2 Repos
Awesome GitHub RepositoriesDeep Learning Workload Optimizers
Tools that collect Python call stack samples and integrate with Jupyter Lab to help maximize GPU utilization in deep learning applications.
Distinct from Python Profilers: Distinct from Python Profilers: focuses specifically on optimizing deep learning workloads with GPU utilization insights, not general Python multi-resource profiling.
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Awesome Deep Learning Workload Optimizers GitHub Repositories
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- nvidia/isaac-gr00t
NVIDIA/Isaac-GR00T
6,222Auf GitHub ansehenCollects Python call stack samples and integrates with Jupyter Lab to help maximize GPU utilization in deep learning applications.
Jupyter Notebook - meta-pytorch/segment-anything-fast
meta-pytorch/segment-anything-fast
1,320Auf GitHub ansehenSegment Anything Fast is a high-performance computer vision inference engine and image segmentation framework built for PyTorch. It provides a specialized environment for automated object isolation and mask generation, designed to process large-scale visual datasets with increased throughput. The project distinguishes itself through a suite of system-level optimization strategies that accelerate deep learning model performance. By utilizing graph-based model compilation, just-in-time kernel fusion, and hardware-aware quantization, it reduces computational latency and memory footprint. These t
Collects performance samples to help maximize GPU utilization in deep learning applications.
Python
