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-Registrierungspfade und Beispielcode für AWS GovCloud (US-East) (us-gov-east-1)
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.
Themen
AutoGluon (Algorithmus)
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='us-gov-east-1',image_scope='inference',version='0.4')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
1.3.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
1.3.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
1.2.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
1.2.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
1.1.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
1.1.1 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
1.1.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
1.1.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
1.0.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
1.0.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.8.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.8.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.7.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.7.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.6.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.6.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.6.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.6.1 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.5.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.5.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.4.3 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.4.3 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.4.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.4.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0,4,0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0,4,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.3.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.3.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-training:<tag> |
0.3.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/autogluon-inference:<tag> |
0.3.1 | Inferenz |
BlazingText (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/blazingtext:<tag> |
1 | Inferenz, Training |
DJL DeepSpeed (Algorithmus)
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) |
|---|---|---|
446045086412.dkr.ecr.us-gov-east-1.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 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.27.0 ----sep----:0.26.0-deepspeed0.12.6-cu121- <tag> |
0,26.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.26.0 ----sep----:0.25.0-deepspeed0.11.0-cu118- <tag> |
0,25,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.25.0 ----sep----:0.24.0-deepspeed0.10.0-cu118- <tag> |
0,24,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.24.0 ----sep----:0.23.0-deepspeed0.9.5-cu118- <tag> |
0,23,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.23.0 ----sep----:0.22.1-deepspeed0.9.2-cu118- <tag> |
0,22,1 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.22.1 ----sep----:0.21.0-deepspeed0.8.3-cu117- <tag> |
0,21,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.21.0 ----sep----:0.20.0-deepspeed0.7.5-cu116- <tag> |
0,20,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/djl-inference0.20.0 ----sep----:0.19.0-deepspeed0.7.3-cu113- <tag> |
0.19.0 | Inferenz |
DeepAR Forecasting (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/forecasting-deepar:<tag> |
1 | Inferenz, Training |
Factorization Machines (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/factorization-machines:<tag> |
1 | Inferenz, Training |
Hugging Face (Algorithmus)
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='us-gov-east-1',version='4.4.2',image_scope='training',base_framework_version='tensorflow2.4.1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.49.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4,49,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4,48,0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4,48,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4,46,1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4,37,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4,36,0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4,28,1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4,28,1 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4,26,0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4,26,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4,26,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.17,0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.17,0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.17,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.17,0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.12.3 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.12.3 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.12.3 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.12.3 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.11.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.11.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.11.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.11.0 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.10.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.10.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.10.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.10.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.10.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.10.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.10.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.10.2 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.6.1 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.6.1 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.6.1 | Inferenz |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.5.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.5.0 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.4.2 | Training |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.4.2 | Training |
IP Insights (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/ipinsights:<tag> |
1 | Inferenz, Training |
Bildklassifizierung (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/image-classification:<tag> |
1 | Inferenz, Training |
Inferentia MXNet (DLC)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='inferentia-mxnet',region='us-gov-east-1',version='1.5.1',instance_type='ml.inf1.6xlarge')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-mxnet:<tag> |
1.8 | Inferenz | inf | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-mxnet:<tag> |
1.5.1 | Inferenz | inf | py3 |
Inferentia PyTorch (DLC)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='inferentia-pytorch',region='us-gov-east-1',version='1.9',py_version='py3')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-pytorch:<tag> |
1.9 | Inferenz | inf | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-pytorch:<tag> |
1.8 | Inferenz | inf | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-pytorch:<tag> |
1,7 | Inferenz | inf | py3 |
K-Means (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/kmeans:<tag> |
1 | Inferenz, Training |
KNN (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/knn:<tag> |
1 | Inferenz, Training |
Linear Learner (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/linear-learner:<tag> |
1 | Inferenz, Training |
MXNet (DLC)
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='us-gov-east-1',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 |
|---|---|---|---|---|
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-training:<tag> |
1.9.0 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference:<tag> |
1.9.0 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-training:<tag> |
1.8.0 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference:<tag> |
1.8.0 | Inferenz | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-training:<tag> |
1.7.0 | Training | CPU, GPU | py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference:<tag> |
1.7.0 | Inferenz | CPU, GPU | py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.7.0 | eia | CPU | py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-training:<tag> |
1.6.0 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference:<tag> |
1.6.0 | Inferenz | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.5.1 | eia | CPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-training:<tag> |
1.4.1 | Training | CPU, GPU | py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference:<tag> |
1.4.1 | Inferenz | CPU, GPU | py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.4.1 | eia | CPU | py2, py3 |
NTM (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/ntm:<tag> |
1 | Inferenz, Training |
Neo Bildklassifizierung (Algorithmus)
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-neo',region='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/image-classification-neo:<tag> |
brandneue | Inferenz |
Neo MXNet (DLC)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='neo-mxnet',region='us-gov-east-1',version='1.8',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-mxnet:<tag> |
1.8 | Inferenz | CPU, GPU | py3 |
Neo PyTorch (DLC)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='neo-pytorch',region='us-gov-east-1',version='1.6',image_scope='inference',instance_type='ml.c5.4xlarge')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
2.0 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.13 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.12 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.8 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1,7 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1,6 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.5 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.4 | Inferenz | CPU, GPU | py3 |
Neo Tensorflow (DLC)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='neo-tensorflow',region='us-gov-east-1',version='1.15.3',py_version='py3',instance_type='ml.c5.4xlarge')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-tensorflow:<tag> |
2.9.2 | Inferenz | CPU, GPU | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-inference-tensorflow:<tag> |
1.15,3 | Inferenz | CPU, GPU | py3 |
Neo XGBoost (Algorithmus)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='xgboost-neo',region='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/xgboost-neo:<tag> |
brandneue | Inferenz |
Object Detection (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/object-detection:<tag> |
1 | Inferenz, Training |
Object2Vec (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/object2vec:<tag> |
1 | Inferenz, Training |
PCA (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/pca:<tag> |
1 | Inferenz, Training |
PyTorch (DLC)
Informationen zu den unterstützten und nicht unterstützten PyTorch Versionen finden Sie in der Framework-Support-Richtlinientabelle 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='us-gov-east-1',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 |
|---|---|---|---|---|
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.7.1 | Training | CPU, GPU | py312 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.6.0 | Inferenz | CPU, GPU | py312 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.6.0 | Training | CPU, GPU | py312 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.5.1 | Inferenz | CPU, GPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.5.1 | Training | CPU, GPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.4.0 | Inferenz | CPU, GPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.4.0 | inference_graviton | CPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.4.0 | Training | CPU, GPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.3.0 | Inferenz | CPU, GPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.3.0 | inference_graviton | CPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.3.0 | Training | CPU, GPU | py311 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.2.1 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.2.0 | Inferenz | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.2.0 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.1.0 | Inferenz | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.1.0 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.1.0 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.0.1 | Inferenz | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.0.1 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.0.1 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
2.0.0 | Inferenz | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.0.0 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
2.0.0 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.13.1 | Inferenz | CPU, GPU | py39 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.13.1 | Training | CPU, GPU | py39 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.12.1 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference-graviton:<tag> |
1.12.1 | inference_graviton | CPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.12.1 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.12.0 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.12.0 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.11.0 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.11.0 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.10.2 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.10.2 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.10.0 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.10.0 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.9.1 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.9.1 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.9.0 | Inferenz | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.9.0 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.8.1 | Inferenz | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.8.1 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.8.0 | Inferenz | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.8.0 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.7.1 | Inferenz | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.7.1 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.6.0 | Inferenz | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.6.0 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.5.0 | Inferenz | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.5.0 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.4.0 | Inferenz | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.4.0 | Training | CPU, GPU | py2, py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.3.1 | Inferenz | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.3.1 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-inference:<tag> |
1.2.0 | Inferenz | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/pytorch-training:<tag> |
1.2.0 | Training | CPU, GPU | py2, py3 |
Random Cut Forest (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/randomcutforest:<tag> |
1 | Inferenz, Training |
Scikit-learn (Algorithmus)
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='us-gov-east-1',version='0.23-1',image_scope='inference')
| Registry-Pfad | Version | Paketversion | Auftragstypen (Bildbereich) |
|---|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.2-1 | 1.2.1 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1,2-1 | 1.2.1 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1,0-1 | 1.0.2 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1,0-1 | 1.0.2 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1,0-1 | 1.0.2 | inference_graviton |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0,23-1 | 0,23,2 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0,23-1 | 0,23,2 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0,20,0 | 0,20,0 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0,20,0 | 0,20,0 | Training |
Semantic Segmentation (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/semantic-segmentation:<tag> |
1 | Inferenz, Training |
Seq2Seq (Algorithmus)
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='us-gov-east-1')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/seq2seq:<tag> |
1 | Inferenz, Training |
Spark (Algorithmus)
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='us-gov-east-1',version='3.0',image_scope='processing')
| Registry-Pfad | Version | Jobtypen (Bildbereich) |
|---|---|---|
260923028637.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.3 | Verarbeitung |
260923028637.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.2 | Verarbeitung |
260923028637.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.1 | Verarbeitung |
260923028637.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.0 | Verarbeitung |
260923028637.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-spark-processing:<tag> |
2.4 | Verarbeitung |
Tensorflow (DLC)
Informationen zu den unterstützten und nicht unterstützten TensorFlow Versionen finden Sie in der Framework-Support-Richtlinientabelle 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='us-gov-east-1',version='1.12.0',image_scope='inference',instance_type='ml.c5.4xlarge')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.19.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.19.0 | Training | CPU, GPU | py312 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.18.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.18.0 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.16.2 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.16.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.16.1 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.14.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.14.1 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.14.1 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.13.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.13.0 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.13.0 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.12.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.12.1 | inference_graviton | CPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.12.0 | Training | CPU, GPU | py310 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.11.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.11.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.11.0 | Training | CPU, GPU | py39 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.10.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.10.1 | Training | CPU, GPU | py39 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.10.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.9.3 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.9.2 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.9.2 | Training | CPU, GPU | py39 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.9.1 | inference_graviton | CPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.8.4 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.8.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.8.0 | Training | CPU, GPU | py39 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.7.1 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.7.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.6.3 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.6.3 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.6.2 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.6.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.6.0 | Training | CPU, GPU | py38 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.5.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.5.1 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.5.0 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.4.3 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.4.3 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.4.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.4.1 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.2 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.3.2 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.3.1 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-eia:<tag> |
2.3.0 | eia | CPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.3.0 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.2 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.2.2 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.2.1 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.2.0 | Training | CPU, GPU | py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.3 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.1.3 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.2 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.1.2 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.1.1 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.1.0 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.4 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.0.4 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.3 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.0.3 | Training | CPU, GPU | py3, py36 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.2 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.0.2 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.1 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.0.1 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-eia:<tag> |
2.0.0 | eia | CPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
2.0.0 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.15,5 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1,1,5 | Training | CPU, GPU | py3, py36, py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.15,4 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1.15,4 | Training | CPU, GPU | py3, py36, py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.15,3 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1.15,3 | Training | CPU, GPU | py2, py3, py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.2 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1.15.2 | Training | CPU, GPU | py2, py3, py37 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-eia:<tag> |
1.15.0 | eia | CPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1.15.0 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference-eia:<tag> |
1.14.0 | eia | CPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.14.0 | Inferenz | CPU, GPU | - |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1.14.0 | Training | CPU, GPU | py2, py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-training:<tag> |
1.13.1 | Training | CPU, GPU | py3 |
446045086412.dkr.ecr.us-gov-east-1.amazonaws.com/tensorflow-inference:<tag> |
1.13.0 | Inferenz | CPU, GPU | - |
Tensorflow Inferentia (DLC)
Das folgende SageMaker AI-Python-SDK-Beispiel zeigt, wie ein bestimmter Registrierungspfad abgerufen wird.
from sagemaker import image_uris image_uris.retrieve(framework='inferentia-tensorflow',region='us-gov-east-1',version='1.15.0',instance_type='ml.inf1.6xlarge')
| Registry-Pfad | Version | Auftragstypen (Bildbereich) | Typen von Prozessoren | Python Versionen |
|---|---|---|---|---|
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-tensorflow:<tag> |
2.5.2 | Inferenz | inf | py3 |
227234621604.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-neo-tensorflow:<tag> |
1.15.0 | Inferenz | inf | py3 |
XGBoost (Algorithmus)
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='us-gov-east-1',version='1.5-1')
| Registry-Pfad | Version | Paketversion | Auftragstypen (Bildbereich) |
|---|---|---|---|
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.7-1 | 1,7.4 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,7-1 | 1,7.4 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,5-1 | 1.5.2 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,5-1 | 1.5.2 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,5-1 | 1.5.2 | inference_graviton |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,3-1 | 1.3.3 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,3-1 | 1.3.3 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,3-1 | 1.3.3 | inference_graviton |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,2-2 | 1.2.0 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,2-2 | 1.2.0 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,2-1 | 1.2.0 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,2-1 | 1.2.0 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,0-1 | 1.0.0 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,0-1 | 1.0.0 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/xgboost:<tag> |
1 | 0,72 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/xgboost:<tag> |
1 | 0,72 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-2 | 0.90 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-2 | 0.90 | Training |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-1 | 0.90 | Inferenz |
237065988967.dkr.ecr.us-gov-east-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-1 | 0.90 | Training |
