{"action":"created","issue":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/7680","repository_url":"https://api.github.com/repos/tensorflow/tensorflow","labels_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/7680/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/7680/comments","events_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/7680/events","html_url":"https://github.com/tensorflow/tensorflow/issues/7680","id":208778134,"number":7680,"title":"CUDA_ERROR_OUT_OF_MEMORY with tf.contrib.learn (basic linear regression model)","user":{"login":"gajop","id":64901,"avatar_url":"https://avatars2.githubusercontent.com/u/64901?v=3","gravatar_id":"","url":"https://api.github.com/users/gajop","html_url":"https://github.com/gajop","followers_url":"https://api.github.com/users/gajop/followers","following_url":"https://api.github.com/users/gajop/following{/other_user}","gists_url":"https://api.github.com/users/gajop/gists{/gist_id}","starred_url":"https://api.github.com/users/gajop/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gajop/subscriptions","organizations_url":"https://api.github.com/users/gajop/orgs","repos_url":"https://api.github.com/users/gajop/repos","events_url":"https://api.github.com/users/gajop/events{/privacy}","received_events_url":"https://api.github.com/users/gajop/received_events","type":"User","site_admin":false},"labels":[{"id":404586594,"url":"https://api.github.com/repos/tensorflow/tensorflow/labels/stat:awaiting%20tensorflower","name":"stat:awaiting tensorflower","color":"b60205","default":false}],"state":"open","locked":false,"assignee":{"login":"zheng-xq","id":15736910,"avatar_url":"https://avatars3.githubusercontent.com/u/15736910?v=3","gravatar_id":"","url":"https://api.github.com/users/zheng-xq","html_url":"https://github.com/zheng-xq","followers_url":"https://api.github.com/users/zheng-xq/followers","following_url":"https://api.github.com/users/zheng-xq/following{/other_user}","gists_url":"https://api.github.com/users/zheng-xq/gists{/gist_id}","starred_url":"https://api.github.com/users/zheng-xq/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zheng-xq/subscriptions","organizations_url":"https://api.github.com/users/zheng-xq/orgs","repos_url":"https://api.github.com/users/zheng-xq/repos","events_url":"https://api.github.com/users/zheng-xq/events{/privacy}","received_events_url":"https://api.github.com/users/zheng-xq/received_events","type":"User","site_admin":false},"assignees":[{"login":"zheng-xq","id":15736910,"avatar_url":"https://avatars3.githubusercontent.com/u/15736910?v=3","gravatar_id":"","url":"https://api.github.com/users/zheng-xq","html_url":"https://github.com/zheng-xq","followers_url":"https://api.github.com/users/zheng-xq/followers","following_url":"https://api.github.com/users/zheng-xq/following{/other_user}","gists_url":"https://api.github.com/users/zheng-xq/gists{/gist_id}","starred_url":"https://api.github.com/users/zheng-xq/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/zheng-xq/subscriptions","organizations_url":"https://api.github.com/users/zheng-xq/orgs","repos_url":"https://api.github.com/users/zheng-xq/repos","events_url":"https://api.github.com/users/zheng-xq/events{/privacy}","received_events_url":"https://api.github.com/users/zheng-xq/received_events","type":"User","site_admin":false}],"milestone":null,"comments":17,"created_at":"2017-02-20T03:53:06Z","updated_at":"2017-06-03T23:32:55Z","closed_at":null,"body":"### Environment info\r\nOperating System: Ubuntu 16.04,  GeForce GTX TITAN X\r\n\r\nInstalled version of CUDA and cuDNN: \r\n(please attach the output of `ls -l /path/to/cuda/lib/libcud*`):\r\nls /usr/local/cuda-8.0/lib64/libcud*\r\n/usr/local/cuda-8.0/lib64/libcudadevrt.a\r\n/usr/local/cuda-8.0/lib64/libcudart.so\r\n/usr/local/cuda-8.0/lib64/libcudart.so.8.0\r\n/usr/local/cuda-8.0/lib64/libcudart.so.8.0.44\r\n/usr/local/cuda-8.0/lib64/libcudart_static.a\r\n/usr/local/cuda-8.0/lib64/libcudnn.so\r\n/usr/local/cuda-8.0/lib64/libcudnn.so.5\r\n/usr/local/cuda-8.0/lib64/libcudnn.so.5.1.10\r\n/usr/local/cuda-8.0/lib64/libcudnn.so.5.1.5\r\n/usr/local/cuda-8.0/lib64/libcudnn_static.a\r\n\r\n### If installed from binary pip package, provide:\r\n\r\n1) Installed with pip tensorflow-gpu\r\n2) ~ python -c \"import tensorflow; print(tensorflow.__version__)\r\n1.0.0\r\n\r\n### If possible, provide a minimal reproducible example (We usually don't have time to read hundreds of lines of your code)\r\nUsing the provided tf.contrib.learn model in get_started/get_started documentation\r\n```\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nfeatures = [tf.contrib.layers.real_valued_column(\"x\", dimension=1)]\r\nestimator = tf.contrib.learn.LinearRegressor(feature_columns=features)\r\nx = np.array([1., 2., 3., 4.])\r\ny = np.array([0., -1., -2., -3.])\r\ninput_fn = tf.contrib.learn.io.numpy_input_fn({\"x\":x}, y, batch_size=4, num_epochs=1000)\r\nestimator.fit(input_fn=input_fn, steps=10)\r\n\r\n```\r\n\r\nThe Core model works fine.\r\n\r\n\r\n### Logs or other output that would be helpful\r\n(If logs are large, please upload as attachment or provide link).\r\n\r\n```\r\npython test-oom.py \r\nI tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally\r\nI tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally\r\nI tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally\r\nI tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally\r\nI tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally\r\nWARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpew3U6z\r\nWARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change.\r\nWARNING:tensorflow:From /home/gajop/projekti/ubuntu-ranking-dataset-creator/env/local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\r\nInstructions for updating:\r\nPlease switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\r\nW tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.\r\nW tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.\r\nW tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.\r\nW tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.\r\nW tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.\r\nW tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.\r\nI tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\nI tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: \r\nname: GeForce GTX TITAN X\r\nmajor: 5 minor: 2 memoryClockRate (GHz) 1.076\r\npciBusID 0000:01:00.0\r\nTotal memory: 11.92GiB\r\nFree memory: 11.52GiB\r\nI tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 \r\nI tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y \r\nI tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0)\r\nE tensorflow/stream_executor/cuda/cuda_driver.cc:1002] failed to allocate 11.92G (12799180800 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\r\n```"},"comment":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/comments/306007705","html_url":"https://github.com/tensorflow/tensorflow/issues/7680#issuecomment-306007705","issue_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/7680","id":306007705,"user":{"login":"secsilm","id":8894643,"avatar_url":"https://avatars0.githubusercontent.com/u/8894643?v=3","gravatar_id":"","url":"https://api.github.com/users/secsilm","html_url":"https://github.com/secsilm","followers_url":"https://api.github.com/users/secsilm/followers","following_url":"https://api.github.com/users/secsilm/following{/other_user}","gists_url":"https://api.github.com/users/secsilm/gists{/gist_id}","starred_url":"https://api.github.com/users/secsilm/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/secsilm/subscriptions","organizations_url":"https://api.github.com/users/secsilm/orgs","repos_url":"https://api.github.com/users/secsilm/repos","events_url":"https://api.github.com/users/secsilm/events{/privacy}","received_events_url":"https://api.github.com/users/secsilm/received_events","type":"User","site_admin":false},"created_at":"2017-06-03T23:32:55Z","updated_at":"2017-06-03T23:32:55Z","body":"@gunan I'm using MINGW32 bash on Windows and that didn't work for me. TensorFlow still use GPU to compute."}}
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