{"action":"closed","issue":{"url":"https://api.github.com/repos/sailorsb/caffe-parallel/issues/5","labels_url":"https://api.github.com/repos/sailorsb/caffe-parallel/issues/5/labels{/name}","comments_url":"https://api.github.com/repos/sailorsb/caffe-parallel/issues/5/comments","events_url":"https://api.github.com/repos/sailorsb/caffe-parallel/issues/5/events","html_url":"https://github.com/sailorsb/caffe-parallel/issues/5","id":87564043,"number":5,"title":"ERROR when \"mpiexec -n 1 xxxxxx\"","user":{"login":"wuyuebupt","id":5171396,"avatar_url":"https://avatars.githubusercontent.com/u/5171396?v=3","gravatar_id":"","url":"https://api.github.com/users/wuyuebupt","html_url":"https://github.com/wuyuebupt","followers_url":"https://api.github.com/users/wuyuebupt/followers","following_url":"https://api.github.com/users/wuyuebupt/following{/other_user}","gists_url":"https://api.github.com/users/wuyuebupt/gists{/gist_id}","starred_url":"https://api.github.com/users/wuyuebupt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wuyuebupt/subscriptions","organizations_url":"https://api.github.com/users/wuyuebupt/orgs","repos_url":"https://api.github.com/users/wuyuebupt/repos","events_url":"https://api.github.com/users/wuyuebupt/events{/privacy}","received_events_url":"https://api.github.com/users/wuyuebupt/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":2,"created_at":"2015-06-12T01:12:15Z","updated_at":"2015-06-12T03:28:15Z","closed_at":"2015-06-12T03:28:15Z","body":"hi, I run the program successfully with the configuration \"-n 2\" with 2 k40.\r\nHowever, the program gets stuck when I use \"-n 1\".\r\n\r\nThe example I use is MNIST.\r\n\r\nDetails are below:\r\nI0611 23:32:33.560036 25939 caffe.cpp:107] Use GPU with device ID 0\r\nI0611 23:32:53.725818 25939 caffe.cpp:115] Starting Optimization\r\nI0611 23:32:53.869191 25939 solver.cpp:223] Initializing solver from parameters: \r\ntest_iter: 100\r\ntest_interval: 500\r\nbase_lr: 0.01\r\ndisplay: 100\r\nmax_iter: 10000\r\nlr_policy: \"inv\"\r\ngamma: 0.0001\r\npower: 0.75\r\nmomentum: 0.9\r\nweight_decay: 0.0005\r\nsnapshot: 5000\r\nsnapshot_prefix: \"examples/mnist/lenet\"\r\nsolver_mode: GPU\r\nnet: \"examples/mnist/lenet_train_test.prototxt\"\r\nI0611 23:32:53.933658 25939 solver.cpp:271] Creating training net from net file: examples/mnist/lenet_train_test.prototxt\r\nI0611 23:32:54.124568 25939 net.cpp:277] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist\r\nI0611 23:32:54.124601 25939 net.cpp:277] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\nI0611 23:32:54.124693 25939 net.cpp:40] Initializing net from parameters: \r\nname: \"LeNet\"\r\nlayers {\r\n  top: \"data\"\r\n  top: \"label\"\r\n  name: \"mnist\"\r\n  type: DATA\r\n  data_param {\r\n    source: \"examples/mnist/mnist_train_lmdb\"\r\n    batch_size: 64\r\n    backend: LMDB\r\n  }\r\n  include {\r\n    phase: TRAIN\r\n  }\r\n  transform_param {\r\n    scale: 0.00390625\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"data\"\r\n  top: \"conv1\"\r\n  name: \"conv1\"\r\n  type: CONVOLUTION\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  convolution_param {\r\n    num_output: 20\r\n    kernel_size: 5\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"conv1\"\r\n  top: \"pool1\"\r\n  name: \"pool1\"\r\n  type: POOLING\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 2\r\n    stride: 2\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"pool1\"\r\n  top: \"conv2\"\r\n  name: \"conv2\"\r\n  type: CONVOLUTION\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  convolution_param {\r\n    num_output: 50\r\n    kernel_size: 5\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"conv2\"\r\n  top: \"pool2\"\r\n  name: \"pool2\"\r\n  type: POOLING\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 2\r\n    stride: 2\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"pool2\"\r\n  top: \"ip1\"\r\n  name: \"ip1\"\r\n  type: INNER_PRODUCT\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  inner_product_param {\r\n    num_output: 500\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"ip1\"\r\n  top: \"ip1\"\r\n  name: \"relu1\"\r\n  type: RELU\r\n}\r\nlayers {\r\n  bottom: \"ip1\"\r\n  top: \"ip2\"\r\n  name: \"ip2\"\r\n  type: INNER_PRODUCT\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  inner_product_param {\r\n    num_output: 10\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"ip2\"\r\n  bottom: \"label\"\r\n  top: \"loss\"\r\n  name: \"loss\"\r\n  type: SOFTMAX_LOSS\r\n}\r\nstate {\r\n  phase: TRAIN\r\n}\r\nI0611 23:32:54.124752 25939 net.cpp:69] Creating Layer mnist\r\nI0611 23:32:54.124758 25939 net.cpp:358] mnist -> data\r\nI0611 23:32:54.124773 25939 net.cpp:358] mnist -> label\r\nI0611 23:32:54.191617 25939 net.cpp:98] Setting up mnist\r\nI0611 23:32:55.070849 25939 data_layer.cpp:80] Opening lmdb examples/mnist/mnist_train_lmdb\r\nI0611 23:32:55.151121 25939 data_layer.cpp:162] output data size: 64,1,28,28\r\nI0611 23:32:55.151221 25939 net.cpp:105] Top shape: 64 1 28 28 (50176)\r\nI0611 23:32:55.151226 25939 net.cpp:105] Top shape: 64 1 1 1 (64)\r\nI0611 23:32:55.151239 25939 net.cpp:69] Creating Layer conv1\r\nI0611 23:32:55.151242 25939 net.cpp:396] conv1 <- data\r\nI0611 23:32:55.151259 25939 net.cpp:358] conv1 -> conv1\r\nI0611 23:32:55.151267 25939 net.cpp:98] Setting up conv1\r\nI0611 23:32:55.151628 25939 net.cpp:105] Top shape: 64 20 24 24 (737280)\r\nI0611 23:32:55.151664 25939 net.cpp:69] Creating Layer pool1\r\nI0611 23:32:55.151667 25939 net.cpp:396] pool1 <- conv1\r\nI0611 23:32:55.151671 25939 net.cpp:358] pool1 -> pool1\r\nI0611 23:32:55.151676 25939 net.cpp:98] Setting up pool1\r\nI0611 23:32:55.151686 25939 net.cpp:105] Top shape: 64 20 12 12 (184320)\r\nI0611 23:32:55.151692 25939 net.cpp:69] Creating Layer conv2\r\nI0611 23:32:55.151695 25939 net.cpp:396] conv2 <- pool1\r\nI0611 23:32:55.151698 25939 net.cpp:358] conv2 -> conv2\r\nI0611 23:32:55.151702 25939 net.cpp:98] Setting up conv2\r\nI0611 23:32:55.151856 25939 net.cpp:105] Top shape: 64 50 8 8 (204800)\r\nI0611 23:32:55.151883 25939 net.cpp:69] Creating Layer pool2\r\nI0611 23:32:55.151886 25939 net.cpp:396] pool2 <- conv2\r\nI0611 23:32:55.151890 25939 net.cpp:358] pool2 -> pool2\r\nI0611 23:32:55.151895 25939 net.cpp:98] Setting up pool2\r\nI0611 23:32:55.151897 25939 net.cpp:105] Top shape: 64 50 4 4 (51200)\r\nI0611 23:32:55.151901 25939 net.cpp:69] Creating Layer ip1\r\nI0611 23:32:55.151903 25939 net.cpp:396] ip1 <- pool2\r\nI0611 23:32:55.151907 25939 net.cpp:358] ip1 -> ip1\r\nI0611 23:32:55.151911 25939 net.cpp:98] Setting up ip1\r\nI0611 23:32:55.154345 25939 net.cpp:105] Top shape: 64 500 1 1 (32000)\r\nI0611 23:32:55.154355 25939 net.cpp:69] Creating Layer relu1\r\nI0611 23:32:55.154357 25939 net.cpp:396] relu1 <- ip1\r\nI0611 23:32:55.154361 25939 net.cpp:347] relu1 -> ip1 (in-place)\r\nI0611 23:32:55.154366 25939 net.cpp:98] Setting up relu1\r\nI0611 23:32:55.154367 25939 net.cpp:105] Top shape: 64 500 1 1 (32000)\r\nI0611 23:32:55.154371 25939 net.cpp:69] Creating Layer ip2\r\nI0611 23:32:55.154373 25939 net.cpp:396] ip2 <- ip1\r\nI0611 23:32:55.154377 25939 net.cpp:358] ip2 -> ip2\r\nI0611 23:32:55.154381 25939 net.cpp:98] Setting up ip2\r\nI0611 23:32:55.154414 25939 net.cpp:105] Top shape: 64 10 1 1 (640)\r\nI0611 23:32:55.154422 25939 net.cpp:69] Creating Layer loss\r\nI0611 23:32:55.154424 25939 net.cpp:396] loss <- ip2\r\nI0611 23:32:55.154428 25939 net.cpp:396] loss <- label\r\nI0611 23:32:55.154431 25939 net.cpp:358] loss -> loss\r\nI0611 23:32:55.154438 25939 net.cpp:98] Setting up loss\r\nI0611 23:32:55.154443 25939 net.cpp:105] Top shape: 1 1 1 1 (1)\r\nI0611 23:32:55.154446 25939 net.cpp:111]     with loss weight 1\r\nI0611 23:32:55.154475 25939 net.cpp:172] loss needs backward computation.\r\nI0611 23:32:55.154479 25939 net.cpp:172] ip2 needs backward computation.\r\nI0611 23:32:55.154480 25939 net.cpp:172] relu1 needs backward computation.\r\nI0611 23:32:55.154484 25939 net.cpp:172] ip1 needs backward computation.\r\nI0611 23:32:55.154485 25939 net.cpp:172] pool2 needs backward computation.\r\nI0611 23:32:55.154487 25939 net.cpp:172] conv2 needs backward computation.\r\nI0611 23:32:55.154489 25939 net.cpp:172] pool1 needs backward computation.\r\nI0611 23:32:55.154492 25939 net.cpp:172] conv1 needs backward computation.\r\nI0611 23:32:55.154495 25939 net.cpp:174] mnist does not need backward computation.\r\nI0611 23:32:55.154496 25939 net.cpp:210] This network produces output loss\r\nI0611 23:32:55.154507 25939 net.cpp:469] Collecting Learning Rate and Weight Decay.\r\nI0611 23:32:55.154512 25939 net.cpp:221] Network initialization done.\r\nI0611 23:32:55.154515 25939 net.cpp:222] Memory required for data: 5169924\r\nI0611 23:32:55.154808 25939 solver.cpp:355] Creating test net (#0) specified by net file: examples/mnist/lenet_train_test.prototxt\r\nI0611 23:32:55.154839 25939 net.cpp:277] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist\r\nI0611 23:32:55.154935 25939 net.cpp:40] Initializing net from parameters: \r\nname: \"LeNet\"\r\nlayers {\r\n  top: \"data\"\r\n  top: \"label\"\r\n  name: \"mnist\"\r\n  type: DATA\r\n  data_param {\r\n    source: \"examples/mnist/mnist_test_lmdb\"\r\n    batch_size: 100\r\n    backend: LMDB\r\n  }\r\n  include {\r\n    phase: TEST\r\n  }\r\n  transform_param {\r\n    scale: 0.00390625\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"data\"\r\n  top: \"conv1\"\r\n  name: \"conv1\"\r\n  type: CONVOLUTION\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  convolution_param {\r\n    num_output: 20\r\n    kernel_size: 5\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"conv1\"\r\n  top: \"pool1\"\r\n  name: \"pool1\"\r\n  type: POOLING\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 2\r\n    stride: 2\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"pool1\"\r\n  top: \"conv2\"\r\n  name: \"conv2\"\r\n  type: CONVOLUTION\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  convolution_param {\r\n    num_output: 50\r\n    kernel_size: 5\r\n    stride: 1\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"conv2\"\r\n  top: \"pool2\"\r\n  name: \"pool2\"\r\n  type: POOLING\r\n  pooling_param {\r\n    pool: MAX\r\n    kernel_size: 2\r\n    stride: 2\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"pool2\"\r\n  top: \"ip1\"\r\n  name: \"ip1\"\r\n  type: INNER_PRODUCT\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  inner_product_param {\r\n    num_output: 500\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"ip1\"\r\n  top: \"ip1\"\r\n  name: \"relu1\"\r\n  type: RELU\r\n}\r\nlayers {\r\n  bottom: \"ip1\"\r\n  top: \"ip2\"\r\n  name: \"ip2\"\r\n  type: INNER_PRODUCT\r\n  blobs_lr: 1\r\n  blobs_lr: 2\r\n  inner_product_param {\r\n    num_output: 10\r\n    weight_filler {\r\n      type: \"xavier\"\r\n    }\r\n    bias_filler {\r\n      type: \"constant\"\r\n    }\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"ip2\"\r\n  bottom: \"label\"\r\n  top: \"accuracy\"\r\n  name: \"accuracy\"\r\n  type: ACCURACY\r\n  include {\r\n    phase: TEST\r\n  }\r\n}\r\nlayers {\r\n  bottom: \"ip2\"\r\n  bottom: \"label\"\r\n  top: \"loss\"\r\n  name: \"loss\"\r\n  type: SOFTMAX_LOSS\r\n}\r\nstate {\r\n  phase: TEST\r\n}\r\nI0611 23:32:55.154999 25939 net.cpp:69] Creating Layer mnist\r\nI0611 23:32:55.155004 25939 net.cpp:358] mnist -> data\r\nI0611 23:32:55.155009 25939 net.cpp:358] mnist -> label\r\nI0611 23:32:55.155014 25939 net.cpp:98] Setting up mnist\r\nI0611 23:32:55.860347 25939 data_layer.cpp:80] Opening lmdb examples/mnist/mnist_test_lmdb\r\nI0611 23:32:55.933403 25939 data_layer.cpp:162] output data size: 100,1,28,28\r\nI0611 23:32:55.933531 25939 net.cpp:105] Top shape: 100 1 28 28 (78400)\r\nI0611 23:32:55.933539 25939 net.cpp:105] Top shape: 100 1 1 1 (100)\r\nI0611 23:32:55.933552 25939 net.cpp:69] Creating Layer label_mnist_1_split\r\nI0611 23:32:55.933557 25939 net.cpp:396] label_mnist_1_split <- label\r\nI0611 23:32:55.933563 25939 net.cpp:358] label_mnist_1_split -> label_mnist_1_split_0\r\nI0611 23:32:55.933573 25939 net.cpp:358] label_mnist_1_split -> label_mnist_1_split_1\r\nI0611 23:32:55.933578 25939 net.cpp:98] Setting up label_mnist_1_split\r\nI0611 23:32:55.933584 25939 net.cpp:105] Top shape: 100 1 1 1 (100)\r\nI0611 23:32:55.933588 25939 net.cpp:105] Top shape: 100 1 1 1 (100)\r\nI0611 23:32:55.933596 25939 net.cpp:69] Creating Layer conv1\r\nI0611 23:32:55.933601 25939 net.cpp:396] conv1 <- data\r\nI0611 23:32:55.933606 25939 net.cpp:358] conv1 -> conv1\r\nI0611 23:32:55.933612 25939 net.cpp:98] Setting up conv1\r\nI0611 23:32:55.933632 25939 net.cpp:105] Top shape: 100 20 24 24 (1152000)\r\nI0611 23:32:55.933645 25939 net.cpp:69] Creating Layer pool1\r\nI0611 23:32:55.933648 25939 net.cpp:396] pool1 <- conv1\r\nI0611 23:32:55.933655 25939 net.cpp:358] pool1 -> pool1\r\nI0611 23:32:55.933660 25939 net.cpp:98] Setting up pool1\r\nI0611 23:32:55.933665 25939 net.cpp:105] Top shape: 100 20 12 12 (288000)\r\nI0611 23:32:55.933672 25939 net.cpp:69] Creating Layer conv2\r\nI0611 23:32:55.933676 25939 net.cpp:396] conv2 <- pool1\r\nI0611 23:32:55.933682 25939 net.cpp:358] conv2 -> conv2\r\nI0611 23:32:55.933687 25939 net.cpp:98] Setting up conv2\r\nI0611 23:32:55.933881 25939 net.cpp:105] Top shape: 100 50 8 8 (320000)\r\nI0611 23:32:55.933893 25939 net.cpp:69] Creating Layer pool2\r\nI0611 23:32:55.933897 25939 net.cpp:396] pool2 <- conv2\r\nI0611 23:32:55.933902 25939 net.cpp:358] pool2 -> pool2\r\nI0611 23:32:55.933907 25939 net.cpp:98] Setting up pool2\r\nI0611 23:32:55.933912 25939 net.cpp:105] Top shape: 100 50 4 4 (80000)\r\nI0611 23:32:55.933918 25939 net.cpp:69] Creating Layer ip1\r\nI0611 23:32:55.933923 25939 net.cpp:396] ip1 <- pool2\r\nI0611 23:32:55.933928 25939 net.cpp:358] ip1 -> ip1\r\nI0611 23:32:55.933935 25939 net.cpp:98] Setting up ip1\r\nI0611 23:32:55.937114 25939 net.cpp:105] Top shape: 100 500 1 1 (50000)\r\nI0611 23:32:55.937126 25939 net.cpp:69] Creating Layer relu1\r\nI0611 23:32:55.937130 25939 net.cpp:396] relu1 <- ip1\r\nI0611 23:32:55.937135 25939 net.cpp:347] relu1 -> ip1 (in-place)\r\nI0611 23:32:55.937140 25939 net.cpp:98] Setting up relu1\r\nI0611 23:32:55.937144 25939 net.cpp:105] Top shape: 100 500 1 1 (50000)\r\nI0611 23:32:55.937152 25939 net.cpp:69] Creating Layer ip2\r\nI0611 23:32:55.937156 25939 net.cpp:396] ip2 <- ip1\r\nI0611 23:32:55.937161 25939 net.cpp:358] ip2 -> ip2\r\nI0611 23:32:55.937167 25939 net.cpp:98] Setting up ip2\r\nI0611 23:32:55.937238 25939 net.cpp:105] Top shape: 100 10 1 1 (1000)\r\nI0611 23:32:55.937245 25939 net.cpp:69] Creating Layer ip2_ip2_0_split\r\nI0611 23:32:55.937249 25939 net.cpp:396] ip2_ip2_0_split <- ip2\r\nI0611 23:32:55.937258 25939 net.cpp:358] ip2_ip2_0_split -> ip2_ip2_0_split_0\r\nI0611 23:32:55.937283 25939 net.cpp:358] ip2_ip2_0_split -> ip2_ip2_0_split_1\r\nI0611 23:32:55.937288 25939 net.cpp:98] Setting up ip2_ip2_0_split\r\nI0611 23:32:55.937293 25939 net.cpp:105] Top shape: 100 10 1 1 (1000)\r\nI0611 23:32:55.937296 25939 net.cpp:105] Top shape: 100 10 1 1 (1000)\r\nI0611 23:32:55.937304 25939 net.cpp:69] Creating Layer accuracy\r\nI0611 23:32:55.937307 25939 net.cpp:396] accuracy <- ip2_ip2_0_split_0\r\nI0611 23:32:55.937312 25939 net.cpp:396] accuracy <- label_mnist_1_split_0\r\nI0611 23:32:55.937317 25939 net.cpp:358] accuracy -> accuracy\r\nI0611 23:32:55.937324 25939 net.cpp:98] Setting up accuracy\r\nI0611 23:32:55.937327 25939 net.cpp:105] Top shape: 1 1 1 1 (1)\r\nI0611 23:32:55.937333 25939 net.cpp:69] Creating Layer loss\r\nI0611 23:32:55.937337 25939 net.cpp:396] loss <- ip2_ip2_0_split_1\r\nI0611 23:32:55.937341 25939 net.cpp:396] loss <- label_mnist_1_split_1\r\nI0611 23:32:55.937346 25939 net.cpp:358] loss -> loss\r\nI0611 23:32:55.937351 25939 net.cpp:98] Setting up loss\r\nI0611 23:32:55.937358 25939 net.cpp:105] Top shape: 1 1 1 1 (1)\r\nI0611 23:32:55.937362 25939 net.cpp:111]     with loss weight 1\r\nI0611 23:32:55.937372 25939 net.cpp:172] loss needs backward computation.\r\nI0611 23:32:55.937376 25939 net.cpp:174] accuracy does not need backward computation.\r\nI0611 23:32:55.937381 25939 net.cpp:172] ip2_ip2_0_split needs backward computation.\r\nI0611 23:32:55.937384 25939 net.cpp:172] ip2 needs backward computation.\r\nI0611 23:32:55.937387 25939 net.cpp:172] relu1 needs backward computation.\r\nI0611 23:32:55.937391 25939 net.cpp:172] ip1 needs backward computation.\r\nI0611 23:32:55.937396 25939 net.cpp:172] pool2 needs backward computation.\r\nI0611 23:32:55.937398 25939 net.cpp:172] conv2 needs backward computation.\r\nI0611 23:32:55.937402 25939 net.cpp:172] pool1 needs backward computation.\r\nI0611 23:32:55.937407 25939 net.cpp:172] conv1 needs backward computation.\r\nI0611 23:32:55.937410 25939 net.cpp:174] label_mnist_1_split does not need backward computation.\r\nI0611 23:32:55.937414 25939 net.cpp:174] mnist does not need backward computation.\r\nI0611 23:32:55.937417 25939 net.cpp:210] This network produces output accuracy\r\nI0611 23:32:55.937422 25939 net.cpp:210] This network produces output loss\r\nI0611 23:32:55.937433 25939 net.cpp:469] Collecting Learning Rate and Weight Decay.\r\nI0611 23:32:55.937439 25939 net.cpp:221] Network initialization done.\r\nI0611 23:32:55.937443 25939 net.cpp:222] Memory required for data: 8086808\r\nI0611 23:32:55.937486 25939 solver.cpp:245] Solver scaffolding done.\r\nI0611 23:32:55.937494 25939 solver.cpp:364] Solving LeNet\r\n"}}