aws-samples/awsome-distributed-training
Mar 2026 – May 2026
3 Months active
Languages Used
PythonShellYAMLbashBashDockerfile
Technical Skills
DevOpsDistributed SystemsKubernetesProfilingPython Scriptingcloud computing
nkumaraws
PROFILE
Nkumaraws
Over a three-month period, contributed to the aws-samples/awsome-distributed-training repository by developing scalable distributed training pipelines and enhancing observability for deep learning workloads on Kubernetes. Delivered end-to-end GRPO training recipes for large language models such as gpt-oss-20b, integrating DeepSpeed ZeRO-3, FSDP2, and vLLM inference, while automating evaluation workflows for language compliance. Improved profiling and bottleneck analysis using Python scripting and Nsight Systems, and stabilized infrastructure with Docker and KubeRay manifests. Addressed dynamic configuration issues in training scripts, updated documentation, and maintained CI dependencies, resulting in reproducible, scalable training environments leveraging Python, Docker, and distributed systems expertise.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
3
Lines of code
4,356
Activity Months3
Your Network
1806 peopleWork History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for aws-samples/awsome-distributed-training: Delivered a complete GRPO training recipe for the OpenAI GPT-OSS-20B MoE model on g5.12xlarge (4x A10G 24GB GPUs), enabling scalable training with FSDP2 and CPU offloading. Added veRL GRPO evaluation scripts and a post-training evaluation workflow using vLLM for multilingual language compliance. Updated Dockerfile, environment, and docs to support new configurations and training optimizations. Achieved strong results: GRPO step 80 achieved 98% reasoning and 80% language accuracy vs SFT baseline 96% and 74%. Also updated CI/dependency points (EFA_VERSION bump) and captured key lessons for future iterations. Commit: 54360503c2e4074861c8c2cbac6b3a227d76244a.
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for aws-samples/awsome-distributed-training: Delivered a complete GRPO training recipe for the OpenAI GPT-OSS-20B MoE model on g5.12xlarge (4x A10G 24GB GPUs), enabling scalable training with FSDP2 and CPU offloading. Added veRL GRPO evaluation scripts and a post-training evaluation workflow using vLLM for multilingual language compliance. Updated Dockerfile, environment, and docs to support new configurations and training optimizations. Achieved strong results: GRPO step 80 achieved 98% reasoning and 80% language accuracy vs SFT baseline 96% and 74%. Also updated CI/dependency points (EFA_VERSION bump) and captured key lessons for future iterations. Commit: 54360503c2e4074861c8c2cbac6b3a227d76244a.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for aws-samples/awsome-distributed-training: Delivered a complete OpenRLHF GRPO training recipe for gpt-oss-20b on HyperPod EKS, including infrastructure, training scripts, and evaluation pipeline; stabilized the Non-Hybrid Engine with DeepSpeed ZeRO-3 and vLLM inference; added Dockerfile and KubeRay manifest, custom reward function for language compliance, and validation tooling. Achieved reproducible training with 60+ steps validated and HF checkpoints saved. This work enables scalable RLHF training on HyperPod, shortening lead times and improving model alignment.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for aws-samples/awsome-distributed-training: Delivered a complete OpenRLHF GRPO training recipe for gpt-oss-20b on HyperPod EKS, including infrastructure, training scripts, and evaluation pipeline; stabilized the Non-Hybrid Engine with DeepSpeed ZeRO-3 and vLLM inference; added Dockerfile and KubeRay manifest, custom reward function for language compliance, and validation tooling. Achieved reproducible training with 60+ steps validated and HF checkpoints saved. This work enables scalable RLHF training on HyperPod, shortening lead times and improving model alignment.
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly performance summary for aws-samples/awsome-distributed-training. Focused on expanding observability for distributed training on EKS and improving scalability across clusters. Delivered Nsight Systems host-mount profiling for distributed PyTorch on EKS with a profiling wrapper, automated bottleneck analysis, and consolidated assets; fixed dynamic configuration gaps in Megatron-LM and BioNeMo scripts to support arbitrary cluster sizes; refined packaging, licensing headers, and docs to improve reproducibility and developer experience. Outcomes include faster bottleneck detection, easier on-boarding for users on EKS/DLAMI, and stronger alignment with performance engineering practices.
2 Commits • 1 Features
Mar 1, 2026March 2026 monthly performance summary for aws-samples/awsome-distributed-training. Focused on expanding observability for distributed training on EKS and improving scalability across clusters. Delivered Nsight Systems host-mount profiling for distributed PyTorch on EKS with a profiling wrapper, automated bottleneck analysis, and consolidated assets; fixed dynamic configuration gaps in Megatron-LM and BioNeMo scripts to support arbitrary cluster sizes; refined packaging, licensing headers, and docs to improve reproducibility and developer experience. Outcomes include faster bottleneck detection, easier on-boarding for users on EKS/DLAMI, and stronger alignment with performance engineering practices.
March 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture85.0%
Performance80.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
BashDockerfilePythonShellYAMLbash
Technical Skills
Deep LearningDevOpsDistributed SystemsDockerKubernetesMachine LearningProfilingPythonPython ScriptingRayShell Scriptingcloud computingdistributed systemsscripting
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