MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
파일 탐색기
최종 버전 다운로드 (.zip)- 2GPUs.png
- 3GPUs.png
- 4gpus.png
- clusterdelete.png
- completed.png
- CPUBase.png
- Dask2.png
- daskini.png
- daskoutput.png
- datastore.png
- dcf1.png
- dcf2.png
- dcf3.png
- dcf4.png
- DLF1.png
- DLF2.png
- DLF3.png
- downamddecom.png
- ETL.png
- fef1.png
- fef2.png
- fef3.png
- fef4.png
- fef5.png
- fef6.png
- fef7.png
- fef8.png
- fef9.png
- install2.png
- installation.png
- NotebookHome.png
- OOM.png
- PArameters.png
- queue.png
- running.png
- saved_workspace.png
- scriptuploading.png
- submission1.png
- target_creation.png
- targeterror1.png
- targeterror2.png
- targetsuccess.png
- training.png
- wap1.png
- wap2.png
- wap3.png
- wap4.png
- WorkSpaceSetUp.png
- wrapper.png
- azure-ml-with-nvidia-rapids.ipynb
- process_data.py
- README.md
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- Dockerfile
- auto-ml-classification-bank-marketing-all-features.ipynb
- auto-ml-classification-credit-card-fraud.ipynb
- auto-ml-continuous-retraining.ipynb
- check_data.py
- register_model.py
- upload_weather_data.py
- codegen-for-autofeaturization.ipynb
- custom-model-training-from-autofeaturization-run.ipynb
- auto-ml-regression-model-proxy.ipynb
- automl_setup_thin_client.cmd
- automl_setup_thin_client_linux.sh
- automl_setup_thin_client_mac.sh
- automl_thin_client_env.yml
- automl_thin_client_env_mac.yml
- README.md
- score.py
- auto-ml-forecasting-backtest-many-models.ipynb
- Backtesting.png
- data_split.py
- retrain_models.py
- score.py
- auto-ml-forecasting-backtest-single-model.ipynb
- Backtesting.png
- pipeline_helper.py
- auto-ml-forecasting-bike-share.ipynb
- bike-no.csv
- forecasting_script.py
- metrics_helper.py
- run_forecast.py
- auto-ml-forecasting-energy-demand.ipynb
- forecasting_script.py
- run_forecast.py
- auto-ml-forecasting-function.ipynb
- forecast_function_at_train.png
- forecast_function_away_from_train.png
- recursive_forecast_iter1.png
- recursive_forecast_iter2.png
- recursive_forecast_overview_small.png
- auto-ml-forecasting-github-dau.ipynb
- github_dau_2011-2018_test.csv
- github_dau_2011-2018_train.csv
- helper.py
- infer.py
- hts-sample-test.csv
- hts-sample-train.csv
- auto-ml-forecasting-hierarchical-timeseries.ipynb
- 01_userfilesupdate.PNG
- ai show.gif
- computes_view.png
- create_notebook_vm.png
- Flow_map.png
- mmsa-overview.png
- mmsa.png
- terminal.png
- data_preprocessing_file.py
- data_preprocessing_tabular.py
- auto-ml-forecasting-many-models.ipynb
- mm-1.png
- mm-2.png
- mm-3.png
- mm-4.png
- README.md
- update_env.yml
- auto-ml-forecasting-orange-juice-sales.ipynb
- dominicks_OJ.csv
- forecasting_script.py
- run_forecast.py
- infer.py
- register_model.py
- auto-ml-forecasting-pipelines.ipynb
- oj-test.csv
- oj-train.csv
- S4248SM144SCEN.csv
- ACF_PACF_for_AR2.png
- univariate_settings_map_20210408.jpg
- auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb
- auto-ml-forecasting-univariate-recipe-run-experiment.ipynb
- forecasting_script.py
- helper_functions.py
- run_forecast.py
- auto-ml-classification-credit-card-fraud-local.ipynb
- auto-ml-regression.ipynb
- auto-ml-regression-explanation-featurization.ipynb
- score_explain.py
- train_explainer.py
- README.md
- automl_env.yml
- automl_env_linux.yml
- automl_env_mac.yml
- automl_setup.cmd
- automl_setup_linux.sh
- automl_setup_mac.sh
- check_conda_version.py
- README.md
- automl-databricks-local-01.ipynb
- automl-databricks-local-with-deployment.ipynb
- README.md
- README.md
- shakespeare.txt
- spark_job_on_synapse_spark_pool.ipynb
- spark_session_on_synapse_spark_pool.ipynb
- start_script.py
- Synapse_Job_Scala_Support.ipynb
- Synapse_Session_Scala_Support.ipynb
- Titanic.csv
- AzureMachineLearningCycle.png
- explanations-run-history.png
- explain-model-on-amlcompute.ipynb
- train_explain.py
- save-retrieve-explanations-run-history.ipynb
- azure-machine-learning-cycle.png
- score_local_explain.py
- score_remote_explain.py
- train-explain-model-locally-and-deploy.ipynb
- train-explain-model-on-amlcompute-and-deploy.ipynb
- train_explain.py
- README.md
- calculate.py
- accidents.R
- accidents.Rd
- Dockerfile
- conda_dependencies.yml
- train.py
- compare.py
- compare.py
- extract.py
- train.py
- train-db-local.py
- extract.py
- training_notebook.ipynb
- compare.py
- extract.py
- train.py
- train.py
- aml-pipelines-data-transfer.ipynb
- aml-pipelines-getting-started.ipynb
- aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb
- aml-pipelines-how-to-use-modulestep.ipynb
- aml-pipelines-how-to-use-pipeline-drafts.ipynb
- aml-pipelines-parameter-tuning-with-hyperdrive.ipynb
- aml-pipelines-publish-and-run-using-rest-endpoint.ipynb
- aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb
- aml-pipelines-setup-versioned-pipeline-endpoints.ipynb
- aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb
- aml-pipelines-showcasing-dataset-and-pipelineparameter.ipynb
- aml-pipelines-use-adla-as-compute-target.ipynb
- aml-pipelines-use-databricks-as-compute-target.ipynb
- aml-pipelines-use-kusto-as-compute-target.ipynb
- aml-pipelines-with-automated-machine-learning-step.ipynb
- aml-pipelines-with-commandstep-r.ipynb
- aml-pipelines-with-commandstep.ipynb
- aml-pipelines-with-data-dependency-steps.ipynb
- aml-pipelines-with-notebook-runner-step.ipynb
- README.md
- register_model.py
- testdata.txt
- tf_mnist.py
- train-db-dbfs.py
- utils.py
- cleanse.py
- filter.py
- merge.py
- normalize.py
- transform.py
- train_test_split.py
- nyc-taxi-data-regression-model-building.ipynb
- digit_identification.py
- iris_score.py
- total_file_size.py
- total_income.py
- disco.wav
- orchestra.wav
- piano.wav
- spirituality.wav
- file-dataset-image-inference-mnist.ipynb
- file-dataset-partition-per-folder.ipynb
- README.md
- tabular-dataset-inference-iris.ipynb
- tabular-dataset-partition-per-column.ipynb
- aml-pipelines-concept.png
- README.md
- svc-pr-1.PNG
- svc-pr-2.PNG
- svc-pr-3.PNG
- svc-pr-4.PNG
- authentication-in-azureml.ipynb
- fastai-with-custom-docker.ipynb
- keras_mnist.py
- nn.png
- train-hyperparameter-tune-deploy-with-keras.ipynb
- utils.py
- distributed-pytorch-with-distributeddataparallel.ipynb
- train.py
- pytorch_score.py
- pytorch_train.py
- test_img.jpg
- train-hyperparameter-tune-deploy-with-pytorch.ipynb
- train-hyperparameter-tune-deploy-with-sklearn.ipynb
- train_iris.py
- hyperparameter-tune-and-warm-start-with-tensorflow.ipynb
- nn.png
- tf_mnist.py
- utils.py
- tf_mnist_with_checkpoint.py
- train-tensorflow-resume-training.ipynb
- utils.py
- train.py
- train-and-deploy-keras-auto-logging.ipynb
- train.py
- train-and-deploy-pytorch.ipynb
- README.md
- Dockerfile-gpu
- callbacks.py
- pong-impala-vectorized.yaml
- pong_rllib.py
- pong.gif
- pong_rllib.ipynb
- README.md
- run_details.PNG
- run_history.PNG
- logging-api.ipynb
- hello.py
- hello_with_children.py
- hello_with_delay.py
- manage-runs.ipynb
- export-run-history-to-tensorboard.ipynb
- tensorboard.ipynb
- train-local.ipynb
- backend_config.json
- conda.yaml
- MLproject
- train-projects-local.ipynb
- train.py
- wine-quality.csv
- backend_config.json
- conda.yaml
- MLproject
- train-projects-remote.ipynb
- train.py
- wine-quality.csv
- train-remote.ipynb
- train_diabetes.py
- README.md
- iris.csv
- train-in-spark.ipynb
- train-spark.py
- train-on-amlcompute.ipynb
- train.py
- mylib.py
- train.py
- train-on-local.ipynb
- train-on-remote-vm.ipynb
- train.py
- train2.py
- example.py
- using-environments.ipynb
- README.md
- prepare.py
- train.py
- pipeline-for-image-classification.ipynb
- dummy_train.py
- how-to-use-scriptrun.ipynb
- iris.csv
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- data.parquet
- tabular-timeseries-dataset-filtering.ipynb
- iris.csv
- train-with-datasets.ipynb
- dataset-api-change-notice.md
- README.md
- README.md
- LICENSE
- LICENSE
- configuration.ipynb
- configuration.yml
- NBSETUP.md
- quickstart-azureml-automl.ipynb
- quickstart-azureml-automl.yml
- quickstart-azureml-in-10mins.ipynb
- quickstart-azureml-in-10mins.yml
- score.py
- utils.py
- train.py
- utils.py
- quickstart-azureml-python-sdk.ipynb
- quickstart-azureml-python-sdk.yml
- experiment_main.png
- flow2.png
- model_download.png
- tutorial-1st-experiment-sdk-train.ipynb
- tutorial-1st-experiment-sdk-train.yml
- img-classification-part1-training.ipynb
- img-classification-part1-training.yml
- img-classification-part2-deploy.ipynb
- img-classification-part2-deploy.yml
- img-classification-part3-deploy-encrypted.ipynb
- img-classification-part3-deploy-encrypted.yml
- sklearn_mnist_model.pkl
- utils.py
- batch_scoring.py
- tutorial-pipeline-batch-scoring-classification.ipynb
- tutorial-pipeline-batch-scoring-classification.yml
- regression-automated-ml.ipynb
- regression-automated-ml.yml
- README.md
- .gitignore
- CODE_OF_CONDUCT.md
- configuration.ipynb
- configuration.yml
- index.md
- LICENSE
- NBSETUP.md
- README.md
- SECURITY.md
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/Azure/MachineLearningNotebooks
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd MachineLearningNotebooks
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. 공식 설치 스크립트
쉬움 추천사전 준비물
- Python 3 pip 명령어를 쓰려면 Python이 필요합니다.
pip install azureml-core
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install azureml-mlflow
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install azureml-dataset-runtime
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
pip install azureml-automl-runtime
PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.
설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
3. Docker
쉬움사전 준비물
- Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
- Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.
⚠️ 이 프로젝트는 규모가 큰 저장소라, 이 방법이 실제 핵심 제품이 아니라 내부 하위 패키지를 가리키는 것일 수 있습니다. README 전체를 함께 확인해보세요.
docker build -f Dockerfiles/1.0.10/Dockerfile -t machinelearningnotebooks .
Dockerfile을 기반으로 실행 가능한 이미지를 빌드합니다.
docker run -p 8080:80 machinelearningnotebooks
빌드된 이미지를 실제 컨테이너로 실행합니다.
터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.
// repository documentation
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