Baolin Li
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    • Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference
    • Interpretable Analysis of Production GPU Clusters Monitoring Data via Association Rule Mining
    • Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service
    • Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems
    • Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
    • Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources
    • MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant GPU Clusters
    • AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications
    • Great Power, Great Respobsibility: Recommendations for Reducing Energy for Training Language Models
    • RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances
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scikit-learn

Oct 26, 2023 ยท 1 min read
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scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Jun 17, 2024
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Baolin Li
Authors
Baolin Li
Ph.D.

โ† PyTorch Oct 26, 2023

ยฉ 2024 Baolin Li. Contact: li.baol@northeastern.edu

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