Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.
Jalur Registri Docker dan Kode Contoh untuk AWS GovCloud (AS-Barat) (us-gov-west-1)
Topik berikut mencantumkan parameter untuk masing-masing algoritme dan wadah pembelajaran mendalam yang disediakan oleh Amazon SageMaker AI dalam hal ini Wilayah AWS.
Topik
AutoGluon (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='autogluon',region='us-gov-west-1',image_scope='inference',version='0.4')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
1.3.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
1.3.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
1.2.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
1.2.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
1.1.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
1.1.1 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
1.1.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
1.1.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
1.0.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
1.0.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.8.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.8.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.7.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.7.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.6.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.6.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.6.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.6.1 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.5.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.5.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.4.3 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.4.3 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.4.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.4.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.4.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.4.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.3.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.3.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-training:<tag> |
0.3.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/autogluon-inference:<tag> |
0.3.1 | inferensi |
BlazingText (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='blazingtext',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/blazingtext:<tag> |
1 | inferensi, pelatihan |
Rantai (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='chainer',region='us-gov-west-1',version='5.0.0',py_version='py3',image_scope='inference',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-chainer:<tag> |
5.0.0 | inferensi, pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-chainer:<tag> |
4.1.0 | inferensi, pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-chainer:<tag> |
4.0.0 | inferensi, pelatihan | CPU, GPU | py2, py3 |
Klarifikasi (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='clarify',region='us-gov-west-1',version='1.0',image_scope='processing')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
598674086554.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-clarify-processing:<tag> |
1.0 | pengolahan |
DJL DeepSpeed (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='djl-deepspeed', region='us-west-2',py_version='py3',image_scope='inference')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.27.0-deepspeed0.12.6-cu121- <tag> |
0.27.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.26.0-deepspeed0.12.6-cu121- <tag> |
0.26.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.25.0-deepspeed0.11.0-cu118- <tag> |
0.25.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.24.0-deepspeed0.10.0-cu118- <tag> |
0.24.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.23.0-deepspeed0.9.5-cu118- <tag> |
0.23.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.22.1-deepspeed0.9.2-cu118- <tag> |
0.22.1 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.21.0-deepspeed0.8.3-cu117- <tag> |
0.21.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.20.0-deepspeed0.7.5-cu116- <tag> |
0.20.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/djl-inference:0.19.0-deepspeed0.7.3-cu113- <tag> |
0.19.0 | inferensi |
Debugger (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='debugger',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
515509971035.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-debugger-rules:<tag> |
terbaru | debugger |
Peramalan DeepAR (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='forecasting-deepar',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/forecasting-deepar:<tag> |
1 | inferensi, pelatihan |
Mesin Faktorisasi (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='factorization-machines',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/factorization-machines:<tag> |
1 | inferensi, pelatihan |
Hugging Face (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='huggingface',region='us-gov-west-1',version='4.4.2',image_scope='training',base_framework_version='tensorflow2.4.1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.49.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.49.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.48.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.48.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.46.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.37.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.36.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.28.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.28.1 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.26.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.26.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.26.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.17.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.17.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.17.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.17.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.12.3 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.12.3 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.12.3 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.12.3 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.11.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.11.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.11.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.11.0 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.10.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.10.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.10.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.10.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.10.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.10.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.10.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.10.2 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.6.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.6.1 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-inference:<tag> |
4.6.1 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-inference:<tag> |
4.6.1 | inferensi |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.5.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.5.0 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-pytorch-training:<tag> |
4.4.2 | pelatihan |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/huggingface-tensorflow-training:<tag> |
4.4.2 | pelatihan |
Wawasan IP (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='ipinsights',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/ipinsights:<tag> |
1 | inferensi, pelatihan |
Klasifikasi gambar (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='image-classification',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/image-classification:<tag> |
1 | inferensi, pelatihan |
Inferensia MXNet (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='inferentia-mxnet',region='us-gov-west-1',version='1.5.1',instance_type='ml.inf1.6xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-mxnet:<tag> |
1.8 | inferensi | inf | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-mxnet:<tag> |
1.5.1 | inferensi | inf | py3 |
Inferensia PyTorch (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='inferentia-pytorch',region='us-gov-west-1',version='1.9',py_version='py3')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-pytorch:<tag> |
1.9 | inferensi | inf | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-pytorch:<tag> |
1.8 | inferensi | inf | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-pytorch:<tag> |
1.7 | inferensi | inf | py3 |
K-Means (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='kmeans',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/kmeans:<tag> |
1 | inferensi, pelatihan |
KNN (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='knn',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/knn:<tag> |
1 | inferensi, pelatihan |
LDA (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='lda',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/lda:<tag> |
1 | inferensi, pelatihan |
Linear Learner (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='linear-learner',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/linear-learner:<tag> |
1 | inferensi, pelatihan |
MXNet (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='mxnet',region='us-gov-west-1',version='1.4.1',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-training:<tag> |
1.9.0 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference:<tag> |
1.9.0 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-training:<tag> |
1.8.0 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference:<tag> |
1.8.0 | inferensi | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-training:<tag> |
1.7.0 | pelatihan | CPU, GPU | py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference:<tag> |
1.7.0 | inferensi | CPU, GPU | py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.7.0 | eia | CPU | py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-training:<tag> |
1.6.0 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference:<tag> |
1.6.0 | inferensi | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.5.1 | eia | CPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.4.1 | pelatihan | CPU, GPU | py2 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-training:<tag> |
1.4.1 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet-serving:<tag> |
1.4.1 | inferensi | CPU, GPU | py2 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference:<tag> |
1.4.1 | inferensi | CPU, GPU | py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/mxnet-inference-eia:<tag> |
1.4.1 | eia | CPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.4.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet-serving:<tag> |
1.4.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet-serving-eia:<tag> |
1.4.0 | eia | CPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.3.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.3.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet-eia:<tag> |
1.3.0 | eia | CPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.2.1 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.2.1 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.1.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.1.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.0.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
1.0.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
0.12.1 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-mxnet:<tag> |
0.12.1 | inferensi | CPU, GPU | py2, py3 |
MXNet Pelatih (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='coach-mxnet',region='us-gov-west-1',version='0.11',py_version='py3',image_scope='training',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-mxnet:pelatih0.11.0- <tag> |
0.11.0 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-mxnet:pelatih0.11- <tag> |
0,11 | pelatihan | CPU, GPU | py3 |
NTM (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='ntm',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/ntm:<tag> |
1 | inferensi, pelatihan |
Klasifikasi Gambar Neo (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='image-classification-neo',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/image-classification-neo:<tag> |
terbaru | inferensi |
Neo MXNet (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='neo-mxnet',region='us-gov-west-1',version='1.8',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-mxnet:<tag> |
1.8 | inferensi | CPU, GPU | py3 |
Neo PyTorch (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='neo-pytorch',region='us-gov-west-1',version='1.6',image_scope='inference',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
2.0 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.13 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.12 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.8 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.7 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.6 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.5 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-pytorch:<tag> |
1.4 | inferensi | CPU, GPU | py3 |
Neo Tensorflow (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='neo-tensorflow',region='us-gov-west-1',version='1.15.3',py_version='py3',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-tensorflow:<tag> |
2.9.2 | inferensi | CPU, GPU | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-inference-tensorflow:<tag> |
1.15.3 | inferensi | CPU, GPU | py3 |
Neo XGBoost (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='xgboost-neo',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/xgboost-neo:<tag> |
terbaru | inferensi |
Deteksi Objek (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='object-detection',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/object-detection:<tag> |
1 | inferensi, pelatihan |
Object2Vec (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='object2vec',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/object2vec:<tag> |
1 | inferensi, pelatihan |
PCA (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='pca',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/pca:<tag> |
1 | inferensi, pelatihan |
PyTorch (DLC)
Untuk informasi tentang PyTorch versi yang didukung dan tidak didukung, lihat Tabel Kebijakan Dukungan Framework di Panduan Pengembang AWS Deep Learning Containers.
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='pytorch',region='us-gov-west-1',version='1.8.0',py_version='py3',image_scope='inference', instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.7.1 | pelatihan | CPU, GPU | py312 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.6.0 | inferensi | CPU, GPU | py312 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.6.0 | pelatihan | CPU, GPU | py312 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.5.1 | inferensi | CPU, GPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.5.1 | pelatihan | CPU, GPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.4.0 | inferensi | CPU, GPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.4.0 | inferensi_graviton | CPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.4.0 | pelatihan | CPU, GPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.3.0 | inferensi | CPU, GPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.3.0 | inferensi_graviton | CPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.3.0 | pelatihan | CPU, GPU | py311 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.2.1 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.2.0 | inferensi | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.2.0 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.1.0 | inferensi | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.1.0 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.1.0 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.0.1 | inferensi | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.0.1 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.0.1 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
2.0.0 | inferensi | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
2.0.0 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
2.0.0 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.13.1 | inferensi | CPU, GPU | py39 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.13.1 | pelatihan | CPU, GPU | py39 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.12.1 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference-graviton:<tag> |
1.12.1 | inferensi_graviton | CPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.12.1 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.12.0 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.12.0 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.11.0 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.11.0 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.10.2 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.10.2 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.10.0 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.10.0 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.9.1 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.9.1 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.9.0 | inferensi | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.9.0 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.8.1 | inferensi | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.8.1 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.8.0 | inferensi | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.8.0 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.7.1 | inferensi | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.7.1 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.6.0 | inferensi | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.6.0 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.5.0 | inferensi | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.5.0 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.4.0 | inferensi | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.4.0 | pelatihan | CPU, GPU | py2, py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.3.1 | inferensi | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.3.1 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-inference:<tag> |
1.2.0 | inferensi | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/pytorch-training:<tag> |
1.2.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-pytorch:<tag> |
1.1.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-pytorch:<tag> |
1.1.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-pytorch:<tag> |
1.0.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-pytorch:<tag> |
1.0.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-pytorch:<tag> |
0.4.0 | inferensi | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-pytorch:<tag> |
0.4.0 | pelatihan | CPU, GPU | py2, py3 |
Random Cut Forest (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='randomcutforest',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/randomcutforest:<tag> |
1 | inferensi, pelatihan |
Scikit-learn (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='sklearn',region='us-gov-west-1',version='0.23-1',image_scope='inference')
| Jalur registri | Versi | Versi paket | Jenis pekerjaan (cakupan gambar) |
|---|---|---|---|
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.2-1 | 1.2.1 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.2-1 | 1.2.1 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.0-1 | 1.0.2 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.0-1 | 1.0.2 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
1.0-1 | 1.0.2 | inferensi_graviton |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0,23-1 | 0.23.2 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0,23-1 | 0.23.2 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0.20.0 | 0.20.0 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-scikit-learn:<tag> |
0.20.0 | 0.20.0 | pelatihan |
Segmentasi Semantik (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='semantic-segmentation',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/semantic-segmentation:<tag> |
1 | inferensi, pelatihan |
Seq2Seq (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='seq2seq',region='us-gov-west-1')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/seq2seq:<tag> |
1 | inferensi, pelatihan |
Spark (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='spark',region='us-gov-west-1',version='3.0',image_scope='processing')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
271483468897.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.3 | pengolahan |
271483468897.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.2 | pengolahan |
271483468897.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.1 | pengolahan |
271483468897.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-spark-processing:<tag> |
3.0 | pengolahan |
271483468897.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-spark-processing:<tag> |
2.4 | pengolahan |
Penyajian SparkMl (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='sparkml-serving',region='us-gov-west-1',version='2.4')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) |
|---|---|---|
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-sparkml-serving:<tag> |
3.3 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-sparkml-serving:<tag> |
2.4 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-sparkml-serving:<tag> |
2.2 | inferensi |
Tensorflow (DLC)
Untuk informasi tentang TensorFlow versi yang didukung dan tidak didukung, lihat Tabel Kebijakan Dukungan Framework di Panduan Pengembang AWS Deep Learning Containers.
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='tensorflow',region='us-gov-west-1',version='1.12.0',image_scope='inference',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.19.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.19.0 | pelatihan | CPU, GPU | py312 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.18.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.18.0 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.16.2 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.16.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.16.1 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.14.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.14.1 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.14.1 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.13.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.13.0 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.13.0 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.12.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.12.1 | inferensi_graviton | CPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.12.0 | pelatihan | CPU, GPU | py310 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.11.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.11.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.11.0 | pelatihan | CPU, GPU | py39 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.10.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.10.1 | pelatihan | CPU, GPU | py39 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.10.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.9.3 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.9.2 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.9.2 | pelatihan | CPU, GPU | py39 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-graviton:<tag> |
2.9.1 | inferensi_graviton | CPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.8.4 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.8.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.8.0 | pelatihan | CPU, GPU | py39 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.7.1 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.7.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.6.3 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.6.3 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.6.2 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.6.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.6.0 | pelatihan | CPU, GPU | py38 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.5.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.5.1 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.5.0 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.4.3 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.4.3 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.4.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.4.1 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.2 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.3.2 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.3.1 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-eia:<tag> |
2.3.0 | eia | CPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.3.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.3.0 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.2 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.2.2 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.2.1 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.2.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.2.0 | pelatihan | CPU, GPU | py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.3 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.1.3 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.2 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.1.2 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.1.1 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.1.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.1.0 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.4 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.0.4 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.3 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.0.3 | pelatihan | CPU, GPU | py3, py36 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.2 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.0.2 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.1 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.0.1 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-eia:<tag> |
2.0.0 | eia | CPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
2.0.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
2.0.0 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.5 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.15.5 | pelatihan | CPU, GPU | py3, py36, py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.4 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.15.4 | pelatihan | CPU, GPU | py3, py36, py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.3 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.15.3 | pelatihan | CPU, GPU | py2, py3, py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.2 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.15.2 | pelatihan | CPU, GPU | py2, py3, py37 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-eia:<tag> |
1.15.0 | eia | CPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.15.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.15.0 | pelatihan | CPU, GPU | py2, py3 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference-eia:<tag> |
1.14.0 | eia | CPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.14.0 | inferensi | CPU, GPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.14.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-scriptmode:<tag> |
1.13.1 | pelatihan | CPU, GPU | py2 |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-training:<tag> |
1.13.1 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-serving-eia:<tag> |
1.13.0 | eia | CPU | - |
442386744353.dkr.ecr.us-gov-west-1.amazonaws.com/tensorflow-inference:<tag> |
1.13.0 | inferensi | CPU, GPU | - |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-serving-eia:<tag> |
1.12.0 | eia | CPU | - |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-serving:<tag> |
1.12.0 | inferensi | CPU, GPU | - |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-scriptmode:<tag> |
1.12.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-serving-eia:<tag> |
1.11.0 | eia | CPU | - |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-serving:<tag> |
1.11.0 | inferensi | CPU, GPU | - |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-scriptmode:<tag> |
1.11.0 | pelatihan | CPU, GPU | py2, py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow-eia:<tag> |
1.10.0 | eia | CPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.10.0 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.10.0 | pelatihan | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.9.0 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.9.0 | pelatihan | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.8.0 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.8.0 | pelatihan | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.7.0 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.7.0 | pelatihan | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.6.0 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.6.0 | pelatihan | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.5.0 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.5.0 | pelatihan | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.4.1 | inferensi | CPU, GPU | py2 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-tensorflow:<tag> |
1.4.1 | pelatihan | CPU, GPU | py2 |
Pelatih Tensorflow (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='coach-tensorflow',region='us-gov-west-1',version='1.0.0',image_scope='training',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:pelatih0.11.1- <tag> |
0.11.1 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:pelatih0.11.0- <tag> |
0.11.0 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:pelatih0.11- <tag> |
0,11 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:pelatih0.10.1- <tag> |
0.10.1 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:pelatih0.10- <tag> |
0,10 | pelatihan | CPU, GPU | py3 |
Inferensia Tensorflow (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='inferentia-tensorflow',region='us-gov-west-1',version='1.15.0',instance_type='ml.inf1.6xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-tensorflow:<tag> |
2.5.2 | inferensi | inf | py3 |
263933020539.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-neo-tensorflow:<tag> |
1.15.0 | inferensi | inf | py3 |
Sinar Tensorflow (DLC)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='ray-tensorflow',region='us-gov-west-1',version='0.8.5',instance_type='ml.c5.4xlarge')
| Jalur registri | Versi | Jenis pekerjaan (cakupan gambar) | Jenis prosesor | Versi Python |
|---|---|---|---|---|
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:ray0.6.5- <tag> |
0.6.5 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:ray0.6- <tag> |
0,6 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:ray0.5.3- <tag> |
0.5.3 | pelatihan | CPU, GPU | py3 |
246785580436.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-rl-tensorflow:ray0.5- <tag> |
0,5 | pelatihan | CPU, GPU | py3 |
XGBoost (algoritma)
Contoh SageMaker AI Python SDK berikut menunjukkan cara mengambil jalur registri tertentu.
from sagemaker import image_uris image_uris.retrieve(framework='xgboost',region='us-gov-west-1',version='1.5-1')
| Jalur registri | Versi | Versi paket | Jenis pekerjaan (cakupan gambar) |
|---|---|---|---|
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,7-1 | 1.7.4 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,7-1 | 1.7.4 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,5-1 | 1.5.2 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,5-1 | 1.5.2 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1,5-1 | 1.5.2 | inferensi_graviton |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.3-1 | 1.3.3 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.3-1 | 1.3.3 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.3-1 | 1.3.3 | inferensi_graviton |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-2 | 1.2.0 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-2 | 1.2.0 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-1 | 1.2.0 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.2-1 | 1.2.0 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.0-1 | 1.0.0 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
1.0-1 | 1.0.0 | pelatihan |
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/xgboost:<tag> |
1 | 0,72 | inferensi |
226302683700.dkr.ecr.us-gov-west-1.amazonaws.com/xgboost:<tag> |
1 | 0,72 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-2 | 0,90 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-2 | 0,90 | pelatihan |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-1 | 0,90 | inferensi |
414596584902.dkr.ecr.us-gov-west-1.amazonaws.com/sagemaker-xgboost:<tag> |
0,90-1 | 0,90 | pelatihan |
