Amazon SageMaker Training Managed Warm Pools gives you the flexibility to opt in to reuse and hold on to the underlying infrastructure for a user-defined period of time. This is done while also maintaining the benefit of passing the undifferentiated heavy lifting of managing compute instances in to Amazon SageMaker Model Training. In this post, we outline the key benefits and pain points addressed by SageMaker Training Managed Warm Pools, as well as benchmarks and best practices.
Overview of SageMaker Training Managed Warm Pools
SageMaker Model Training is a fully managed capability that spins up instances for every job, trains a model, runs and then spins down instances after the job. You’re only billed for the duration of the job down to the second. This fully managed capability gives you the freedom to focus on your machine learning (ML) algorithm and not worry about undifferentiated heavy lifting like infrastructure management while training your models.
This mechanism necessitates a finite startup time for a training job. Although this startup time, also known as cold-start startup time, is fairly low, some of our most demanding customer use cases require even lower startup times, such as under 20 seconds. There are two prominent use cases that have these requirements:
- The first is active ML experimentation by data scientists using the Amazon SageMaker training platform, especially while training large models, like GPT3, that require multiple iterations to get to a production-ready state.
- The second is the programmatic launch of a large number (in the order of several hundred or thousands) of consecutive jobs on the same kind of instances on a scheduled cadence. For example, parameter search or incremental training.
For such use cases, every second spent on overhead, like the startup time for a training job, has a cumulative effect on all these jobs.
With SageMaker Training Managed Warm Pools, data scientists and ML engineers have the ability to opt in to keep SageMaker training instances or multi-instance clusters warm for a prespecified and reconfigurable time (
keep_alive_period_in_seconds) after each training job completes. So even though you incur a cold-start penalty for the first training job run on an instance or cluster, for all the subsequent training jobs, the instances are already up and running. As a result, these subsequent training jobs that start on an instance before the
keep_alive_period_in_seconds expires don’t incur the cold-start startup time overhead. This can reduce training job startup times to roughly less than 20 seconds (P90).
Data scientists and ML engineers can use SageMaker Training Managed Warm Pools to keep single or multiple instances warm in between training runs for experimentation or run multiple jobs consecutively on the same single or multi-instance cluster. You only pay for the duration of training jobs and the reconfigurable
keep_alive_period_in_seconds like everywhere else you specify for every single instance.
In essence, with SageMaker Training Managed Warm Pools, you get a combination of SageMaker managed instance utilization with the ability to opt in and provision capacity and self-manage utilization for short intervals of time. These intervals are configurable before a job, but if during the
keep_alive_period_in_seconds interval, you need to reduce or increase it, you can do so. Increases to
keep_alive_period_in_seconds can be done in intervals of up to 60 minutes, with a max period for an instance or cluster being 7 days.
We performed benchmarking tests to measure job startup latency using a 1.34 GB TensorFlow image, 2 GB of data, and different training data input modes (Amazon FSx, Fast File Mode, File Mode). The tests were run across a variety of instance types from the m4, c4, m5, and c5 families in the us-east-2 Region. The startup latency was measured as the time of job creation to the start of the actual training job on the instances. The first jobs that started the cluster and created the warm pool had a startup latency of 2–3 minutes. This higher latency is due to the time taken to provision the infrastructure, download the image, and download the data. The consequent jobs that utilized the warm pool cluster had a startup latency of approximately 20 seconds for Fast File Mode (FFM) or Amazon FSx, and 70 seconds for File Mode (FM). This delta is a result of FM requiring the entire dataset to be downloaded from Amazon S3 prior to the start of the job.
Your choice of training data input mode affects the startup time, even with Warm Pools. Guidance on what input mode to select is in the best practices section later in this post.
The following table summarizes the job startup latency P90 for different training data input modes.
|Data Input Mode||Startup Latency P90 (seconds)|
|First Job||Warm Pool Jobs (second job onwards)|
|Fast File Mode||143||21|
Best practices for using warm pools
In the following section, we share some best practices when using warm pools.
When should you use warm pools?
Warm pools are recommended in the following scenarios:
- You are interactively experimenting and tuning your script over a series of short jobs.
- You are running your own custom-made, large-scale hyperparameter optimization (for example, Syne Tune).
- You have a batch process that runs a large number (in the order of several hundreds or thousands) of consecutive jobs on the same kind of instances on a daily or weekly cadence. For example, training an ML model per city.
Warm pools are not recommended when it’s unlikely that someone will reuse the warm pool before it expires. For example, a single lengthy job that runs via an automated ML pipeline.
Minimize warm pool training job startup latency
Training jobs that reuse a warm pool start faster than the first job that created the warm pool. This is due to keeping the ML instances running between jobs with a cached training container Docker image to skip pulling the container from Amazon Elastic Container Registry (Amazon ECR). However, even when reusing a warm pool, certain initialization steps occur for all jobs. Optimizing these steps can reduce your job startup time (both first and subsequent jobs). Consider the following:
- Training data input mode can affect startup time – Managed training data input channels are recreated for each training job, contributing to job startup latency. So doing initial experiments over a smaller dataset will allow for faster startup time (and faster training time). For later stages of experimentation, when a large dataset is needed, consider using an input mode type that has minimal or fixed initialization time. For example, FILE input mode copies the entire dataset from Amazon Simple Storage Service (Amazon S3) to the training instance, which is time-consuming for large datasets (even with warm pools). Fast File Mode is better suited for lower startup latency because only S3 object metadata needs to be read from Amazon S3 before the workload can start. The Amazon FSx for Lustre, or Amazon Elastic File System (Amazon EFS) file system input mode, has a fixed initialization time regardless of the number of files in the file system, which is beneficial when working with a large dataset.
For more information on how to choose an input channel, see Choose the best data source for your Amazon SageMaker training job.
- Reduce runtime installation of packages – Any software installation that takes place during container startup, for example, Python’s pip or operating system apt-get, will increase training job latency. Minimizing this startup latency requires making a trade-off between the flexibility and simplicity of runtime installations vs. installation at container build time. If you use your own Docker container with SageMaker, refer to Adapting Your Own Docker Container to Work with SageMaker. If you rely on prebuilt SageMaker container images, you’ll need to extend a prebuilt container and explicitly manage these containers. Consider this if your runtime installs significantly increase startup latency.
- Avoid updating your Docker image frequently – If you use your own Docker container with SageMaker, try to avoid updating it every job run. If the Docker image changes between the job submissions, the warm pool will be reused, but the startup process will need to re-pull the container image from Amazon ECR instead of reusing a cached container image. If the Docker image must be updated, confine the updates to the last Docker layer to take advantage of Docker layer caching. Ideally, you should remove the Dockerfile content that’s likely to change over iterations, like hyperparameter, dataset definitions, and the ML code itself. To iterate on ML code without having to rebuild Docker images with each change, you can adopt the framework container paradigm advocated in the SageMaker Training Toolkit. If you’d like to develop a framework container with your own code, refer to this Amazon SageMaker tutorial.
Share warm pools between multiple users
Let’s look at an example timeline. User-1 starts a training job that completes and results in a new warm pool created. When user-2 starts a training job, the job will reuse the existing warm pool, resulting in a fast job startup. While user-2’s job is running with the warm pool in use, if another user starts a training job, then a second warm pool will be created.
This reuse behavior helps reduce costs by sharing warm pools between users that start similar jobs. If you want to avoid sharing warm pools between users, then users’ jobs must not have matching job criteria (for example, they must use a different IAM role).
Notify users on job completion
When using warm pools for experimentation, we recommend notifying users when their job is complete. This allows users to resume experimentation before the warm pool expires or stop the warm pool if it’s no longer needed. You can also automatically trigger notifications through Amazon EventBridge.
Further tools for fast experimentation and troubleshooting training jobs
With warm pools, you can start a job in less than 20 seconds. Some scenarios require real-time, hands-on interactive experimentation and troubleshooting. The open-source SageMaker SSH Helper library allows you to shell into a SageMaker training container and conduct remote development and debugging.
With SageMaker Training Managed Warm Pools, you can keep your model training hardware instances warm after every job for a specified period. This can reduce the startup latency for a model training job by up to 8x. SageMaker Training Managed Warm Pools are available in all public AWS Regions where SageMaker Model Training is available.
To get started, see Train Using SageMaker Managed Warm Pools.
About the authors
Dr. Romi Datta is a Senior Manager of Product Management in the Amazon SageMaker team responsible for training, processing and feature store. He has been in AWS for over 4 years, holding several product management leadership roles in SageMaker, S3 and IoT. Prior to AWS he worked in various product management, engineering and operational leadership roles at IBM, Texas Instruments and Nvidia. He has an M.S. and Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin, and an MBA from the University of Chicago Booth School of Business.
Arun Nagarajan is a Principal Engineer with the Amazon SageMaker team focussing on the Training and MLOps areas. He has been with the SageMaker team from the launch year, enjoyed contributing to different areas in SageMaker including the realtime inference and Model Monitor products. He likes to explore the outdoors in the Pacific Northwest area and climb mountains.
Amy You is a Software Development Manager at AWS SageMaker. She focuses on bringing together a team of software engineers to build, maintain and develop new capabilities of the SageMaker Training platform that helps customers train their ML models more efficiently and easily. She has a passion for ML and AI technology, especially related to image and vision from her graduate studies. In her spare time, she loves working on music and art with her family.
Sifei Li is a Software Engineer in Amazon AI where she’s working on building Amazon Machine Learning Platforms and was part of the launch team for Amazon SageMaker. In her spare time, she likes playing music and reading.
Jenna Zhao is a Software Development Engineer at AWS SageMaker. She is passionate about ML/AI technology and has been focusing on building SageMaker Training platform that enables customers to quickly and easily train machine learning models. Outside of work, she enjoys traveling and spending time with her family.
Paras Mehra is a Senior Product Manager at AWS. He is focused on helping build Amazon SageMaker Training and Processing. In his spare time, Paras enjoys spending time with his family and road biking around the Bay Area. You can find him on LinkedIn.
Gili Nachum is a senior AI/ML Specialist Solutions Architect who works as part of the EMEA Amazon Machine Learning team. Gili is passionate about the challenges of training deep learning models, and how machine learning is changing the world as we know it. In his spare time, Gili enjoy playing table tennis.
Olivier Cruchant is a Machine Learning Specialist Solutions Architect at AWS, based in France. Olivier helps AWS customers – from small startups to large enterprises – develop and deploy production-grade machine learning applications. In his spare time, he enjoys reading research papers and exploring the wilderness with friends and family.
Emily Webber joined AWS just after SageMaker launched, and has been trying to tell the world about it ever since! Outside of building new ML experiences for customers, Emily enjoys meditating and studying Tibetan Buddhism.