[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9637":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":38,"readmeContent":39,"aiSummary":40,"trendingCount":16,"starSnapshotCount":16,"syncStatus":41,"lastSyncTime":42,"discoverSource":43},9637,"tvm","apache\u002Ftvm","apache","Open Machine Learning Compiler Framework","https:\u002F\u002Ftvm.apache.org\u002F",null,"Python",13460,3891,365,144,0,5,47,113,30,45,"Apache License 2.0",false,"main",[26,27,28,29,30,31,32,33,34,35,36,5,37],"compiler","deep-learning","gpu","javascript","machine-learning","metal","opencl","performance","rocm","spirv","tensor","vulkan","2026-06-12 02:02:10","\u003C!--- Licensed to the Apache Software Foundation (ASF) under one -->\n\u003C!--- or more contributor license agreements.  See the NOTICE file -->\n\u003C!--- distributed with this work for additional information -->\n\u003C!--- regarding copyright ownership.  The ASF licenses this file -->\n\u003C!--- to you under the Apache License, Version 2.0 (the -->\n\u003C!--- \"License\"); you may not use this file except in compliance -->\n\u003C!--- with the License.  You may obtain a copy of the License at -->\n\n\u003C!---   http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0 -->\n\n\u003C!--- Unless required by applicable law or agreed to in writing, -->\n\u003C!--- software distributed under the License is distributed on an -->\n\u003C!--- \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->\n\u003C!--- KIND, either express or implied.  See the License for the -->\n\u003C!--- specific language governing permissions and limitations -->\n\u003C!--- under the License. -->\n\n\u003Cimg src=https:\u002F\u002Fraw.githubusercontent.com\u002Fapache\u002Ftvm-site\u002Fmain\u002Fimages\u002Flogo\u002Ftvm-logo-small.png width=128\u002F> Open Machine Learning Compiler Framework\n==============================================\n[Documentation](https:\u002F\u002Ftvm.apache.org\u002Fdocs) |\n[Contributors](CONTRIBUTORS.md) |\n[Community](https:\u002F\u002Ftvm.apache.org\u002Fcommunity) |\n[Release Notes](NEWS.md)\n\nApache TVM is an open machine learning compilation framework,\nfollowing the following principles:\n\n- Python-first development that enables quick customization of machine learning compiler pipelines.\n- Universal deployment to bring models into minimum deployable modules.\n\nLicense\n-------\nTVM is licensed under the [Apache-2.0](LICENSE) license.\n\nGetting Started\n---------------\nCheck out the [TVM Documentation](https:\u002F\u002Ftvm.apache.org\u002Fdocs\u002F) site for installation instructions, tutorials, examples, and more.\nThe [Getting Started with TVM](https:\u002F\u002Ftvm.apache.org\u002Fdocs\u002Fget_started\u002Foverview.html) tutorial is a great\nplace to start.\n\nContribute to TVM\n-----------------\nTVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community.\nCheck out the [Contributor Guide](https:\u002F\u002Ftvm.apache.org\u002Fdocs\u002Fcontribute\u002F).\n\nHistory and Acknowledgement\n---------------------------\nTVM started as a research project for deep learning compilation.\nThe first version of the project benefited a lot from the following projects:\n\n- [Halide](https:\u002F\u002Fgithub.com\u002Fhalide\u002FHalide): Part of TVM's TIR and arithmetic simplification module\n originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.\n- [Loopy](https:\u002F\u002Fgithub.com\u002Finducer\u002Floopy): use of integer set analysis and its loop transformation primitives.\n- [Theano](https:\u002F\u002Fgithub.com\u002FTheano\u002FTheano): the design inspiration of symbolic scan operator for recurrence.\n\nSince then, the project has gone through several rounds of redesigns.\nThe current design is also drastically different from the initial design, following the\ndevelopment trend of the ML compiler community.\n\nThe most recent version focuses on a cross-level design with TensorIR as the tensor-level representation\nand Relax as the graph-level representation and Python-first transformations.\nThe project's current design goal is to make the ML compiler accessible by enabling most\ntransformations to be customizable in Python and bringing a cross-level representation that can jointly\noptimize computational graphs, tensor programs, and libraries. The project is also a foundation\ninfra for building Python-first vertical compilers for domains, such as LLMs.\n","Apache TVM 是一个开放的机器学习编译框架，旨在优化和部署深度学习模型。它支持Python优先开发，允许用户快速定制机器学习编译流程，并具备跨平台部署能力，可以将模型转换为最小可部署模块。TVM利用多种后端技术如GPU、OpenCL、Vulkan等来提升模型在不同硬件上的运行效率。该框架适用于需要高性能推理引擎或希望在边缘设备上高效运行AI模型的各种场景。",2,"2026-06-11 03:23:53","top_topic"]