# Available Resources for Amazon SageMaker Studio Classic Notebooks
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
The following sections list the available resources for Amazon SageMaker Studio Classic notebooks.
**Topics**
+ [Instance Types Available for Use With Amazon SageMaker Studio Classic Notebooks](notebooks-available-instance-types.md)
+ [Amazon SageMaker Images Available for Use With Studio Classic Notebooks](notebooks-available-images.md)
# Instance Types Available for Use With Amazon SageMaker Studio Classic Notebooks
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
Amazon SageMaker Studio Classic notebooks run on Amazon Elastic Compute Cloud (Amazon EC2) instances. The following Amazon EC2 instance types are available for use with Studio Classic notebooks. For detailed information on which instance types fit your use case, and their performance capabilities, see [Amazon Elastic Compute Cloud Instance types](https://aws.amazon.com/ec2/instance-types/). For information about pricing for these instance types, see [Amazon EC2 Pricing](https://aws.amazon.com/ec2/pricing/).
For information about available Amazon SageMaker Notebook Instance types, see [CreateNotebookInstance](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateNotebookInstance.html#sagemaker-CreateNotebookInstance-request-InstanceType).
**Note**
For most use cases, you should use a `ml.t3.medium`. This is the default instance type for CPU-based SageMaker images, and is available as part of the [AWS Free Tier](https://aws.amazon.com/free).
**Topics**
+ [CPU instances](#notebooks-resources-no-gpu)
+ [Instances with 1 or more GPUs](#notebooks-resources-gpu)
## CPU instances
The following table lists the Amazon EC2 CPU instance types with no GPU attached that are available for use with Studio Classic notebooks. It also lists information about the specifications of each instance type. The default instance type for CPU-based images is `ml.t3.medium`.
For detailed information on which instance types fit your use case, and their performance capabilities, see [Amazon Elastic Compute Cloud Instance types](https://aws.amazon.com/ec2/instance-types/). For information about pricing for these instance types, see [Amazon EC2 Pricing](https://aws.amazon.com/ec2/pricing/).
CPU instances
| Instance | Use case | Fast launch | vCPU | Memory (GiB) | Instance Storage (GB) |
| --- | --- | --- | --- | --- | --- |
| ml.t3.medium | General purpose | Yes | 2 | 4 | Amazon EBS Only |
| ml.t3.large | General purpose | No | 2 | 8 | Amazon EBS Only |
| ml.t3.xlarge | General purpose | No | 4 | 16 | Amazon EBS Only |
| ml.t3.2xlarge | General purpose | No | 8 | 32 | Amazon EBS Only |
| ml.m5.large | General purpose | Yes | 2 | 8 | Amazon EBS Only |
| ml.m5.xlarge | General purpose | No | 4 | 16 | Amazon EBS Only |
| ml.m5.2xlarge | General purpose | No | 8 | 32 | Amazon EBS Only |
| ml.m5.4xlarge | General purpose | No | 16 | 64 | Amazon EBS Only |
| ml.m5.8xlarge | General purpose | No | 32 | 128 | Amazon EBS Only |
| ml.m5.12xlarge | General purpose | No | 48 | 192 | Amazon EBS Only |
| ml.m5.16xlarge | General purpose | No | 64 | 256 | Amazon EBS Only |
| ml.m5.24xlarge | General purpose | No | 96 | 384 | Amazon EBS Only |
| ml.m5d.large | General purpose | No | 2 | 8 | 1 x 75 NVMe SSD |
| ml.m5d.xlarge | General purpose | No | 4 | 16 | 1 x 150 NVMe SSD |
| ml.m5d.2xlarge | General purpose | No | 8 | 32 | 1 x 300 NVMe SSD |
| ml.m5d.4xlarge | General purpose | No | 16 | 64 | 2 x 300 NVMe SSD |
| ml.m5d.8xlarge | General purpose | No | 32 | 128 | 2 x 600 NVMe SSD |
| ml.m5d.12xlarge | General purpose | No | 48 | 192 | 2 x 900 NVMe SSD |
| ml.m5d.16xlarge | General purpose | No | 64 | 256 | 4 x 600 NVMe SSD |
| ml.m5d.24xlarge | General purpose | No | 96 | 384 | 4 x 900 NVMe SSD |
| ml.c5.large | Compute optimized | Yes | 2 | 4 | Amazon EBS Only |
| ml.c5.xlarge | Compute optimized | No | 4 | 8 | Amazon EBS Only |
| ml.c5.2xlarge | Compute optimized | No | 8 | 16 | Amazon EBS Only |
| ml.c5.4xlarge | Compute optimized | No | 16 | 32 | Amazon EBS Only |
| ml.c5.9xlarge | Compute optimized | No | 36 | 72 | Amazon EBS Only |
| ml.c5.12xlarge | Compute optimized | No | 48 | 96 | Amazon EBS Only |
| ml.c5.18xlarge | Compute optimized | No | 72 | 144 | Amazon EBS Only |
| ml.c5.24xlarge | Compute optimized | No | 96 | 192 | Amazon EBS Only |
| ml.r5.large | Memory optimized | No | 2 | 16 | Amazon EBS Only |
| ml.r5.xlarge | Memory optimized | No | 4 | 32 | Amazon EBS Only |
| ml.r5.2xlarge | Memory optimized | No | 8 | 64 | Amazon EBS Only |
| ml.r5.4xlarge | Memory optimized | No | 16 | 128 | Amazon EBS Only |
| ml.r5.8xlarge | Memory optimized | No | 32 | 256 | Amazon EBS Only |
| ml.r5.12xlarge | Memory optimized | No | 48 | 384 | Amazon EBS Only |
| ml.r5.16xlarge | Memory optimized | No | 64 | 512 | Amazon EBS Only |
| ml.r5.24xlarge | Memory optimized | No | 96 | 768 | Amazon EBS Only |
## Instances with 1 or more GPUs
The following table lists the Amazon EC2 instance types with 1 or more GPUs attached that are available for use with Studio Classic notebooks. It also lists information about the specifications of each instance type. The default instance type for GPU-based images is `ml.g4dn.xlarge`.
For detailed information on which instance types fit your use case, and their performance capabilities, see [Amazon Elastic Compute Cloud Instance types](https://aws.amazon.com/ec2/instance-types/). For information about pricing for these instance types, see [Amazon EC2 Pricing](https://aws.amazon.com/ec2/pricing/).
Instances with 1 or more GPUs
| Instance | Use case | Fast launch | GPUs | vCPU | Memory (GiB) | GPU Memory (GiB) | Instance Storage (GB) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ml.p3.2xlarge | Accelerated computing | No | 1 | 8 | 61 | 16 | Amazon EBS Only |
| ml.p3.8xlarge | Accelerated computing | No | 4 | 32 | 244 | 64 | Amazon EBS Only |
| ml.p3.16xlarge | Accelerated computing | No | 8 | 64 | 488 | 128 | Amazon EBS Only |
| ml.p3dn.24xlarge | Accelerated computing | No | 8 | 96 | 768 | 256 | 2 x 900 NVMe SSD |
| ml.p4d.24xlarge | Accelerated computing | No | 8 | 96 | 1152 | 320 GB HBM2 | 8 x 1000 NVMe SSD |
| ml.p4de.24xlarge | Accelerated computing | No | 8 | 96 | 1152 | 640 GB HBM2e | 8 x 1000 NVMe SSD |
| ml.g4dn.xlarge | Accelerated computing | Yes | 1 | 4 | 16 | 16 | 1 x 125 NVMe SSD |
| ml.g4dn.2xlarge | Accelerated computing | No | 1 | 8 | 32 | 16 | 1 x 225 NVMe SSD |
| ml.g4dn.4xlarge | Accelerated computing | No | 1 | 16 | 64 | 16 | 1 x 225 NVMe SSD |
| ml.g4dn.8xlarge | Accelerated computing | No | 1 | 32 | 128 | 16 | 1 x 900 NVMe SSD |
| ml.g4dn.12xlarge | Accelerated computing | No | 4 | 48 | 192 | 64 | 1 x 900 NVMe SSD |
| ml.g4dn.16xlarge | Accelerated computing | No | 1 | 64 | 256 | 16 | 1 x 900 NVMe SSD |
| ml.g5.xlarge | Accelerated computing | No | 1 | 4 | 16 | 24 | 1 x 250 NVMe SSD |
| ml.g5.2xlarge | Accelerated computing | No | 1 | 8 | 32 | 24 | 1 x 450 NVMe SSD |
| ml.g5.4xlarge | Accelerated computing | No | 1 | 16 | 64 | 24 | 1 x 600 NVMe SSD |
| ml.g5.8xlarge | Accelerated computing | No | 1 | 32 | 128 | 24 | 1 x 900 NVMe SSD |
| ml.g5.12xlarge | Accelerated computing | No | 4 | 48 | 192 | 96 | 1 x 3800 NVMe SSD |
| ml.g5.16xlarge | Accelerated computing | No | 1 | 64 | 256 | 24 | 1 x 1900 NVMe SSD |
| ml.g5.24xlarge | Accelerated computing | No | 4 | 96 | 384 | 96 | 1 x 3800 NVMe SSD |
| ml.g5.48xlarge | Accelerated computing | No | 8 | 192 | 768 | 192 | 2 x 3800 NVMe SSD |
# Amazon SageMaker Images Available for Use With Studio Classic Notebooks
**Important**
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).
This page lists the SageMaker images and associated kernels that are available in Amazon SageMaker Studio Classic. This page also gives information about the format needed to create the ARN for each image. SageMaker images contain the latest [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) and the latest version of the kernel. For more information, see [Deep Learning Containers Images](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html).
**Topics**
+ [Image ARN format](#notebooks-available-images-arn)
+ [Supported URI tags](#notebooks-available-uri-tag)
+ [Supported images](#notebooks-available-images-supported)
+ [Images slated for deprecation](#notebooks-available-images-deprecation)
+ [Deprecated images](#notebooks-available-images-deprecated)
## Image ARN format
The following table lists the image ARN and URI format for each Region. To create the full ARN for an image, replace the *resource-identifier* placeholder with the corresponding resource identifier for the image. The resource identifier is found in the SageMaker images and kernels table. To create the full URI for an image, replace the *tag* placeholder with the corresponding cpu or gpu tag. For the list of tags you can use, see [Supported URI tags](#notebooks-available-uri-tag).
**Note**
SageMaker Distribution images use a distinct set of image ARNs, which are listed in the following table.
| Region | Image ARN Format | SageMaker Distribution Image ARN Format | SageMaker Distribution Image URI Format |
| --- | --- | --- | --- |
| us-east-1 | arn:aws:sagemaker:us-east-1:081325390199:image/resource-identifier | arn:aws:sagemaker:us-east-1:885854791233:image/resource-identifier | 885854791233.dkr.ecr.us-east-1.amazonaws.com/sagemaker-distribution-prod:tag |
| us-east-2 | arn:aws:sagemaker:us-east-2:429704687514:image/resource-identifier | arn:aws:sagemaker:us-east-2:137914896644:image/resource-identifier | 137914896644.dkr.ecr.us-east-2.amazonaws.com/sagemaker-distribution-prod:tag |
| us-west-1 | arn:aws:sagemaker:us-west-1:742091327244:image/resource-identifier | arn:aws:sagemaker:us-west-1:053634841547:image/resource-identifier | 053634841547.dkr.ecr.us-west-1.amazonaws.com/sagemaker-distribution-prod:tag |
| us-west-2 | arn:aws:sagemaker:us-west-2:236514542706:image/resource-identifier | arn:aws:sagemaker:us-west-2:542918446943:image/resource-identifier | 542918446943.dkr.ecr.us-west-2.amazonaws.com/sagemaker-distribution-prod:tag |
| af-south-1 | arn:aws:sagemaker:af-south-1:559312083959:image/resource-identifier | arn:aws:sagemaker:af-south-1:238384257742:image/resource-identifier | 238384257742.dkr.ecr.af-south-1.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-east-1 | arn:aws:sagemaker:ap-east-1:493642496378:image/resource-identifier | arn:aws:sagemaker:ap-east-1:523751269255:image/resource-identifier | 523751269255.dkr.ecr.ap-east-1.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-south-1 | arn:aws:sagemaker:ap-south-1:394103062818:image/resource-identifier | arn:aws:sagemaker:ap-south-1:245090515133:image/resource-identifier | 245090515133.dkr.ecr.ap-south-1.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-northeast-2 | arn:aws:sagemaker:ap-northeast-2:806072073708:image/resource-identifier | arn:aws:sagemaker:ap-northeast-2:064688005998:image/resource-identifier | 064688005998.dkr.ecr.ap-northeast-2.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-southeast-1 | arn:aws:sagemaker:ap-southeast-1:492261229750:image/resource-identifier | arn:aws:sagemaker:ap-southeast-1:022667117163:image/resource-identifier | 022667117163.dkr.ecr.ap-southeast-1.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-southeast-2 | arn:aws:sagemaker:ap-southeast-2:452832661640:image/resource-identifier | arn:aws:sagemaker:ap-southeast-2:648430277019:image/resource-identifier | 648430277019.dkr.ecr.ap-southeast-2.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-northeast-1 | arn:aws:sagemaker:ap-northeast-1:102112518831:image/resource-identifier | arn:aws:sagemaker:ap-northeast-1:010972774902:image/resource-identifier | 010972774902.dkr.ecr.ap-northeast-1.amazonaws.com/sagemaker-distribution-prod:tag |
| ca-central-1 | arn:aws:sagemaker:ca-central-1:310906938811:image/resource-identifier | arn:aws:sagemaker:ca-central-1:481561238223:image/resource-identifier | 481561238223.dkr.ecr.ca-central-1.amazonaws.com/sagemaker-distribution-prod:tag |
| eu-central-1 | arn:aws:sagemaker:eu-central-1:936697816551:image/resource-identifier | arn:aws:sagemaker:eu-central-1:545423591354:image/resource-identifier | 545423591354.dkr.ecr.eu-central-1.amazonaws.com/sagemaker-distribution-prod:tag |
| eu-west-1 | arn:aws:sagemaker:eu-west-1:470317259841:image/resource-identifier | arn:aws:sagemaker:eu-west-1:819792524951:image/resource-identifier | 819792524951.dkr.ecr.eu-west-1.amazonaws.com/sagemaker-distribution-prod:tag |
| eu-west-2 | arn:aws:sagemaker:eu-west-2:712779665605:image/resource-identifier | arn:aws:sagemaker:eu-west-2:021081402939:image/resource-identifier | 021081402939.dkr.ecr.eu-west-2.amazonaws.com/sagemaker-distribution-prod:tag |
| eu-west-3 | arn:aws:sagemaker:eu-west-3:615547856133:image/resource-identifier | arn:aws:sagemaker:eu-west-3:856416204555:image/resource-identifier | 856416204555.dkr.ecr.eu-west-3.amazonaws.com/sagemaker-distribution-prod:tag |
| eu-north-1 | arn:aws:sagemaker:eu-north-1:243637512696:image/resource-identifier | arn:aws:sagemaker:eu-north-1:175620155138:image/resource-identifier | 175620155138.dkr.ecr.eu-north-1.amazonaws.com/sagemaker-distribution-prod:tag |
| eu-south-1 | arn:aws:sagemaker:eu-south-1:592751261982:image/resource-identifier | arn:aws:sagemaker:eu-south-1:810671768855:image/resource-identifier | 810671768855.dkr.ecr.eu-south-1.amazonaws.com/sagemaker-distribution-prod:tag |
| sa-east-1 | arn:aws:sagemaker:sa-east-1:782484402741:image/resource-identifier | arn:aws:sagemaker:sa-east-1:567556641782:image/resource-identifier | 567556641782.dkr.ecr.sa-east-1.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-northeast-3 | arn:aws:sagemaker:ap-northeast-3:792733760839:image/resource-identifier | arn:aws:sagemaker:ap-northeast-3:564864627153:image/resource-identifier | 564864627153.dkr.ecr.ap-northeast-3.amazonaws.com/sagemaker-distribution-prod:tag |
| ap-southeast-3 | arn:aws:sagemaker:ap-southeast-3:276181064229:image/resource-identifier | arn:aws:sagemaker:ap-southeast-3:370607712162:image/resource-identifier | 370607712162.dkr.ecr.ap-southeast-3.amazonaws.com/sagemaker-distribution-prod:tag |
| me-south-1 | arn:aws:sagemaker:me-south-1:117516905037:image/resource-identifier | arn:aws:sagemaker:me-south-1:523774347010:image/resource-identifier | 523774347010.dkr.ecr.me-south-1.amazonaws.com/sagemaker-distribution-prod:tag |
| me-central-1 | arn:aws:sagemaker:me-central-1:103105715889:image/resource-identifier | arn:aws:sagemaker:me-central-1:358593528301:image/resource-identifier | 358593528301.dkr.ecr.me-central-1.amazonaws.com/sagemaker-distribution-prod:tag |
## Supported URI tags
The following list shows the tags you can include in your image URI.
+ 1-cpu
+ 1-gpu
+ 0-cpu
+ 0-gpu
**The following examples show URIs with various tag formats:**
+ 542918446943.dkr.ecr.us-west-2.amazonaws.com/sagemaker-distribution-prod:1-cpu
+ 542918446943.dkr.ecr.us-west-2.amazonaws.com/sagemaker-distribution-prod:0-gpu
## Supported images
The following table gives information about the SageMaker images and associated kernels that are available in Amazon SageMaker Studio Classic. It also gives information about the resource identifier and Python version included in the image.
SageMaker images and kernels
| SageMaker Image | Description | Resource Identifier | Kernels (and Identifier) | Python Version |
| --- | --- | --- | --- | --- |
| Base Python 4.3 | Official Python 3.11 image from DockerHub with boto3 and AWS CLI included. | sagemaker-base-python-v4 | Python 3 (python3) | Python 3.11 |
| Base Python 4.2 | Official Python 3.11 image from DockerHub with boto3 and AWS CLI included. | sagemaker-base-python-v4 | Python 3 (python3) | Python 3.11 |
| Base Python 4.1 | Official Python 3.11 image from DockerHub with boto3 and AWS CLI included. | sagemaker-base-python-v4 | Python 3 (python3) | Python 3.11 |
| Base Python 4.0 | Official Python 3.11 image from DockerHub with boto3 and AWS CLI included. | sagemaker-base-python-v4 | Python 3 (python3) | Python 3.11 |
| Base Python 3.0 | Official Python 3.10 image from DockerHub with boto3 and AWS CLI included. | sagemaker-base-python-310-v1 | Python 3 (python3) | Python 3.10 |
| Data Science 5.3 | Data Science 5.3 is a Python 3.11 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version jammy-20240212. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-v5 | Python 3 (python3) | Python 3.11 |
| Data Science 5.2 | Data Science 5.2 is a Python 3.11 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version jammy-20240212. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-v5 | Python 3 (python3) | Python 3.11 |
| Data Science 5.1 | Data Science 5.1 is a Python 3.11 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version jammy-20240212. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-v5 | Python 3 (python3) | Python 3.11 |
| Data Science 5.0 | Data Science 5.0 is a Python 3.11 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version jammy-20240212. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-v5 | Python 3 (python3) | Python 3.11 |
| Data Science 4.0 | Data Science 4.0 is a Python 3.11 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version 22.04. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-311-v1 | Python 3 (python3) | Python 3.11 |
| Data Science 3.0 | Data Science 3.0 is a Python 3.10 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version 22.04. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-310-v1 | Python 3 (python3) | Python 3.10 |
| Geospatial 1.0 | Amazon SageMaker geospatial is a Python image consisting of commonly used geospatial libraries such as GDAL, Fiona, GeoPandas, Shapley, and Rasterio. It allows you to visualize geospatial data within SageMaker AI. For more information, see [Amazon SageMaker geospatial Notebook SDK](https://docs.aws.amazon.com/sagemaker/latest/dg/geospatial-notebook-sdk.html) | sagemaker-geospatial-1.0 | Python 3 (python3) | Python 3.10 |
| SparkAnalytics 4.3 | The SparkAnalytics 4.3 image provides Spark and PySpark kernel options on Amazon SageMaker Studio Classic, including SparkMagic Spark, SparkMagic PySpark, Glue Spark, and Glue PySpark, enabling flexible distributed data processing. | sagemaker-spark-analytics-v4 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.11 |
| SparkAnalytics 4.2 | The SparkAnalytics 4.2 image provides Spark and PySpark kernel options on Amazon SageMaker Studio Classic, including SparkMagic Spark, SparkMagic PySpark, Glue Spark, and Glue PySpark, enabling flexible distributed data processing. | sagemaker-spark-analytics-v4 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.11 |
| SparkAnalytics 4.1 | The SparkAnalytics 4.1 image provides Spark and PySpark kernel options on Amazon SageMaker Studio Classic, including SparkMagic Spark, SparkMagic PySpark, Glue Spark, and Glue PySpark, enabling flexible distributed data processing. | sagemaker-spark-analytics-v4 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.11 |
| SparkAnalytics 4.0 | The SparkAnalytics 4.0 image provides Spark and PySpark kernel options on Amazon SageMaker Studio Classic, including SparkMagic Spark, SparkMagic PySpark, Glue Spark, and Glue PySpark, enabling flexible distributed data processing. | sagemaker-spark-analytics-v4 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.11 |
| SparkAnalytics 3.0 | The SparkAnalytics 3.0 image provides Spark and PySpark kernel options on Amazon SageMaker Studio Classic, including SparkMagic Spark, SparkMagic PySpark, Glue Spark, and Glue PySpark, enabling flexible distributed data processing. | sagemaker-sparkanalytics-311-v1 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.11 |
| SparkAnalytics 2.0 | Anaconda Individual Edition with PySpark and Spark kernels. For more information, see [sparkmagic](https://github.com/jupyter-incubator/sparkmagic). | sagemaker-sparkanalytics-310-v1 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.10 |
| PyTorch 2.4.0 Python 3.11 CPU Optimized | The AWS Deep Learning Containers for PyTorch 2.4.0 with CUDA 12.4 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.4.0-cpu-py311 | Python 3 (python3) | Python 3.11 |
| PyTorch 2.4.0 Python 3.11 GPU Optimized | The AWS Deep Learning Containers for PyTorch 2.4.0 with CUDA 12.4 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.4.0-gpu-py311 | Python 3 (python3) | Python 3.11 |
| PyTorch 2.3.0 Python 3.11 CPU Optimized | The AWS Deep Learning Containers for PyTorch 2.3.0 with CUDA 12.1 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.3.0-cpu-py311 | Python 3 (python3) | Python 3.11 |
| PyTorch 2.3.0 Python 3.11 GPU Optimized | The AWS Deep Learning Containers for PyTorch 2.3.0 with CUDA 12.1 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.3.0-gpu-py311 | Python 3 (python3) | Python 3.11 |
| PyTorch 2.2.0 Python 3.10 CPU Optimized | The AWS Deep Learning Containers for PyTorch 2.2 with CUDA 12.1 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.2.0-cpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 2.2.0 Python 3.10 GPU Optimized | The AWS Deep Learning Containers for PyTorch 2.2 with CUDA 12.1 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.2.0-gpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 2.1.0 Python 3.10 CPU Optimized | The AWS Deep Learning Containers for PyTorch 2.1 with CUDA 12.1 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.1.0-cpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 2.1.0 Python 3.10 GPU Optimized | The AWS Deep Learning Containers for PyTorch 2.1 with CUDA 12.1 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.1.0-gpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 1.13 HuggingFace Python 3.10 Neuron Optimized | PyTorch 1.13 image with HuggingFace and Neuron packages installed for training on Trainium instances optimized for performance and scale on AWS. | pytorch-1.13-hf-neuron-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 1.13 Python 3.10 Neuron Optimized | PyTorch 1.13 image with Neuron packages installed for training on Trainium instances optimized for performance and scale on AWS. | pytorch-1.13-neuron-py310 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.14.0 Python 3.10 CPU Optimized | The AWS Deep Learning Containers for TensorFlow 2.14 with CUDA 11.8 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.14.1-cpu-py310-ubuntu20.04-sagemaker-v1.0 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.14.0 Python 3.10 GPU Optimized | The AWS Deep Learning Containers for TensorFlow 2.14 with CUDA 11.8 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.14.1-gpu-py310-cu118-ubuntu20.04-sagemaker-v1.0 | Python 3 (python3) | Python 3.10 |
## Images slated for deprecation
SageMaker AI ends support for images the day after any of the packages in the image reach end-of life by their publisher. The following SageMaker images are slated for deprecation.
Images based on Python 3.8 reached [end-of-life](https://endoflife.date/python) on October 31st, 2024. Starting on November 1, 2024, SageMaker AI will discontinue support for these images and they will not be selectable from the Studio Classic UI. To avoid non-compliance issues, if you're using any of these images, we recommend that you move to an image with a later version.
SageMaker images slated for deprecation
| SageMaker Image | Deprecation date | Description | Resource Identifier | Kernels | Python Version |
| --- | --- | --- | --- | --- | --- |
| SageMaker Distribution v0.12 CPU | November 1, 2024 | SageMaker Distribution v0 CPU is a Python 3.8 image that includes popular frameworks for machine learning, data science and visualization on CPU. This includes deep learning frameworks like PyTorch, TensorFlow and Keras; popular Python packages like numpy, scikit-learn and pandas; and IDEs like Jupyter Lab. For more information, see the [Amazon SageMaker AI Distribution](https://github.com/aws/sagemaker-distribution) repo. | sagemaker-distribution-cpu-v0 | Python 3 (python3) | Python 3.8 |
| SageMaker Distribution v0.12 GPU | November 1, 2024 | SageMaker Distribution v0 GPU is a Python 3.8 image that includes popular frameworks for machine learning, data science and visualization on GPU. This includes deep learning frameworks like PyTorch, TensorFlow and Keras; popular Python packages like numpy, scikit-learn and pandas; and IDEs like Jupyter Lab. For more information, see the [Amazon SageMaker AI Distribution](https://github.com/aws/sagemaker-distribution) repo. | sagemaker-distribution-gpu-v0 | Python 3 (python3) | Python 3.8 |
| Base Python 2.0 | November 1, 2024 | Official Python 3.8 image from DockerHub with boto3 and AWS CLI included. | sagemaker-base-python-38 | Python 3 (python3) | Python 3.8 |
| Data Science 2.0 | November 1, 2024 | Data Science 2.0 is a Python 3.8 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image based on Ubuntu version 22.04. It includes the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | sagemaker-data-science-38 | Python 3 (python3) | Python 3.8 |
| PyTorch 1.13 Python 3.9 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 1.13 with CUDA 11.3 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-1.13-cpu-py39 | Python 3 (python3) | Python 3.9 |
| PyTorch 1.13 Python 3.9 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 1.13 with CUDA 11.7 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-1.13-gpu-py39 | Python 3 (python3) | Python 3.9 |
| PyTorch 1.12 Python 3.8 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 1.12 with CUDA 11.3 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for PyTorch 1.12.0](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-pytorch-1-12-0-on-sagemaker/). | pytorch-1.12-cpu-py38 | Python 3 (python3) | Python 3.8 |
| PyTorch 1.12 Python 3.8 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 1.12 with CUDA 11.3 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for PyTorch 1.12.0](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-pytorch-1-12-0-on-sagemaker/). | pytorch-1.12-gpu-py38 | Python 3 (python3) | Python 3.8 |
| PyTorch 1.10 Python 3.8 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 1.10 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for PyTorch 1.10.2 on SageMaker AI](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-pytorch-1-10-2-on-sagemaker/). | pytorch-1.10-cpu-py38 | Python 3 (python3) | Python 3.8 |
| PyTorch 1.10 Python 3.8 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 1.10 with CUDA 11.3 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for PyTorch 1.10.2 on SageMaker AI](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-pytorch-1-10-2-on-sagemaker/). | pytorch-1.10-gpu-py38 | Python 3 (python3) | Python 3.8 |
| SparkAnalytics 1.0 | November 1, 2024 | Anaconda Individual Edition with PySpark and Spark kernels. For more information, see [sparkmagic](https://github.com/jupyter-incubator/sparkmagic). | sagemaker-sparkanalytics-v1 | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.8 |
| TensorFlow 2.13.0 Python 3.10 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.13 with CUDA 11.8 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers.](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.13.0-cpu-py310-ubuntu20.04-sagemaker-v1.0 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.13.0 Python 3.10 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.13 with CUDA 11.8 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers.](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html) | tensorflow-2.13.0-gpu-py310-cu118-ubuntu20.04-sagemaker-v1.0 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.6 Python 3.8 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.6 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for TensorFlow 2.6](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-tensorflow-2-6/). | tensorflow-2.6-cpu-py38-ubuntu20.04-v1 | Python 3 (python3) | Python 3.8 |
| TensorFlow 2.6 Python 3.8 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.6 with CUDA 11.2 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for TensorFlow 2.6](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-tensorflow-2-6/). | tensorflow-2.6-gpu-py38-cu112-ubuntu20.04-v1 | Python 3 (python3) | Python 3.8 |
| PyTorch 2.0.1 Python 3.10 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 2.0.1 with CUDA 12.1 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.0.1-cpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 2.0.1 Python 3.10 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 2.0.1 with CUDA 12.1 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.0.1-gpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 2.0.0 Python 3.10 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 2.0.0 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.0.0-cpu-py310 | Python 3 (python3) | Python 3.10 |
| PyTorch 2.0.0 Python 3.10 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for PyTorch 2.0.0 with CUDA 11.8 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | pytorch-2.0.0-gpu-py310 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.12.0 Python 3.10 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.12.0 with CUDA 11.2 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.12.0-cpu-py310-ubuntu20.04-sagemaker-v1.0 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.12.0 Python 3.10 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.12.0 with CUDA 11.8 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.12.0-gpu-py310-cu118-ubuntu20.04-sagemaker-v1 | Python 3 (python3) | Python 3.10 |
| TensorFlow 2.11.0 Python 3.9 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.11.0 with CUDA 11.2 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.11.0-cpu-py39-ubuntu20.04-sagemaker-v1.1 | Python 3 (python3) | Python 3.9 |
| TensorFlow 2.11.0 Python 3.9 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.11.0 with CUDA 11.2 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.11.0-gpu-py39-cu112-ubuntu20.04-sagemaker-v1.1 | Python 3 (python3) | Python 3.9 |
| TensorFlow 2.10 Python 3.9 CPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.10 with CUDA 11.2 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.10.1-cpu-py39-ubuntu20.04-sagemaker-v1.2 | Python 3 (python3) | Python 3.9 |
| TensorFlow 2.10 Python 3.9 GPU Optimized | November 1, 2024 | The AWS Deep Learning Containers for TensorFlow 2.10 with CUDA 11.2 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [Release Notes for Deep Learning Containers](https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/dlc-release-notes.html). | tensorflow-2.10.1-gpu-py39-ubuntu20.04-sagemaker-v1.2 | Python 3 (python3) | Python 3.9 |
## Deprecated images
SageMaker AI has ended support for the following images. Deprecation occurs the day after any of the packages in the image reach end-of life by their publisher.
SageMaker images slated for deprecation
| SageMaker Image | Deprecation date | Description | Resource Identifier | Kernels | Python Version |
| --- | --- | --- | --- | --- | --- |
| Data Science | October 30, 2023 | Data Science is a Python 3.7 [conda](https://docs.conda.io/projects/conda/en/latest/index.html) image with the most commonly used Python packages and libraries, such as NumPy and SciKit Learn. | datascience-1.0 | Python 3 | Python 3.7 |
| SageMaker JumpStart Data Science 1.0 | October 30, 2023 | SageMaker JumpStart Data Science 1.0 is a JumpStart image that includes commonly used packages and libraries. | sagemaker-jumpstart-data-science-1.0 | Python 3 | Python 3.7 |
| SageMaker JumpStart MXNet 1.0 | October 30, 2023 | SageMaker JumpStart MXNet 1.0 is a JumpStart image that includes MXNet. | sagemaker-jumpstart-mxnet-1.0 | Python 3 | Python 3.7 |
| SageMaker JumpStart PyTorch 1.0 | October 30, 2023 | SageMaker JumpStart PyTorch 1.0 is a JumpStart image that includes PyTorch. | sagemaker-jumpstart-pytorch-1.0 | Python 3 | Python 3.7 |
| SageMaker JumpStart TensorFlow 1.0 | October 30, 2023 | SageMaker JumpStart TensorFlow 1.0 is a JumpStart image that includes TensorFlow. | sagemaker-jumpstart-tensorflow-1.0 | Python 3 | Python 3.7 |
| SparkMagic | October 30, 2023 | Anaconda Individual Edition with PySpark and Spark kernels. For more information, see [sparkmagic](https://github.com/jupyter-incubator/sparkmagic). | sagemaker-sparkmagic | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html) | Python 3.7 |
| TensorFlow 2.3 Python 3.7 CPU Optimized | October 30, 2023 | The AWS Deep Learning Containers for TensorFlow 2.3 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers with TensorFlow 2.3.0](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-with-tensorflow-2-3-0/). | tensorflow-2.3-cpu-py37-ubuntu18.04-v1 | Python 3 | Python 3.7 |
| TensorFlow 2.3 Python 3.7 GPU Optimized | October 30, 2023 | The AWS Deep Learning Containers for TensorFlow 2.3 with CUDA 11.0 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers for TensorFlow 2.3.1 with CUDA 11.0](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-for-tensorflow-2-3-1-with-cuda-11-0/). | tensorflow-2.3-gpu-py37-cu110-ubuntu18.04-v3 | Python 3 | Python 3.7 |
| TensorFlow 1.15 Python 3.7 CPU Optimized | October 30, 2023 | The AWS Deep Learning Containers for TensorFlow 1.15 include containers for training on CPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers v7.0 for TensorFlow](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-v7-0-for-tensorflow/). | tensorflow-1.15-cpu-py37-ubuntu18.04-v7 | Python 3 | Python 3.7 |
| TensorFlow 1.15 Python 3.7 GPU Optimized | October 30, 2023 | The AWS Deep Learning Containers for TensorFlow 1.15 with CUDA 11.0 include containers for training on GPU, optimized for performance and scale on AWS. For more information, see [AWS Deep Learning Containers v7.0 for TensorFlow](https://aws.amazon.com/releasenotes/aws-deep-learning-containers-v7-0-for-tensorflow/). | tensorflow-1.15-gpu-py37-cu110-ubuntu18.04-v8 | Python 3 | Python 3.7 |