Welcome to ISC18 Student Cluster Competition.
Tensorflow 1.7.1 on Ubuntu 16.04 was used for this document.
Download Tensorflow source code
git clone https://github.com/tensorflow/tensorflow git checkout r1.7 |
Install Tensorflow dependencies
apt install bazel apt install python-numpy python-dev python-pip python-wheel pip install six numpy wheel |
Build Tensorflow
$ cd tensorflow # cd to the top-level directory created $ ./configure # Choose GPU and VERBS support $ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg $ pip install /tmp/tensorflow_pkg/tensorflow-1.7.1-cp27-cp27mu-linux_x86_64.whl # For TensorFlow 1.7.1 |
Validate your installation
$ Python import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello)) If the system outputs the following, then you are ready to begin writing TensorFlow programs: Hello, TensorFlow! |
Download Tensorflow model and benchmark
$ git clone https://github.com/tensorflow/models.git $ git clone https://github.com/tensorflow/benchmarks.git |
Converting ImageNet data to TFRecord format
First, create a login at http://image-net.org and make sure that your hard disk has at least 500 GB of free space for downloading and storing the data. Here we select DATA_DIR=/imagenet-data $ DATA_DIR=/imagenet-data $ cd models/research/inception $ bazel build //inception:download_and_preprocess_imagenet $ bazel-bin/inception/download_and_preprocess_imagenet "${DATA_DIR}" |
Run the Tensorflow benchmark using GPUs
$ DATA_DIR=/imagenet-data $ TRAIN_DIR=/imagenet-train $ cd benchmarks/scripts/tf_cnn_benchmarks $ python tf_cnn_benchmarks.py \ --data_format=NCHW --batch_size=64 \ --model=vgg16 --optimizer=momentum --variable_update=replicated \ --nodistortions --gradient_repacking=8 --num_gpus=2 \ --num_epochs=10 --weight_decay=1e-4 --data_dir=$DATA_DIR --use_fp16 \ --train_dir=$TRAIN_DIR --print_training_accuracy=true |
Sample output
TensorFlow: 1.7 Model: vgg16 Dataset: imagenet Mode: training SingleSess: False Batch size: 128 global 64 per device Num batches: 20018 Num epochs: 2.00 Devices: ['/gpu:0', '/gpu:1'] Data format: NCHW Layout optimizer: False Optimizer: momentum Variables: replicated AllReduce: None ========== Generating model Running warm up Done warm up Step Img/sec total_loss top_1_accuracy top_5_accuracy 1 images/sec: 605.9 +/- 0.0 (jitter = 0.0) 7.774 0.000 0.000 10 images/sec: 598.9 +/- 3.7 (jitter = 8.8) 7.774 0.000 0.000 20 images/sec: 600.9 +/- 2.0 (jitter = 4.8) 7.774 0.000 0.000 30 images/sec: 600.5 +/- 1.6 (jitter = 6.1) 7.774 0.000 0.000 ... 19990 images/sec: 577.4 +/- 0.3 (jitter = 7.0) 4.494 0.234 0.453 20000 images/sec: 577.5 +/- 0.3 (jitter = 7.0) 4.583 0.195 0.438 20010 images/sec: 576.7 +/- 0.3 (jitter = 7.0) 4.828 0.219 0.422 ---------------------------------------------------------------- total images/sec: 576.26 ---------------------------------------------------------------- |