{"action":"created","issue":{"url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/issues/11","repository_url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries","labels_url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/issues/11/labels{/name}","comments_url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/issues/11/comments","events_url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/issues/11/events","html_url":"https://github.com/stanford-futuredata/dawn-bench-entries/pull/11","id":312577720,"number":11,"title":"Intel(R) Optimized Caffe inference on AWS c5.4xlarge","user":{"login":"daisyden","id":27668899,"avatar_url":"https://avatars2.githubusercontent.com/u/27668899?v=4","gravatar_id":"","url":"https://api.github.com/users/daisyden","html_url":"https://github.com/daisyden","followers_url":"https://api.github.com/users/daisyden/followers","following_url":"https://api.github.com/users/daisyden/following{/other_user}","gists_url":"https://api.github.com/users/daisyden/gists{/gist_id}","starred_url":"https://api.github.com/users/daisyden/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/daisyden/subscriptions","organizations_url":"https://api.github.com/users/daisyden/orgs","repos_url":"https://api.github.com/users/daisyden/repos","events_url":"https://api.github.com/users/daisyden/events{/privacy}","received_events_url":"https://api.github.com/users/daisyden/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2018-04-09T15:28:29Z","updated_at":"2018-04-15T21:27:00Z","closed_at":null,"author_association":"NONE","pull_request":{"url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/pulls/11","html_url":"https://github.com/stanford-futuredata/dawn-bench-entries/pull/11","diff_url":"https://github.com/stanford-futuredata/dawn-bench-entries/pull/11.diff","patch_url":"https://github.com/stanford-futuredata/dawn-bench-entries/pull/11.patch"},"body":"Hi, \r\nWe run Intel(R) Optimized Caffe on AWS c5.4xlarge and this  is the result. Please have a review.\r\n\r\nThanks\r\nDaisy"},"comment":{"url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/issues/comments/381439365","html_url":"https://github.com/stanford-futuredata/dawn-bench-entries/pull/11#issuecomment-381439365","issue_url":"https://api.github.com/repos/stanford-futuredata/dawn-bench-entries/issues/11","id":381439365,"user":{"login":"codyaustun","id":549702,"avatar_url":"https://avatars2.githubusercontent.com/u/549702?v=4","gravatar_id":"","url":"https://api.github.com/users/codyaustun","html_url":"https://github.com/codyaustun","followers_url":"https://api.github.com/users/codyaustun/followers","following_url":"https://api.github.com/users/codyaustun/following{/other_user}","gists_url":"https://api.github.com/users/codyaustun/gists{/gist_id}","starred_url":"https://api.github.com/users/codyaustun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/codyaustun/subscriptions","organizations_url":"https://api.github.com/users/codyaustun/orgs","repos_url":"https://api.github.com/users/codyaustun/repos","events_url":"https://api.github.com/users/codyaustun/events{/privacy}","received_events_url":"https://api.github.com/users/codyaustun/received_events","type":"User","site_admin":false},"created_at":"2018-04-15T21:27:00Z","updated_at":"2018-04-15T21:27:00Z","author_association":"CONTRIBUTOR","body":"@daisydeny Is the relevant code [L586-L645](https://github.com/intel/caffe/blob/e1c2efe751f82f0dbe958a02c20257811b509772/tools/caffe.cpp#L586-L645)? The code looks good to me, but I just want to clarify a few points:\r\n\r\n1) is the latency number you're reporting the average forward pass as defined on [L636](https://github.com/intel/caffe/blob/e1c2efe751f82f0dbe958a02c20257811b509772/tools/caffe.cpp#L636)?\r\n2) Should the `--forward_only` flag be included in the [command line you gave](https://github.com/intel/caffe/tree/DAWN_Bench#inference)? I don't think this should affect the result based on your code.\r\n3) For data loading, you can start from memory on the host machine. In this case, you don't need to include the time to read `compressed_lmdb` as you mentioned in your earlier comment\r\n\r\nFrom a reproducibility stand point, I couldn't get your code to work. Both the Docker and from source instructions don't work for this branch. After rebasing on master, your code compiled but running the inference command gave the following error:\r\n\r\n```layer {\r\n  name: \"loss3/top-5\"\r\n  type: \"Accuracy\"\r\n  bottom: \"fc1000_fc1000_0_split_2\"\r\n  bottom: \"label_data_1_split_2\"\r\n  top: \"loss3/top-5\"\r\n  accuracy_param {\r\n    top_k: 5\r\n  }\r\n}\r\nI0415 19:20:55.471315 11546 layer_factory.hpp:114] Creating layer data\r\nI0415 19:20:55.471535 11546 net.cpp:261] Creating Layer data\r\nI0415 19:20:55.471552 11546 net.cpp:1472] data -> data\r\nI0415 19:20:55.471577 11546 net.cpp:1472] data -> label\r\nF0415 19:20:55.471738 11556 db_lmdb.hpp:53] Check failed: mdb_status == 0 (13 vs. 0) Permission denied\r\n*** Check failure stack trace: ***\r\n    @     0x7faf06daf5cd  google::LogMessage::Fail()\r\n    @     0x7faf06db1433  google::LogMessage::SendToLog()\r\n    @     0x7faf06daf15b  google::LogMessage::Flush()\r\n    @     0x7faf06db1e1e  google::LogMessageFatal::~LogMessageFatal()\r\n    @     0x7faf075d4da0  caffe::db::LMDB::Open()\r\n    @     0x7faf072e97b2  caffe::DataReader::DBWrapper::DBWrapper()\r\n    @     0x7faf072eb0eb  caffe::DataReader::Body::InternalThreadEntry()\r\n    @     0x7faef98bc5d5  (unknown)\r\n    @     0x7faf059e46ba  start_thread\r\n    @     0x7faf05d0141d  clone\r\n    @              (nil)  (unknown)\r\nAborted (core dumped)\r\n```\r\n\r\nCan you update the branch so it compiles and add instructions on how to set up lmdb to run this experiment?"}}
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