aws-samples/awsome-distributed-training
Mar 2026 – Jun 2026
3 Months active
Languages Used
bashYAMLMarkdownPythonShell
Technical Skills
environment setuppackage managementscriptingAWSCloudFormationHPC
Daisuke Miyamoto
PROFILE
Daisuke Miyamoto
Worked on the aws-samples/awsome-distributed-training repository to deliver a production-ready AWS ParallelCluster-based machine learning training environment. Focused on scalable HPC deployments, the work included building a reference architecture with integrated monitoring, multi-user OpenLDAP support, and enhanced IAM policies for secure, one-click cluster setup. Leveraged AWS, CloudFormation, and Python to implement modular deployment templates, GPU and CPU EFA support, and FSx Lustre performance tuning. Improved operational reliability through idempotent automation scripts and robust post-install logic, while updating documentation to streamline onboarding. The approach emphasized maintainability, security, and observability, enabling faster, consistent deployments for distributed training workloads across multiple regions.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
4
Lines of code
14,652
Activity Months3
Your Network
60 peopleSame Organization
@amazon.co.jp11
Kazuhito GoMember
kennygt51Member
dayuan.jiangMember
Kouki KishidaMember
Taito KikuchiMember
Masashi TomookaMember
Kousuke SarutaMember
skisimoMember
Takamitsu IwaiMember
Work History
June 2026
4 Commits • 3 Features
Jun 1, 2026June 2026 monthly summary for aws-samples/awsome-distributed-training: Focused on delivering a production-ready AWS PCS-based ML training cluster with integrated monitoring, multi-user IAM, and broader hardware support, while improving stability, security, and maintainability. Highlights include large-scale feature delivery for PCS reference cluster deployment, GPU/CPU EFA support, and FSx enhancements, underpinned by robust monitoring and updated IAM policies. Substantial progress on testing, documentation, and operational hygiene to enable faster, safer one-click deployments across regions and AZs. Key achievements were: 1) Key features delivered: - PCS reference cluster deployment enhancements: integrated monitoring, P6 GPU node groups, container runtime, multi-user OpenLDAP and enhanced admin/user IAM policies; deployment options with 1-click deploy. (Commit 10613e...) - Elastic Fabric Adapter (EFA) support extended to CPU HPC instances with new templates and parameters; improved on-demand GPU/CPU NIC configurations and latest launch-template versioning for reliable updates. (Commit f08a4c...) - AWS ParallelCluster Monitoring integration: added monitoring stack (Prometheus, Grafana, DCGM) with IAM policies, Ubuntu 24.04 compatibility workarounds, and per-node role tagging; Grafana access via Session Manager port-forward enabled. (Series of commits in PCS monitoring-related work) - FSx Lustre/OpenZFS improvements and performance tuning: mount options for Lustre to boost ML workload throughput, and new FSx-related parameters (RootVolumeSize, LustreOpenZFS integration, and EFA-enabled configurations). (Commit 856c633... and related entries) - Improved template and deployment UX: minor reorganizations of parameter groups, improved 1-click experience, and better ops visibility; added GrafanaPublicAccess considerations and IAM policy simplifications; decoupled AMI build from deployment path to support pre-baked AMIs. 2) Major bugs fixed: - Stabilized first-boot provisioning and monitoring by adding Ubuntu 24.04 compatibility shims and symlink workaround for monitoring user home directory; implemented idempotent post-install logic for Enroot/Pyxis; fixed PATH handling for Slurm; ensured correct Slurm version propagation and consistent post-install behavior. - Resolved various monitoring integration issues, including correct IAM policies, session manager access, and cluster-name tagging to ensure metrics are accessible and dashboards populate reliably. - Hardened IAM policy growth by splitting admin/user policies to stay within AWS limits and ensured correct scoping of access to LDAP/SSM/CFN resources; removed brittle external references and aligned with repo structure. - Fixed various template/script escaping, variables, and arguments issues (S3-based script fetch, Slurm PATH derivation, and MonitoringVersion pinning) to ensure reliable bootstrapping and operation across regions. 3) Overall impact and accomplishments: - Faster time-to-value: one-click deployment path for PSC-based ML training clusters with end-to-end monitoring, IAM roles, and OpenLDAP multi-user support across multiple regions/AZs. - Production-grade stability: idempotent PostInstall, single-version Pyxis build aligned with SlurmVersion, and robust monitoring stack integration, reducing operational toil and error-prone manual steps. - Expanded hardware support: added EFA-enabled CPU HPC and GPU P5/P6 templates, improved NIC topology, and FSx/Lustre OpenZFS storage options, enabling more realistic ML workloads and scalable training. - Security and governance: open LDAP multi-user onboarding, fine-grained IAM policies, and session-based access controls to support enterprise usage. - Clear documentation and governance: updated PARAMETERS.md, OPERATIONS.md, ROADMAP.md, and USER-MANAGEMENT docs to reflect new capabilities and best practices, enabling safer rollouts and faster onboarding for teams. 4) Technologies and skills demonstrated: - AWS ParallelCluster, Slurm, and EC2 networking (P5/P6, EFA, multi-NIC templates, P-series), FSx for Lustre/OpenZFS, and DCGM/Grafana/Prometheus monitoring integration. - OpenLDAP and SSSD-based multi-user directory onboarding; IAM policy design and least-privilege scoping; session-based access via AWS SSM. - Advanced templating and deployment orchestration: modular CNG templates, latest-launch-template usage, container runtimes (Enroot/Pyxis), and monitoring integration hooks with conditional deployment. - Robust post-installation automation: idempotent scripts, per-version Slurm handling, and script fetch strategies (S3-based) to minimize downtime and ensure reproducibility. Business value delivered: - Improved reliability and security for ML training workloads; scalable, region-agnostic deployment; better governance and user management; and faster onboarding for data science teams via a strong, one-click experience.
4 Commits • 3 Features
Jun 1, 2026June 2026 monthly summary for aws-samples/awsome-distributed-training: Focused on delivering a production-ready AWS PCS-based ML training cluster with integrated monitoring, multi-user IAM, and broader hardware support, while improving stability, security, and maintainability. Highlights include large-scale feature delivery for PCS reference cluster deployment, GPU/CPU EFA support, and FSx enhancements, underpinned by robust monitoring and updated IAM policies. Substantial progress on testing, documentation, and operational hygiene to enable faster, safer one-click deployments across regions and AZs. Key achievements were: 1) Key features delivered: - PCS reference cluster deployment enhancements: integrated monitoring, P6 GPU node groups, container runtime, multi-user OpenLDAP and enhanced admin/user IAM policies; deployment options with 1-click deploy. (Commit 10613e...) - Elastic Fabric Adapter (EFA) support extended to CPU HPC instances with new templates and parameters; improved on-demand GPU/CPU NIC configurations and latest launch-template versioning for reliable updates. (Commit f08a4c...) - AWS ParallelCluster Monitoring integration: added monitoring stack (Prometheus, Grafana, DCGM) with IAM policies, Ubuntu 24.04 compatibility workarounds, and per-node role tagging; Grafana access via Session Manager port-forward enabled. (Series of commits in PCS monitoring-related work) - FSx Lustre/OpenZFS improvements and performance tuning: mount options for Lustre to boost ML workload throughput, and new FSx-related parameters (RootVolumeSize, LustreOpenZFS integration, and EFA-enabled configurations). (Commit 856c633... and related entries) - Improved template and deployment UX: minor reorganizations of parameter groups, improved 1-click experience, and better ops visibility; added GrafanaPublicAccess considerations and IAM policy simplifications; decoupled AMI build from deployment path to support pre-baked AMIs. 2) Major bugs fixed: - Stabilized first-boot provisioning and monitoring by adding Ubuntu 24.04 compatibility shims and symlink workaround for monitoring user home directory; implemented idempotent post-install logic for Enroot/Pyxis; fixed PATH handling for Slurm; ensured correct Slurm version propagation and consistent post-install behavior. - Resolved various monitoring integration issues, including correct IAM policies, session manager access, and cluster-name tagging to ensure metrics are accessible and dashboards populate reliably. - Hardened IAM policy growth by splitting admin/user policies to stay within AWS limits and ensured correct scoping of access to LDAP/SSM/CFN resources; removed brittle external references and aligned with repo structure. - Fixed various template/script escaping, variables, and arguments issues (S3-based script fetch, Slurm PATH derivation, and MonitoringVersion pinning) to ensure reliable bootstrapping and operation across regions. 3) Overall impact and accomplishments: - Faster time-to-value: one-click deployment path for PSC-based ML training clusters with end-to-end monitoring, IAM roles, and OpenLDAP multi-user support across multiple regions/AZs. - Production-grade stability: idempotent PostInstall, single-version Pyxis build aligned with SlurmVersion, and robust monitoring stack integration, reducing operational toil and error-prone manual steps. - Expanded hardware support: added EFA-enabled CPU HPC and GPU P5/P6 templates, improved NIC topology, and FSx/Lustre OpenZFS storage options, enabling more realistic ML workloads and scalable training. - Security and governance: open LDAP multi-user onboarding, fine-grained IAM policies, and session-based access controls to support enterprise usage. - Clear documentation and governance: updated PARAMETERS.md, OPERATIONS.md, ROADMAP.md, and USER-MANAGEMENT docs to reflect new capabilities and best practices, enabling safer rollouts and faster onboarding for teams. 4) Technologies and skills demonstrated: - AWS ParallelCluster, Slurm, and EC2 networking (P5/P6, EFA, multi-NIC templates, P-series), FSx for Lustre/OpenZFS, and DCGM/Grafana/Prometheus monitoring integration. - OpenLDAP and SSSD-based multi-user directory onboarding; IAM policy design and least-privilege scoping; session-based access via AWS SSM. - Advanced templating and deployment orchestration: modular CNG templates, latest-launch-template usage, container runtimes (Enroot/Pyxis), and monitoring integration hooks with conditional deployment. - Robust post-installation automation: idempotent scripts, per-version Slurm handling, and script fetch strategies (S3-based) to minimize downtime and ensure reproducibility. Business value delivered: - Improved reliability and security for ML training workloads; scalable, region-agnostic deployment; better governance and user management; and faster onboarding for data science teams via a strong, one-click experience.
June 2026
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 monthly work summary focused on delivering a scalable, maintainable AWS PCS HPC reference architecture and Slurm deployment enhancements for the awsome-distributed-training project. The work reduced time-to-value for HPC deployments, improved operations efficiency, and laid a foundation for consistent, observable Clusters-on-AWS across teams and customers.
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 monthly work summary focused on delivering a scalable, maintainable AWS PCS HPC reference architecture and Slurm deployment enhancements for the awsome-distributed-training project. The work reduced time-to-value for HPC deployments, improved operations efficiency, and laid a foundation for consistent, observable Clusters-on-AWS across teams and customers.
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary for aws-samples/awsome-distributed-training focusing on business value and technical achievements. Delivered an environment stability improvement by pinning setuptools to a version below 81 in the virtual environment setup to prevent compatibility issues with other packages and reduce runtime failures in distributed training workflows.
1 Commits
Mar 1, 2026March 2026 monthly summary for aws-samples/awsome-distributed-training focusing on business value and technical achievements. Delivered an environment stability improvement by pinning setuptools to a version below 81 in the virtual environment setup to prevent compatibility issues with other packages and reduce runtime failures in distributed training workflows.
March 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability83.4%
Architecture90.0%
Performance83.4%
AI Usage36.6%
Skills & Technologies
Programming Languages
MarkdownPythonShellYAMLbash
Technical Skills
AWSCloudFormationDevOpsHPCIAMInfrastructure as CodeLustreOpenLDAPSlurmcloud infrastructurecontainerizationenvironment setupmonitoringnetworkingpackage management
Repositories Contributed To
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