Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

# Docker-Registry-Pfade und Beispielcode für Asien-Pazifik (Jakarta) (ap-southeast-3)
<a name="ecr-ap-southeast-3"></a>

In den folgenden Themen sind Parameter für jeden der Algorithmen und Deep-Learning-Container aufgeführt, die Amazon SageMaker AI in diesem Bereich bereitstellt AWS-Region.

**Topics**
+ [AutoGluon (Algorithmus)](#autogluon-ap-southeast-3)
+ [BlazingText (Algorithmus)](#blazingtext-ap-southeast-3)
+ [Clarify (Algorithmus)](#clarify-ap-southeast-3)
+ [DJL DeepSpeed (Algorithmus)](#djl-deepspeed-ap-southeast-3)
+ [DeepAR Forecasting (Algorithmus)](#forecasting-deepar-ap-southeast-3)
+ [Factorization Machines (Algorithmus)](#factorization-machines-ap-southeast-3)
+ [Hugging Face (Algorithmus)](#huggingface-ap-southeast-3)
+ [IP Insights (Algorithmus)](#ipinsights-ap-southeast-3)
+ [Bildklassifizierung (Algorithmus)](#image-classification-ap-southeast-3)
+ [K-Means (Algorithmus)](#kmeans-ap-southeast-3)
+ [KNN (Algorithmus)](#knn-ap-southeast-3)
+ [Linear Learner (Algorithmus)](#linear-learner-ap-southeast-3)
+ [MXNet (DLC)](#mxnet-ap-southeast-3)
+ [Model Monitor (Algorithmus)](#model-monitor-ap-southeast-3)
+ [NTM (Algorithmus)](#ntm-ap-southeast-3)
+ [Objekterkennung (Algorithmus)](#object-detection-ap-southeast-3)
+ [Object2Vec (Algorithmus)](#object2vec-ap-southeast-3)
+ [PCA (Algorithmus)](#pca-ap-southeast-3)
+ [PyTorch (DLC)](#pytorch-ap-southeast-3)
+ [Random Cut Forest (Algorithmus)](#randomcutforest-ap-southeast-3)
+ [Scikit-learn (Algorithmus)](#sklearn-ap-southeast-3)
+ [Semantic Segmentation (Algorithmus)](#semantic-segmentation-ap-southeast-3)
+ [Seq2Seq (Algorithmus)](#seq2seq-ap-southeast-3)
+ [Spark (Algorithmus)](#spark-ap-southeast-3)
+ [SparkML Serving (Algorithmus)](#sparkml-serving-ap-southeast-3)
+ [Tensorflow (DLC)](#tensorflow-ap-southeast-3)
+ [XGBoost (Algorithmus)](#xgboost-ap-southeast-3)

## AutoGluon (Algorithmus)
<a name="autogluon-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='autogluon',region='ap-southeast-3',image_scope='inference',version='0.4')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 1.3.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 1.3.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 1.2.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 1.2.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 1.1.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 1.1.1 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 1.1.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 1.1.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 1.0.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 1.0.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.8.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.8.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.7.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.7.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.6.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.6.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.6.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.6.1 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.5.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.5.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.4.3 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.4.3 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.4.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.4.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0,4,0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0,4,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.3.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.3.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-training:{{<tag>}} | 0.3.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/autogluon-inference:{{<tag>}} | 0.3.1 | Inferenz | 

## BlazingText (Algorithmus)
<a name="blazingtext-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='blazingtext',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/blazingtext:{{<tag>}} | 1 | Inferenz, Training | 

## Clarify (Algorithmus)
<a name="clarify-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='clarify',region='ap-southeast-3',version='1.0',image_scope='processing')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 705930551576.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-clarify-processing:{{<tag>}} | 1,0 | Verarbeitung | 

## DJL DeepSpeed (Algorithmus)
<a name="djl-deepspeed-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='djl-deepspeed', region='us-west-2',py_version='py3',image_scope='inference')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inferenceDas folgende AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird. ----sep----:0.27.0-deepspeed0.12.6-cu121- {{<tag>}} | 0,27,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.27.0 ----sep----:0.26.0-deepspeed0.12.6-cu121- {{<tag>}} | 0,26.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.26.0 ----sep----:0.25.0-deepspeed0.11.0-cu118- {{<tag>}} | 0,25,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.25.0 ----sep----:0.24.0-deepspeed0.10.0-cu118- {{<tag>}} | 0,24,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.24.0 ----sep----:0.23.0-deepspeed0.9.5-cu118- {{<tag>}} | 0,23,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.23.0 ----sep----:0.22.1-deepspeed0.9.2-cu118- {{<tag>}} | 0,22,1 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.22.1 ----sep----:0.21.0-deepspeed0.8.3-cu117- {{<tag>}} | 0,21,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.21.0 ----sep----:0.20.0-deepspeed0.7.5-cu116- {{<tag>}} | 0,20,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/djl-inference0.20.0 ----sep----:0.19.0-deepspeed0.7.3-cu113- {{<tag>}} | 0.19.0 | Inferenz | 

## DeepAR Forecasting (Algorithmus)
<a name="forecasting-deepar-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='forecasting-deepar',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/forecasting-deepar:{{<tag>}} | 1 | Inferenz, Training | 

## Factorization Machines (Algorithmus)
<a name="factorization-machines-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='factorization-machines',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/factorization-machines:{{<tag>}} | 1 | Inferenz, Training | 

## Hugging Face (Algorithmus)
<a name="huggingface-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='huggingface',region='ap-southeast-3',version='4.4.2',image_scope='training',base_framework_version='tensorflow2.4.1')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.49.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4,49,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4,48,0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4,48,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4,46,1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4,37,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4,36,0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4,28,1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4,28,1 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4,26,0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4,26,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4,26,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.17,0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.17,0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4.17,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4.17,0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.12.3 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.12.3 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4.12.3 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4.12.3 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.11.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.11.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4.11.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4.11.0 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.10.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.10.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.10.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.10.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4.10.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4.10.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4.10.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4.10.2 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.6.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.6.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.6.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.6.1 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-inference:{{<tag>}} | 4.6.1 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-inference:{{<tag>}} | 4.6.1 | Inferenz | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.5.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.5.0 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-pytorch-training:{{<tag>}} | 4.4.2 | Training | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/huggingface-tensorflow-training:{{<tag>}} | 4.4.2 | Training | 

## IP Insights (Algorithmus)
<a name="ipinsights-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='ipinsights',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/ipinsights:{{<tag>}} | 1 | Inferenz, Training | 

## Bildklassifizierung (Algorithmus)
<a name="image-classification-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='image-classification',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/image-classification:{{<tag>}} | 1 | Inferenz, Training | 

## K-Means (Algorithmus)
<a name="kmeans-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='kmeans',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/kmeans:{{<tag>}} | 1 | Inferenz, Training | 

## KNN (Algorithmus)
<a name="knn-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='knn',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/knn:{{<tag>}} | 1 | Inferenz, Training | 

## Linear Learner (Algorithmus)
<a name="linear-learner-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='linear-learner',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/linear-learner:{{<tag>}} | 1 | Inferenz, Training | 

## MXNet (DLC)
<a name="mxnet-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='mxnet',region='ap-southeast-3',version='1.4.1',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
```


| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen | 
| --- | --- | --- | --- | --- | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-training:{{<tag>}} | 1.9.0 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference:{{<tag>}} | 1.9.0 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-training:{{<tag>}} | 1.8.0 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference:{{<tag>}} | 1.8.0 | Inferenz | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-training:{{<tag>}} | 1.7.0 | Training | CPU, GPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference:{{<tag>}} | 1.7.0 | Inferenz | CPU, GPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference-eia:{{<tag>}} | 1.7.0 | eia | CPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-training:{{<tag>}} | 1.6.0 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference:{{<tag>}} | 1.6.0 | Inferenz | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference-eia:{{<tag>}} | 1.5.1 | eia | CPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-training:{{<tag>}} | 1.4.1 | Training | CPU, GPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference:{{<tag>}} | 1.4.1 | Inferenz | CPU, GPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/mxnet-inference-eia:{{<tag>}} | 1.4.1 | eia | CPU | py2, py3 | 

## Model Monitor (Algorithmus)
<a name="model-monitor-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='model-monitor',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 669540362728.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-model-monitor-analyzer:{{<tag>}} |  | Überwachung | 

## NTM (Algorithmus)
<a name="ntm-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='ntm',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/ntm:{{<tag>}} | 1 | Inferenz, Training | 

## Objekterkennung (Algorithmus)
<a name="object-detection-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='object-detection',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/object-detection:{{<tag>}} | 1 | Inferenz, Training | 

## Object2Vec (Algorithmus)
<a name="object2vec-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='object2vec',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/object2vec:{{<tag>}} | 1 | Inferenz, Training | 

## PCA (Algorithmus)
<a name="pca-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='pca',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/pca:{{<tag>}} | 1 | Inferenz, Training | 

## PyTorch (DLC)
<a name="pytorch-ap-southeast-3"></a>

Informationen zu den unterstützten und nicht unterstützten PyTorch Versionen finden Sie in der [Framework-Support-Richtlinientabelle](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-framework-support-policy.html) im *AWS Deep Learning Containers Developer Guide*.

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='pytorch',region='ap-southeast-3',version='1.8.0',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
```


| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen | 
| --- | --- | --- | --- | --- | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.7.1 | Training | CPU, GPU | py312 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.6.0 | Inferenz | CPU, GPU | py312 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.6.0 | Training | CPU, GPU | py312 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.5.1 | Inferenz | CPU, GPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.5.1 | Training | CPU, GPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.4.0 | Inferenz | CPU, GPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 2.4.0 | inference\_graviton | CPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.4.0 | Training | CPU, GPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.3.0 | Inferenz | CPU, GPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 2.3.0 | inference\_graviton | CPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.3.0 | Training | CPU, GPU | py311 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 2.2.1 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.2.0 | Inferenz | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.2.0 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.1.0 | Inferenz | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 2.1.0 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.1.0 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.0.1 | Inferenz | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 2.0.1 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.0.1 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 2.0.0 | Inferenz | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 2.0.0 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 2.0.0 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.13.1 | Inferenz | CPU, GPU | py39 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.13.1 | Training | CPU, GPU | py39 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.12.1 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-graviton:{{<tag>}} | 1.12.1 | inference\_graviton | CPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.12.1 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.12.0 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.12.0 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.11.0 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.11.0 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.10.2 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.10.2 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.10.0 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.10.0 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.9.1 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.9.1 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.9.0 | Inferenz | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.9.0 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.8.1 | Inferenz | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.8.1 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.8.0 | Inferenz | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.8.0 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.7.1 | Inferenz | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.7.1 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.6.0 | Inferenz | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.6.0 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-eia:{{<tag>}} | 1.5.1 | eia | CPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.5.0 | Inferenz | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.5.0 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.4.0 | Inferenz | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.4.0 | Training | CPU, GPU | py2, py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference-eia:{{<tag>}} | 1.3.1 | eia | CPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.3.1 | Inferenz | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.3.1 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-inference:{{<tag>}} | 1.2.0 | Inferenz | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/pytorch-training:{{<tag>}} | 1.2.0 | Training | CPU, GPU | py2, py3 | 

## Random Cut Forest (Algorithmus)
<a name="randomcutforest-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='randomcutforest',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/randomcutforest:{{<tag>}} | 1 | Inferenz, Training | 

## Scikit-learn (Algorithmus)
<a name="sklearn-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='sklearn',region='ap-southeast-3',version='0.23-1',image_scope='inference')
```


| Registry-Pfad | Version | Version Package | Auftragstypen (Bildbereich) | 
| --- | --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 1.2-1 | 1.2.1 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 1,2-1 | 1.2.1 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 1,0-1 | 1.0.2 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 1,0-1 | 1.0.2 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 1,0-1 | 1.0.2 | inference\_graviton | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 0,23-1 | 0,23,2 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 0,23-1 | 0,23,2 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 0,20,0 | 0,20,0 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-scikit-learn:{{<tag>}} | 0,20,0 | 0,20,0 | Training | 

## Semantic Segmentation (Algorithmus)
<a name="semantic-segmentation-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='semantic-segmentation',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/semantic-segmentation:{{<tag>}} | 1 | Inferenz, Training | 

## Seq2Seq (Algorithmus)
<a name="seq2seq-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='seq2seq',region='ap-southeast-3')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/seq2seq:{{<tag>}} | 1 | Inferenz, Training | 

## Spark (Algorithmus)
<a name="spark-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='spark',region='ap-southeast-3',version='3.0',image_scope='processing')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 800295151634.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-spark-processing:{{<tag>}} | 3.3 | Verarbeitung | 
| 800295151634.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-spark-processing:{{<tag>}} | 3.2 | Verarbeitung | 
| 800295151634.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-spark-processing:{{<tag>}} | 3.1 | Verarbeitung | 
| 800295151634.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-spark-processing:{{<tag>}} | 3.0 | Verarbeitung | 
| 800295151634.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-spark-processing:{{<tag>}} | 2.4 | Verarbeitung | 

## SparkML Serving (Algorithmus)
<a name="sparkml-serving-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='sparkml-serving',region='ap-southeast-3',version='2.4')
```


| Registry-Pfad | Version | Jobtypen (Bildbereich) | 
| --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-sparkml-serving:{{<tag>}} | 3.3 | Inferenz | 

## Tensorflow (DLC)
<a name="tensorflow-ap-southeast-3"></a>

Informationen zu den unterstützten und nicht unterstützten TensorFlow Versionen finden Sie in der [Framework-Support-Richtlinientabelle](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-framework-support-policy.html) im *AWS Deep Learning Containers Developer Guide*.

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='tensorflow',region='ap-southeast-3',version='1.12.0',image_scope='inference',instance_type='ml.c5.4xlarge')
```


| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen | 
| --- | --- | --- | --- | --- | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.19.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.19.0 | Training | CPU, GPU | py312 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.18.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.18.0 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.16.2 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.16.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-graviton:{{<tag>}} | 2.16.1 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.14.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-graviton:{{<tag>}} | 2.14.1 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.14.1 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.13.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-graviton:{{<tag>}} | 2.13.0 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.13.0 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.12.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-graviton:{{<tag>}} | 2.12.1 | inference\_graviton | CPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.12.0 | Training | CPU, GPU | py310 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.11.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.11.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.11.0 | Training | CPU, GPU | py39 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.10.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.10.1 | Training | CPU, GPU | py39 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.10.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.9.3 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.9.2 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.9.2 | Training | CPU, GPU | py39 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-graviton:{{<tag>}} | 2.9.1 | inference\_graviton | CPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.8.4 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.8.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.8.0 | Training | CPU, GPU | py39 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.7.1 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.7.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.6.3 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.6.3 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.6.2 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.6.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.6.0 | Training | CPU, GPU | py38 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.5.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.5.1 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.5.0 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.4.3 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.4.3 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.4.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.4.1 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.3.2 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.3.2 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.3.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.3.1 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-eia:{{<tag>}} | 2.3.0 | eia | CPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.3.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.3.0 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.2.2 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.2.2 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.2.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.2.1 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.2.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.2.0 | Training | CPU, GPU | py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.1.3 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.1.3 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.1.2 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.1.2 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.1.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.1.1 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.1.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.1.0 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.0.4 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.0.4 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.0.3 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.0.3 | Training | CPU, GPU | py3, py36 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.0.2 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.0.2 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.0.1 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.0.1 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-eia:{{<tag>}} | 2.0.0 | eia | CPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 2.0.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 2.0.0 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.15,5 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1,1,5 | Training | CPU, GPU | py3, py36, py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.15,4 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1.15,4 | Training | CPU, GPU | py3, py36, py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.15,3 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1.15,3 | Training | CPU, GPU | py2, py3, py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.15.2 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1.15.2 | Training | CPU, GPU | py2, py3, py37 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-eia:{{<tag>}} | 1.15.0 | eia | CPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.15.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1.15.0 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference-eia:{{<tag>}} | 1.14.0 | eia | CPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.14.0 | Inferenz | CPU, GPU | - | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1.14.0 | Training | CPU, GPU | py2, py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-training:{{<tag>}} | 1.13.1 | Training | CPU, GPU | py3 | 
| 907027046896.dkr.ecr.ap-southeast-3.amazonaws.com/tensorflow-inference:{{<tag>}} | 1.13.0 | Inferenz | CPU, GPU | - | 

## XGBoost (Algorithmus)
<a name="xgboost-ap-southeast-3"></a>

Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.

```
from sagemaker import image_uris
image_uris.retrieve(framework='xgboost',region='ap-southeast-3',version='1.5-1')
```


| Registry-Pfad | Version | Version Package | Auftragstypen (Bildbereich) | 
| --- | --- | --- | --- | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1.7-1 | 1,7.4 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,7-1 | 1,7.4 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,5-1 | 1.5.2 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,5-1 | 1.5.2 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,5-1 | 1.5.2 | inference\_graviton | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,3-1 | 1.3.3 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,3-1 | 1.3.3 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,3-1 | 1.3.3 | inference\_graviton | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,2-2 | 1.2.0 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,2-2 | 1.2.0 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,2-1 | 1.2.0 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,2-1 | 1.2.0 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,0-1 | 1.0.0 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 1,0-1 | 1.0.0 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/xgboost:{{<tag>}} | 1 | 0,72 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/xgboost:{{<tag>}} | 1 | 0,72 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 0,90-2 | 0.90 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 0,90-2 | 0.90 | Training | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 0,90-1 | 0.90 | Inferenz | 
| 951798379941.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-xgboost:{{<tag>}} | 0,90-1 | 0.90 | Training |