Tensorflow Inception v3 benchmark

Tensorflow™ Inception v3 benchmark

LeaderGPU® is an ambitious player in the GPU computing market intend to change the current state of affairs. According on tests results, the computation speed for the Inception v3 model in LeaderGPU® is 3 times faster comparing to Google Cloud, and in 2.9 times faster comparing to AWS (data is provided with respect to example with 8x GTX 1080). The cost of per-minute leasing of the GPU in LeaderGPU® starts from as little as 0.02 euros, which is more than 4 times lower than the cost of renting in Google Cloud and more than 5 times lower than the cost in AWS (as of July 7, 2017).

Throughout this article, we will be testing the Inception v3 model in such services as LeaderGPU®, AWS, and Google Cloud. We will determine why LeaderGPU® is the leading offer among the considering options.

All tests were performed using python 3.5 and Tensorflow-gpu 1.2 on machines with GTX 1080, GTX 1080 TI and Tesla® P 100 with CentOS 7 operating system installed and CUDA® 8.0 library.

The following commands were used to run the tests:

# git clone https://github.com/tensorflow/benchmarks.git

# python3.5 benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --num_gpus=2(Number of cards on the server) --model inception3 --batch_size 32 (64, 128)

GTX 1080 instances

For the first test, we use instances with the GTX 1080. Testing environment data (with batch sizes 32 and 64) is provided below:

• Instance types: ltbv17, ltbv13, ltbv16
• GPU: 2x GTX 1080, 4x GTX 1080, 8x GTX 1080
• OS: CentOS 7
• CUDA® / cuDNN: 8.0 / 5.1
• TensorFlow™ GitHub hash: b1e174e
• Benchmark GitHub hash: 9165a70
• Command: # python3.5 benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --num_gpus=2 --model inception3 –batch size 32 (optional 64)
• Model: Inception v3
• Date of testing: June 2017

The test results are shown in the following diagram:

GTX 1080TI instances

Now let's use instances with the GTX 1080 Ti. Testing environment data (with batch sizes 32 and 64) is provided below:

• Instance types: ltbv21, ltbv18
• GPU: 2x GTX 1080TI, 4x GTX 1080TI
• OS: CentOS 7
• CUDA® / cuDNN: 8.0 / 5.1
• TensorFlow™ GitHub hash: b1e174e
• Benchmark GitHub hash: 9165a70
• Command: # python3.5 benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --num_gpus=2 (4) --model inception3 --batch_size 32 (optional 64, 128)
• Model: Inception v3
• Date of testing: June 2017

The test results are shown in the following diagram:

Tesla® P100 instance

Finally, it's time to test the model with the Tesla® P100. Testing environment data (with batch sizes 32, 64 and 128) is provided below:

• Instance type: ltbv20
• GPU: 2x NVIDIA® Tesla® P100
• OS: CentOS 7
• CUDA® / cuDNN: 8.0 / 5.1
• TensorFlow™ GitHub hash: b1e174e
• Benchmark GitHub hash: 9165a70
• Command: # python3.5 benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --num_gpus=2 --model inception3 –batch size 32 (optional 64, 128)
• Model: Inception v3
• Date of testing: June 2017

The test results are shown in the following diagram:

In the table below, we collected the results of inception v3 tests on Google cloud and AWS (with the batch size 64)

* Data for the table is quoted from the following sources:

https://www.tensorflow.org/performance/benchmarks#details_for_google_compute_engine_nvidia_tesla_k80
https://www.tensorflow.org/performance/benchmarks#details_for_amazon_ec2_nvidia_tesla_k80

Let's make a calculation of the cost and processing time for 1,000,000 images on each LeaderGPU®, AWS and Google machine. Counting was carried out with a batch size of 64 for all machines.

** The Google cloud service does not offer per minute payment plans. Per minute cost calculations are based on the hourly price ($ 5,645).

As can be concluded from the table, the speed of image processing in the Inception v3 model is the maximum with 8x GTX 1080 from LeaderGPU®, while:

• The initial cost in LeaderGPU® starts from as little as € 1.77, which is about 3.42 times lower than in the instances of 8x Tesla® K80 by Google Cloud, and about 4.38 times lower than in instances of 8x Tesla® K80 from Google AWS;
• processing time was 24 minutes 18 seconds, which is 3.03 times faster than in the instances of 8x Tesla® K80 from the Google Cloud, and 2.99 times faster than in the 8x Tesla® K80 instances from Google AWS.

Testing results leave no doubts. LeaderGPU® is a proven leader in the field of GPU-computing, offering unrivaled solutions at reasonable prices. Take advantage of the cost-effective GPU offer from LeaderGPU® today!

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