Skip to content

Latest commit

 

History

History
93 lines (72 loc) · 3.88 KB

README.md

File metadata and controls

93 lines (72 loc) · 3.88 KB

Tensorflow benchmark collection

Tensorflow

Tested with 1.2.0, 1.3.0, 1.4.1, 1.5.0, 1.6.0, 1.7.0 TF versions.

Benchmark files

CNN

convnet-benchmarks:

(based on https://github.com/soumith/convnet-benchmarks/tree/master/tensorflow)

  • benchmark_alexnet.py
  • benchmark_googlenet.py
  • benchmark_overfeat.py
  • benchmark_vgg.py

Parameters:

--batch_size   Batch size 
--num_bathces  Number of batches to run
--data_format  The data format for Convnet operations. Can be either NHWC (CPU) or NCHW (default)
--csv_file     File to output script results and information

MNIST example:

(based on: https://www.tensorflow.org/versions/r1.2/get_started/mnist/pros
https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/examples/tutorials/mnist/mnist_deep.py)

  • mnist_deep.py

Parameters:

--data_dir       Directory with MNIST input data
--mnist_batch    Batch size
--mnist_steps    Number of steps to train
--csv_file       Same as for benchmark_xxx.py (see above)

MNIST database of handwritten digits: http://yann.lecun.com/exdb/mnist/

tf-benchmarks.sh

Bash script to run either one or all benchmarks listed above, one after another. Same parameters as for individual benchmark files are accepted. For example:

$> ./tf-benchmarks.sh all --csv_file=benchmark_results.csv --batch_size=256 --mnist_batch=128

N.B. if --csv_file is specified, output of all benchmarks is added to the same .csv file.

Datasets

By default datasets are expected as subdirectories in datasets directory.

For MNIST data this can however be re-defined with --data_dir setting (see above).

Usage

If you have TensorFlow installed, you can:

  • execute each individual file
  • use tf-benchmarks.sh shell script
  • for an advanced logging you may have a look into job_examples/job_bmetal.sh

If you have no TensorFlow installed: use Docker containers!

To run a container you can use:

  • docker (CPU only! root rights are required)
  • nvidia-docker (root rights are required)
  • singularity (installation requires root rights)
  • udocker (no root privileges for installation or running)

Official TF Docker images

Official TensorFlow Docker images from https://hub.docker.com/r/tensorflow/tensorflow/tags/

Example how to use official TensorFlow images with udocker:

  1. install udocker in your preferred directory:
$> curl https://raw.githubusercontent.com/mariojmdavid/udocker/devel/udocker.py > udocker
$> chmod u+rx ./udocker
$> ./udocker install

  1. look into tools/udocker_pull_n_setup.sh script and specify which TensorFlow you want to pull. The script will also create container and setup its execution mode. If --nvidia option is given, you need CUDA installed. Note the name of the created container. List of available containers is given by udocker ps

  2. have a look into job_examples/job_udocker.sh to adjust e.g. where tf-benchmarks are installed ($PROJECT/workspace/tf-benchmarks by default). Run the script with the proper udocker container name (check with udocker ps).

Dockerfiles

To build your own docker images, one needs docker-ce (e.g. https://docs.docker.com/install/linux/docker-ce/ubuntu/)

Dockerfiles here are based on https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker

Dockerfile Versions

Dockerfile.cpu: CPU version based on Tensorflow for CPU (starts with FROM tensorflow/tensorflow:1.8.0)

Dockerfile.gpu: GPU version based on Tensorflow for GPU (starts with FROM tensorflow/tensorflow:1.8.0-gpu)

Dockerfile-tf{141|150}-nv384.81.gpu: GPU versions based on TF Dockerfile for GPU (CUDA{8|9} and CuDNN{6|7}). nvidia driver 384.81 is installed in the produced container(s). If a host machine has the same nvidia driver version (384.81), it allows to run containers on GPUs of such a host machine by means of e.g. singularity 2.2.1.

Compiled docker images

e.g. https://hub.docker.com/r/vykozlov/tensorflow/tags/