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COMPANY · ENTITY #3411

Amazon EKS

Related event timeline, sources and context from the news index.

EVENT TIMELINE

16

MODELS · 1 SOURCE · AWS Machine Learning

NVIDIA publishes Nemotron 3.5 Super VL (120B MoE) and provides AWS EKS / SageMaker deployment guide

NVIDIA released Nemotron 3.5 Super VL, an open multimodal 120B-parameter mixture-of-experts (MoE) model that activates about 12B parameters per token and is distilled from Nemotron 3 Ultra. NVIDIA published the model weights, training datasets, and post-training recipes and published reference implementations and an aws-do-eks walkthrough showing how to deploy Nemotron models on Amazon EKS, EKS Auto Mode, and SageMaker HyperPod EKS; the model is validated on AWS GPU instances and integrates with major agent frameworks via the NVIDIA Agent Toolkit.

7.0

CODING · 1 SOURCE · AWS Machine Learning

AWS Ray Train DLC (train-ml-cuda-v1.1) enables EFA-backed multi-node PyTorch on EKS

AWS provides a Ray Train Deep Learning Container (public.ecr.aws/deep-learning-containers/ray:train-ml-cuda-v1.1) that bundles CUDA, NCCL, EFA userspace libraries and Ray, allowing multi-node PyTorch training on EKS without building custom images. A provided sample shows fine-tuning Qwen2.5-1.5B with PyTorch FSDP across eight NVIDIA L4 GPUs on two g6.12xlarge instances using KubeRay and EFA.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Hands-on guide: Multi-Turn Reinforcement Learning (MTRL) on Amazon SageMaker AI with AgentCore

Amazon published a hands-on guide demonstrating Multi-Turn Reinforcement Learning (MTRL) on SageMaker AI that uses an agent hosted on Amazon Bedrock AgentCore (or on user infrastructure) to sample policy models and return episode-level rewards. The post walks an end-to-end pipeline (deployment, dataset prep, LoRA training with GRPO, MLflow monitoring, evaluation), uses Wordle as a toy example, reports improving GPT-OSS-20B solve rate from 61% to 84% (and from 15% to 69% at a lower reasoning effort) for about $339 in training tokens, and provides code in an aws-samples repo.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Rescale builds automated test-triage agent using Strands Agents and AI-DLC

Rescale used the open-source AI-Driven Development Lifecycle (AI-DLC) and Strands Agents to build an agent that classifies and routes nightly end-to-end test failures. The agent runs in Argo Workflows on Amazon EKS, invokes Claude Opus 4.8 via Amazon Bedrock, queries logs through the Model Context Protocol (MCP), and produced a staged deployment in five days and production deployment within weeks.

6.0

MODELS · 1 SOURCE · AWS Machine Learning

GPT-OSS-20B fine-tuned for multilingual reasoning on Amazon SageMaker HyperPod

Teams fine-tuned the open-weight reasoning model GPT-OSS-20B on a multilingual chain-of-thought dataset (HuggingFaceH4/Multilingual-Thinking) using supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) on Amazon SageMaker HyperPod with EKS, LoRA, TRL/veRL, vLLM, Ray/KubeRay and Amazon FSx for Lustre. Results: the base model almost always produced English chains-of-thought; SFT corrected most of the language behavior gap, and GRPO provided additional improvements only when the desired behavior had non-zero probability in the base model; a Qwen2.5-7B control experiment supported the same conclusion.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AtScale C2026.7 walkthrough: build a semantic layer for Amazon Bedrock AgentCore agents

A technical walkthrough demonstrates using AtScale (C2026.7) as a dimensional semantic layer (modeled in SML) for agentic workflows on Amazon Bedrock AgentCore. The post shows a Strands agent running on AgentCore (with Anthropic Claude Sonnet 4.6 for the demo), how AtScale exposes a single model over SQL/XMLA/MCP, identity and RBAC propagation through AgentCore to AtScale, query pushdown to Amazon Redshift Serverless, and deployment on Amazon EKS.

5.0

CODING · 1 SOURCE · AWS Machine Learning

AWS launches Ray LLM DLC for multi-node LLM inference on Amazon EKS

AWS published a guide and released a Ray LLM Deep Learning Container (DLC) that supports deploying a single large language model across multiple GPU nodes on Amazon EKS, using a disaggregated prefill/decode setup. The Ray LLM DLC packages vLLM, Ray Serve LLM, NIXL for KV-cache transfer, and the EFA stack, exposes an OpenAI‑compatible endpoint, and includes a helper (build_pd_openai_app) plus example code in a repository.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Amazon outlines multi-tenant reference architecture for SageMaker HyperPod on EKS

AWS describes a reference architecture for using Amazon SageMaker HyperPod with Amazon EKS to let multiple teams share GPU clusters while preserving isolation, fair resource quotas, and per-team cost allocation. The design uses AWS IAM Identity Center for authentication, per-team SageMaker AI domains and Kubernetes namespaces for isolation, HyperPod Task Governance for quotas, and HyperPod Observability for monitoring.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

AWS added UI support in SageMaker Studio to create and manage SageMaker Spaces on HyperPod EKS clusters, enabling data scientists to launch JupyterLab or Code Editor environments and start/stop Spaces without using the CLI. Administrators must install the SageMaker Spaces add-on on HyperPod clusters, attach required IAM policies, and enable per-user identity propagation for Studio domains.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS documents Amazon Bedrock AgentCore multi-agent workflows and integrations for cloud migrations and agent workloads

An AWS technical post explains how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate end-to-end enterprise cloud migrations and reduce infrastructure-as-code development time from weeks to minutes. The post also covers related how-tos and integrations — Live Data in Amazon QuickSight, using Amazon S3 Vectors as persistent memory with NVIDIA NeMo Agent Toolkit on EKS, ambient/ambient-agent patterns, Claude Platform configuration on AWS, personalization with SageMaker AI, fine-tuning Nova 2 Lite for uniopen, a Bedrock Knowledge Bases conversational claims assistant, and AgentCore Runtime Instances for colocated GPU agent workflows.

7.0

CODING · 1 SOURCE · AWS Machine Learning

How to use Amazon S3 Vectors as persistent memory for NVIDIA NeMo Agent Toolkit (NAT v1.6)

A how-to showing how to implement Amazon S3 Vectors as a custom memory provider for the open-source NVIDIA NeMo Agent Toolkit (NAT), including a MemoryEditor plugin, YAML configuration, and deployment on Amazon EKS; the post was tested with NAT v1.6 and uses Amazon Titan Text Embeddings V2 for embeddings. It demonstrates mapping NAT's memory model (MemoryItem/MemoryEditor) to S3 Vectors indexes and metadata, and explains required tooling and configuration for a multi-agent investment research example.

5.0

CODING · 1 SOURCE · AWS Machine Learning

Anthropic guide: implement multi-environment access for Claude Platform on AWS

Anthropic published a step‑by‑step implementation guide for Claude Platform on AWS (CPonAWS) that shows how to enable inference from three environments—AWS production workloads via cross‑account SigV4, developer laptops via workspace‑scoped API keys, and external services via OIDC federation and short‑lived tokens—using a dedicated AI Services linked account and workspace isolation. The walkthrough includes CLI commands, console instructions, code snippets, and a recommended three‑account topology (payer, AI Services, workload) with examples for IAM roles, workspace ARNs, and region‑specific endpoints.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Uniopen fine-tunes Amazon Nova 2 Lite for retail content moderation

Uniopen (part of Taiwan’s Uni‑President Enterprises Group) adapted Amazon Nova 2 Lite for its retail moderation taxonomy by supervised fine-tuning in Amazon SageMaker AI and additional prompt‑level output optimization, using Nova 2 Pro to generate candidate corrections that humans verify before inclusion in training data. The deployment pipeline uses Amazon S3, DynamoDB, Argo Workflows on EKS, Argo CD, SNS, CloudWatch and Bedrock Guardrails with hard/soft gate checks, fixed test sets, and mandatory human review for ambiguous cases.

6.0

COMPANIES · 1 SOURCE · AWS Machine Learning

Run SkyRL GRPO post-training of Qwen3‑VL‑8B on Amazon SageMaker HyperPod

Amazon demonstrates running the open-source SkyRL framework on SageMaker HyperPod (EKS) to post-train a Qwen3‑VL‑8B vision‑language model with Group Relative Policy Optimization (GRPO). Using a HyperPod Ray cluster with shared FSx storage, the workflow improved maze solve rate from 43.75% to over 95% on a fixed 64‑maze evaluation set and uses colocated vLLM inference and FSDP-sharded policy training with LoRA adapters.

6.0

MODELS · 1 SOURCE · AWS Machine Learning

Amazon SageMaker HyperPod and Qumulo enable multi‑Region model training without copying data

Amazon and Qumulo validated an architecture pairing Amazon SageMaker HyperPod with Cloud Native Qumulo (CNQ) and Qumulo Cloud Data Fabric (CDF) so HyperPod clusters can train from a single dataset stored in a different AWS Region without replicating data. In a cross‑Region test (hub in us-east-2, spoke in us-west-2) running a 1.02B‑parameter LLaMA v3 job on two ml.p5.48xlarge instances per cluster (16 H100 GPUs total), the remote spoke converged to hub throughput (≈115–117 samples/sec) after a short NeuralCache warmup, achieving near‑full GPU utilization thereafter.

7.0

CODING · 1 SOURCE · AWS Machine Learning

NVIDIA NVRx adds fault-tolerance to PyTorch FSDP training on Amazon EKS

NVIDIA describes how to integrate its Resiliency Extension (NVRx) with PyTorch FSDP on Amazon EKS to reduce downtime for large multi-node GPU training. The post demonstrates async checkpointing (TorchAsyncCheckpoint), in-process restart, and an in-job restart launcher (ft_launcher), provides H100 2–8 node benchmarks, and publishes reproducible code and a pip-installable nvidia-resiliency-ext package.

6.0