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-09-01T13:42:48Z","updated_at":"2018-09-01T13:42:48Z","closed_at":null,"author_association":"NONE","body":"My training data is about 200 million, and the size is 50G. I found that each GPU's process reads a copy of data into memory, resulting in more than 400G of memory usage. Then the program ran out of memory and crashed. Have you encountered similar problems in the experiment of Paracrawl data?  How can I solve this problem?"}}
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