awslabs/sagemaker-hyperpod-usage-report
Apr 2025 – Jul 2025
3 Months active
Languages Used
MarkdownPythonYAMLpythonsqlyamlJSON
Technical Skills
AWSAWS AthenaAWS CloudFormationAWS GlueAWS LambdaAWS S3
zicanl-amazon
PROFILE
Zicanl-amazon
Zican Li enhanced the awslabs/sagemaker-hyperpod-usage-report platform by delivering end-to-end improvements to usage reporting, including a modular report generation system and a robust data model supporting historical accuracy and replayability. Leveraging Python, AWS CloudFormation, and SQL, Zican refactored the repository for maintainability and streamlined deployment across AWS regions. He addressed operational needs by extending IAM policies for secure S3 bucket cleanup and corrected utilization aggregation logic to improve reporting accuracy. Zican also expanded deployment support to new AWS regions using Helm, demonstrating depth in cloud infrastructure, DevOps, and data engineering while ensuring the platform’s reliability, scalability, and compliance.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
3
Lines of code
3,195
Activity Months3
Your Network
1390 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for awslabs/sagemaker-hyperpod-usage-report. Focused on expanding regional deployment coverage and validating the Helm-based deployment flow for new AWS regions.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for awslabs/sagemaker-hyperpod-usage-report. Focused on expanding regional deployment coverage and validating the Helm-based deployment flow for new AWS regions.
July 2025
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for awslabs/sagemaker-hyperpod-usage-report: Delivered two prioritized improvements to usage reporting. 1) Feature: S3 Bucket Versioned Objects Handling — extended the installer IAM policy to list and delete object versions, enabling complete cleanup of versioned S3 buckets used for usage reports. 2) Bug fix: Correct Utilization Aggregation — replaced instance-count based calculation with sum-based aggregation of CPU, GPU, and neuron core utilization; added 'pod' to team-level utilization grouping for accurate cross-team reporting. Impact: improved data accuracy, security/compliance through thorough cleanup, and reliable cost/resource visibility; reduced risk of stale data and misinterpretation of usage metrics. Tech/skills: IAM policy enhancement, S3 versioned object handling, precise data aggregation logic, and maintainable code changes with auditable commits.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for awslabs/sagemaker-hyperpod-usage-report: Delivered two prioritized improvements to usage reporting. 1) Feature: S3 Bucket Versioned Objects Handling — extended the installer IAM policy to list and delete object versions, enabling complete cleanup of versioned S3 buckets used for usage reports. 2) Bug fix: Correct Utilization Aggregation — replaced instance-count based calculation with sum-based aggregation of CPU, GPU, and neuron core utilization; added 'pod' to team-level utilization grouping for accurate cross-team reporting. Impact: improved data accuracy, security/compliance through thorough cleanup, and reliable cost/resource visibility; reduced risk of stale data and misinterpretation of usage metrics. Tech/skills: IAM policy enhancement, S3 versioned object handling, precise data aggregation logic, and maintainable code changes with auditable commits.
April 2025
6 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for awslabs/sagemaker-hyperpod-usage-report focusing on delivering business value and technical achievements, including end-to-end HyperPod usage reporting enhancement, data model upgrades, and streamlined deployment and governance.
6 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for awslabs/sagemaker-hyperpod-usage-report focusing on delivering business value and technical achievements, including end-to-end HyperPod usage reporting enhancement, data model upgrades, and streamlined deployment and governance.
April 2025
Activity
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Quality Metrics
Correctness93.2%
Maintainability92.2%
Architecture93.2%
Performance84.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONMarkdownPythonYAMLpythonsqlyaml
Technical Skills
AWSAWS AthenaAWS CloudFormationAWS GlueAWS LambdaAWS S3Amazon AthenaAmazon S3AthenaCloud InfrastructureCloudFormationCode OrganizationData EngineeringData ReportingDevOps
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