Hi All, TVM is very promising and I am also so excited to see such a great project's proposal! I would love to be a mentor too if it is possible.
Kind Regards, Furkan KAMACI On Fri, Feb 15, 2019 at 9:52 PM Timothy Chen <tnac...@apache.org> wrote: > Very excited to see this proposed as well. > > I’d also like to volunteer mentoring if the community is open too. > > Tim > > On Fri, Feb 15, 2019 at 10:48 Henry Saputra <henry.sapu...@gmail.com> > wrote: > > > 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 > > > > > > > > >
