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ease
features
design
support

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Write a Review

Description

Amazon SageMaker HyperPod is a specialized and robust computing infrastructure designed to streamline and speed up the creation of extensive AI and machine learning models by managing distributed training, fine-tuning, and inference across numerous clusters equipped with hundreds or thousands of accelerators, such as GPUs and AWS Trainium chips. By alleviating the burdens associated with developing and overseeing machine learning infrastructure, it provides persistent clusters capable of automatically identifying and rectifying hardware malfunctions, resuming workloads seamlessly, and optimizing checkpointing to minimize the risk of interruptions — thus facilitating uninterrupted training sessions that can last for months. Furthermore, HyperPod features centralized resource governance, allowing administrators to establish priorities, quotas, and task-preemption rules to ensure that computing resources are allocated effectively among various tasks and teams, which maximizes utilization and decreases idle time. It also includes support for “recipes” and pre-configured settings, enabling rapid fine-tuning or customization of foundational models, such as Llama. This innovative infrastructure not only enhances efficiency but also empowers data scientists to focus more on developing their models rather than managing the underlying technology.

Description

Tinker is an innovative training API tailored for researchers and developers, providing comprehensive control over model fine-tuning while simplifying the complexities of infrastructure management. It offers essential primitives that empower users to create bespoke training loops, supervision techniques, and reinforcement learning workflows. Currently, it facilitates LoRA fine-tuning on open-weight models from both the LLama and Qwen families, accommodating a range of model sizes from smaller variants to extensive mixture-of-experts configurations. Users can write Python scripts to manage data, loss functions, and algorithmic processes, while Tinker autonomously takes care of scheduling, resource distribution, distributed training, and recovery from failures. The platform allows users to download model weights at various checkpoints without the burden of managing the computational environment. Delivered as a managed service, Tinker executes training jobs on Thinking Machines’ proprietary GPU infrastructure, alleviating users from the challenges of cluster orchestration and enabling them to focus on building and optimizing their models. This seamless integration of capabilities makes Tinker a vital tool for advancing machine learning research and development.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS EC2 Trn3 Instances
AWS Trainium
Amazon SageMaker
Amazon Web Services (AWS)
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Python
Qwen
Qwen3

Integrations

AWS EC2 Trn3 Instances
AWS Trainium
Amazon SageMaker
Amazon Web Services (AWS)
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Python
Qwen
Qwen3

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/ai/hyperpod/

Vendor Details

Company Name

Thinking Machines Lab

Country

United States

Website

thinkingmachines.ai/tinker/

Alternatives

Alternatives

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