{"id":19009901,"url":"https://github.com/matcom/ml","last_synced_at":"2025-08-11T23:05:20.862Z","repository":{"id":75594924,"uuid":"162164653","full_name":"matcom/ml","owner":"matcom","description":"Base machine learning image and environment.","archived":false,"fork":false,"pushed_at":"2025-06-21T13:56:19.000Z","size":59,"stargazers_count":16,"open_issues_count":30,"forks_count":6,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-07-10T06:58:15.212Z","etag":null,"topics":["docker","keras","machine-learning","python","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"","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/matcom.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,"zenodo":null}},"created_at":"2018-12-17T17:10:42.000Z","updated_at":"2023-10-28T13:08:48.000Z","dependencies_parsed_at":"2023-02-26T11:46:13.084Z","dependency_job_id":"9da867c5-ce12-4c2d-8ec3-547701ef39c1","html_url":"https://github.com/matcom/ml","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/matcom/ml","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matcom%2Fml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matcom%2Fml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matcom%2Fml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matcom%2Fml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/matcom","download_url":"https://codeload.github.com/matcom/ml/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matcom%2Fml/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269970120,"owners_count":24505466,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-11T02:00:10.019Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["docker","keras","machine-learning","python","sklearn","tensorflow"],"created_at":"2024-11-08T19:09:14.106Z","updated_at":"2025-08-11T23:05:20.834Z","avatar_url":"https://github.com/matcom.png","language":"Python","readme":"# MatCom Machine Learning image\n\nBase machine learning images and environment with CPU and GPU support.\n\nThis repository contains two images:\n* `matcomuh/ml:cpu` is a basic machine learning image with several popular ML tools.\n* `matcomuh/hub:cpu` is a fully-functional JupyterHub on top of the basic ML image.\n\nAlso, the same images with GPU support:\n* `matcomuh/ml:gpu`\n* `matcomuh/hub:gpu`\n\n## Basic ML usage\n\nIf you just want to hack machine learning on your own, you can use the basic image.\nClone this repository and run:\n\n```bash\ndocker-compose up ml\n```\n\nIn [localhost:8888](http://localhost:8888) you will find an instance of [JupyterLab](https://github.com/jupyterlab/jupyterlab).\nThe notebooks are stored in the local `notebooks` folder.\n\n## JupyterHub\n\nIf you need a more advanced multi-user JupyterHub scenario, then run:\n\n```bash\ndocker-compose up hub\n```\n\nIn [localhost:8000](http://localhost:8000) you will find an instance of [JupyterHub](https://github.com/jupyterhub/jupyterhub).\n* The default user is `admin` with password `admin`.\n* The file `hub/config.py` contains the configuration file for this instance.\n\nUsers are by default added to the system, and their data folders are mounted in a `docker` volume.\nHence when the container is re-created the data and users will still be there.\n\n\u003e **NOTE**: New users are created by default with the same username as password. When the container is destroyed and re-created, **password changes are not saved** for now.\n\n## Running the GPU version\n\nBy default the CPU version of the services are run. If you want to try the GPU version, you will need [nvidia-docker2](https://github.com/NVIDIA/nvidia-docker) installed, and suitable NVIDIA drivers for your box.\n\nWith all prerequisites, you are ready to run the GPU version of the services:\n\n```bash\ndocker-compose -f docker-compose.yml -f docker-compose.gpu.yml up [ml|hub]\n```\n\nIf you are gonna be running GPU all the time, consider creating a `docker-compose.override.yml` link to simplify things:\n\n```bash\nln -s docker-compose.gpu.yml docker-compose.override.yml\n```\n\nThen just running `docker-compose up` as usual will automatically use the GPU version of the services.\n\n## What's included\n\n* Jupyter Notebook / Lab / Hub\n* Tensorflow (1.12.0)\n* Keras (2.1.6-tf) _(see note)_\n* Scikit-learn (0.20)\n  * hmmlearn\n  * sklearn-crfsuite\n  * seqlearn\n* Flask \u0026 Flask-RESTful\n* Gensim\n* Graphviz\n* NLTK\n* owlready (1 \u0026 2)\n* Spacy\n  * (`en` and `es` corpora)\n  * `neuralcoref`\n\nPlus small utilities such as `psutils`. Take a look at the [requirements.txt](requirements.txt) file.\n\n\u003e **NOTE:** To use `keras`, you have to import it as `from tensorflow import keras`.\n\n## Contributors:\n\n* [Alejandro Piad](https://github.com/apiad)\n* [Hian Cañizares](https://github.com/hiancdtrsnm)\n\n## License \u0026 Contributions\n\nAll contributions are appreciated! Licensed under MIT.\nMake sure to add your name to the previous list.\n\n\u003e MIT License\n\u003e\n\u003e Copyright (c) 2018 Faculty of Math and Computer Science, University of Havana\n\u003e\n\u003e Permission is hereby granted, free of charge, to any person obtaining a copy\n\u003e of this software and associated documentation files (the \"Software\"), to deal\n\u003e in the Software without restriction, including without limitation the rights\n\u003e to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n\u003e copies of the Software, and to permit persons to whom the Software is\n\u003e furnished to do so, subject to the following conditions:\n\u003e\n\u003e The above copyright notice and this permission notice shall be included in all\n\u003e copies or substantial portions of the Software.\n\u003e\n\u003e THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n\u003e IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n\u003e FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n\u003e AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n\u003e LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n\u003e OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n\u003e SOFTWARE.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatcom%2Fml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatcom%2Fml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatcom%2Fml/lists"}