SageMaker Distribution Now Accessible on Amazon SageMaker Studio, Making it Search Engine Optimized and Appealing to Users
Introduction:
SageMaker Distribution is a powerful tool designed for machine learning (ML), data science, and data visualization. It is a pre-built Docker image that contains popular packages such as PyTorch, TensorFlow, Keras, NumPy, scikit-learn, pandas, and JupyterLab. Recently, SageMaker Distribution was launched as an open-source project at JupyterCon.
With SageMaker Distribution, you can easily run experiments on your local environments. It is now available as a first-party image in Amazon SageMaker Studio, providing you with high performance, compute, and security benefits. The SageMaker Python SDK package is also built-in with the image.
In this article, we will explore the features and advantages of using the SageMaker Distribution image. We will also discuss how to use it in SageMaker Studio, run code remotely, reproduce Conda environments, and customize the image.
Whether you are a data scientist, machine learning engineer, or developer, SageMaker Distribution offers a convenient and efficient solution for your ML and data science projects. Try it out and share your feedback with us on GitHub!
Full Article: SageMaker Distribution Now Accessible on Amazon SageMaker Studio, Making it Search Engine Optimized and Appealing to Users
SageMaker Distribution Launches as an Open-Source Project at JupyterCon
SageMaker Distribution, a pre-built Docker image containing popular packages for machine learning (ML), data science, and data visualization, was officially launched as an open-source project at JupyterCon in May 2023. This release allows users to leverage SageMaker Distribution for running experiments on local environments. Now, the image is available natively in Amazon SageMaker Studio, offering high performance, compute, and security benefits for conducting experiments.
Additional Capabilities of the SageMaker Distribution Image
Compared to its earlier open-source launch, the SageMaker Distribution image now offers additional capabilities. These include:
1. Integration with SageMaker Studio: The open-source image is now available as a first-party image in SageMaker Studio. This means users can simply select the open-source SageMaker Distribution from the image and kernel list for their notebooks, eliminating the need to create a custom image.
2. Built-in SageMaker Python SDK: The SageMaker Python SDK package is now built-in with the SageMaker Distribution image, providing users with easy access to the SDK’s features and functionalities.
Using SageMaker Distribution in SageMaker Studio
To use SageMaker Distribution in SageMaker Studio, follow these steps:
1. Launch SageMaker Studio from an existing Studio domain. For instructions on creating a Studio domain, refer to the documentation on “Onboard to Amazon SageMaker Domain.”
2. In the SageMaker Studio UI, go to the menu bar and choose File > New > Notebook.
3. When prompted for the image and instance, select either the “SageMaker Distribution v0 CPU” or “SageMaker Distribution v0 GPU” image.
4. Choose the desired kernel and click Select.
Now, users can run commands without the need to install common ML packages and frameworks. They can also execute notebooks running on supported frameworks like PyTorch and TensorFlow from the SageMaker examples repository without switching active kernels.
Running Code Remotely with SageMaker Distribution
Apart from running code locally, SageMaker Distribution allows users to execute their local notebook code as a SageMaker training job by adding a “@remote” decorator to their function. Here’s an example:
“`python
from sagemaker.remote_function import remote
@remote(instance_type=”ml.m5.xlarge”, dependencies=”./requirements.txt”)
def divide(x, y):
return x / y
divide(2, 3.0)
“`
When this code is run, the function is executed as a remote SageMaker training job on an ml.m5.xlarge notebook. The SageMaker Distribution image is automatically selected as the training image in Amazon Elastic Container Registry (Amazon ECR). For deep learning workloads, scripts can be executed on multiple parallel instances.
Reproducing Conda Environments from SageMaker Distribution
For data scientists familiar with Conda environments, the SageMaker Distribution GitHub repository provides environment files for each image build, allowing them to build Conda environments for both CPU and GPU versions. These files can be found under the “sagemaker-distribution/build_artifacts” directory.
To create the same environment as any available SageMaker Distribution version, run the following command:
“`bash
conda create –name conda-sagemaker-distribution –file sagemaker-distribution/build_artifacts/v0/v0.2/v0.2.1/cpu.env.out
“`
After creating the environment, activate it with the following command:
“`bash
conda activate conda-sagemaker-distribution
“`
Customizing the Open-Source SageMaker Distribution Image
While the open-source SageMaker Distribution image includes commonly used packages for data science and ML, data scientists may require access to additional packages. Enterprise customers might also have proprietary packages that offer additional capabilities. In such cases, there are multiple options for having a runtime environment with all the required packages:
1. Install packages directly on the notebook using Conda, micromamba, or pip.
2. Reproduce the Conda environment from SageMaker Distribution elsewhere and install/manage additional packages in that environment.
3. For administrators who need a repeatable and controlled runtime environment, it is possible to extend SageMaker Distribution’s Docker images and maintain a custom image. Detailed instructions for creating and using a custom image in SageMaker Studio can be found in the “Bring your own SageMaker image” guide.
Cleaning Up
To avoid unnecessary charges, it is recommended to shut down all Studio apps in SageMaker Studio if they are no longer in use. Refer to the “Shut down and Update Studio Apps” guide for instructions on shutting down Studio apps.
Conclusion
The launch of the open-source SageMaker Distribution image within SageMaker Studio provides users with a powerful tool for conducting machine learning experiments. This article demonstrated how to use the image in SageMaker Studio, run code remotely, reproduce Conda environments, and customize the image. Users are encouraged to try out SageMaker Distribution and provide feedback through the GitHub repository.
Additional References
Summary: SageMaker Distribution Now Accessible on Amazon SageMaker Studio, Making it Search Engine Optimized and Appealing to Users
SageMaker Distribution is a pre-built Docker image that includes popular packages for machine learning, data science, and data visualization. It supports deep learning frameworks like PyTorch, TensorFlow, and Keras, as well as Python packages like NumPy and scikit-learn.
In May 2023, SageMaker Distribution was launched as an open-source project, allowing you to run experiments on local environments. Now, the image is natively provided in Amazon SageMaker Studio, giving you access to high performance and security benefits. You can easily choose the SageMaker Distribution image when creating notebooks and have built-in support for the SageMaker Python SDK. Additionally, you can use SageMaker Distribution to run code remotely and reproduce Conda environments. Customize the image if needed by installing additional packages or creating a custom image.
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