Hi +1 also, excited to see TVM proposal.
Regards Liang Timothy Chen-2 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.saputra@ > > 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 < > weimer@ > > 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: >> > >> > * > private@.apache >> > * > dev@.apache > , subscribe github issues. >> > * > discuss-archive@.apache > , 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 > tqchen@ >> > - Ziheng Jiang > ziheng@ >> > - Yizhi Liu > liuyizhi@ >> > - Thierry Moreau > moreau@.washington >> > - Haichen Shen > shenhaichen@ >> > - Lianmin Zheng > lianminzheng@ >> > - Markus Weimer > weimer@ >> > - Sebastian Schelter >> > - Byung-Gon Chun >> > >> > Initial Committers (Including PPMCs) >> > - Aditya Atluri > Aditya.Atluri@ > AMD >> > - Tianqi Chen > tqchen@ > University of Washington >> > - Yuwei Hu > huyuwei1995@ > Cornell >> > - Nick Hynes > nhynes@ > UC Berkeley >> > - Ziheng Jiang > ziheng@ > University of Washington >> > - Yizhi Liu > liuyizhi@ > AWS >> > - Thierry Moreau > moreau@.washington > University of Washington >> > - Siva > srk.it38@ > Huawei >> > - Haichen Shen > shenhaichen@ > AWS >> > - Masahiro Masuda > masahi129@ > Ziosoft >> > - Zhixun Tan > phisiart@ > Google >> > - Leyuan Wang > laurawly@ > AWS >> > - Eddie Yan > eqy@.washington > University of Washington >> > - Lianming Zheng > lianminzheng@ > 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-unsubscribe@.apache >> > For additional commands, e-mail: > general-help@.apache >> > >> > >> -- Sent from: http://apache-incubator-general.996316.n3.nabble.com/ --------------------------------------------------------------------- To unsubscribe, e-mail: general-unsubscr...@incubator.apache.org For additional commands, e-mail: general-h...@incubator.apache.org
