AWS Documentation » Amazon SageMaker » Developer Guide » Using Built-in Algorithms with Amazon SageMaker » Algorithms Provided by Amazon SageMaker: Common Information » Algorithms Provided by Amazon SageMaker: Common Parameters

Algorithms Provided by Amazon SageMaker: Common Parameters

The following table lists parameters for each of the algorithms provided by Amazon SageMaker.

Algorithm Name	Channel Name	Training Image and Inference Image Registry Path	Training Input Mode	File Type	Instance Class
BlazingText	train	<ecr_path>/blazingtext:<tag>	File	Text file (one sentence per line with with space-separated tokens)	GPU (single instance only) or CPU
DeepAR Forecasting	train and (optionally) test	<ecr_path>/forecasting-deepar:<tag>	File	JSON Lines or Parquet	GPU or CPU
Factorization Machines	train and (optionally) test	<ecr_path>/factorization-machines:<tag>	File or Pipe	recordIO-protobuf	CPU (GPU for dense data)
Image Classification	train and validation, (optionally) train_lst and validation_lst	<ecr_path>/image-classification:<tag>	File	recordIO or image files (.jpg or .png)	GPU
IP Insights	train and (optionally) validation	<ecr_path>/ipinsights:<tag>	File	CSV	GPU or GPU
k-means	train and (optionally) test	<ecr_path>/kmeans:<tag>	File or Pipe	recordIO-protobuf or CSV	CPU or GPUCommon (single GPU device on one or more instances)
k-nearest-neighbor (k-NN)	train and (optionally) test	<ecr_path>/knn:<tag>	File or Pipe	recordIO-protobuf or CSV	CPU or GPU (single GPU device on one or more instances)
LDA	train and (optionally) test	<ecr_path>/lda:<tag>	File or Pipe	recordIO-protobuf or CSV	CPU (single instance only)
Linear Learner	train and (optionally) validation, test, or both	<ecr_path>/linear-learner:<tag>	File or Pipe	recordIO-protobuf or CSV	CPU or GPU
Neural Topic Model	train and (optionally) validation, test, or both	<ecr_path>/ntm:<tag>	File or Pipe	recordIO-protobuf or CSV	GPU or CPU
Object2Vec	train and (optionally) validation, test, or both	<ecr_path>/object2vec:<tag>	File	JSON Lines	GPU or CPU (single instance only)
Object Detection	train and validation, (optionally) train_annotation and validation_annotation	<ecr_path>/object-detection:<tag>	File	recordIO or image files (.jpg or .png)	GPU
PCA	train and (optionally) test	<ecr_path>/pca:<tag>	File or Pipe	recordIO-protobuf or CSV	GPU or CPU
Random Cut Forest	train and (optionally) test	<ecr_path>/randomcutforest:<tag>	File or Pipe	recordIO-protobuf or CSV	CPU
Seq2Seq Modeling	train, validation, and vocab	<ecr_path>/seq2seq:<tag>	File	recordIO-protobuf	GPU (single instance only)
XGBoost	train and (optionally) validation	<ecr_path>/xgboost:<tag>	File	CSV or LibSVM	CPU

For the Training Image and Inference Image Registry Path column, use the :1 version tag to ensure that you are using a stable version of the algorithm. You can reliably host a model trained using an image with the :1 tag on an inference image that has the :1 tag. Using the :latest tag in the registry path provides you with the most up-to-date version of the algorithm, but might cause problems with backward compatibility. Avoid using the :latest tag for production purposes.

For the Training Image and Inference Image Registry Path column, depending on algorithm and region use one of the following values for <ecr_path>.

Algorithm Name	AWS Region	Training Image and Inference Image Registry Path
Factorization Machines, IP Insights, k-means, k-nearest-neighbor, Linear Learner, Object2Vec, Neural Topic Model,PCA, and Random Cut Forest	us-west-1	632365934929.dkr.ecr.us-west-1.amazonaws.com
	us-west-2	174872318107.dkr.ecr.us-west-2.amazonaws.com
	us-east-1	382416733822.dkr.ecr.us-east-1.amazonaws.com
	us-east-2	404615174143.dkr.ecr.us-east-2.amazonaws.com
	us-gov-west-1	226302683700.dkr.ecr.us-gov-west-1.amazonaws.com
	ap-northeast-1	351501993468.dkr.ecr.ap-northeast-1.amazonaws.com
	ap-northeast-2	835164637446.dkr.ecr.ap-northeast-2.amazonaws.com
	ap-south-1	991648021394.dkr.ecr.ap-south-1.amazonaws.com
	ap-southeast-1	475088953585.dkr.ecr.ap-southeast-1.amazonaws.com
	ap-southeast-2	712309505854.dkr.ecr.ap-southeast-2.amazonaws.com
	ca-central-1	469771592824.dkr.ecr.ca-central-1.amazonaws.com
	eu-central-1	664544806723.dkr.ecr.eu-central-1.amazonaws.com
	eu-west-1	438346466558.dkr.ecr.eu-west-1.amazonaws.com
	eu-west-2	644912444149.dkr.ecr.eu-west-2.amazonaws.com
LDA	us-west-1	632365934929.dkr.ecr.us-west-1.amazonaws.com
	us-west-2	266724342769.dkr.ecr.us-west-2.amazonaws.com
	us-east-1	766337827248.dkr.ecr.us-east-1.amazonaws.com
	us-east-2	999911452149.dkr.ecr.us-east-2.amazonaws.com
	us-gov-west-1	226302683700.dkr.ecr.us-gov-west-1.amazonaws.com
	ap-northeast-1	258307448986.dkr.ecr.ap-northeast-1.amazonaws.com
	ap-northeast-2	293181348795.dkr.ecr.ap-northeast-2.amazonaws.com
	ap-south-1	991648021394.dkr.ecr.ap-south-1.amazonaws.com
	ap-southeast-1	475088953585.dkr.ecr.ap-southeast-1.amazonaws.com
	ap-southeast-2	297031611018.dkr.ecr.ap-southeast-2.amazonaws.com
	ca-central-1	469771592824.dkr.ecr.ca-central-1.amazonaws.com
	eu-central-1	353608530281.dkr.ecr.eu-central-1.amazonaws.com
	eu-west-1	999678624901.dkr.ecr.eu-west-1.amazonaws.com ​
	eu-west-2	644912444149.dkr.ecr.eu-west-2.amazonaws.com
BlazingText, Image Classification, Object Detection, Seq2Seq, and XGBoost	us-west-1	632365934929.dkr.ecr.us-west-1.amazonaws.com​
	us-west-2	433757028032.dkr.ecr.us-west-2.amazonaws.com
	us-east-1	811284229777.dkr.ecr.us-east-1.amazonaws.com
	us-east-2	825641698319.dkr.ecr.us-east-2.amazonaws.com
	us-gov-west-1	226302683700.dkr.ecr.us-gov-west-1.amazonaws.com
	ap-northeast-1	501404015308.dkr.ecr.ap-northeast-1.amazonaws.com
	ap-northeast-2	306986355934.dkr.ecr.ap-northeast-2.amazonaws.com
	ap-south-1	991648021394.dkr.ecr.ap-south-1.amazonaws.com
	ap-southeast-1	475088953585.dkr.ecr.ap-southeast-1.amazonaws.com
	ap-southeast-2	544295431143.dkr.ecr.ap-southeast-2.amazonaws.com
	ca-central-1	469771592824.dkr.ecr.ca-central-1.amazonaws.com
	eu-central-1	813361260812.dkr.ecr.eu-central-1.amazonaws.com
	eu-west-1	685385470294.dkr.ecr.eu-west-1.amazonaws.com
	eu-west-2	644912444149.dkr.ecr.eu-west-2.amazonaws.com
DeepAR Forecasting	us-west-1	632365934929.dkr.ecr.us-west-1.amazonaws.com​
	us-west-2	156387875391.dkr.ecr.us-west-2.amazonaws.com​
	us-east-1	522234722520.dkr.ecr.us-east-1.amazonaws.com
	us-east-2	566113047672.dkr.ecr.us-east-2.amazonaws.com
	us-gov-west-1	226302683700.dkr.ecr.us-gov-west-1.amazonaws.com
	ap-northeast-1	633353088612.dkr.ecr.ap-northeast-1.amazonaws.com
	ap-northeast-2	204372634319.dkr.ecr.ap-northeast-2.amazonaws.com
	ap-south-1	991648021394.dkr.ecr.ap-south-1.amazonaws.com
	ap-southeast-1	475088953585.dkr.ecr.ap-southeast-1.amazonaws.com
	ap-southeast-2	514117268639.dkr.ecr.ap-southeast-2.amazonaws.com
	ca-central-1	469771592824.dkr.ecr.ca-central-1.amazonaws.com
	eu-central-1	495149712605.dkr.ecr.eu-central-1.amazonaws.com
	eu-west-1	224300973850.dkr.ecr.eu-west-1.amazonaws.com
	eu-west-2	644912444149.dkr.ecr.eu-west-2.amazonaws.com

Use the paths and training input mode as follows:

• To create a training job (with a request to the CreateTrainingJob API), specify the Docker Registry path and the training input mode for the training image. You create a training job to train a model using a specific dataset.

   

• To create a model (with a CreateModel request), specify the Docker Registry path for the inference image. Amazon SageMaker launches machine learning compute instances that are based on the endpoint configuration and deploys the model, which includes the artifacts (the result of model training).