{"id":21844223,"url":"https://github.com/altescy/sagemaker-example","last_synced_at":"2026-04-08T18:32:00.948Z","repository":{"id":37686409,"uuid":"424177239","full_name":"altescy/sagemaker-example","owner":"altescy","description":"Example of training and deploying your own machine learning model with AWS SageMaker","archived":false,"fork":false,"pushed_at":"2023-02-08T02:20:21.000Z","size":432,"stargazers_count":1,"open_issues_count":7,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-21T16:21:23.008Z","etag":null,"topics":["fastapi","machine-learning","python","sagemaker"],"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/altescy.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":"2021-11-03T10:19:52.000Z","updated_at":"2022-11-08T14:24:48.000Z","dependencies_parsed_at":"2025-01-26T11:41:38.474Z","dependency_job_id":"ef3cc6f3-eb88-46c9-b76f-8e97f4edf965","html_url":"https://github.com/altescy/sagemaker-example","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/altescy/sagemaker-example","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/altescy%2Fsagemaker-example","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/altescy%2Fsagemaker-example/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/altescy%2Fsagemaker-example/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/altescy%2Fsagemaker-example/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/altescy","download_url":"https://codeload.github.com/altescy/sagemaker-example/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/altescy%2Fsagemaker-example/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31568620,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","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":["fastapi","machine-learning","python","sagemaker"],"created_at":"2024-11-27T22:18:46.700Z","updated_at":"2026-04-08T18:32:00.928Z","avatar_url":"https://github.com/altescy.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# sagemaker-example\n\nThis repository provides a simple example of training and deploying your own machine learning model with AWS SageMaker.\n\n## Usage\n\n1. Upload dataset files to S3\n\n```\naws s3 cp ./data/iris.csv s3://your-bucket/path/to/dataset/iris.csv\n```\n\n2. Build and push the docker image to ECR\n\n```\n./scripts/build_and_push_ecr.sh your-image-name\n```\n\n3. Train a model with SageMaker\n\n```\npoetry run python scripts/train.py \\\n    --dataset-path s3://your-bucket/path/to/dataset \\\n    --artifact-path s3://your-bucket/path/to/artifacts \\\n    --image-uri xxxxxxxxxxxx.dkr.ecr.ap-northeast-1.amazonaws.com/your-image-name \\\n    --execution-role arn:aws:iam::xxxxxxxxxxxx:role/SageMakerExecutionRole\n```\n\n4. Deploy the trained model\n\n```\npoetry run python scripts/deploy.py \\\n    --endpoint-name your-endpoint-name \\\n    --training-job training-job-name\n```\n\n5. Invoke the endpoint\n\n```\npoetry run python scripts/predict.py -n your-endpoint-name data/test.json\n```\n\n6. Delete the endpoint\n\n```\naws sagemaker delete-endpoint --endpoint-name your-endpoint-name\n```\n\n## Local mode\n\n1. Train a model on your local machie\n\n```\npoetry run python -m sagemaker_example train --local\n```\n\n2. Serve the endpoint on your local machine\n\n```\npoetry run python -m sagemaker_example serve --local --port 8080\n```\n\n3. Train a model with local mode\n\n```\npoetry run python scripts/train.py \\\n    --local \\\n    --dataset-path file://`pwd`/data \\\n    --artifact-path file://`pwd`/data \\\n    --image-uri xxxxxxxxxxxx.dkr.ecr.ap-northeast-1.amazonaws.com/your-image-name \\\n    --execution-role dummy/dummy\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faltescy%2Fsagemaker-example","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faltescy%2Fsagemaker-example","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faltescy%2Fsagemaker-example/lists"}