{"id":20812372,"url":"https://github.com/osmr/utct","last_synced_at":"2026-04-23T06:33:18.167Z","repository":{"id":131249523,"uuid":"99340559","full_name":"osmr/utct","owner":"osmr","description":"Universal Training Control Tools","archived":false,"fork":false,"pushed_at":"2017-12-19T08:25:59.000Z","size":72,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-12-26T18:52:14.645Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/osmr.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-08-04T12:24:31.000Z","updated_at":"2017-08-04T12:26:17.000Z","dependencies_parsed_at":"2023-03-10T16:45:50.572Z","dependency_job_id":null,"html_url":"https://github.com/osmr/utct","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/osmr/utct","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/osmr%2Futct","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/osmr%2Futct/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/osmr%2Futct/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/osmr%2Futct/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/osmr","download_url":"https://codeload.github.com/osmr/utct/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/osmr%2Futct/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32169657,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-23T02:19:40.750Z","status":"ssl_error","status_checked_at":"2026-04-23T02:17:55.737Z","response_time":53,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":"2024-11-17T20:53:38.725Z","updated_at":"2026-04-23T06:33:18.140Z","avatar_url":"https://github.com/osmr.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Universal Training Control Tools \n\nA set of utilities and wrappers for uniform work with various frameworks such as Tensorflow, TFLearn, Keras, MXNet, Lasagne, and CNTK. This allows us to apply universal methods for hyper parameters tuning, overfitting detection, etc. Also, it simplifies porting/converting models from one framework to another.\n\n## Instructions for using TFLearn-branch on Linux (GPU):\n1. Install TensorFlow (https://www.tensorflow.org/install/install_linux):\n```\nsudo pip install tensorflow-gpu\n```\n2. Install TFLearn (http://tflearn.org/installation):\n```\nsudo pip install git+https://github.com/tflearn/tflearn.git\n```\n3. Install extra python packages:\n```\nsudo pip install --upgrade pip\nsudo pip install opencv-python\nsudo pip install bayesian-optimization\nsudo pip install pandas\nsudo pip install h5py\n```\n## Instructions for using TFLearn-branch on Win10 x64 (CPU):\n1. Install Python3:\n- Install Anaconda3 x64 for Windows (https://www.continuum.io/downloads).\n- Add the path to Anaconda for ability of running the python from a console:\n```\nPATH=%PATH%; %USERPROFILE%\\Anaconda3\n```\n2. Install TensorFlow (https://www.tensorflow.org/install/install_windows):\n```\npip install --upgrade tensorflow\n```\n3. Install TFLearn (http://tflearn.org/installation):\n```\npip install git+https://github.com/tflearn/tflearn.git\n```\n4. Install OpenCV (from http://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv):\n```\npip install opencv_python-3.2.0+contrib-cp36-cp36m-win_amd64.whl\n```\n5. Install h5py (http://www.lfd.uci.edu/~gohlke/pythonlibs/#h5py):\n```\npip install h5py-2.7.0-cp36-cp36m-win_amd64.whl\n```\n6. Install extra python packages:\n```\npip install bayesian-optimization\npip install pandas\n```\n\n## Instructions for using MXNet-branch on Linux (GPU):\n1. Install extra python packages:\n```\nsudo pip install --upgrade pip\nsudo pip install opencv-python\nsudo pip install bayesian-optimization\nsudo pip install pandas\nsudo pip install h5py\nsudo pip install graphviz\n```\n2. Install MXNet (http://mxnet.io/get_started/install.html):\n```\nsudo pip install mxnet-cu80\n```\n\n## Instructions for using MXNet-branch on Win10 x64 (CPU):\n1. Install Python3:\n- Install Anaconda3 x64 for Windows (https://www.continuum.io/downloads).\n- Add the path to Anaconda for ability of running the python from a console:\n```\nPATH=%PATH%; %USERPROFILE%\\Anaconda3\n```\n2. Install MXNet:\n- Download MXNet binary packages for Windows x64 (https://github.com/yajiedesign/mxnet/releases).\n- Unpack packages into `%LANGS_LIBS%/mxnet` directory.\n- Setup environmental variables (from script `setupenv.cmd`, NB: This bugged script slightly breaks existing paths!):\n```\nset MXNET_HOME=%LANGS_LIBS%\\mxnet\nset PATH=%PATH%; %MXNET_HOME%\\3rdparty\\openblas\\bin\nset PATH=%PATH%; %MXNET_HOME%\\3rdparty\\gnuwin\nset PATH=%PATH%; %MXNET_HOME%\\3rdparty\\vc\nset PATH=%PATH%; %MXNET_HOME%\\3rdparty\\opencv\nset PATH=%PATH%; %MXNET_HOME%\\3rdparty\\cudart\nset PATH=%PATH%; %MXNET_HOME%\\3rdparty\\cudnn\\bin\nset PATH=%PATH%; %MXNET_HOME%\\lib\n```\n- Install the MXNet python package by running `python setup.py install`.\n- Copy content of folder `%MXNET_HOME%\\python\\mxnet` to `%USERPROFILE%\\Anaconda3\\Lib\\site-packages\\mxnet-0.10.1-py3.6.egg\\mxnet`.\n3. Install OpenCV (from http://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv):\n```\npip install opencv_python-3.2.0+contrib-cp36-cp36m-win_amd64.whl\n```\n4. Install h5py (http://www.lfd.uci.edu/~gohlke/pythonlibs/#h5py):\n```\npip install h5py-2.7.0-cp36-cp36m-win_amd64.whl\n```\n5. Install Graphviz (http://www.graphviz.org/Download_windows.php) and add path to the program in to the environmental variables:\n```\nset PATH=%PATH%; C:\\Program Files (x86)\\Graphviz2.38\\bin\n```\n6. Install extra python packages:\n```\npip install bayesian-optimization\npip install pandas\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fosmr%2Futct","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fosmr%2Futct","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fosmr%2Futct/lists"}