Monday, February 17, 2025

Amazon SageMaker HyperPod Training Plans Are Now Available

SageMaker HyperPod training plans on Amazon! Optimise resources and speed up machine learning training. You may reserve and optimise the utilisation of GPU resources for large-scale AI model training workloads with Amazon SageMaker training plans. Access to a variety of GPU-accelerated computing solutions, such as the newest NVIDIA GPUs technology and AWS Trainium chips, is made possible via this capability. Without having to worry about maintaining underlying infrastructure, SageMaker training programs allow you to guarantee consistent access to these high-demand, high-performance computing resources within your designated timeframes and financial constraints. For enterprises facing the difficulties of obtaining and scheduling these oversubscribed compute instances for their mission-critical AI workloads, this flexibility is very helpful.

What are SageMaker training plans?

With SageMaker HyperPod training, you may reserve compute capacity for SageMaker training jobs or SageMaker HyperPod clusters based on your unique resource requirements. Workload execution, infrastructure setup, accelerated computing resource supply, and infrastructure failure recovery are all automatically managed by the service.

Advantages of SageMaker training plans

The following advantages are provided by SageMaker training programs:

Predictable Access

Set aside GPU power for your workloads related to machine learning during predetermined window of time.

Cost management

Arrange and set aside money ahead of time for extensive training needs.

Automated Resource administration

Infrastructure provisioning and administration are handled using SageMaker training programs.

Flexibility

Develop training programs for a range of resources, such as SageMaker HyperPod clusters and SageMaker AI training jobs.

Fault Tolerance

For SageMaker AI training jobs, take advantage of automatic recovery from infrastructure failures and workload movement between Availability Zones.

User workflow is planned by SageMaker training

The following steps are used in SageMaker training plans:

Administrative actions:

  • Look up and evaluate: Look for plan options that fit your computation needs, including instance type, count, duration, and start time.
  • Make a strategy: Using the ID of the plan option you have selected, reserve a training program that suits your needs.
  • Payment and scheduling: The plan status changes to Scheduled after a successful upfront payment.

Action items for ML engineers and plan users:

  • Resource allocation: Allocate to a SageMaker HyperPod cluster instance group or queue SageMaker AI training jobs according to your plan.
  • Activation: The plan becomes active on the scheduled start date. SageMaker training plans immediately start training jobs or provision instance groups based on available reserved capacity.

The following pictures show the lifespan of a plan and its function in resource allocation for both SageMaker AI training jobs and SageMaker HyperPod clusters, giving a thorough overview of how SageMaker training plans interact with various target resources.

  • SageMaker Training Job training plans: The first diagram shows the complete process of how a training plan and SageMaker Training Job interact.
SageMaker Training Job training plans
Image credit to AWS
  • SageMaker HyperPod cluster training plans: The second diagram shows the training plan-SageMaker HyperPod instance group workflow.
SageMaker HyperPod cluster training plans
Image credit to AWS

AWS Regions and Instance Types Supported

Reservations for the following particular high-performance instance types, which are accessible in particular AWS Regions, are supported by training plans:

  • ml.p4d.24xlarge
  • ml.p5.48xlarge
  • ml.p5e.48xlarge
  • ml.p5en.48xlarge
  • ml.trn2.48xlarge

The availability in several regions makes it possible to select the best site for workloads, taking into account elements like data residency requirements and accessibility to other AWS services.

Composition of the plan

One or more Reserved Capacity blocks, each defined by the following, may be included in a SageMaker training program:

  • Particular instance type
  • Number of occurrences
  • Zone of Availability
  • Time frame
  • Start and finish times

SageMaker training plans search behavior

When looking for a training plan, SageMaker training plans employ the following strategy to optimise user flexibility and resource availability, even in situations when demand is high and continuous time blocks are limited:

  • Finding a single, continuous block of reserved capacity that satisfies all of the requirements (target resource, requested instance type, number of instances, length of the reservation, start and finish dates) is the system’s initial continuous search.
  • Two-block lookup:
    • If a single continuous Reserved Capacity block that satisfies all requirements is not available, SageMaker training programs do not instantly return a “no capacity” result. Instead, it uses two different Reserved Capacity blocks in an attempt to automatically fulfil the request.
    • In this case, the request’s entire duration is divided into two non-contiguous time periods. The system might provide a plan with two 24-hour chunks, possibly on different days or weeks, based on availability and the start and finish dates, for instance, if a user requested a 48-hour reservation.
    • More resource allocation flexibility is offered by this two-block method, which can enable you to obtain instances with high demand that would not otherwise be accessible for the entire time you have asked.

SageMaker training plans modify their search approach according to the target resource while looking for training plan offerings:

  • For clusters of SageMaker HyperPods:
    • There is only one Availability Zone (AZ) where offerings are available.
    • This guarantees data locality inside the cluster and constant network performance.
  • Regarding SageMaker training positions:
    • Offerings may fall under more than one Availability Zone.
    • This is especially important when there are several discontinuous reserved capacity in the plan offering.
    • For instance, a plan might allocate capacity to one Reserved Capacity block in AZ-A and another in AZ-B. Workloads can be automatically transferred across Availability Zones (AZs) via SageMaker training plans in accordance with resource availability.

More resource allocation flexibility is offered by this multi-AZ approach to training tasks, which raises the likelihood that you will locate capacity that meets your workload. Users should be mindful, nevertheless, that during different times of their reservation term, their jobs might run in different AZs.

The Amazon SageMaker HyperPod flexible training plans are now generally available, saving data scientists weeks of work in controlling the training process based on compute availability and enabling them to train huge foundation models (FMs) within their budgets and timeframes.

AWS SageMaker HyperPod, which uses preconfigured distributed training libraries and built-in robustness to scale over thousands of compute resources in parallel and cut down on training time for FMs by up to 40%. The majority of generative AI model construction tasks require concurrent acceleration of computational resources. To finish their training within their time and financial limits, the customers find it difficult to obtain timely access to computational resources.

With today’s innovation, you can determine the accelerated computing resources needed for training, design the best training schedules, and distribute training workloads among various capacity blocks according to the compute resources’ availability. Without the need for manual involvement, you can quickly determine the budget, the date of training completion, the resources needed, the best training strategies, and the completely controlled training jobs.

SageMaker HyperPod training plans in action

Start by selecting Training plans in the left navigation pane of the Amazon SageMaker AI console, then select Create training plan.

SageMaker HyperPod training plans in action
Image credit to AWS

For instance, select the instance type and count (16 ml.p5.48xlarge) for the SageMaker HyperPod training, your desired training date and time (10 days), and then select Find training plan.

Two five-day training sessions are recommended by SageMaker HyperPod. This covers the entire plan’s initial cost.

If you agree to this training plan, select Create your plan and enter your training information in the following step.

You can view the list of training plans once you’ve created your own. After creating a training plan, you have 12 hours to make the upfront payment. One plan has already begun and is in the Active stage, using every instance. You can already submit jobs that will start automatically when the second plan starts, even though it is scheduled to start later.

The computational resources in SageMaker HyperPod are available in the active state, which automatically resumes after interruptions in availability and ends at the conclusion of the plan. A first section is executing at the moment, while a second segment is waiting to execute after it.

This is comparable to SageMaker AI’s Managed Spot training, where SageMaker AI handles instance disruptions and carries on with training without the need for human involvement. See the Amazon SageMaker AI Developer Guide’s SageMaker HyperPod training plans for additional information.

Now available

Amazon SageMaker HyperPod training plans support ml.p4d.48xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, and ml.trn2.48xlarge instances and are now available in the US East (N.Virginia, Ohio, and Oregon AWS Regions. Only the US East (Ohio) Region has occurrences of Trn2 and P5en.

Thota nithya
Thota nithya
Thota Nithya has been writing Cloud Computing articles for govindhtech from APR 2023. She was a science graduate. She was an enthusiast of cloud computing.
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