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GitHub / aws / amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws%2Famazon-sagemaker-examples
Stars: 9,541
Forks: 6,610
Open Issues: 936
License: apache-2.0
Language: Jupyter Notebook
Repo Size: 618 MB
Dependencies:
426
Created: over 6 years ago
Updated: about 7 hours ago
Last pushed: about 7 hours ago
Last synced: about 5 hours ago
Commit Stats
Commits: 2697
Authors: 579
Mean commits per author: 4.66
Development Distribution Score: 0.88
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/aws/amazon-sagemaker-examples
Topics: aws, data-science, deep-learning, examples, inference, jupyter-notebook, machine-learning, mlops, reinforcement-learning, sagemaker, training
Files
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Readme
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Dependencies
- sagemaker-training ==3.4.1
- nltk *
- rouge_score *
- transformers ==4.7.0
- ubuntu 20.04 build
- ubuntu 18.04 build
- ubuntu 16.04 build
- ubuntu 16.04 build
- ubuntu 16.04 build
- ubuntu 16.04 build
advanced_functionality/distributed_tensorflow_mask_rcnn/container-optimized-script-mode/Dockerfile
docker
- 763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training 2.12.0-gpu-py310-cu118-ubuntu20.04-sagemaker build
- 763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training 2.12.0-gpu-py310-cu118-ubuntu20.04-sagemaker build
- 763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training 2.12.0-gpu-py310-cu118-ubuntu20.04-sagemaker build
advanced_functionality/distributed_tensorflow_mask_rcnn/container-serving-optimized/Dockerfile
docker
- 763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training 2.12.0-gpu-py310-cu118-ubuntu20.04-sagemaker build
- nvidia/cuda 9.0-cudnn7-devel-ubuntu16.04 build
- nvidia/cuda 11.1-cudnn8-devel-ubuntu18.04 build
- ubuntu 18.04 build
- 763104351884.dkr.ecr.${region}.amazonaws.com/pytorch-training 1.5.1-cpu-py36-ubuntu16.04 build
- 763104351884.dkr.ecr.$REGION.amazonaws.com/pytorch-training 1.12.1-gpu-py38-cu113-ubuntu20.04-sagemaker build
- 763104351884.dkr.ecr.${REGION}.amazonaws.com/pytorch-training 1.11.0-cpu-py38 build
- ubuntu 18.04 build
- ubuntu 18.04 build
- jupyter/scipy-notebook latest build
- accelerate *
- diffusers *
- transformers *
- ubuntu 16.04 build
- ubuntu 16.04 build
- python 3.7-slim-buster build
- tensorflow/tensorflow latest-py3 build
- tensorflow/tensorflow $VERSION build
- rapidsai/rapidsai 21.06-cuda11.0-base-ubuntu18.04-py3.7 build
- ubuntu 18.04 build
- openjdk 19 build
- python 3.9-slim-buster build
- ubuntu 18.04 build
- $BASE_IMAGE latest build
- public.ecr.aws/u6k6n4j8/r-base latest build
- r-base 3.6.3 build
- r-base 3.6.3 build
- r-base 3.6.3 build
- alpine 3.6 build
- openjdk 8u171-jre-alpine build
- alpine 3.6 build
- 520713654638.dkr.ecr.${AWS_REGION}.amazonaws.com/sagemaker-rl-mxnet coach0.11.0-${CPU_OR_GPU}-py3 build
- continuumio/miniconda3 latest build
- ubuntu 18.04 build
- tensorflow/tensorflow 2.2.0rc2-py3-jupyter build
- ubuntu 18.04 build
- pytorch/pytorch 1.12.1-cuda11.3-cudnn8-runtime build
- nvcr.io/nvidia/pytorch 22.10-py3 build
- ${SM_TRITON_IMAGE_URI} latest build
- ubuntu 16.04 build
- nvidia/cuda 10.2-cudnn7-runtime-ubuntu18.04 build
sagemaker_processing/feature_transformation_with_sagemaker_processing_dask/container/Dockerfile
docker
- continuumio/miniconda3 4.7.12 build
- ${dlc_account_id}.dkr.ecr.${region}.amazonaws.com/pytorch-training 1.10.2-gpu-py38-cu113-ubuntu20.04-sagemaker build
- 763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training 2.4.1-gpu-py37-cu110-ubuntu18.04 build
- com.fasterxml.jackson.core:jackson-annotations 2.14.2
- com.fasterxml.jackson.core:jackson-databind 2.14.2
- com.fasterxml.jackson.datatype:jackson-datatype-jsr310
- commons-io:commons-io 2.4
- javax.xml.bind:jaxb-api 2.3.1
- org.glassfish.jaxb:jaxb-runtime 2.3.5
- org.jpmml:pmml-evaluator 1.1.14
- org.jpmml:pmml-evaluator-metro 1.6.4
- org.jpmml:pmml-model 1.1.12
- org.projectlombok:lombok 1.18.20
- org.springframework.boot:spring-boot-starter-web
environment.yml
pypi
- nbsphinx ==0.8.7
- prompt-toolkit ==2.0.10
- sagemaker *
- sphinx-rtd-theme ==1.0.0
- boto3 ==1.16.24
- boto3 ==1.26.150
- botocore ==1.29.150
- faiss-cpu ==1.7.4
- langchain ==0.0.195
- langchainplus-sdk ==0.0.8
- streamlit ==1.23.1
- streamlit-chat ==0.0.2.2
- transformers ==4.30.2
- unstructured ==0.9.3
- Pillow *
- requests *
- safetensors *
- tqdm *
- transformers >=4.27.1
- flask *
- gevent *
- gunicorn *
- numpy *
- pandas *
- retrying *
- sagemaker-inference *
- scikit-learn *
- numpy *
- pandas *
- sagemaker-inference *
- xgboost *
- albumentations ==0.5.2
- easydict *
- hydra-core *
- joblib *
- kornia ==0.5.0
- matplotlib *
- numpy *
- opencv-python *
- packaging *
- pandas *
- pytorch-lightning *
- pyyaml *
- scikit-image *
- scikit-learn *
- tabulate *
- tensorflow *
- timm *
- tqdm *
- webdataset *
- matplotlib ==3.6.3
- opencv-python-headless ==4.7.0.68
- segment-anything-py ==1.0
- accelerate *
- diffusers ==0.13.0
- ftfy *
- opencv-python-headless ==4.7.0.68
- scipy *
- transformers *
- xformers ==0.0.23
- scikit-learn ==0.24.2
- sentence-transformers *
- torch *
introduction_to_applying_machine_learning/deep_demand_forecasting/lstnet/requirements_inference.txt
pypi
- awscli *
- sagemaker ==1.72.0
- watchtower ==1.0.0
- datasets ==2.12.0
- spacy *
- spacy *
- boto3 *
- pytorch_lightning ==1.0.2
- sentencepiece *
- transformers *
- boto3 *
- protobuf ==3.20.0
- pytorch_lightning ==1.0.2
- sentencepiece *
- transformers *
- accelerate ==0.21.0
- bitsandbytes ==0.40.2
- peft ==0.4.0
- safetensors >=0.3.1
- tokenizers >=0.13.3
- transformers ==4.36.2
- matplotlib *
- numpy *
- pandas *
- streamlit *
- gluoncv ==0.10.4.post4
- fsspec *
- matplotlib *
- numpy *
- pandas *
- s3fs *
- sagemaker *
- scikit-learn *
- fsspec ==2022.11.0
- matplotlib ==3.6.2
- numpy ==1.24.0
- pandas ==1.5.2
- s3fs ==2022.11.0
- sagemaker ==2.126.0
- scikit-learn *
- pandas *
- transformers ==4.2.1
- boto3 *
- botocore *
- matplotlib *
- sagemaker *
- shap *
- boto3 *
- matplotlib *
- sagemaker *
- shap *
- pandas *
- torch ==1.13.1
- transformers ==4.2.1
- gluonnlp ==0.8.3
- gevent *
- geventhttpclient *
- pandas >=2.0.0
- s3fs ==0.4.2
- sagemaker >=2.199.0,<3
- scikit-learn *
- xgboost ==1.7.1
- gevent *
- geventhttpclient *
- pandas >=2.0.0
- s3fs ==0.4.2
- sagemaker >=2.199.0,<3
- scikit-learn *
- xgboost ==1.7.1
- pandas *
- s3fs ==0.4.2
- sagemaker >=2.199.0,<3
- scikit-learn *
- xgboost ==1.7.1
- sagemaker >=2.199.0,<3
sagemaker-pipelines/tabular/local-mode/framework-processor-byoc/code/processing/requirements.txt
pypi
- pandas *
- datasets *
- pandas >=2.0.0
- s3fs ==0.4.2
- sagemaker >=2.156.0,<3
- scikit-learn *
- torch *
- transformers *
- pandas >=2.0.0
- sagemaker >=2.156.0,<3
- scikit-learn *
- torch *
- torchvision *
- sagemaker >=2.156.0,<3
- torch *
- torchvision *
- sagemaker >=2.156.0,<3
- s3fs ==0.4.2
- sagemaker >=2.156.0,<3
- scikit-learn *
- xgboost *
- datasets ==2.10.1
- transformers ==4.26
sagemaker-training-compiler/huggingface/pytorch_multiple_gpu_multiple_node/scripts/requirements.txt
pypi
- datasets *
- transformers ==4.25.1
- datasets *
- transformers ==4.25.1
- datasets *
- transformers ==4.25.1
- datasets *
- transformers ==4.25.1
- datasets *
- transformers ==4.25.1
- transformers ==4.15.0
- keras *
- pandas *
- pillow *
- seaborn *
- tensorflow *
- Pydantic ==1.10.15
- accelerate >=0.21
- bitsandbytes *
- datasets *
- deepspeed ==0.14.0
- peft *
- transformers ==4.38.0
- accelerate >=0.21
- bitsandbytes *
- datasets *
- einops *
- transformers ==4.33.0
- datasets *
- humanize *
- numpy *
- sagemaker *
- sagemaker-experiments *
- scipy *
- smdebug *
- torchnet *
- transformers ==4.21.0
- datasets *
- humanize *
- numpy *
- sagemaker *
- sagemaker-experiments *
- scipy *
- smdebug *
- torchnet *
- transformers ==4.21.0
- datasets *
- humanize *
- numpy *
- sagemaker *
- sagemaker-experiments *
- scipy *
- smdebug *
- torchnet *
- transformers ==4.21.0
- datasets *
- humanize *
- numpy *
- sagemaker *
- sagemaker-experiments *
- scipy *
- smdebug *
- torchnet *
- transformers ==4.21.0
- datasets *
- humanize *
- numpy *
- sagemaker *
- sagemaker-experiments *
- scipy *
- smdebug *
- torchnet *
- transformers ==4.21.0
- accelerate >=0.12.0
- datasets >=2.16.1
- einops *
- evaluate *
- expecttest *
- flash-attn >=2.3.6
- h5py *
- humanize *
- hypothesis *
- ninja *
- numpy *
- packaging *
- protobuf *
- scikit-learn *
- sentencepiece *
- tensorboard *
- transformers >=4.37.1
- grpcio-tools *
- protobuf ==3.20.2
- torchvision *
- accelerate *
- botocore *
- datasets *
- evaluate *
- sagemaker *
- transformers *
- accelerate *
- botocore *
- datasets *
- evaluate *
- pytorch-lightning ==1.9.4
- sagemaker *
- transformers *