Commit
·
349fec6
1
Parent(s):
8721530
Sorted + removed duplicate TorchTitan and opencv
Browse files- PyTorchConference2025_GithubRepos.json +869 -881
PyTorchConference2025_GithubRepos.json
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| 871 |
-
"repo_link": "https://github.com/ROCm/rocRAND",
|
| 872 |
-
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|
| 873 |
-
"github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo",
|
| 874 |
-
"homepage_link": "https://github.com/ROCm/rocm-libraries",
|
| 875 |
-
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|
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|
| 877 |
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{
|
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-
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|
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|
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-
"github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo",
|
| 881 |
-
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|
| 882 |
-
}
|
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|
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|
| 1 |
[
|
| 2 |
+
{
|
| 3 |
+
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|
| 4 |
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"repo_link": "https://github.com/block/goose",
|
| 5 |
+
"category": "agent",
|
| 6 |
+
"github_about_section": "an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM",
|
| 7 |
+
"homepage_link": "https://block.github.io/goose/",
|
| 8 |
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"github_topic_closest_fit": "mcp"
|
| 9 |
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|
| 10 |
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{
|
| 11 |
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"repo_name": "ray",
|
| 12 |
+
"repo_link": "https://github.com/ray-project/ray",
|
| 13 |
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"category": "ai compute engine",
|
| 14 |
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"github_about_section": "Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.",
|
| 15 |
+
"homepage_link": "https://ray.io",
|
| 16 |
+
"github_topic_closest_fit": "machine-learning"
|
| 17 |
+
},
|
| 18 |
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{
|
| 19 |
+
"repo_name": "flashinfer-bench",
|
| 20 |
+
"repo_link": "https://github.com/flashinfer-ai/flashinfer-bench",
|
| 21 |
+
"category": "benchmark",
|
| 22 |
+
"github_about_section": "Building the Virtuous Cycle for AI-driven LLM Systems",
|
| 23 |
+
"homepage_link": "https://bench.flashinfer.ai"
|
| 24 |
+
},
|
| 25 |
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{
|
| 26 |
+
"repo_name": "KernelBench",
|
| 27 |
+
"repo_link": "https://github.com/ScalingIntelligence/KernelBench",
|
| 28 |
+
"category": "benchmark",
|
| 29 |
+
"github_about_section": "KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems",
|
| 30 |
+
"homepage_link": "https://scalingintelligence.stanford.edu/blogs/kernelbench/",
|
| 31 |
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"github_topic_closest_fit": "benchmark"
|
| 32 |
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|
| 33 |
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{
|
| 34 |
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"repo_name": "SWE-bench",
|
| 35 |
+
"repo_link": "https://github.com/SWE-bench/SWE-bench",
|
| 36 |
+
"category": "benchmark",
|
| 37 |
+
"github_about_section": "SWE-bench: Can Language Models Resolve Real-world Github Issues?",
|
| 38 |
+
"homepage_link": "https://www.swebench.com",
|
| 39 |
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"github_topic_closest_fit": "benchmark"
|
| 40 |
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|
| 41 |
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{
|
| 42 |
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"repo_name": "terminal-bench",
|
| 43 |
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"repo_link": "https://github.com/laude-institute/terminal-bench",
|
| 44 |
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"category": "benchmark",
|
| 45 |
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"github_about_section": "A benchmark for LLMs on complicated tasks in the terminal",
|
| 46 |
+
"homepage_link": "https://www.tbench.ai"
|
| 47 |
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},
|
| 48 |
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{
|
| 49 |
+
"repo_name": "TritonBench",
|
| 50 |
+
"repo_link": "https://github.com/thunlp/TritonBench",
|
| 51 |
+
"category": "benchmark",
|
| 52 |
+
"github_about_section": "TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators"
|
| 53 |
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},
|
| 54 |
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{
|
| 55 |
+
"repo_name": "BitBLAS",
|
| 56 |
+
"repo_link": "https://github.com/microsoft/BitBLAS",
|
| 57 |
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"category": "BLAS",
|
| 58 |
+
"github_about_section": "BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment."
|
| 59 |
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},
|
| 60 |
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{
|
| 61 |
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"repo_name": "hipBLAS",
|
| 62 |
+
"repo_link": "https://github.com/ROCm/hipBLAS",
|
| 63 |
+
"category": "BLAS",
|
| 64 |
+
"github_about_section": "[DEPRECATED] Moved to ROCm/rocm-libraries repo",
|
| 65 |
+
"homepage_link": "https://github.com/ROCm/rocm-libraries",
|
| 66 |
+
"github_topic_closest_fit": "hip"
|
| 67 |
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},
|
| 68 |
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{
|
| 69 |
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"repo_name": "hipBLASLt",
|
| 70 |
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"repo_link": "https://github.com/AMD-AGI/hipBLASLt",
|
| 71 |
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"category": "BLAS",
|
| 72 |
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"github_about_section": "hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library",
|
| 73 |
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"homepage_link": "https://rocm.docs.amd.com/projects/hipBLASLt/en/latest/index.html"
|
| 74 |
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|
| 75 |
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{
|
| 76 |
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|
| 77 |
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"repo_link": "https://github.com/AdaptiveCpp/AdaptiveCpp",
|
| 78 |
+
"category": "compiler",
|
| 79 |
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"github_about_section": "Compiler for multiple programming models (SYCL, C++ standard parallelism, HIP/CUDA) for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!",
|
| 80 |
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"homepage_link": "https://adaptivecpp.github.io",
|
| 81 |
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|
| 82 |
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|
| 83 |
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{
|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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"github_topic_closest_fit": "compiler"
|
| 90 |
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|
| 91 |
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{
|
| 92 |
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|
| 93 |
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|
| 94 |
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"category": "compiler",
|
| 95 |
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"github_about_section": "NumPy aware dynamic Python compiler using LLVM",
|
| 96 |
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|
| 97 |
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"github_topic_closest_fit": "compiler"
|
| 98 |
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},
|
| 99 |
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{
|
| 100 |
+
"repo_name": "nvcc4jupyter",
|
| 101 |
+
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|
| 102 |
+
"category": "compiler",
|
| 103 |
+
"github_about_section": "A plugin for Jupyter Notebook to run CUDA C/C++ code"
|
| 104 |
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},
|
| 105 |
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{
|
| 106 |
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|
| 107 |
+
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|
| 108 |
+
"category": "CUDA / OpenCL",
|
| 109 |
+
"github_about_section": "A prototype CUDA-to-OpenCL source-to-source translator, built on the Clang compiler framework",
|
| 110 |
+
"homepage_link": "http://chrec.cs.vt.edu/cu2cl",
|
| 111 |
+
"github_topic_closest_fit": "opencl"
|
| 112 |
+
},
|
| 113 |
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{
|
| 114 |
+
"repo_name": "cuda-python",
|
| 115 |
+
"repo_link": "https://github.com/NVIDIA/cuda-python",
|
| 116 |
+
"category": "CUDA / OpenCL",
|
| 117 |
+
"github_about_section": "CUDA Python: Performance meets Productivity",
|
| 118 |
+
"homepage_link": "https://nvidia.github.io/cuda-python/"
|
| 119 |
+
},
|
| 120 |
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{
|
| 121 |
+
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|
| 122 |
+
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|
| 123 |
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"category": "CUDA / OpenCL",
|
| 124 |
+
"github_about_section": "OpenCL SDK",
|
| 125 |
+
"github_topic_closest_fit": "opencl"
|
| 126 |
+
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|
| 127 |
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{
|
| 128 |
+
"repo_name": "pocl",
|
| 129 |
+
"repo_link": "https://github.com/pocl/pocl",
|
| 130 |
+
"category": "CUDA / OpenCL",
|
| 131 |
+
"github_about_section": "pocl - Portable Computing Language",
|
| 132 |
+
"homepage_link": "https://portablecl.org",
|
| 133 |
+
"github_topic_closest_fit": "opencl"
|
| 134 |
+
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|
| 135 |
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{
|
| 136 |
+
"repo_name": "SYCL-Docs",
|
| 137 |
+
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|
| 138 |
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"category": "CUDA / OpenCL",
|
| 139 |
+
"github_about_section": "SYCL Open Source Specification",
|
| 140 |
+
"github_topic_closest_fit": "opencl"
|
| 141 |
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|
| 142 |
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{
|
| 143 |
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|
| 144 |
+
"repo_link": "https://github.com/triSYCL/triSYCL",
|
| 145 |
+
"category": "CUDA / OpenCL",
|
| 146 |
+
"github_about_section": "Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group",
|
| 147 |
+
"github_topic_closest_fit": "opencl"
|
| 148 |
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|
| 149 |
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{
|
| 150 |
+
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|
| 151 |
+
"repo_link": "https://github.com/vosen/ZLUDA",
|
| 152 |
+
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|
| 153 |
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"github_about_section": "CUDA on non-NVIDIA GPUs",
|
| 154 |
+
"homepage_link": "https://vosen.github.io/ZLUDA/",
|
| 155 |
+
"github_topic_closest_fit": "cuda"
|
| 156 |
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|
| 157 |
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{
|
| 158 |
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|
| 159 |
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|
| 160 |
+
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|
| 161 |
+
"github_about_section": "LLM inference in C/C++",
|
| 162 |
+
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|
| 163 |
+
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|
| 164 |
+
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|
| 165 |
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{
|
| 166 |
+
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|
| 167 |
+
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|
| 168 |
+
"category": "inference engine",
|
| 169 |
+
"github_about_section": "Official inference library for Mistral models",
|
| 170 |
+
"homepage_link": "https://mistral.ai/",
|
| 171 |
+
"github_topic_closest_fit": "llm-inference"
|
| 172 |
+
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|
| 173 |
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{
|
| 174 |
+
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|
| 175 |
+
"repo_link": "https://github.com/ollama/ollama",
|
| 176 |
+
"category": "inference engine",
|
| 177 |
+
"github_about_section": "Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.",
|
| 178 |
+
"homepage_link": "https://ollama.com",
|
| 179 |
+
"github_topic_closest_fit": "inference"
|
| 180 |
+
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|
| 181 |
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{
|
| 182 |
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|
| 183 |
+
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|
| 184 |
+
"category": "inference engine",
|
| 185 |
+
"github_about_section": "SGLang is a fast serving framework for large language models and vision language models.",
|
| 186 |
+
"homepage_link": "https://docs.sglang.ai",
|
| 187 |
+
"github_topic_closest_fit": "inference"
|
| 188 |
+
},
|
| 189 |
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{
|
| 190 |
+
"repo_name": "TensorRT",
|
| 191 |
+
"repo_link": "https://github.com/NVIDIA/TensorRT",
|
| 192 |
+
"category": "inference engine",
|
| 193 |
+
"github_about_section": "NVIDIA TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.",
|
| 194 |
+
"homepage_link": "https://developer.nvidia.com/tensorrt",
|
| 195 |
+
"github_topic_closest_fit": "inference"
|
| 196 |
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|
| 197 |
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{
|
| 198 |
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|
| 199 |
+
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|
| 200 |
+
"category": "inference engine",
|
| 201 |
+
"github_about_section": "A high-throughput and memory-efficient inference and serving engine for LLMs",
|
| 202 |
+
"homepage_link": "https://docs.vllm.ai",
|
| 203 |
+
"github_topic_closest_fit": "inference"
|
| 204 |
+
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|
| 205 |
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{
|
| 206 |
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|
| 207 |
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"repo_link": "https://github.com/huggingface/kernels",
|
| 208 |
+
"category": "kernels",
|
| 209 |
+
"github_about_section": "Load compute kernels from the Hub"
|
| 210 |
+
},
|
| 211 |
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{
|
| 212 |
+
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|
| 213 |
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|
| 214 |
+
"category": "kernels",
|
| 215 |
+
"github_about_section": "Kernel sources for https://huggingface.co/kernels-community"
|
| 216 |
+
},
|
| 217 |
+
{
|
| 218 |
+
"repo_name": "Liger-Kernel",
|
| 219 |
+
"repo_link": "https://github.com/linkedin/Liger-Kernel",
|
| 220 |
+
"category": "kernels",
|
| 221 |
+
"github_about_section": "Efficient Triton Kernels for LLM Training",
|
| 222 |
+
"homepage_link": "https://openreview.net/pdf?id=36SjAIT42G",
|
| 223 |
+
"github_topic_closest_fit": "triton"
|
| 224 |
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|
| 225 |
+
{
|
| 226 |
+
"repo_name": "quack",
|
| 227 |
+
"repo_link": "https://github.com/Dao-AILab/quack",
|
| 228 |
+
"category": "kernels",
|
| 229 |
+
"github_about_section": "A Quirky Assortment of CuTe Kernels"
|
| 230 |
+
},
|
| 231 |
+
{
|
| 232 |
+
"repo_name": "reference-kernels",
|
| 233 |
+
"repo_link": "https://github.com/gpu-mode/reference-kernels",
|
| 234 |
+
"category": "kernels",
|
| 235 |
+
"github_about_section": "Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!",
|
| 236 |
+
"homepage_link": "https://gpumode.com",
|
| 237 |
+
"github_topic_closest_fit": "gpu"
|
| 238 |
+
},
|
| 239 |
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{
|
| 240 |
+
"repo_name": "pytorch",
|
| 241 |
+
"repo_link": "https://github.com/pytorch/pytorch",
|
| 242 |
+
"category": "machine learning framework",
|
| 243 |
+
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| 568 |
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| 570 |
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| 571 |
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| 572 |
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"github_about_section": "Build, Manage and Deploy AI/ML Systems",
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| 573 |
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|
| 574 |
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"github_topic_closest_fit": "machine-learning"
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| 575 |
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| 576 |
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{
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| 577 |
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| 578 |
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| 579 |
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| 580 |
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| 581 |
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| 583 |
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| 584 |
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| 585 |
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"github_about_section": "Specification and documentation for the Model Context Protocol",
|
| 586 |
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| 587 |
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| 588 |
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{
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| 589 |
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| 590 |
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| 591 |
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| 592 |
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|
| 593 |
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| 594 |
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| 595 |
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| 598 |
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| 599 |
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| 600 |
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| 602 |
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| 603 |
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| 604 |
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| 606 |
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| 609 |
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| 611 |
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| 612 |
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| 616 |
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| 621 |
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| 622 |
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| 624 |
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| 625 |
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| 626 |
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| 627 |
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| 634 |
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| 675 |
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| 677 |
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| 702 |
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| 706 |
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| 710 |
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| 712 |
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| 714 |
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| 715 |
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| 717 |
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| 720 |
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| 722 |
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| 730 |
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| 733 |
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| 735 |
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| 740 |
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| 741 |
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| 744 |
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| 745 |
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| 747 |
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| 752 |
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|
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| 759 |
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| 769 |
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|
| 770 |
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| 771 |
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|
| 772 |
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| 773 |
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| 777 |
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| 778 |
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|
| 779 |
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"github_topic_closest_fit": "gpu"
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| 780 |
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| 784 |
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| 785 |
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| 789 |
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| 794 |
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|
| 799 |
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|
| 800 |
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| 802 |
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|
| 804 |
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|
| 805 |
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| 808 |
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|
| 809 |
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|
| 810 |
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|
| 811 |
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| 813 |
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| 816 |
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| 817 |
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| 823 |
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| 824 |
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| 830 |
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| 831 |
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| 836 |
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|
| 838 |
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{
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|
| 843 |
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|
| 844 |
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|
| 845 |
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| 846 |
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{
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| 847 |
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| 848 |
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|
| 849 |
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|
| 850 |
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| 851 |
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| 852 |
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{
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| 853 |
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| 854 |
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|
| 855 |
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|
| 856 |
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| 857 |
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{
|
| 858 |
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|
| 859 |
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|
| 860 |
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"github_about_section": "Wan: Open and Advanced Large-Scale Video Generative Models",
|
| 861 |
+
"homepage_link": "https://wan.video",
|
| 862 |
+
"github_topic_closest_fit": "video-generation"
|
| 863 |
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},
|
| 864 |
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{
|
| 865 |
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|
| 866 |
+
"repo_link": "https://github.com/NVIDIA/warp",
|
| 867 |
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"github_about_section": "A Python framework for accelerated simulation, data generation and spatial computing.",
|
| 868 |
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|
| 869 |
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"github_topic_closest_fit": "gpu"
|
| 870 |
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|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 871 |
]
|