Nicolai van der Smagt
193524afaa
* Add Mixtral MoE QLoRA finetuning example * Correcting for PR comments * Remove notebook kernel restart cell * Remove references to bucket name and HF auth token * Correcting cell formatting issues * Update mixtral-8x7b.ipynb Improve example prompt
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5.8 KiB
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235 lines
5.8 KiB
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.. image:: _static/sagemaker_gears.jpg
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:width: 600
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:alt: sagemaker_logo
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Amazon SageMaker Example Notebooks
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==================================
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.. image:: https://readthedocs.org/projects/sagemaker-examples-test-website/badge/?version=latest
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Welcome to Amazon SageMaker.
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This site highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker.
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This site is based on the `SageMaker Examples repository <https://github.com/aws/amazon-sagemaker-examples>`_ on GitHub.
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To run these notebooks, you will need a SageMaker Notebook Instance or SageMaker Studio.
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Refer to the SageMaker developer guide's `Get Started <https://docs.aws.amazon.com/sagemaker/latest/dg/gs.html>`_ page to get one of these set up.
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On a Notebook Instance, the examples are pre-installed and available from the examples menu item in JupyterLab.
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On SageMaker Studio, you will need to open a terminal, go to your home folder, then clone the repo with the following::
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git clone https://github.com/aws/amazon-sagemaker-examples.git
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----
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.. toctree::
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:maxdepth: 1
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:caption: Introduction
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intro.rst
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We recommend the following notebooks as a broad introduction to the capabilities that SageMaker offers. To explore in even more depth, we provide additional notebooks covering even more use cases and frameworks.
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.. toctree::
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:maxdepth: 1
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:caption: Get started on SageMaker
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introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn_outputs
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.. toctree::
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:maxdepth: 1
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:caption: Prepare data
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sagemaker-datawrangler/index
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sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing_outputs
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sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing_outputs
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.. toctree::
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:maxdepth: 1
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:caption: Train and tune models
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hyperparameter_tuning/tensorflow2_mnist/hpo_tensorflow2_mnist_outputs
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sagemaker-script-mode/sklearn/sklearn_byom_outputs
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sagemaker-experiments/mnist-handwritten-digits-classification-experiment/mnist-handwritten-digits-classification-experiment_outputs
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.. toctree::
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:maxdepth: 1
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:caption: Deploy models
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sagemaker-script-mode/pytorch_bert/deploy_bert_outputs
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sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo_outputs
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sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform_outputs
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.. toctree::
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:maxdepth: 1
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:caption: Track, monitor, and explain models
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sagemaker-lineage/sagemaker-lineage-multihop-queries_outputs
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sagemaker_model_monitor/introduction/SageMaker-ModelMonitoring_outputs
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sagemaker-clarify/fairness_and_explainability/fairness_and_explainability_outputs
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.. toctree::
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:maxdepth: 1
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:caption: Orchestrate workflows
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sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform_outputs
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sagemaker-pipelines/tabular/lambda-step/sagemaker-pipelines-lambda-step_outputs
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.. toctree::
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:maxdepth: 1
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:caption: Popular frameworks
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introduction_to_amazon_algorithms/xgboost_abalone/xgboost_abalone_dist_script_mode_outputs
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introduction_to_applying_machine_learning/huggingface_sentiment_classification/huggingface_sentiment_outputs
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sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform_outputs
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sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon_outputs
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frameworks/tensorflow/get_started_mnist_train_outputs
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frameworks/pytorch/get_started_mnist_train_outputs
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introduction_to_applying_machine_learning/mixtral_tune_and_deploy/mixtral-8x7b
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-----
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More examples
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=============
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.. toctree::
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:maxdepth: 1
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:caption: SageMaker Studio
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aws_sagemaker_studio/index
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sagemaker-lineage/index
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.. toctree::
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:maxdepth: 1
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:caption: Introduction to Amazon Algorithms
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introduction_to_amazon_algorithms/index
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.. toctree::
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:maxdepth: 1
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:caption: SageMaker End-to-End Examples
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end_to_end/fraud_detection/index
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end_to_end/music_recommendation/index
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end_to_end/nlp_mlops_company_sentiment/index
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.. toctree::
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:maxdepth: 1
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:caption: Patterns
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patterns/ml_gateway/index
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.. toctree::
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:maxdepth: 1
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:caption: SageMaker Use Cases
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use-cases/index
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use-cases/examples_by_problem_type
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.. toctree::
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:maxdepth: 1
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:caption: Autopilot
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autopilot/index
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.. toctree::
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:maxdepth: 1
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:caption: Ingest Data
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ingest_data/index
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.. toctree::
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:maxdepth: 1
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:caption: Label Data
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label_data/index
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.. toctree::
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:maxdepth: 1
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:caption: Prep Data
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prep_data/index
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.. toctree::
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:maxdepth: 1
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:caption: Feature Store
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sagemaker-featurestore/index
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.. toctree::
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:maxdepth: 1
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:caption: Frameworks
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training/frameworks
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.. toctree::
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:maxdepth: 1
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:caption: Training
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training/algorithms
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reinforcement_learning/index
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sagemaker-experiments/index
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sagemaker-debugger/index
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training/tuning
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training/distributed_training/index
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sagemaker-training-compiler/index
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sagemaker-script-mode/index
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training/bring_your_own_container
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training/management
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training/heterogeneous-clusters/index
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.. toctree::
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:maxdepth: 1
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:caption: Inference
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inference/index
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model-governance/index
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sagemaker-shadow-variant/index
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.. toctree::
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:maxdepth: 1
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:caption: Workflows
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sagemaker-pipelines/index
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sagemaker_processing/index
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sagemaker-spark/index
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step-functions-data-science-sdk/index
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sagemaker-notebook-jobs/index
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.. toctree::
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:maxdepth: 1
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:caption: Advanced Functionality
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advanced_functionality/index
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serverless-inference/index
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.. toctree::
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:maxdepth: 1
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:caption: Advanced examples
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sagemaker-clarify/index
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scientific_details_of_algorithms/index
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aws_marketplace/index
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sagemaker-geospatial/index
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.. toctree::
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:maxdepth: 1
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:caption: Community examples
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contrib/index
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