[chibi@centos8 ~]$ sudo nvidia-docker run --rm -ti nvcr.io/nvidia/tensorflow:19.04-py3 [sudo] chibi のパスワード: Unable to find image 'nvcr.io/nvidia/tensorflow:19.04-py3' locally 19.04-py3: Pulling from nvidia/tensorflow 34667c7e4631: Pulling fs layer d18d76a881a4: Pulling fs layer 119c7358fbfc: Pulling fs layer 2aaf13f3eff0: Waiting 202fa0f8874b: Waiting 3b700a61ede6: Waiting 87e6ca450d3f: Pulling fs layer a1e76dce1aec: Waiting 9b91fa2f9276: Waiting b5877a9add73: Waiting bab74df105f1: Pulling fs layer 534bbf505504: Waiting 4956bf3bbbb9: Waiting f4371944c97d: Waiting 4615a735431d: Waiting 5db2639932b5: Waiting 629d5c9d75a4: Waiting 8071b94b5429: Waiting 6eb8eba2ad5a: Waiting e32e86c15b8b: Waiting 08db5b51b243: Waiting f71ce95fb406: Waiting 3498ed8c5685: Pulling fs layer 62819d8896c1: Waiting 34bc85bf8bef: Waiting 4a95ca3431c4: Waiting 41bc2d0a4d4d: Waiting a2ceadc61854: Waiting 2d0c5308ff92: Waiting a531832992b8: Waiting b24a8fd8f2e1: Waiting 8d9313624ab7: Waiting e5cafe011f22: Pull complete eca19a329cd4: Pull complete 65ee50af0bcc: Pull complete 5f60ec8c32f4: Pull complete d7dcb657fa13: Pull complete 1f6ef6575fbe: Pull complete d1ef346a3015: Pull complete 4ef9cb404fd5: Pull complete f6797f45a018: Pull complete 1d4380527325: Pull complete 965f2629db02: Pull complete 5debff4c8c0a: Pull complete b3a3a9d82be6: Pull complete eac05f20b729: Pull complete 3ce0a7f80167: Pull complete 2a21e34a5784: Pull complete c1ccf19e258e: Pull complete 0b6ea9d0652b: Pull complete 307bc8c3f024: Pull complete ca75fd593a79: Pull complete 0cd3cdca1af7: Pull complete 48e857e9d372: Pull complete 3264ea403ca9: Pull complete Digest: sha256:aaebc136d5d50937362675c77afd908bd96cded68846f39163050a023c8a9851 Status: Downloaded newer image for nvcr.io/nvidia/tensorflow:19.04-py3 ================ == TensorFlow == ================ NVIDIA Release 19.04 (build 6132408) TensorFlow Version 1.13.1 Container image Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. Copyright 2017-2019 The TensorFlow Authors. All rights reserved. Various files include modifications (c) NVIDIA CORPORATION. All rights reserved. NVIDIA modifications are covered by the license terms that apply to the underlying project or file. NOTE: MOFED driver for multi-node communication was not detected. Multi-node communication performance may be reduced. NOTE: The SHMEM allocation limit is set to the default of 64MB. This may be insufficient for TensorFlow. NVIDIA recommends the use of the following flags: nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 ... root@6643139b67ae:/workspace# ls README.md docker-examples nvidia-examples root@6643139b67ae:/workspace# cd nvidia-examples root@6643139b67ae:/workspace/nvidia-examples# ls NCF bert cnn ssdv1.2 OpenSeq2Seq big_lstm gnmt_v2 tensorrt UNet_Industrial build_imagenet_data resnet50v1.5 root@6643139b67ae:/workspace/nvidia-examples# cd big_lstm root@6643139b67ae:/workspace/nvidia-examples/big_lstm# ls 1b_word_vocab.txt data_utils_test.py language_model_test.py README.md download_1b_words_data.sh model_utils.py __init__.py hparams.py run_utils.py common.py hparams_test.py single_lm_train.py data_utils.py language_model.py testdata root@6643139b67ae:/workspace/nvidia-examples/big_lstm# ./download_1b_words_data.sh Please specify root of dataset directory: data Success: dataset root dir validated --2020-01-23 19:56:03-- http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz Resolving www.statmt.org (www.statmt.org)... 129.215.197.184 Connecting to www.statmt.org (www.statmt.org)|129.215.197.184|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 1792209805 (1.7G) [application/x-gzip] Saving to: ‘1-billion-word-language-modeling-benchmark-r13output.tar.gz’ 1-billion-word-lang 100%[===================>] 1.67G 1.56MB/s in 13m 13s 2020-01-23 20:09:16 (2.16 MB/s) - ‘1-billion-word-language-modeling-benchmark-r13output.tar.gz’ saved [1792209805/1792209805] 1-billion-word-language-modeling-benchmark-r13output/ 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00024-of-00100 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00057-of-00100 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00055-of-00100 1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00096-of-00100 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1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00044-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00045-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00016-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00004-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00035-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00038-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00009-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00024-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00022-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00021-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00032-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00011-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00049-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00041-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00019-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00023-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00040-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00014-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00007-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00017-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00012-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00018-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00003-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00028-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en-00000-of-00100 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00043-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00005-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00036-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00026-of-00050 1-billion-word-language-modeling-benchmark-r13output/heldout-monolingual.tokenized.shuffled/news.en.heldout-00047-of-00050 1-billion-word-language-modeling-benchmark-r13output/README Success! One billion words dataset ready at: data/1-billion-word-language-modeling-benchmark-r13output/ Please pass this dir to single_lm_train.py via the --datadir option. root@6643139b67ae:/workspace/nvidia-examples/big_lstm# time python single_lm_train.py --mode=train --logdir=./logs --num_gpus=4 --datadir=./data/1-billion-word-language-modeling-benchmark-r13output WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md * https://github.com/tensorflow/addons If you depend on functionality not listed there, please file an issue. *****HYPER PARAMETERS***** {'max_time': 180, 'do_summaries': False, 'num_shards': 8, 'num_layers': 1, 'num_delayed_steps': 150, 'num_gpus': 4, 'optimizer': 0, 'num_steps': 20, 'num_sampled': 8192, 'average_params': True, 'max_grad_norm': 10.0, 'keep_prob': 0.9, 'run_profiler': False, 'emb_size': 512, 'learning_rate': 0.2, 'projected_size': 512, 'state_size': 2048, 'batch_size': 128, 'vocab_size': 793470} ************************** WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From /opt/tensorflow/nvidia-examples/big_lstm/model_utils.py:33: UniformUnitScaling.__init__ (from tensorflow.python.ops.init_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.initializers.variance_scaling instead with distribution=uniform to get equivalent behavior. WARNING:tensorflow:From /opt/tensorflow/nvidia-examples/big_lstm/language_model.py:75: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`. WARNING:tensorflow:From /opt/tensorflow/nvidia-examples/big_lstm/language_model.py:107: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_impl.py:1444: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version. Instructions for updating: Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead. WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/array_grad.py:425: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. Current time: 1579810262.728985 ALL VARIABLES WARNING:tensorflow:From /opt/tensorflow/nvidia-examples/big_lstm/run_utils.py:18: all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02. Instructions for updating: Please use tf.global_variables instead. model/emb_0:0 (99184, 512) /gpu:0 model/emb_1:0 (99184, 512) /gpu:0 model/emb_2:0 (99184, 512) /gpu:0 model/emb_3:0 (99184, 512) /gpu:0 model/emb_4:0 (99184, 512) /gpu:0 model/emb_5:0 (99184, 512) /gpu:0 model/emb_6:0 (99184, 512) /gpu:0 model/emb_7:0 (99184, 512) /gpu:0 model/lstm_0/LSTMCell/W_0:0 (1024, 8192) /gpu:0 model/lstm_0/LSTMCell/B:0 (8192,) /gpu:0 model/lstm_0/LSTMCell/W_P_0:0 (2048, 512) /gpu:0 model/softmax_w_0:0 (99184, 512) /gpu:0 model/softmax_w_1:0 (99184, 512) /gpu:0 model/softmax_w_2:0 (99184, 512) /gpu:0 model/softmax_w_3:0 (99184, 512) /gpu:0 model/softmax_w_4:0 (99184, 512) /gpu:0 model/softmax_w_5:0 (99184, 512) /gpu:0 model/softmax_w_6:0 (99184, 512) /gpu:0 model/softmax_w_7:0 (99184, 512) /gpu:0 model/softmax_b:0 (793470,) /gpu:0 model/global_step:0 () model/model/emb_0/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_1/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_2/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_3/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_4/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_5/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_6/Adagrad:0 (99184, 512) /gpu:0 model/model/emb_7/Adagrad:0 (99184, 512) /gpu:0 model/model/lstm_0/LSTMCell/W_0/Adagrad:0 (1024, 8192) /gpu:0 model/model/lstm_0/LSTMCell/B/Adagrad:0 (8192,) /gpu:0 model/model/lstm_0/LSTMCell/W_P_0/Adagrad:0 (2048, 512) /gpu:0 model/model/softmax_w_0/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_1/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_2/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_3/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_4/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_5/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_6/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_w_7/Adagrad:0 (99184, 512) /gpu:0 model/model/softmax_b/Adagrad:0 (793470,) /gpu:0 model/model/lstm_0/LSTMCell/W_0/ExponentialMovingAverage:0 (1024, 8192) /gpu:0 model/model/lstm_0/LSTMCell/B/ExponentialMovingAverage:0 (8192,) /gpu:0 model/model/lstm_0/LSTMCell/W_P_0/ExponentialMovingAverage:0 (2048, 512) /gpu:0 TRAINABLE VARIABLES model/emb_0:0 (99184, 512) /gpu:0 model/emb_1:0 (99184, 512) /gpu:0 model/emb_2:0 (99184, 512) /gpu:0 model/emb_3:0 (99184, 512) /gpu:0 model/emb_4:0 (99184, 512) /gpu:0 model/emb_5:0 (99184, 512) /gpu:0 model/emb_6:0 (99184, 512) /gpu:0 model/emb_7:0 (99184, 512) /gpu:0 model/lstm_0/LSTMCell/W_0:0 (1024, 8192) /gpu:0 model/lstm_0/LSTMCell/B:0 (8192,) /gpu:0 model/lstm_0/LSTMCell/W_P_0:0 (2048, 512) /gpu:0 model/softmax_w_0:0 (99184, 512) /gpu:0 model/softmax_w_1:0 (99184, 512) /gpu:0 model/softmax_w_2:0 (99184, 512) /gpu:0 model/softmax_w_3:0 (99184, 512) /gpu:0 model/softmax_w_4:0 (99184, 512) /gpu:0 model/softmax_w_5:0 (99184, 512) /gpu:0 model/softmax_w_6:0 (99184, 512) /gpu:0 model/softmax_w_7:0 (99184, 512) /gpu:0 model/softmax_b:0 (793470,) /gpu:0 LOCAL VARIABLES model/model/state_0_0:0 (128, 2560) /gpu:0 model/model_1/state_1_0:0 (128, 2560) /gpu:1 model/model_2/state_2_0:0 (128, 2560) /gpu:2 model/model_3/state_3_0:0 (128, 2560) /gpu:3 WARNING:tensorflow:From /opt/tensorflow/nvidia-examples/big_lstm/run_utils.py:32: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version. Instructions for updating: Please switch to tf.train.MonitoredTrainingSession 2020-01-23 20:11:03.661272: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200025000 Hz 2020-01-23 20:11:03.667451: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0xacb6f60 executing computations on platform Host. Devices: 2020-01-23 20:11:03.667490: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): , 2020-01-23 20:11:04.730632: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0xa890960 executing computations on platform CUDA. Devices: 2020-01-23 20:11:04.730693: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): TITAN RTX, Compute Capability 7.5 2020-01-23 20:11:04.730707: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (1): TITAN RTX, Compute Capability 7.5 2020-01-23 20:11:04.730719: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (2): GeForce RTX 2080 Ti, Compute Capability 7.5 2020-01-23 20:11:04.730730: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (3): GeForce RTX 2080 Ti, Compute Capability 7.5 2020-01-23 20:11:04.732670: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: name: TITAN RTX major: 7 minor: 5 memoryClockRate(GHz): 1.77 pciBusID: 0000:82:00.0 totalMemory: 23.65GiB freeMemory: 23.48GiB 2020-01-23 20:11:04.732742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 1 with properties: name: TITAN RTX major: 7 minor: 5 memoryClockRate(GHz): 1.77 pciBusID: 0000:83:00.0 totalMemory: 23.65GiB freeMemory: 23.48GiB 2020-01-23 20:11:04.732786: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 2 with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635 pciBusID: 0000:02:00.0 totalMemory: 10.76GiB freeMemory: 10.32GiB 2020-01-23 20:11:04.732848: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 3 with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635 pciBusID: 0000:03:00.0 totalMemory: 10.76GiB freeMemory: 10.60GiB 2020-01-23 20:11:04.733078: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1, 2, 3 2020-01-23 20:11:05.984676: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-01-23 20:11:05.984735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 1 2 3 2020-01-23 20:11:05.984745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N N N N 2020-01-23 20:11:05.984751: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1: N N N N 2020-01-23 20:11:05.984759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 2: N N N N 2020-01-23 20:11:05.984783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 3: N N N N 2020-01-23 20:11:05.985028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22757 MB memory) -> physical GPU (device: 0, name: TITAN RTX, pci bus id: 0000:82:00.0, compute capability: 7.5) 2020-01-23 20:11:05.985817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 22757 MB memory) -> physical GPU (device: 1, name: TITAN RTX, pci bus id: 0000:83:00.0, compute capability: 7.5) 2020-01-23 20:11:05.986076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 9958 MB memory) -> physical GPU (device: 2, name: GeForce RTX 2080 Ti, pci bus id: 0000:02:00.0, compute capability: 7.5) 2020-01-23 20:11:05.986727: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 10224 MB memory) -> physical GPU (device: 3, name: GeForce RTX 2080 Ti, pci bus id: 0000:03:00.0, compute capability: 7.5) Processing file: ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00093-of-00100 Finished processing! 2020-01-23 20:11:48.002131: I tensorflow/stream_executor/dso_loader.cc:153] successfully opened CUDA library libcublas.so.10 locally Iteration 1, time = 22.85s, wps = 448, train loss = 13.0120 Iteration 2, time = 19.26s, wps = 532, train loss = 12.9460 Iteration 3, time = 0.11s, wps = 89834, train loss = 12.8581 Iteration 4, time = 0.11s, wps = 97158, train loss = 11.3665 Iteration 5, time = 0.11s, wps = 96928, train loss = 111.7584 Iteration 6, time = 0.10s, wps = 104338, train loss = 69.0413 Iteration 7, time = 0.10s, wps = 98282, train loss = 15.8646 Iteration 8, time = 0.10s, wps = 103919, train loss = 36.4781 Iteration 9, time = 0.10s, wps = 100877, train loss = 14.5212 Iteration 20, time = 1.14s, wps = 99192, train loss = 16.2836 Iteration 40, time = 2.06s, wps = 99658, train loss = 9.6829 Iteration 60, time = 2.06s, wps = 99541, train loss = 8.1851 Iteration 80, time = 2.06s, wps = 99457, train loss = 7.8727 Iteration 100, time = 2.07s, wps = 98918, train loss = 7.7115 Iteration 120, time = 2.18s, wps = 94003, train loss = 7.3383 Iteration 140, time = 2.03s, wps = 100829, train loss = 6.9771 Iteration 160, time = 2.07s, wps = 98937, train loss = 6.7965 Iteration 180, time = 2.05s, wps = 99871, train loss = 6.4957 Iteration 200, time = 2.05s, wps = 99677, train loss = 6.2136 Iteration 220, time = 2.07s, wps = 98844, train loss = 6.2540 Iteration 240, time = 2.11s, wps = 96980, train loss = 6.2228 Iteration 260, time = 2.07s, wps = 98961, train loss = 6.1349 Iteration 280, time = 2.16s, wps = 94698, train loss = 6.1524 Iteration 300, time = 2.08s, wps = 98407, train loss = 6.0408 Iteration 320, time = 2.08s, wps = 98337, train loss = 6.0841 Iteration 340, time = 2.27s, wps = 90260, train loss = 5.9012 Iteration 360, time = 2.06s, wps = 99375, train loss = 5.9277 Iteration 380, time = 2.07s, wps = 99112, train loss = 5.8590 Iteration 400, time = 2.06s, wps = 99409, train loss = 5.8469 Iteration 420, time = 2.07s, wps = 98920, train loss = 5.7539 Iteration 440, time = 2.06s, wps = 99459, train loss = 5.7758 Iteration 460, time = 2.08s, wps = 98439, train loss = 5.7263 Iteration 480, time = 2.04s, wps = 100631, train loss = 5.7645 Iteration 500, time = 2.27s, wps = 90339, train loss = 5.7509 Iteration 520, time = 2.13s, wps = 96372, train loss = 5.6470 Iteration 540, time = 2.04s, wps = 100514, train loss = 5.6136 Iteration 560, time = 2.19s, wps = 93644, train loss = 5.5383 Iteration 580, time = 2.06s, wps = 99401, train loss = 5.5840 Iteration 600, time = 2.09s, wps = 97987, train loss = 5.5494 Iteration 620, time = 2.08s, wps = 98567, train loss = 5.6220 Iteration 640, time = 2.22s, wps = 92124, train loss = 5.5799 Iteration 660, time = 2.13s, wps = 95992, train loss = 5.4912 Iteration 680, time = 2.23s, wps = 91693, train loss = 5.4705 Iteration 700, time = 2.06s, wps = 99284, train loss = 5.4976 Iteration 720, time = 2.25s, wps = 91076, train loss = 5.4388 Iteration 740, time = 2.18s, wps = 93782, train loss = 5.4567 Iteration 760, time = 2.14s, wps = 95479, train loss = 5.3508 Iteration 780, time = 2.06s, wps = 99215, train loss = 5.3709 Processing file: ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00016-of-00100 Finished processing! Iteration 800, time = 4.32s, wps = 47359, train loss = 5.4229 Iteration 820, time = 2.07s, wps = 98700, train loss = 5.3804 Iteration 840, time = 2.11s, wps = 96860, train loss = 5.3702 Iteration 860, time = 2.15s, wps = 95360, train loss = 5.3632 Iteration 880, time = 2.26s, wps = 90809, train loss = 5.2485 Iteration 900, time = 2.07s, wps = 99079, train loss = 5.2911 Iteration 920, time = 2.17s, wps = 94420, train loss = 5.2802 Iteration 940, time = 2.08s, wps = 98226, train loss = 5.1818 Iteration 960, time = 2.15s, wps = 95155, train loss = 5.2136 Iteration 980, time = 2.11s, wps = 97243, train loss = 5.1938 Iteration 1000, time = 2.12s, wps = 96817, train loss = 5.2737 Iteration 1020, time = 2.07s, wps = 99097, train loss = 5.2151 Iteration 1040, time = 2.12s, wps = 96600, train loss = 5.1828 Iteration 1060, time = 2.23s, wps = 91669, train loss = 5.1354 /usr/local/lib/python3.5/dist-packages/tensorflow/python/summary/writer/writer.py:386: UserWarning: Attempting to use a closed FileWriter. The operation will be a noop unless the FileWriter is explicitly reopened. warnings.warn("Attempting to use a closed FileWriter. " real 3m22.637s user 16m3.827s sys 2m1.227s root@6643139b67ae:/workspace/nvidia-examples/big_lstm# cat /etc/os-release NAME="Ubuntu" VERSION="16.04.6 LTS (Xenial Xerus)" ID=ubuntu ID_LIKE=debian PRETTY_NAME="Ubuntu 16.04.6 LTS" VERSION_ID="16.04" HOME_URL="http://www.ubuntu.com/" SUPPORT_URL="http://help.ubuntu.com/" BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/" VERSION_CODENAME=xenial UBUNTU_CODENAME=xenial root@6643139b67ae:/workspace/nvidia-examples/big_lstm# nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Fri_Feb__8_19:08:17_PST_2019 Cuda compilation tools, release 10.1, V10.1.105 root@6643139b67ae:/workspace/nvidia-examples/big_lstm# cd data root@6643139b67ae:/workspace/nvidia-examples/big_lstm/data# ls 1-billion-word-language-modeling-benchmark-r13output root@6643139b67ae:/workspace/nvidia-examples/big_lstm/data# cd 1-billion-word-language-modeling-benchmark-r13output root@6643139b67ae:/workspace/nvidia-examples/big_lstm/data/1-billion-word-language-modeling-benchmark-r13output# ls 1b_word_vocab.txt heldout-monolingual.tokenized.shuffled README training-monolingual.tokenized.shuffled root@6643139b67ae:/workspace/nvidia-examples/big_lstm/data/1-billion-word-language-modeling-benchmark-r13output# cd training-monolingual.tokenized.shuffled root@6643139b67ae:/workspace/nvidia-examples/big_lstm/data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled# ls news.en-00001-of-00100 news.en-00034-of-00100 news.en-00067-of-00100 news.en-00002-of-00100 news.en-00035-of-00100 news.en-00068-of-00100 news.en-00003-of-00100 news.en-00036-of-00100 news.en-00069-of-00100 news.en-00004-of-00100 news.en-00037-of-00100 news.en-00070-of-00100 news.en-00005-of-00100 news.en-00038-of-00100 news.en-00071-of-00100 news.en-00006-of-00100 news.en-00039-of-00100 news.en-00072-of-00100 news.en-00007-of-00100 news.en-00040-of-00100 news.en-00073-of-00100 news.en-00008-of-00100 news.en-00041-of-00100 news.en-00074-of-00100 news.en-00009-of-00100 news.en-00042-of-00100 news.en-00075-of-00100 news.en-00010-of-00100 news.en-00043-of-00100 news.en-00076-of-00100 news.en-00011-of-00100 news.en-00044-of-00100 news.en-00077-of-00100 news.en-00012-of-00100 news.en-00045-of-00100 news.en-00078-of-00100 news.en-00013-of-00100 news.en-00046-of-00100 news.en-00079-of-00100 news.en-00014-of-00100 news.en-00047-of-00100 news.en-00080-of-00100 news.en-00015-of-00100 news.en-00048-of-00100 news.en-00081-of-00100 news.en-00016-of-00100 news.en-00049-of-00100 news.en-00082-of-00100 news.en-00017-of-00100 news.en-00050-of-00100 news.en-00083-of-00100 news.en-00018-of-00100 news.en-00051-of-00100 news.en-00084-of-00100 news.en-00019-of-00100 news.en-00052-of-00100 news.en-00085-of-00100 news.en-00020-of-00100 news.en-00053-of-00100 news.en-00086-of-00100 news.en-00021-of-00100 news.en-00054-of-00100 news.en-00087-of-00100 news.en-00022-of-00100 news.en-00055-of-00100 news.en-00088-of-00100 news.en-00023-of-00100 news.en-00056-of-00100 news.en-00089-of-00100 news.en-00024-of-00100 news.en-00057-of-00100 news.en-00090-of-00100 news.en-00025-of-00100 news.en-00058-of-00100 news.en-00091-of-00100 news.en-00026-of-00100 news.en-00059-of-00100 news.en-00092-of-00100 news.en-00027-of-00100 news.en-00060-of-00100 news.en-00093-of-00100 news.en-00028-of-00100 news.en-00061-of-00100 news.en-00094-of-00100 news.en-00029-of-00100 news.en-00062-of-00100 news.en-00095-of-00100 news.en-00030-of-00100 news.en-00063-of-00100 news.en-00096-of-00100 news.en-00031-of-00100 news.en-00064-of-00100 news.en-00097-of-00100 news.en-00032-of-00100 news.en-00065-of-00100 news.en-00098-of-00100 news.en-00033-of-00100 news.en-00066-of-00100 news.en-00099-of-00100 root@6643139b67ae:/workspace/nvidia-examples/big_lstm/data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled# exit exit [chibi@centos8 ~]$ cat /etc/redhat-release CentOS Linux release 8.1.1911 (Core) [chibi@centos8 ~]$ sudo hddtemp /dev/sda [sudo] chibi のパスワード: /dev/sda: Samsung SSD 840 PRO Series: 18°C [chibi@centos8 ~]$ nvidia-smi nvlink -c GPU 0: GeForce RTX 2080 Ti (UUID: GPU-1ac935c2-557f-282e-14e5-3f749ffd63ac) GPU 1: GeForce RTX 2080 Ti (UUID: GPU-13277ce5-e1e9-0cb1-8cee-6c9e6618e774) GPU 2: TITAN RTX (UUID: GPU-5a71d61e-f130-637a-b33d-4df555b0ed88) GPU 3: TITAN RTX (UUID: GPU-7fb51c1d-c1e7-35cc-aad7-66971f05ddb7) [chibi@centos8 ~]$