neuralmagic/compressed-tensors
Jun 2026 – Jun 2026
1 Month active
Languages Used
BashYAML
Technical Skills
BuildkiteCI/CDDevOpsGPU ProgrammingKubernetesPipeline Configuration
Deepak Kumar
PROFILE
Deepak Kumar
Worked on the neuralmagic/compressed-tensors repository to deliver GPU-enabled continuous integration by migrating the project’s CI from GitHub Actions to Buildkite. Designed and implemented a robust pipeline using YAML and Python, enabling GPU-based test runs on H100duo and L4solo hardware. Introduced a runner selection interface and a build summary banner to improve configurability and observability of test results. Addressed NVIDIA driver issues by stabilizing the default GPU runner and decommissioned the legacy GitHub Actions workflow. Updated test tooling and ensured proper environment variable handling, resulting in more reliable, environment-aware CI pipelines and faster feedback for GPU workload validation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
187
Activity Months1
Your Network
3236 peopleWork History
June 2026
3 Commits • 1 Features
Jun 1, 2026June 2026: Delivered GPU-enabled CI for the compressed-tensors project by migrating from GitHub Actions to Buildkite, enabling GPU tests on H100duo and L4solo. Implemented a robust pipeline with per-runner selection, a build summary banner, and environment-aware configurations. Fixed NVIDIA driver-related issues by stabilizing the default GPU runner to H100duo and removing the older GHA test-check workflow. Updated test tooling (CUDA 13.0, Python 3.12) and ensured proper escaping of environment variables in pipeline definitions. These changes improved test reliability, observability, and cross-cloud GPU coverage, delivering faster feedback and higher confidence in GPU workloads.
3 Commits • 1 Features
Jun 1, 2026June 2026: Delivered GPU-enabled CI for the compressed-tensors project by migrating from GitHub Actions to Buildkite, enabling GPU tests on H100duo and L4solo. Implemented a robust pipeline with per-runner selection, a build summary banner, and environment-aware configurations. Fixed NVIDIA driver-related issues by stabilizing the default GPU runner to H100duo and removing the older GHA test-check workflow. Updated test tooling (CUDA 13.0, Python 3.12) and ensured proper escaping of environment variables in pipeline definitions. These changes improved test reliability, observability, and cross-cloud GPU coverage, delivering faster feedback and higher confidence in GPU workloads.
June 2026
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
BashYAML
Technical Skills
BuildkiteCI/CDDevOpsGPU ProgrammingKubernetesPipeline ConfigurationPython
Repositories Contributed To
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