{"id":31895505,"url":"https://github.com/igorcosta/deep-docker","last_synced_at":"2026-05-05T14:07:59.937Z","repository":{"id":142023261,"uuid":"132989252","full_name":"igorcosta/deep-docker","owner":"igorcosta","description":"Docker image for Deep Learning on AWS Cloud","archived":false,"fork":false,"pushed_at":"2018-05-11T04:42:31.000Z","size":3,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-13T10:26:51.523Z","etag":null,"topics":["cuda","deep-learning","docker","docker-image","tensorflow"],"latest_commit_sha":null,"homepage":null,"language":null,"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/igorcosta.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-05-11T04:28:39.000Z","updated_at":"2018-05-14T00:14:27.000Z","dependencies_parsed_at":"2023-07-07T08:01:56.178Z","dependency_job_id":null,"html_url":"https://github.com/igorcosta/deep-docker","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/igorcosta/deep-docker","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igorcosta%2Fdeep-docker","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igorcosta%2Fdeep-docker/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igorcosta%2Fdeep-docker/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igorcosta%2Fdeep-docker/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/igorcosta","download_url":"https://codeload.github.com/igorcosta/deep-docker/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igorcosta%2Fdeep-docker/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32652592,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-05T11:29:49.557Z","status":"ssl_error","status_checked_at":"2026-05-05T11:29:48.587Z","response_time":54,"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":["cuda","deep-learning","docker","docker-image","tensorflow"],"created_at":"2025-10-13T10:26:11.850Z","updated_at":"2026-05-05T14:07:59.931Z","avatar_url":"https://github.com/igorcosta.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# deep-docker\n\n\u003e For running deeplearning experiments on AWS EC2 g2.2xlarge advised or bigger with\n\u003e Docker and Docker machine\n\nThis image includes\n- Optimized Python 3.6 for Docker, find out more there [https://www.revsys.com/tidbits/optimized-python/]\n- Nvidia driver 346.46\n- CUDA 7.0\n- Anaconda 3.18.8 (Python 2.7.11)\n- Preconfigured .theanorc to use GPU and float32 by default\n\n## Useful Commands\n\n### Preparing the host machine\n\nThe host machine needs to run the **same version** of the NVidia driver as inside the container. So I built an AMI based on the Ubuntu 14.04 HBM SSD AMI (ami-5c207736) by the following script.\n\n    sudo su -\n    apt-get update\n    apt-get install -y build-essential\n    apt-get install -y linux-headers-$(uname -r) linux-image-$(uname -r) linux-image-extra-$(uname -r)\n    echo \"blacklist nouveau\\nblacklist lbm-nouveau\\noptions nouveau modeset=0\\nalias nouveau off\\nalias lbm-nouveau off\" \u003e /etc/modprobe.d/blacklist-nouveau.conf\n    echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf\n    update-initramfs -u\n    reboot\n\n    sudo su -\n    cd /opt\n    wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run\n    chmod +x cuda_*_linux.run\n    ./cuda_*_linux.run -extract=`pwd`/nvidia_installers\n    cd nvidia_installers\n    ./NVIDIA-Linux-x86_64-*.run -s\n    ./cuda-linux64-rel-*.run -noprompt\n    ./cuda-samples-linux-7.0.28-19326674.run -noprompt -cudaprefix=/usr/local/cuda\n    cd /usr/local/cuda/samples/1_Utilities/deviceQuery\n    make\n    ./deviceQuery\n    ls /dev | grep nvidia\n\n    rm /opt/cuda_7.0.28_linux.run\n    rm -r /opt/nvidia_installers\n\nYou should save the instance as an AMI so you can reuse it later.\n\nTo create a host using spot instance\n\n    docker-machine create --driver amazonec2 \\\n        --amazonec2-ami ami-... \\\n        --amazonec2-access-key $AWS_ACCESS_KEY_ID \\\n        --amazonec2-secret-key $AWS_SECRET_ACCESS_KEY \\\n        --amazonec2-vpc-id vpc-... \\\n        --amazonec2-root-size 60 \\\n        --amazonec2-instance-type g2.2xlarge \\\n        --amazonec2-request-spot-instance \\\n        --amazonec2-spot-price 0.15 \\\n        aws01\n\nTo activate the newly created instance\n\n    eval \"$(docker-machine env aws01)\"\n\nTo view all created hosts\n\n    docker-machine ls\n\nSSH into the instance and sanity check\n\n    docker-machine ssh aws01\n    nvidia-smi\n    # Should see information about the GPU\n    ls /dev | grep nvidia\n    # Should see nvidia0 nvidiactl nvidia-uvm\n\nIf nvidia-uvm is not found\n\n    docker-machine ssh aws01\n    /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery\n    ls /dev | grep nvidia\n    exit\n\nTo terminate and remove the instance\n\n    docker-machine rm aws01\n\n### Running the image\n\nTo build this image\n\n    docker build -t igorcosta/deeplearning .\n\nMake sure the GPU is working inside the container\n\n    docker run -ti --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm igorcosta/deeplearning python -c \"import theano\"\n    # Should see \"Using gpu device 0: GRID K520\"\n\nDebug inside the container\n\n    docker run -ti --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm igorcosta/deeplearning /bin/bash\n\nTo publish the image\n\n    docker push igorcosta/deeplearning\n\nTo start over\n\n    docker stop $(docker ps -a -q)\n    docker rm $(docker ps -a -q)\n    docker rmi $(docker images -q)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figorcosta%2Fdeep-docker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Figorcosta%2Fdeep-docker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figorcosta%2Fdeep-docker/lists"}