aws-samples/awsome-distributed-training
Mar 2026 – Jun 2026
2 Months active
Languages Used
PythonShellYAMLBashDockerfile
Technical Skills
Deep LearningDockerKubernetesMachine LearningNatural Language ProcessingContainerization
Mijanur Palash
PROFILE
Mijanur Palash
Worked on aws-samples/awsome-distributed-training, delivering end-to-end fine-tuning workflows for large language models such as GPT-OSS 20B and OpenVLA-7B. Developed multilingual fine-tuning capabilities using LoRA and GRPO, providing training scripts, Docker configurations, and evaluation pipelines to support scalable, reproducible deployments. Enhanced reliability and maintainability by reorganizing Kubernetes manifests, hardening environments, and addressing compatibility issues. Introduced a Slurm-based test workflow for OpenVLA-7B on HyperPod clusters, including Dockerfile setup and batch scripts for rapid iteration. Utilized Python, Docker, and Kubernetes to streamline onboarding, automate testing, and ensure production readiness, with a focus on distributed computing and machine learning.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
3,341
Activity Months2
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026Month 2026-06 summary for aws-samples/awsome-distributed-training: - Delivered an end-to-end OpenVLA HyperPod Slurm fine-tuning test workflow, including a Dockerfile for environment setup, a Slurm batch script for job submission, and a user-facing README. This enables reproducible single-node LoRA fine-tuning of OpenVLA-7B on an 8-GPU HyperPod setup. - Consolidated testing artifacts to support rapid iteration: ~10 minutes for 500 steps on the validated configuration. - Fixed critical provenance and documentation issues to ensure correct references and model sources: updated to use openvla/openvla-7b (HF AutoModel checkpoint) and corrected repository naming from awsome-distributed-training to awsome-distributed-ai. - Prepared groundwork for CI/test automation with pinned environments and clear setup/run instructions, improving onboarding and long-term maintainability.
1 Commits • 1 Features
Jun 1, 2026Month 2026-06 summary for aws-samples/awsome-distributed-training: - Delivered an end-to-end OpenVLA HyperPod Slurm fine-tuning test workflow, including a Dockerfile for environment setup, a Slurm batch script for job submission, and a user-facing README. This enables reproducible single-node LoRA fine-tuning of OpenVLA-7B on an 8-GPU HyperPod setup. - Consolidated testing artifacts to support rapid iteration: ~10 minutes for 500 steps on the validated configuration. - Fixed critical provenance and documentation issues to ensure correct references and model sources: updated to use openvla/openvla-7b (HF AutoModel checkpoint) and corrected repository naming from awsome-distributed-training to awsome-distributed-ai. - Prepared groundwork for CI/test automation with pinned environments and clear setup/run instructions, improving onboarding and long-term maintainability.
June 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — End-to-end multilingual fine-tuning capability delivered for GPT-OSS 20B using LoRA and GRPO, plus stability and maintainability improvements in aws-samples/awsome-distributed-training. Achievements include a complete finetuning setup with training scripts, Docker configurations, and evaluation mechanisms; repo reorganization for scalable deployment; critical fixes for reliability and compatibility; and environment hardening for production readiness.
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — End-to-end multilingual fine-tuning capability delivered for GPT-OSS 20B using LoRA and GRPO, plus stability and maintainability improvements in aws-samples/awsome-distributed-training. Achievements include a complete finetuning setup with training scripts, Docker configurations, and evaluation mechanisms; repo reorganization for scalable deployment; critical fixes for reliability and compatibility; and environment hardening for production readiness.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
BashDockerfilePythonShellYAML
Technical Skills
ContainerizationDeep LearningDistributed ComputingDockerKubernetesMachine LearningNatural Language ProcessingRobotics
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