[chibi@rhel8 ~]$ sudo nvidia-docker run --rm -ti nvcr.io/nvidia/tensorflow:19.04-py3
[sudo] chibi のパスワード:
                                                                                
================
== 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@67d1a4a7e39f:/workspace# ls
README.md  docker-examples  nvidia-examples
root@67d1a4a7e39f:/workspace# cd nvidia-examples
root@67d1a4a7e39f:/workspace/nvidia-examples# ls
NCF              bert                 cnn           ssdv1.2
OpenSeq2Seq      big_lstm             gnmt_v2       tensorrt
UNet_Industrial  build_imagenet_data  resnet50v1.5
root@67d1a4a7e39f:/workspace/nvidia-examples# cd big_lstm
root@67d1a4a7e39f:/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@67d1a4a7e39f:/workspace/nvidia-examples/big_lstm# ./download_1b_words_data.sh
Please specify root of dataset directory: data

Success: dataset root dir validated

--2020-03-10 18:38:42--  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
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Saving to: ‘1-billion-word-language-modeling-benchmark-r13output.tar.gz’

1-billion-word-lang 100%[===================>]   1.67G  2.29MB/s    in 19m 4s

2020-03-10 18:57:47 (1.49 MB/s) - ‘1-billion-word-language-modeling-benchmark-r13output.tar.gz’ saved [1792209805/1792209805]

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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@67d1a4a7e39f:/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*****
{'optimizer': 0, 'do_summaries': False, 'num_sampled': 8192, 'max_time': 180, 'num_gpus': 4, 'num_steps': 20, 'num_shards': 8, 'average_params': True, 'keep_prob': 0.9, 'state_size': 2048, 'num_delayed_steps': 150, 'max_grad_norm': 10.0, 'vocab_size': 793470, 'run_profiler': False, 'num_layers': 1, 'emb_size': 512, 'batch_size': 128, 'learning_rate': 0.2, 'projected_size': 512}
**************************
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: 1583866746.6295264
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) <dtype: 'float32_ref'> /gpu:0
model/emb_1:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_2:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_3:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_4:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_5:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_6:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_7:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/lstm_0/LSTMCell/W_0:0 (1024, 8192) <dtype: 'float32_ref'> /gpu:0
model/lstm_0/LSTMCell/B:0 (8192,) <dtype: 'float32_ref'> /gpu:0
model/lstm_0/LSTMCell/W_P_0:0 (2048, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_0:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_1:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_2:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_3:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_4:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_5:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_6:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_7:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_b:0 (793470,) <dtype: 'float32_ref'> /gpu:0
model/global_step:0 () <dtype: 'int32_ref'>
model/model/emb_0/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_1/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_2/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_3/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_4/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_5/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_6/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/emb_7/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/lstm_0/LSTMCell/W_0/Adagrad:0 (1024, 8192) <dtype: 'float32_ref'> /gpu:0
model/model/lstm_0/LSTMCell/B/Adagrad:0 (8192,) <dtype: 'float32_ref'> /gpu:0
model/model/lstm_0/LSTMCell/W_P_0/Adagrad:0 (2048, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_0/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_1/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_2/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_3/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_4/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_5/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_6/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_w_7/Adagrad:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/model/softmax_b/Adagrad:0 (793470,) <dtype: 'float32_ref'> /gpu:0
model/model/lstm_0/LSTMCell/W_0/ExponentialMovingAverage:0 (1024, 8192) <dtype: 'float32_ref'> /gpu:0
model/model/lstm_0/LSTMCell/B/ExponentialMovingAverage:0 (8192,) <dtype: 'float32_ref'> /gpu:0
model/model/lstm_0/LSTMCell/W_P_0/ExponentialMovingAverage:0 (2048, 512) <dtype: 'float32_ref'> /gpu:0
TRAINABLE VARIABLES
model/emb_0:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_1:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_2:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_3:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_4:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_5:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_6:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/emb_7:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/lstm_0/LSTMCell/W_0:0 (1024, 8192) <dtype: 'float32_ref'> /gpu:0
model/lstm_0/LSTMCell/B:0 (8192,) <dtype: 'float32_ref'> /gpu:0
model/lstm_0/LSTMCell/W_P_0:0 (2048, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_0:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_1:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_2:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_3:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_4:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_5:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_6:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_w_7:0 (99184, 512) <dtype: 'float32_ref'> /gpu:0
model/softmax_b:0 (793470,) <dtype: 'float32_ref'> /gpu:0
LOCAL VARIABLES
model/model/state_0_0:0 (128, 2560) <dtype: 'float32_ref'> /gpu:0
model/model_1/state_1_0:0 (128, 2560) <dtype: 'float32_ref'> /gpu:1
model/model_2/state_2_0:0 (128, 2560) <dtype: 'float32_ref'> /gpu:2
model/model_3/state_3_0:0 (128, 2560) <dtype: 'float32_ref'> /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
2020-03-10 18:59:07.641545: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200005000 Hz
2020-03-10 18:59:07.647890: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0xbd0c120 executing computations on platform Host. Devices:
2020-03-10 18:59:07.647918: I tensorflow/compiler/xla/service/service.cc:168]   StreamExecutor device (0): <undefined>, <undefined>
2020-03-10 18:59:08.656863: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0xbbfdeb0 executing computations on platform CUDA. Devices:
2020-03-10 18:59:08.656901: I tensorflow/compiler/xla/service/service.cc:168]   StreamExecutor device (0): TITAN RTX, Compute Capability 7.5
2020-03-10 18:59:08.656910: I tensorflow/compiler/xla/service/service.cc:168]   StreamExecutor device (1): TITAN RTX, Compute Capability 7.5
2020-03-10 18:59:08.656917: I tensorflow/compiler/xla/service/service.cc:168]   StreamExecutor device (2): GeForce RTX 2080 Ti, Compute Capability 7.5
2020-03-10 18:59:08.656926: I tensorflow/compiler/xla/service/service.cc:168]   StreamExecutor device (3): GeForce RTX 2080 Ti, Compute Capability 7.5
2020-03-10 18:59:08.658368: 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
2020-03-10 18:59:08.658422: 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
2020-03-10 18:59:08.658464: 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: 10.37GiB
2020-03-10 18:59:08.658505: 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
2020-03-10 18:59:08.658711: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1, 2, 3
2020-03-10 18:59:14.012284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-10 18:59:14.012337: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 1 2 3
2020-03-10 18:59:14.012346: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N N N N
2020-03-10 18:59:14.012352: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1:   N N N N
2020-03-10 18:59:14.012359: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 2:   N N N N
2020-03-10 18:59:14.012365: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 3:   N N N N
2020-03-10 18:59:14.012594: 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)
2020-03-10 18:59:14.013354: 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)
2020-03-10 18:59:14.013606: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 10003 MB memory) -> physical GPU (device: 2, name: GeForce RTX 2080 Ti, pci bus id: 0000:02:00.0, compute capability: 7.5)
2020-03-10 18:59:14.014306: 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-00094-of-00100
Finished processing!
2020-03-10 18:59:57.353898: I tensorflow/stream_executor/dso_loader.cc:153] successfully opened CUDA library libcublas.so.10 locally
Iteration 1, time = 35.02s, wps = 292, train loss = 13.0194
Iteration 2, time = 19.07s, wps = 537, train loss = 12.9299
Iteration 3, time = 0.11s, wps = 91111, train loss = 12.8328
Iteration 4, time = 0.11s, wps = 96463, train loss = 11.7931
Iteration 5, time = 0.11s, wps = 94252, train loss = 12.7502
Iteration 6, time = 0.11s, wps = 92579, train loss = 11.6831
Iteration 7, time = 0.11s, wps = 91576, train loss = 77.3567
Iteration 8, time = 0.10s, wps = 102658, train loss = 35.6945
Iteration 9, time = 0.10s, wps = 98318, train loss = 16.8084
Iteration 20, time = 1.13s, wps = 100104, train loss = 12.1319
Iteration 40, time = 2.07s, wps = 98743, train loss = 9.4442
Iteration 60, time = 2.09s, wps = 97814, train loss = 8.4348
Iteration 80, time = 2.08s, wps = 98469, train loss = 8.1801
Iteration 100, time = 2.10s, wps = 97698, train loss = 8.0459
Iteration 120, time = 2.08s, wps = 98479, train loss = 7.6409
Iteration 140, time = 2.08s, wps = 98444, train loss = 7.1072
Iteration 160, time = 2.10s, wps = 97674, train loss = 6.9134
Iteration 180, time = 2.11s, wps = 96956, train loss = 6.9025
Iteration 200, time = 2.07s, wps = 98951, train loss = 6.6642
Iteration 220, time = 2.07s, wps = 98722, train loss = 6.3750
Iteration 240, time = 2.06s, wps = 99576, train loss = 6.2454
Iteration 260, time = 2.08s, wps = 98488, train loss = 6.2133
Iteration 280, time = 2.06s, wps = 99414, train loss = 6.2046
Iteration 300, time = 2.04s, wps = 100482, train loss = 6.0895
Iteration 320, time = 2.09s, wps = 97774, train loss = 6.0206
Iteration 340, time = 2.09s, wps = 98024, train loss = 5.8916
Iteration 360, time = 2.10s, wps = 97707, train loss = 5.9628
Iteration 380, time = 2.08s, wps = 98535, train loss = 5.8795
Iteration 400, time = 2.09s, wps = 97974, train loss = 5.9173
Iteration 420, time = 2.07s, wps = 99107, train loss = 5.8642
Iteration 440, time = 2.09s, wps = 98176, train loss = 5.8204
Iteration 460, time = 2.07s, wps = 98885, train loss = 5.7758
Iteration 480, time = 2.07s, wps = 99022, train loss = 5.8255
Iteration 500, time = 2.08s, wps = 98481, train loss = 5.6615
Iteration 520, time = 2.07s, wps = 98921, train loss = 5.7176
Iteration 540, time = 2.06s, wps = 99420, train loss = 5.6750
Iteration 560, time = 2.07s, wps = 99115, train loss = 5.7206
Iteration 580, time = 2.08s, wps = 98236, train loss = 5.6646
Iteration 600, time = 2.08s, wps = 98697, train loss = 5.5937
Iteration 620, time = 2.08s, wps = 98620, train loss = 5.5907
Iteration 640, time = 2.07s, wps = 98828, train loss = 5.4982
Iteration 660, time = 2.05s, wps = 99678, train loss = 5.5273
Iteration 680, time = 2.05s, wps = 99677, train loss = 5.5539
Iteration 700, time = 2.08s, wps = 98344, train loss = 5.4627
Iteration 720, time = 2.06s, wps = 99490, train loss = 5.4245
Iteration 740, time = 2.06s, wps = 99212, train loss = 5.4144
Iteration 760, time = 2.11s, wps = 97291, train loss = 5.3394
Iteration 780, time = 2.08s, wps = 98363, train loss = 5.4100
Processing file: ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/news.en-00087-of-00100
Finished processing!
Iteration 800, time = 4.26s, wps = 48049, train loss = 5.3839
Iteration 820, time = 2.08s, wps = 98455, train loss = 5.3893
Iteration 840, time = 2.07s, wps = 98805, train loss = 5.3327
Iteration 860, time = 2.07s, wps = 98961, train loss = 5.3127
Iteration 880, time = 2.08s, wps = 98694, train loss = 5.2900
Iteration 900, time = 2.06s, wps = 99202, train loss = 5.3383
Iteration 920, time = 2.08s, wps = 98627, train loss = 5.3023
/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    3m53.215s
user    14m6.734s
sys     1m50.252s
root@67d1a4a7e39f:/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@67d1a4a7e39f:/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@67d1a4a7e39f:/workspace/nvidia-examples/big_lstm# cd data
root@67d1a4a7e39f:/workspace/nvidia-examples/big_lstm/data# ls
1-billion-word-language-modeling-benchmark-r13output
root@67d1a4a7e39f:/workspace/nvidia-examples/big_lstm/data# cd 1-billion-word-language-modeling-benchmark-r13output
root@67d1a4a7e39f:/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@67d1a4a7e39f:/workspace/nvidia-examples/big_lstm/data/1-billion-word-language-modeling-benchmark-r13output# cd training-monolingual.tokenized.shuffled
root@67d1a4a7e39f:/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@67d1a4a7e39f:/workspace/nvidia-examples/big_lstm/data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled# exit
exit
[chibi@rhel8 ~]$ cat /etc/redhat-release
Red Hat Enterprise Linux release 8.1 (Ootpa)
[chibi@rhel8 ~]$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
[chibi@rhel8 ~]$ sudo hddtemp /dev/sda
[sudo] chibi のパスワード:
/dev/sda: WDC WD10EZEX-00BN5A0: 24°C
[chibi@rhel8 ~]$ 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@rhel8 ~]$