+1 binding with a caveat:
You need mentors and champions from Apache who are available and ideally
active in the incubator. Markus had to step down on hivemail last
year. Has his situation changed?
Some comments:
The hardware artifacts being donated is interesting and something I
would support helping. We might want to loop in the secretary and legal
vps to discuss.
The reviewer status is something the pmc can elect to do. They might
end up with the same karma as committers on any repos if they need that
karma is the only hurdle I can think of. But we like a model of trust
for people so it should be a good thing.
But otherwise looks like a great start!
KAM
On Fri, Feb 15, 2019, 13:42 Markus Weimer <wei...@apache.org
<mailto: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 <mailto:priv...@tvm.apache.org>
* d...@tvm.apache.org <mailto:d...@tvm.apache.org> , subscribe github
issues.
* discuss-arch...@tvm.apache.org
<mailto: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 <mailto:tqc...@apache.org>
- Ziheng Jiang zih...@apache.org <mailto:zih...@apache.org>
- Yizhi Liu liuyi...@apache.org <mailto:liuyi...@apache.org>
- Thierry Moreau mor...@cs.washington.edu
<mailto:mor...@cs.washington.edu>
- Haichen Shen shenhaic...@gmail.com <mailto:shenhaic...@gmail.com>
- Lianmin Zheng lianminzh...@gmail.com <mailto:lianminzh...@gmail.com>
- Markus Weimer wei...@apache.org <mailto:wei...@apache.org>
- Sebastian Schelter
- Byung-Gon Chun
Initial Committers (Including PPMCs)
- Aditya Atluri aditya.atl...@amd.com <mailto:aditya.atl...@amd.com> AMD
- Tianqi Chen tqc...@apache.org <mailto:tqc...@apache.org>
University of Washington
- Yuwei Hu huyuwei1...@gmail.com <mailto:huyuwei1...@gmail.com> Cornell
- Nick Hynes nhy...@berkeley.edu <mailto:nhy...@berkeley.edu> UC
Berkeley
- Ziheng Jiang zih...@apache.org <mailto:zih...@apache.org>
University of Washington
- Yizhi Liu liuyi...@apache.org <mailto:liuyi...@apache.org> AWS
- Thierry Moreau mor...@cs.washington.edu
<mailto:mor...@cs.washington.edu> University of Washington
- Siva srk.i...@gmail.com <mailto:srk.i...@gmail.com> Huawei
- Haichen Shen shenhaic...@gmail.com <mailto:shenhaic...@gmail.com> AWS
- Masahiro Masuda masahi...@gmail.com <mailto:masahi...@gmail.com>
Ziosoft
- Zhixun Tan phisi...@gmail.com <mailto:phisi...@gmail.com> Google
- Leyuan Wang laura...@gmail.com <mailto:laura...@gmail.com> AWS
- Eddie Yan e...@cs.washington.edu <mailto:e...@cs.washington.edu>
University of Washington
- Lianming Zheng lianminzh...@gmail.com
<mailto: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
<mailto:general-unsubscr...@incubator.apache.org>
For additional commands, e-mail: general-h...@incubator.apache.org
<mailto:general-h...@incubator.apache.org>
