HI Markus, I have been using TVM as part of ML platform work as consumer of the project, this is great news!
Would love to come in and help as a Mentor of this project if it is Ok with the community. Thanks, - Henry On Fri, Feb 15, 2019 at 10:42 AM Markus Weimer <wei...@apache.org> wrote: > Hi, > > we'd like to start the discussion of accepting TVM into the incubator. > Please see the proposal below. I'd like to highlight a few things for > our discussion: > > (1) The project already follows many Apache ways like meritocracy, > open development and such. > > (2) The project recognizes an in-between state of "reviewer" that it > nominates people for between contributor and committer status. We'd > like to learn if and how to maintain that in the future. > > (3) The project contains hardware as a software artifact. We are not > aware of another ASF project like that and wonder if and how it > affects its acceptance into the incubator. > > Thanks! > > Markus > > === Proposal === > > We propose to incubate the TVM project the Apache Software Foundation. TVM > is a > full stack open deep learning compiler stack for CPUs, GPUs, and > specialized > accelerators. It aims to close the gap between the productivity-focused > deep > learning frameworks, and the performance- or efficiency-oriented hardware > backends. > > === Background === > > There is an increasing need to bring machine learning to a wide diversity > of > hardware devices. Current frameworks rely on vendor-specific operator > libraries > and optimize for a narrow range of server-class GPUs. Deploying workloads > to new > platforms -- such as mobile phones, embedded devices, and accelerators > (e.g., > FPGAs, ASICs) -- requires significant manual effort. TVM is an end to end > deep > learning a compiler that exposes graph-level and operator-level > optimizations to > provide performance portability to deep learning workloads across diverse > hardware back-ends. TVM solves optimization challenges specific to deep > learning, such as high-level operator fusion, mapping to arbitrary hardware > primitives, and memory latency hiding. It also automates optimization of > low-level programs to hardware characteristics by employing a novel, > learning-based cost modeling method for rapid exploration of program > optimizations. > > Moreover, there is increasing interest in designing specialized hardware > which > accelerates machine learning. Towards this goal, TVM introduces VTA, an > open > source deep learning accelerator as part of its stack. The open source VTA > driver and hardware design is a crucial step toward building software > support > for future ASICs. The TVM-VTA flow acts as a is the great frontier for > researchers and practitioners to explore specialized hardware designs. > > > === Rationale === > > Deep learning compilation will be the next frontier of machine learning > systems. > TVM is already one of the leading open source projects pursuing this > direction. > > Specifically, TVM provides infrastructure to use machine learning to > automatically optimize deployment of deep learning programs on diverse > hardware > backends. > > > === VTA: Open Source Hardware Design === > > TVM also contains open source hardware as part of its stack. The VTA > hardware > design is a fully open sourced deep learning accelerator that allows us to > experiment with compiler, driver, runtime, and execute the code on FPGA. > VTA > provides a path to target future ASICs, and build software-driven > solutions to > co-design future deep learning accelerators. > > Having an open source hardware design in an ASF project is rare and perhaps > unprecedented. We put some of our rationale on why it is necessary for the > community. > > Deep learning specialized ASICs are going to be at the center of the AI > revolution. However, given its early shape, there is no open standard, or > even > any available information hardware interface that allows an open source > software > to target to. VTA provides such open source hardware abstraction layer and > allows us to build in abstractions that can be effectively used to target > other > deep learning accelerators. > > Moreover, there is an increasing need for co-designing future of machine > learning systems with the hardware abstraction. Having a co-designed open > source > hardware stack along with the software creates a path for this route. In > short, > we need open-source hardware to build the best open source software. > > Finally, we can still view VTA design as “software”, as its source code is > written in source description language and can generate “binary” which can > run > on FPGA and possibly simulators. > > > === Current Status === > > TVM is open sourced under the Apache License for one and half years. See > the > current project website (https://tvm.ai/), Github > (https://github.com/dmlc/tvm/), as well as TVM Conference > (https://sampl.cs.washington.edu/tvmconf/#about-tvmconf) > > TVM has already been used in production, some highlights are AWS (Sagemaker > Neo), Huawei (AI Chip compilation) and Facebook (mobile optimization). We > anticipate the list of adopters to grow over the next few years. > > === Meritocracy === > > The TVM stack began as a research project of the SAMPL group at Paul G. > Allen > School of Computer Science & Engineering, University of Washington. The > project > is now driven by an open source community involving multiple industry and > academic institutions. The project is currently governed by the Apache Way > (https://docs.tvm.ai/contribute/community.html). The project now has 14 > committers and 6 PMCs, and the list is actively growing. The PMCs uses a > google > group mail-list to vote in new committers/PMCs, which will be moved to > private@ > after incubation. > > The community highly values open collaboration among contributors from > different > backgrounds.The current committers come from UW, Berkeley, Cornell, SJTU, > AMD, > AWS, Huawei, Google, Facebook, Ziosoft. > > > === Community === > > The project currently has 173 contributors. As per the Apache way, all the > discussions are conducted in publicly archivable places. > > - Github issues are used to track development activities and RFC. > - The roadmap is public and encourages participation from everyone in the > community. > - Discussion forums for general discussions. https://discuss.tvm.ai > - The content of the discourse forum can be considered as a public archive > as it is searchable with all the content > - We also created a mail-list archive of the forum, which we will forward > to > an Apache mail-list after incubation > https://groups.google.com/forum/#!forum/tvm-discuss-archive > > - See https://tvm.ai/community > - See https://github.com/dmlc/tvm/releases for past releases. > > Currently, Github issue serves as dev@ channel. Notably, major features > always > start from RFCs discussions to encourage broad participation in the > community. > > The community recognizes potential committers early by bringing > contributors as > code reviewers and encourages them to participate in code reviews. Code > reviews > and high-quality code are fundamental to the long-term success of the > project. > The reviewer mechanism in the community serves a way to highlight this > aspect as > well as helping the community find good candidates to promote to > committers. > > > > ==== Development and Decision Process ==== > > See > https://docs.tvm.ai/contribute/community.html#general-development-process > for the current development guideline. The key points are: Open public > roadmap > during development, which turns into release notes Major features start > with an > RFC, everything happens in public Encourage public discussion via > archivable > channels Strive to reach a consensus on technical decisions through > discussion > Moderation from committers and encourage everyone’s participation > > Example Roadmap: https://github.com/dmlc/tvm/issues/1170 > The idea is to keep an active list of roadmaps that can be turned directly > into a release note. Public roadmap helps to encourage general > participation > from all contributors. > > Example 1: > Recently a major proposal in the community is to bring in a new > high-level IR, RFC thread: https://github.com/dmlc/tvm/issues/1673 The > pull > request: https://github.com/dmlc/tvm/pull/1672 Everyone who participated > in the > RFC is invited to review the code as well - Follow up features are > proposed as > follow up RFCs. > > Example 2: Community guideline improvements > RFC thread: https://github.com/dmlc/tvm/issues/2017 > Slack channel setup as per community suggestion, but still encourage the > community to only use it for quick communication and use publicly archived > channels for development: https://github.com/dmlc/tvm/issues/2174 > > Example 3: Python3 timeline proposal > RFC thread: https://github.com/dmlc/tvm/issues/1602 > Finished with the decision to respect backward compatibility and keep > python2 > support. > > See > > https://github.com/dmlc/tvm/issues?utf8=%E2%9C%93&q=label%3A%22status%3A+RFC%22+ > for a full list of RFCs. > > > === Alignment === > > TVM is useful for building deep learning deployment solutions. It is > perhaps > also the first Apache incubator proposal that includes both open source > software > and hardware system design. > > It has the potential to benefit existing related ML projects such as MXNet, > Singa, SystemML, and Mahout by providing powerful low-level primitives for > matrix operations. > > > === Known Risks === > > ==== Orphaned products ==== > > The project has a diverse contributor base. As an example, the current > committers come from: UW, Berkeley, Cornell, SJTU, AMD, AWS, Google, > Facebook, > Ziosoft, Huawei. We are actively growing this list. Given that the project > has > already been used in production, there is a minimum risk of the project > being > abandoned. > > ==== Inexperience with Open Source ==== > > The TVM community has extensive experience in open source. Three of > current five > PMCs are already PPMCs of existing Apache projects. Over the course of > development, the community already has a good way bringing RFCs, > discussions and > most importantly, welcoming new contributors in the Apache way. > > ==== Homogenous Developers ==== > > The project has a diverse contributor base. As an example, the current > committers comes from: UW, Berkeley, Cornell, SJTU, AMD, AWS, Huawei, > Google, > Facebook, Ziosoft. The community actively seeks to collaborative broadly. > The > PMCs followed a principle to *only* nominate committers outside their own > organizations. > > > === Reliance on Salaried Developers === > > Most of the current committers are volunteers. > > === Relationships with Other Apache Products === > > TVM can serve as a fundamental compiler stack for deep learning and machine > learning in general. We expect it can benefit projects like MXNet, Spark, > Flink, > Mahout, and SystemML. > > === Documentation === > > See https://tvm.ai/ > > === Initial Source === > > https://github.com/dmlc/tvm > > We plan to move our repository to https://github.com/apache/incubator-tvm > > > === Source and Intellectual Property Submission Plan === > > TVM source code is available under Apache V2 license. We will work with the > committers to get ICLAs signed. > > === External Dependencies === > > We put all the source level dependencies under > https://github.com/dmlc/tvm/tree/master/3rdparty > > - dmlc-core (Apache2): https://github.com/dmlc/dmlc-core > - dlpack (Apache2): https://github.com/dmlc/dlpack > - HalideIR (MIT): https://github.com/dmlc/HalideIR > - range(Unlicense): https://github.com/agauniyal/rang > - Compiler-RT (BSD) > - LLVM > > All of the current he dependencies are stable, which means that the > current TVM > repo is standalone and main development activities only happen at the TVM > repo. > The dependencies are periodically updated in the rate about once a month > when > necessary. For source level dependencies, we will always point to a stable > release version for software release in the future. > > > === External Dependencies on DMLC projects === > > There are three dependencies to dmlc projects in the 3rdparty. The current > proposal is to keep the current dependencies in the 3rdparty. We elaborate > on > the background of these dependencies below: > > - dmlc-core: is a minimum module for logging and memory serialization. It > is > currently used by projects including ApacheMXNet, TVM, and XGBoost. The > project is relatively stable, with around one change a week(most recent > changes comes from XGBoost project). TVM’s dependency on dmlc-core is > minimum > and only uses its feature for logging. > - dlpack: is a minimum consensus standard for in-memory Tensor format. It > is > currently used by PyTorch, ApacheMXNet, Chainer, and a few other projects. > - HalideIR: is a minimum IR data structure that is isolated from a fork of > Halide project. We keep the license to be MIT to respect the original > license > and its origin. A common consensus in the TVM project is that we keep the > old > derived code in HalideIR (which are stable), and all new developments > happen > in the TVM repo. > > The main reason to propose keep these dependencies are: > - Each of the dependencies has the user and developer community of its own > which is larger than the TVM community or different license options(MIT in > HalideIR) > - These dependencies are stable and update at a monthly rate. > > While it is possible to fork the code in the tvm repo, given that the > current > tvm repo is self-contained, and community development is stand-alone, we > feel > that there are have enough justifications to treat these as 3rdparty > dependencies. > > > === Required Resources === > > ==== Mailing List: ==== > The usual mailing lists are expected to be set up when entering incubation: > > * priv...@tvm.apache.org > * d...@tvm.apache.org , subscribe github issues. > * discuss-arch...@tvm.apache.org, Archive the discuss content of the > discourse user forum > > > Currently, we only use issues for developments and encourage community to > use > discuss forums when possible. As a result, the current github issues serves > similar purposes as dev@, so we propose to subscribe github issues to dev@ > after > incubation. > > The current community use https://discuss.tvm.ai/ for general technical > and > support discussions. The community forum is maintained by PMCs. We propose > to > continue to use the forum and archive the posts to an Apache mail-list. We > already have the mechanism to do so (see > https://groups.google.com/forum/#!forum/tvm-discuss-archive) > > > > ==== Git Repositories: ==== > > Upon entering incubation, we plan to transfer the existing repo from > https://github.com/dmlc/tvm to https://github.com/apache/incubator-tvm. > > > > > ==== Issue Tracking: ==== > > TVM currently uses GitHub to track issues. We would like to continue to do > so > while we discuss migration possibilities with the ASF Infra team. > > ==== URL: ==== > > Current project website: https://tvm.ai/, as we proceed website will > migrate to > https://tvm.incubator.apache.org and hopefully https://tvm.apache.org > > === Initial Committers and PMCs === > > As the project has already followed the Apache way of development(in terms > of > meritocracy, community, and archive of public discussion). We plan to > transition > the current PMCs to PPMCs , and committers to apache committers. There are > also > ongoing votes and discussions in the current tvm PMC private mail-list > about new > committers/PMCs(we also invited our tentative mentors as observers to the > mail-list). We plan to migrate the discussions to private@ after the > proposal > has been accepted and bring in the new committers/PPMCs according to the > standard Apache community procedure. > > > Initial PPMCs > - Tianqi Chen tqc...@apache.org > - Ziheng Jiang zih...@apache.org > - Yizhi Liu liuyi...@apache.org > - Thierry Moreau mor...@cs.washington.edu > - Haichen Shen shenhaic...@gmail.com > - Lianmin Zheng lianminzh...@gmail.com > - Markus Weimer wei...@apache.org > - Sebastian Schelter > - Byung-Gon Chun > > Initial Committers (Including PPMCs) > - Aditya Atluri aditya.atl...@amd.com AMD > - Tianqi Chen tqc...@apache.org University of Washington > - Yuwei Hu huyuwei1...@gmail.com Cornell > - Nick Hynes nhy...@berkeley.edu UC Berkeley > - Ziheng Jiang zih...@apache.org University of Washington > - Yizhi Liu liuyi...@apache.org AWS > - Thierry Moreau mor...@cs.washington.edu University of Washington > - Siva srk.i...@gmail.com Huawei > - Haichen Shen shenhaic...@gmail.com AWS > - Masahiro Masuda masahi...@gmail.com Ziosoft > - Zhixun Tan phisi...@gmail.com Google > - Leyuan Wang laura...@gmail.com AWS > - Eddie Yan e...@cs.washington.edu University of Washington > - Lianming Zheng lianminzh...@gmail.com Shanghai Jiao Tong University > > > === Sponsors: === > > ==== Champion: ==== > * Markus Weimer, Microsoft > > ==== Mentors: ==== > * Sebastian Schelter, New York University > * Byung-Gon Chun, Seoul National University > > ==== Sponsoring Entity ==== > We are requesting the Incubator to sponsor this project. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org > For additional commands, e-mail: general-h...@incubator.apache.org > >
