{"action":"created","issue":{"url":"https://api.github.com/repos/NifTK/NiftyNet/issues/185","repository_url":"https://api.github.com/repos/NifTK/NiftyNet","labels_url":"https://api.github.com/repos/NifTK/NiftyNet/issues/185/labels{/name}","comments_url":"https://api.github.com/repos/NifTK/NiftyNet/issues/185/comments","events_url":"https://api.github.com/repos/NifTK/NiftyNet/issues/185/events","html_url":"https://github.com/NifTK/NiftyNet/issues/185","id":346316583,"node_id":"MDU6SXNzdWUzNDYzMTY1ODM=","number":185,"title":"Ensure efficient IO when training on large sets of 2D images","user":{"login":"tvercaut","id":1614505,"node_id":"MDQ6VXNlcjE2MTQ1MDU=","avatar_url":"https://avatars2.githubusercontent.com/u/1614505?v=4","gravatar_id":"","url":"https://api.github.com/users/tvercaut","html_url":"https://github.com/tvercaut","followers_url":"https://api.github.com/users/tvercaut/followers","following_url":"https://api.github.com/users/tvercaut/following{/other_user}","gists_url":"https://api.github.com/users/tvercaut/gists{/gist_id}","starred_url":"https://api.github.com/users/tvercaut/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tvercaut/subscriptions","organizations_url":"https://api.github.com/users/tvercaut/orgs","repos_url":"https://api.github.com/users/tvercaut/repos","events_url":"https://api.github.com/users/tvercaut/events{/privacy}","received_events_url":"https://api.github.com/users/tvercaut/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-07-31T18:58:18Z","updated_at":"2018-07-31T19:14:08Z","closed_at":null,"author_association":"MEMBER","body":"As discussed with @atbenmurray and previously with @wyli and @luiscarlosgph, this is a follow up of [cmiclab issue #205](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/issues/205). We now have support for 2D images but it's rather crude and would benefit from being optimised for example by storing the images in a dedicated high-performance database (LMDB?).\r\n\r\nThe first task would be to look into the current state of the art in other TF-based projects for that."},"comment":{"url":"https://api.github.com/repos/NifTK/NiftyNet/issues/comments/409335581","html_url":"https://github.com/NifTK/NiftyNet/issues/185#issuecomment-409335581","issue_url":"https://api.github.com/repos/NifTK/NiftyNet/issues/185","id":409335581,"node_id":"MDEyOklzc3VlQ29tbWVudDQwOTMzNTU4MQ==","user":{"login":"tvercaut","id":1614505,"node_id":"MDQ6VXNlcjE2MTQ1MDU=","avatar_url":"https://avatars2.githubusercontent.com/u/1614505?v=4","gravatar_id":"","url":"https://api.github.com/users/tvercaut","html_url":"https://github.com/tvercaut","followers_url":"https://api.github.com/users/tvercaut/followers","following_url":"https://api.github.com/users/tvercaut/following{/other_user}","gists_url":"https://api.github.com/users/tvercaut/gists{/gist_id}","starred_url":"https://api.github.com/users/tvercaut/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tvercaut/subscriptions","organizations_url":"https://api.github.com/users/tvercaut/orgs","repos_url":"https://api.github.com/users/tvercaut/repos","events_url":"https://api.github.com/users/tvercaut/events{/privacy}","received_events_url":"https://api.github.com/users/tvercaut/received_events","type":"User","site_admin":false},"created_at":"2018-07-31T19:14:08Z","updated_at":"2018-07-31T19:14:08Z","author_association":"MEMBER","body":"Probably best to stick to the recommended TFRecord if possible. Some relevant links:\r\nhttps://www.tensorflow.org/performance/datasets_performance\r\nhttps://github.com/tensorflow/tensorflow/issues/21129\r\nhttps://stackoverflow.com/questions/48309631/tensorflow-tf-data-dataset-reading-large-hdf5-files"}}
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