{"id":37736559,"url":"https://github.com/datasystemsgrouput/dlbench","last_synced_at":"2026-01-16T14:01:01.545Z","repository":{"id":39281854,"uuid":"156101304","full_name":"DataSystemsGroupUT/DLBench","owner":"DataSystemsGroupUT","description":"A repository for benchmarking deep learning frameworks on different data sets","archived":false,"fork":false,"pushed_at":"2023-01-06T00:52:23.000Z","size":70182,"stargazers_count":4,"open_issues_count":40,"forks_count":2,"subscribers_count":7,"default_branch":"master","last_synced_at":"2023-08-04T05:56:20.802Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DataSystemsGroupUT.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-11-04T16:21:57.000Z","updated_at":"2023-07-13T09:52:36.000Z","dependencies_parsed_at":"2023-02-05T01:03:08.207Z","dependency_job_id":null,"html_url":"https://github.com/DataSystemsGroupUT/DLBench","commit_stats":null,"previous_names":[],"tags_count":0,"template":null,"template_full_name":null,"purl":"pkg:github/DataSystemsGroupUT/DLBench","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataSystemsGroupUT%2FDLBench","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataSystemsGroupUT%2FDLBench/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataSystemsGroupUT%2FDLBench/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataSystemsGroupUT%2FDLBench/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DataSystemsGroupUT","download_url":"https://codeload.github.com/DataSystemsGroupUT/DLBench/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataSystemsGroupUT%2FDLBench/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28479074,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T11:59:17.896Z","status":"ssl_error","status_checked_at":"2026-01-16T11:55:55.838Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-16T14:01:00.300Z","updated_at":"2026-01-16T14:01:01.499Z","avatar_url":"https://github.com/DataSystemsGroupUT.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Benchmarking-Deep-Learning-Frameworks\n## The frameworks that are included in this benchmark are:\n  1-[Keras](https://keras.io/) \u003cbr /\u003e \n  2-[Chainer](https://docs.chainer.org/en/stable/glance.html) \u003cbr /\u003e \n  3-[Tensorflow](https://www.tensorflow.org/) \u003cbr /\u003e \n  4-[Pytorch](https://pytorch.org/) \u003cbr /\u003e\n  5-[Theano](http://deeplearning.net/software/theano/)  \u003cbr /\u003e\n  6-[Mxnet](https://mxnet.apache.org/) \u003cbr /\u003e\n\n## The CNN Experiments comparing those frameworks over 4 datasets:\n  1- [MNIST](http://yann.lecun.com/exdb/mnist/) \u003cbr /\u003e\n  2- [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) \u003cbr /\u003e\n  3- [CIFAR100](https://www.cs.toronto.edu/~kriz/cifar.html) \u003cbr /\u003e\n  4- [SVHN](http://ufldl.stanford.edu/housenumbers/) \u003cbr /\u003e\n  \n\n## The LSTM Experiments comparing those frameworks over 3 datasets:\n  1- [PTB](https://corochann.com/penn-tree-bank-ptb-dataset-introduction-1456.html) \u003cbr /\u003e\n  2- [Manythings](https://www.manythings.org/anki/) \u003cbr /\u003e\n  3- [IMDB](https://keras.io/api/datasets/imdb/) \u003cbr /\u003e\n    \n## The FRCNN Experiment comparing those frameworks over 1 dataset:\n  1- [VOC12](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) \u003cbr /\u003e\n\n\nThere are two experiments one of them uses CPU and the other uses GPU.\n\n## This repository is divided into 3 folders:\n ### 1- CPU Experiment \u003cbr /\u003e\n    * The CPU experiments are performed on a single machine running on Centos release 7.5.1804 with 32 core Intel Xeon Processor (Skylake,  IBRS) @ 2.00GHz;64 GB DIMM memory; and 240 GB SSD hard drive.\n    * For Keras, version 2.2.4 is used on Tensorflow 1.11.0. \n    * For Chainer, version 4.5.0 is used. \n    * For Tensorflow, version 1.11 is used. \n    * For Pytorch, version 0.4.1 is used\n    * For Theano, version 1.0.2 is used. \n    * For MXNet, version 1.3.0 is used.\n    * It contain the CPU source code \n    * The Generated graphs \n    * The logs of the experiment \n       \n ### 2- GPU Experiment \u003cbr /\u003e\n    * The GPU experiments are performed on a single machine running on Debian GNU/Linux 9 (stretch) with 8 core Intel(R) Xeon(R) CPU @ 2.00GHz; NVIDIA Tesla P4;36 GB DIMM memory; and 300 GB SSD hard drive.\n    * For Keras, version 2.2.4 is used and run on Tensorflow version 1.11.0. \n    * For Chainer, version 4.5.0 is used. \n    * For Tensorflow, version 1.11.0 is used. \n    * For Pytorch, version 0.4.1 is used, For Theano, version 1.0.2 is used. \n    * For MXNet, version 1.3.0 is used.\n    * For Chainer, MXNet and Pytorch, we used CUDA 9.2 and cuDNN 7.2.1, and for Tensorflow, Keras, and Theano, we used CUDA 10.0 and cuDNN 7.3\n    * It contain the GPU experiment \n    * The Generated graphs \n    * The logs of the experiment \n    \n ### 3- Installation Guide \u003cbr /\u003e\n    * It contain file for each framework with the commands needed for installation \n    * It's recommended to create different environmnet for each framework using conda or virtualenvs\n\n  \n ## Experiment Logging:\n  There exist 3 files for logging the resources during the experiment: CPU log, GPU Log, memory Log.\u003cbr /\u003e\n  \n## How to run? \n 1- Install the environment for each framework using the installation  guide \u003cbr /\u003e\n 2- Clone the project \u003cbr /\u003e\n 3- For running the experiment over MNIST datset for Keras framework for example, you will find in CPU folder the source code, There is a file for each framework. \u003cbr /\u003e\n 4- There is a method in the main function that is called runModel, this methods holds the name of the dataset and the number of       epochs needed for this run.\n\n## Optional hyperparameters:\u003cbr /\u003e\nThere are  many other optional parameters with the following default values, these paramters including: \u003cbr /\u003e\n     1- Learning Rate=0.01 \u003cbr /\u003e\n     2- momentum=0.5 \u003cbr /\u003e\n     3- Weight Decay=1e-6 \u003cbr /\u003e\n     4- batch size = 128 \u003cbr /\u003e\n     \n ## #Installation steps:\n create a new environment with conda\n  $ conda create -n [my-env-name]\n activate the environment you created\n  $ source activate [my-env-name]\n install pip in the virtual environment\n  $ conda install pip\n  \n  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatasystemsgrouput%2Fdlbench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatasystemsgrouput%2Fdlbench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatasystemsgrouput%2Fdlbench/lists"}