aws-samples/awsome-distributed-training
Aug 2025 – Jun 2026
7 Months active
Languages Used
DockerfileMarkdownYAMLShellBashPython
Technical Skills
AWSBuild SystemsCI/CDContainerizationDevOpsNVIDIA CUDA
Pavel Belevich
PROFILE
Pavel Belevich
Worked on the aws-samples/awsome-distributed-training repository, delivering features to enhance distributed training and inference workflows on AWS. Focused on upgrading and integrating libraries such as NCCL, NVSHMEM, and DeepEP, the work improved compatibility, performance, and deployment reliability for large-scale machine learning benchmarks. Leveraged technologies including Docker, Kubernetes, and Python to refactor build systems, streamline containerization, and enable architecture-aware deployments. Addressed reproducibility and maintainability by consolidating Dockerfiles, updating documentation, and implementing robust health checks. Collaborated on benchmarking frameworks and deployment scripts, ensuring scalable, low-latency LLM serving and efficient distributed training across diverse cloud and hardware environments.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
16Total
Bugs
1
Commits
16
Features
9
Lines of code
5,194
Activity Months7
Your Network
1806 peopleWork History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026: Key feature delivered is the DeepEP v1 integration with NVSHMEM v3.7.0-0 for AWS EFA distributed training on the aws-samples/awsome-distributed-training repo. This work updates to NVSHMEM v3.7.0-0, enhances distributed training performance on AWS EFA clusters, and includes benchmark/readme updates plus a new EFA deployment setup script. No major bugs fixed this month; the focus was on feature delivery, performance enablement, and deployment readiness. Impact: improved training throughput and scalability for AWS-based distributed workloads, with an easier path to production EFA deployments and up-to-date benchmarking.
1 Commits • 1 Features
Jun 1, 2026June 2026: Key feature delivered is the DeepEP v1 integration with NVSHMEM v3.7.0-0 for AWS EFA distributed training on the aws-samples/awsome-distributed-training repo. This work updates to NVSHMEM v3.7.0-0, enhances distributed training performance on AWS EFA clusters, and includes benchmark/readme updates plus a new EFA deployment setup script. No major bugs fixed this month; the focus was on feature delivery, performance enablement, and deployment readiness. Impact: improved training throughput and scalability for AWS-based distributed workloads, with an easier path to production EFA deployments and up-to-date benchmarking.
June 2026
May 2026
1 Commits • 1 Features
May 1, 2026Month: 2026-05 — Delivered Disaggregated Inference for DeepSeek-V3 on EKS using vLLM, UCCL-EP, and NIXL, enabling scalable, low-latency LLM serving. Reworked deployment architecture and test infra: migrated components to Deployments with liveness/readiness probes, a single replica, and robust health checks; improved reliability and maintainability. Implemented build-time smoke tests, multi-seed benchmarks, and environment hardening (pinned AWS EFA plugin, NIXL v1.1.0, Ray 2.55.1), and updated docs/license headers. Teardown and test scripts were adjusted to reflect Deployment-centric cleanup. This work reduces operational risk and enables customers to run larger LLM workloads on EKS with better observability and reproducibility.
May 2026
1 Commits • 1 Features
May 1, 2026Month: 2026-05 — Delivered Disaggregated Inference for DeepSeek-V3 on EKS using vLLM, UCCL-EP, and NIXL, enabling scalable, low-latency LLM serving. Reworked deployment architecture and test infra: migrated components to Deployments with liveness/readiness probes, a single replica, and robust health checks; improved reliability and maintainability. Implemented build-time smoke tests, multi-seed benchmarks, and environment hardening (pinned AWS EFA plugin, NIXL v1.1.0, Ray 2.55.1), and updated docs/license headers. Teardown and test scripts were adjusted to reflect Deployment-centric cleanup. This work reduces operational risk and enables customers to run larger LLM workloads on EKS with better observability and reproducibility.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered Benchmark Framework enhancements for expert parallelism in the aws-samples/awsome-distributed-training repo. Refactored and reorganized expert parallelism benchmarks by moving from 3.test_cases to micro-benchmarks, improving clarity and maintainability. Performed a small cleanup by removing a redundant .gitignore entry. Implemented in commit e62a04e9c797bfed284b418c9e5a48b1c82f1e8c (#901) with Keita Watanabe as co-author, reflecting strong cross-team collaboration. Business impact: more reliable performance metrics, faster iteration, and clearer signals to guide optimization and capacity planning for distributed training workloads. Technologies demonstrated: benchmark tooling, performance evaluation, code refactoring, and Git hygiene.
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered Benchmark Framework enhancements for expert parallelism in the aws-samples/awsome-distributed-training repo. Refactored and reorganized expert parallelism benchmarks by moving from 3.test_cases to micro-benchmarks, improving clarity and maintainability. Performed a small cleanup by removing a redundant .gitignore entry. Implemented in commit e62a04e9c797bfed284b418c9e5a48b1c82f1e8c (#901) with Keita Watanabe as co-author, reflecting strong cross-team collaboration. Business impact: more reliable performance metrics, faster iteration, and clearer signals to guide optimization and capacity planning for distributed training workloads. Technologies demonstrated: benchmark tooling, performance evaluation, code refactoring, and Git hygiene.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026: Stabilized NCCL-based distributed training tests in Kubernetes through environment-path refinements and tuner-plugin wiring. Delivered targeted NCCL test environment enhancements and corrected plugin references in nccl-tests.yaml, aligning with related PRs to ensure reliable distributed training validations.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026: Stabilized NCCL-based distributed training tests in Kubernetes through environment-path refinements and tuner-plugin wiring. Delivered targeted NCCL test environment enhancements and corrected plugin references in nccl-tests.yaml, aligning with related PRs to ensure reliable distributed training validations.
November 2025
1 Commits
Nov 1, 2025Concise monthly summary for 2025-11 focusing on aws-samples/awsome-distributed-training. The primary activity was a Dockerfile cleanup: reverting custom AWS OFI NCCL support to align with standard NCCL configurations, reducing maintenance burden and potential incompatibilities.
1 Commits
Nov 1, 2025Concise monthly summary for 2025-11 focusing on aws-samples/awsome-distributed-training. The primary activity was a Dockerfile cleanup: reverting custom AWS OFI NCCL support to align with standard NCCL configurations, reducing maintenance burden and potential incompatibilities.
November 2025
September 2025
8 Commits • 4 Features
Sep 1, 2025September 2025 focused on delivering architecture-aware improvements for the aws-samples/awsome-distributed-training project, emphasizing portability, stability, and maintainability. The work enhances cross-architecture deployment readiness and reduces build surface area while keeping dependencies up to date.
September 2025
8 Commits • 4 Features
Sep 1, 2025September 2025 focused on delivering architecture-aware improvements for the aws-samples/awsome-distributed-training project, emphasizing portability, stability, and maintainability. The work enhances cross-architecture deployment readiness and reduces build surface area while keeping dependencies up to date.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025: Key feature delivery focused on NCCL tests dependency upgrades to improve compatibility and performance across distributed benchmarks in the aws-samples/awsome-distributed-training repo. Updated GDRcopy, EFA installer, AWS OFI NCCL, NCCL, and NCCL tests in configuration and Dockerfiles to ensure the build uses current library versions, enabling more reliable benchmarks and faster iteration. No major bugs fixed this month; work emphasized stability and reproducibility of distributed workloads.
2 Commits • 1 Features
Aug 1, 2025August 2025: Key feature delivery focused on NCCL tests dependency upgrades to improve compatibility and performance across distributed benchmarks in the aws-samples/awsome-distributed-training repo. Updated GDRcopy, EFA installer, AWS OFI NCCL, NCCL, and NCCL tests in configuration and Dockerfiles to ensure the build uses current library versions, enabling more reliable benchmarks and faster iteration. No major bugs fixed this month; work emphasized stability and reproducibility of distributed workloads.
August 2025
Activity
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Quality Metrics
Correctness96.2%
Maintainability93.8%
Architecture93.8%
Performance93.8%
AI Usage26.2%
Skills & Technologies
Programming Languages
BashDockerfileMarkdownPythonShellYAML
Technical Skills
AWSBuild SystemsCI/CDCUDAConfiguration ManagementContainerizationDependency ManagementDevOpsDistributed SystemsDockerDockerfile ManagementDocumentationKubernetesLinuxMachine Learning
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