Docker Registry Paths and Example Code for Middle East (UAE) (me-central-1)
The following topics list parameters for each of the algorithms and deep learning containers that are provided by Amazon SageMaker AI in this Amazon Web Services Region.
Topics
AutoGluon (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='autogluon',region='me-central-1',image_scope='inference',version='0.4')
| Registry path | Version | Job types (image scope) |
|---|---|---|
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-training:<tag> |
0.5.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-inference:<tag> |
0.5.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-training:<tag> |
0.4.3 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-inference:<tag> |
0.4.3 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-training:<tag> |
0.4.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-inference:<tag> |
0.4.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-training:<tag> |
0.4.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-inference:<tag> |
0.4.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-training:<tag> |
0.3.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-inference:<tag> |
0.3.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-training:<tag> |
0.3.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/autogluon-inference:<tag> |
0.3.1 | inference |
BlazingText (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='blazingtext',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/blazingtext:<tag> |
1 | inference, training |
DeepAR Forecasting (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='forecasting-deepar',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/forecasting-deepar:<tag> |
1 | inference, training |
Factorization Machines (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='factorization-machines',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/factorization-machines:<tag> |
1 | inference, training |
Hugging Face (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='huggingface',region='me-central-1',version='4.4.2',image_scope='training',base_framework_version='tensorflow2.4.1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.49.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.49.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.48.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.48.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.46.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.37.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.36.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.28.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.28.1 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.26.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.26.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.26.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.17.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.17.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.17.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.17.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.12.3 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.12.3 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.12.3 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.12.3 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.11.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.11.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.11.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.11.0 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.10.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.10.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.10.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.10.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.10.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.10.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.10.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.10.2 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.6.1 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.6.1 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.6.1 | inference |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.5.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.5.0 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.4.2 | training |
914824155844.dkr.ecr.me-central-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.4.2 | training |
IP Insights (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='ipinsights',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/ipinsights:<tag> |
1 | inference, training |
Image classification (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='image-classification',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/image-classification:<tag> |
1 | inference, training |
K-Means (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='kmeans',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/kmeans:<tag> |
1 | inference, training |
KNN (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='knn',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/knn:<tag> |
1 | inference, training |
Linear Learner (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='linear-learner',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/linear-learner:<tag> |
1 | inference, training |
MXNet (DLC)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='mxnet',region='me-central-1',version='1.4.1',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
| Registry path | Version | Job types (image scope) | Processor types | Python versions |
|---|---|---|---|---|
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-training:<tag> |
1.9.0 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference:<tag> |
1.9.0 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-training:<tag> |
1.8.0 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference:<tag> |
1.8.0 | inference | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-training:<tag> |
1.7.0 | training | CPU, GPU | py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference:<tag> |
1.7.0 | inference | CPU, GPU | py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.7.0 | eia | CPU | py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-training:<tag> |
1.6.0 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference:<tag> |
1.6.0 | inference | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.5.1 | eia | CPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-training:<tag> |
1.4.1 | training | CPU, GPU | py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference:<tag> |
1.4.1 | inference | CPU, GPU | py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.4.1 | eia | CPU | py2, py3 |
Model Monitor (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='model-monitor',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
588750061953.dkr.ecr.me-central-1.amazonaws.com/sagemaker-model-monitor-analyzer:<tag> |
monitoring |
NTM (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='ntm',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/ntm:<tag> |
1 | inference, training |
Object Detection (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='object-detection',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/object-detection:<tag> |
1 | inference, training |
Object2Vec (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='object2vec',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/object2vec:<tag> |
1 | inference, training |
PCA (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='pca',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/pca:<tag> |
1 | inference, training |
PyTorch (DLC)
For information about the supported and unsupported PyTorch versions, see the Framework Support Policy Table in the Amazon Deep Learning Containers Developer Guide.
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='pytorch',region='me-central-1',version='1.8.0',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
| Registry path | Version | Job types (image scope) | Processor types | Python versions |
|---|---|---|---|---|
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.7.1 | training | CPU, GPU | py312 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.6.0 | inference | CPU, GPU | py312 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.6.0 | training | CPU, GPU | py312 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.5.1 | inference | CPU, GPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.5.1 | training | CPU, GPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.4.0 | inference | CPU, GPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.4.0 | inference_graviton | CPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.4.0 | training | CPU, GPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.3.0 | inference | CPU, GPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.3.0 | inference_graviton | CPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.3.0 | training | CPU, GPU | py311 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.2.1 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.2.0 | inference | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.2.0 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.1.0 | inference | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.1.0 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.1.0 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.0.1 | inference | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.0.1 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.0.1 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
2.0.0 | inference | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.0.0 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
2.0.0 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.13.1 | inference | CPU, GPU | py39 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.13.1 | training | CPU, GPU | py39 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.12.1 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference-graviton:<tag> |
1.12.1 | inference_graviton | CPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.12.1 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.12.0 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.12.0 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.11.0 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.11.0 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.10.2 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.10.2 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.10.0 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.10.0 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.9.1 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.9.1 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.9.0 | inference | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.9.0 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.8.1 | inference | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.8.1 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.8.0 | inference | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.8.0 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.7.1 | inference | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.7.1 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.6.0 | inference | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.6.0 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.5.0 | inference | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.5.0 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.4.0 | inference | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.4.0 | training | CPU, GPU | py2, py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.3.1 | inference | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.3.1 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-inference:<tag> |
1.2.0 | inference | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/pytorch-training:<tag> |
1.2.0 | training | CPU, GPU | py2, py3 |
Random Cut Forest (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='randomcutforest',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/randomcutforest:<tag> |
1 | inference, training |
Scikit-learn (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='sklearn',region='me-central-1',version='0.23-1',image_scope='inference')
| Registry path | Version | Package version | Job types (image scope) |
|---|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.2-1 | 1.2.1 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.2-1 | 1.2.1 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.0-1 | 1.0.2 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.0-1 | 1.0.2 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.0-1 | 1.0.2 | inference_graviton |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0.23-1 | 0.23.2 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0.23-1 | 0.23.2 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0.20.0 | 0.20.0 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0.20.0 | 0.20.0 | training |
Semantic Segmentation (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='semantic-segmentation',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/semantic-segmentation:<tag> |
1 | inference, training |
Seq2Seq (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='seq2seq',region='me-central-1')
| Registry path | Version | Job types (image scope) |
|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/seq2seq:<tag> |
1 | inference, training |
Spark (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='spark',region='me-central-1',version='3.0',image_scope='processing')
| Registry path | Version | Job types (image scope) |
|---|---|---|
395420993607.dkr.ecr.me-central-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.3 | processing |
395420993607.dkr.ecr.me-central-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.2 | processing |
395420993607.dkr.ecr.me-central-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.1 | processing |
395420993607.dkr.ecr.me-central-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.0 | processing |
395420993607.dkr.ecr.me-central-1.amazonaws.com/sagemaker-spark-processing:<tag> |
2.4 | processing |
Tensorflow (DLC)
For information about the supported and unsupported TensorFlow versions, see the Framework Support Policy Table in the Amazon Deep Learning Containers Developer Guide.
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='tensorflow',region='me-central-1',version='1.12.0',image_scope='inference',instance_type='ml.c5.4xlarge')
| Registry path | Version | Job types (image scope) | Processor types | Python versions |
|---|---|---|---|---|
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.19.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.19.0 | training | CPU, GPU | py312 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.18.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.18.0 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.16.2 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.16.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.16.1 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.14.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.14.1 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.14.1 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.13.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.13.0 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.13.0 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.12.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.12.1 | inference_graviton | CPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.12.0 | training | CPU, GPU | py310 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.11.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.11.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.11.0 | training | CPU, GPU | py39 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.10.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.10.1 | training | CPU, GPU | py39 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.10.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.9.3 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.9.2 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.9.2 | training | CPU, GPU | py39 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.9.1 | inference_graviton | CPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.8.4 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.8.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.8.0 | training | CPU, GPU | py39 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.7.1 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.7.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.6.3 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.6.3 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.6.2 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.6.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.6.0 | training | CPU, GPU | py38 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.5.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.5.1 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.5.0 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.4.3 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.4.3 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.4.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.4.1 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.2 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.3.2 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.3.1 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-eia:<tag> |
2.3.0 | eia | CPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.3.0 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.2 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.2.2 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.2.1 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.2.0 | training | CPU, GPU | py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.3 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.1.3 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.2 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.1.2 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.1.1 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.1.0 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.4 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.0.4 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.3 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.0.3 | training | CPU, GPU | py3, py36 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.2 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.0.2 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.1 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.0.1 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-eia:<tag> |
2.0.0 | eia | CPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
2.0.0 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.5 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.15.5 | training | CPU, GPU | py3, py36, py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.4 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.15.4 | training | CPU, GPU | py3, py36, py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.3 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.15.3 | training | CPU, GPU | py2, py3, py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.2 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.15.2 | training | CPU, GPU | py2, py3, py37 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-eia:<tag> |
1.15.0 | eia | CPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.15.0 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference-eia:<tag> |
1.14.0 | eia | CPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.14.0 | inference | CPU, GPU | - |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.14.0 | training | CPU, GPU | py2, py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-training:<tag> |
1.13.1 | training | CPU, GPU | py3 |
914824155844.dkr.ecr.me-central-1.amazonaws.com/tensorflow-inference:<tag> |
1.13.0 | inference | CPU, GPU | - |
XGBoost (algorithm)
The following SageMaker AI Python SDK example shows how to retrieve a specific registry path.
from sagemaker import image_uris image_uris.retrieve(framework='xgboost',region='me-central-1',version='1.5-1')
| Registry path | Version | Package version | Job types (image scope) |
|---|---|---|---|
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.7-1 | 1.7.4 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.7-1 | 1.7.4 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.5-1 | 1.5.2 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.5-1 | 1.5.2 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.5-1 | 1.5.2 | inference_graviton |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.3-1 | 1.3.3 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.3-1 | 1.3.3 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.3-1 | 1.3.3 | inference_graviton |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-2 | 1.2.0 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-2 | 1.2.0 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-1 | 1.2.0 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-1 | 1.2.0 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.0-1 | 1.0.0 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.0-1 | 1.0.0 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/xgboost:<tag> |
1 | 0.72 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/xgboost:<tag> |
1 | 0.72 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
0.90-2 | 0.90 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
0.90-2 | 0.90 | training |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
0.90-1 | 0.90 | inference |
272398656194.dkr.ecr.me-central-1.amazonaws.com/sagemaker-xgboost:<tag> |
0.90-1 | 0.90 | training |