{"id":13682424,"url":"https://github.com/anurag/fastai-course-1","last_synced_at":"2025-03-16T10:31:00.826Z","repository":{"id":152170835,"uuid":"79759675","full_name":"anurag/fastai-course-1","owner":"anurag","description":"Docker environment for fast.ai Deep Learning Course 1 at http://course.fast.ai","archived":false,"fork":false,"pushed_at":"2020-08-28T15:17:07.000Z","size":19,"stargazers_count":100,"open_issues_count":8,"forks_count":37,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-10-17T08:50:25.233Z","etag":null,"topics":["deep-learning","docker","fastai","jupyter","notebook","nvidia-docker"],"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/anurag.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}},"created_at":"2017-01-23T01:38:56.000Z","updated_at":"2023-12-13T17:49:47.000Z","dependencies_parsed_at":"2024-01-14T15:23:16.162Z","dependency_job_id":"1c42f173-21ff-4e14-b9bb-4cd29313be3d","html_url":"https://github.com/anurag/fastai-course-1","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anurag%2Ffastai-course-1","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anurag%2Ffastai-course-1/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anurag%2Ffastai-course-1/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anurag%2Ffastai-course-1/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anurag","download_url":"https://codeload.github.com/anurag/fastai-course-1/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221662696,"owners_count":16859732,"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","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":["deep-learning","docker","fastai","jupyter","notebook","nvidia-docker"],"created_at":"2024-08-02T13:01:45.857Z","updated_at":"2024-10-27T10:25:15.042Z","avatar_url":"https://github.com/anurag.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# Docker for [fast.ai](http://course.fast.ai) Course 1\nA Jupyter environment for fast.ai's Deep Learning MOOC at http://course.fast.ai.\n\nRuns a Jupyter notebook on port 8888 with the default password used in the course ('dl_course').\n\nUses CPUs by default and NVIDIA GPUs when run with [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).\n\nThe container comes with:\n* All notebooks from https://github.com/fastai/courses/tree/master/deeplearning1/nbs\n* Python 2.7 (the default Python version used in the course)\n* Conda\n* Theano\n* Keras\n* PIL\n* Jupyter\n* bcolz\n* kaggle-cli\n* ...all other libraries needed for the course.\n\n## Usage\n\n#### CPU Only\n```bash\ndocker run -it -p 8888:8888 deeprig/fastai-course-1\n```\n\n#### With GPU\n```bash\nnvidia-docker run -it -p 8888:8888 deeprig/fastai-course-1\n```\n\n## Data management\nDocker containers are designed to be ephemeral, so if you need persistent data for Kaggle competitions you should download it on your local machine and [mount the directory as a host volume](https://docs.docker.com/engine/tutorials/dockervolumes/#/mount-a-host-directory-as-a-data-volume) when you run the container.\n\nFor example, if your data directory is at `/Users/yourname/data`, start your container with this command:\n\n```bash\ndocker run -it -p 8888:8888 -v /Users/yourname/data:/home/docker/data deeprig/fastai-course-1\n```\n\nYour local data directory will now be visible in the container at `/home/docker/data`.\n\n**Don't forget to change the path to the data folder in your notebooks as well!**\n\n## Installing packages\nAll packages should ideally be part of the Dockerfile. If something is missing, please open an issue or submit a PR to update the Dockerfile. If you need to install something as a workaround, follow the steps below:\n1. Get a shell into the running container with `docker exec -it \u003ccontainer_name\u003e /bin/bash`\n2. `sudo apt-get update \u0026\u0026 sudo apt-get install package_name`\n\n## Running on AWS\nYou can also use `docker-machine` and preconfigured AMIs for `us-west-2` using the commands below.\n\n### GPU instance\n```bash\n# spin up a p2.xlarge instance\ndocker-machine create \\\n  --driver amazonec2 \\\n  --amazonec2-region='us-west-2' \\\n  --amazonec2-root-size=50 \\\n  --amazonec2-ami='ami-e03a8480' \\\n  --amazonec2-instance-type='p2.xlarge' \\\n  fastai-p2\n\n# open Jupyter port 8888\naws ec2 authorize-security-group-ingress --group-name docker-machine --port 8888 --protocol tcp --cidr 0.0.0.0/0\n\n# open an SSH shell on the new machine\ndocker-machine ssh fastai-p2\n\n# (on the remote machine fastai-p2) run Jupyter interactively\nnvidia-docker run -it -p 8888:8888 deeprig/fastai-course-1\n\n# (on your local machine) get the IP of the new machine:\ndocker-machine ip fastai-p2\n```\nOpen http://[NEW_MACHINE_IP]:8888 in your browser to view notebooks.\n\n### CPU instance\n```bash\n# spin up a t2.xlarge instance\ndocker-machine create \\\n  --driver amazonec2 \\\n  --amazonec2-region='us-west-2' \\\n  --amazonec2-root-size=50 \\\n  --amazonec2-ami='ami-a073cdc0' \\\n  --amazonec2-instance-type='t2.xlarge' \\\n  fastai-t2\n\n# open Jupyter port 8888\naws ec2 authorize-security-group-ingress --group-name docker-machine --port 8888 --protocol tcp --cidr 0.0.0.0/0\n\n# open an SSH shell on the new machine\ndocker-machine ssh fastai-t2\n\n# (on the remote machine fastai-t2) run Jupyter interactively\ndocker run -it -p 8888:8888 deeprig/fastai-course-1\n\n# (on your local machine) get the IP of the new machine:\ndocker-machine ip fastai-t2\n```\nOpen http://[NEW_MACHINE_IP]:8888 in your browser to view notebooks.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanurag%2Ffastai-course-1","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanurag%2Ffastai-course-1","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanurag%2Ffastai-course-1/lists"}