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04:26:06 <RRSAgent> logging to https://www.w3.org/2019/09/17-webmachinelearning-irc
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04:26:17 <anssik> RRSAgent, make logs public
04:26:28 <anssik> Meeting: WebML CG F2F Day 1 – 17 September 2019
04:26:32 <anssik> Chair: Anssi
04:26:36 <anssik> Agenda: https://github.com/webmachinelearning/meetings/blob/master/2019-09-17-fukuoka/
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04:26:48 <anssik> Scribe: Anssi
04:26:50 <anssik> scribeNick: anssik
04:27:10 <anssik> Present+ Anssi_Kostiainen
04:27:26 <ningxin_hu> Present+ Ningxin_Hu
04:27:37 <takio> takio has joined #webmachinelearning
04:27:43 <niwamoto> Present+ Narifumi_Iwamoto
04:27:55 <Youngsun_Ryu> Present+ Youngsun_Ryu
04:27:58 <David_Marsh> Present+ David Marsh
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04:28:20 <HelloFillip> Present+ Phil_Laszkowicz
04:28:24 <Kangchan> present+ Kangchan_Lee
04:28:25 <riju> Present+ Rijubrata Bhaumik
04:28:26 <takio> Present+ Takio_Yamaoka
04:32:15 <anssik> RRSAgent, draft minutes v2
04:32:15 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
04:33:04 <anssik> TOPIC: Welcome and intros
04:33:24 <anssik> anssik: welcome to the WebML CG's 2nd F2F, happy to see both new and old faces around
04:33:58 <Bruce> Bruce has joined #webmachinelearning
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04:34:37 <anssik> ... on the agenda today on Day 1: intros, custom operations, MLIR (Multi-Level Intermediate Representation) exploration, Operation set
04:34:48 <anssik> ... on Friday Day 2 exploratory topics, standards track next steps, W3C workshop planning
04:35:00 <anssik> anssik: Let's do a roundtable 30-sec intros: your affiliation & interests toward the group
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04:35:43 <dino> present+
04:35:45 <anssik> anssik: I'm the chair working for Intel
04:36:06 <anssik> nikhil: working for Google, Deeplearn.js co-author, want to bring the ecosystem forward, not familiar with W3C
04:36:23 <anssik> ningxin_hu: Intel, CV and ML interest, OpenCV.js background
04:36:42 <anssik> kenneth: Intel architect, W3C TAG rep, overseeing the architecture of the Web
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04:37:33 <anssik> Yongsun: Samsung, interested in ML in general
04:37:47 <anssik> s/replacethis/withthis/
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04:38:13 <anssik> Dave: Payments network with many members, just interested in ML
04:38:43 <anssik> Chimiming: University affiliated
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04:39:31 <anssik> Dean: Apple, interested in everything the group does, not ML specialist but I'll do my best connecting Apple experts, I work on WebKit project also Safari
04:40:11 <anssik> Philip: Omnijar, working with DL for 13 years, with large companies, automotive, NVIDIA, ARM, interest to continue move commercial project to the Web
04:40:44 <anssik> Riju: Intel, Chromium developer, sensors, NFC, media capture, OpenCV, not using ML currently
04:41:15 <anssik> Kangchan: ETRI Korea, working on standards in ITU, ML as a Service
04:42:04 <anssik> Wenson: Apple, WebKit, interest in ML
04:42:26 <anssik> Diogo: Brazil W3C office, NLP background and interest
04:42:38 <Bruce> Bruce has joined #webmachinelearning
04:42:58 <anssik> Takio: Yahoo Japan, sensor processing, transcoding, interest in CV w/ ML
04:43:34 <anssik> Sangwhan: TAG member, used to work for Opera, CV startup not affiliated with Web, I also do NLP
04:43:53 <anssik> Frank: Inria France, curious of the group
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04:44:40 <anssik> Belem: Intel, responsible to WebML polyfill
04:45:16 <anssik> James: Google, working on Chrome, WebGL/GPU, interested in ML in Chrome
04:46:08 <anssik> TOPIC: Custom operations
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04:49:15 <Big_Screen> https://docs.google.com/presentation/d/1KGRc1RnnYt_1JK2Pk6r2xRkD60v4F8jc4beHMv0crng/edit#slide=id.p
04:50:44 <anssik> q+ to say something
04:50:53 <anssik> ack anssik
04:50:53 <Zakim> anssik, you wanted to say something
04:51:17 <anssik> [ningxin presents the slides]
04:53:17 <anssik> ningxin_hu: ML field is fast moving. Model architecture and the ops are evolving quickly. This leads JS ML frameworks usually have big op set (e.g. TF.js has over 200 ops)
04:53:25 <anssik> ... Today’s framework’s ops are implemented in WebGL and WASM, and WebGPU
04:53:32 <anssik> ... WebNN’s built-in op set that focuses on hardware acceleration will be small and grow slowly
04:53:52 <anssik> ... Problem: It demands a way for library authors to write ops that can interop with built-in ops.
04:54:08 <anssik> Options: WebNN built-in ops interop with framework ops in WASM and WebGL/WebGPU (focus of this investigation)
04:54:49 <anssik> Kenneth: can you mix Wasm and WebNN ops?
04:55:14 <anssik> Shangwan: there's a GPU-CPU transfer with a performance cost
04:55:38 <anssik> ... WebNN provides a way to write custom op by a domain specific language (e.g. Kai’s proposal) (future exploration)
04:55:57 <anssik> ningxin_hu: next subtopic, WebNN-WebGPU Interop
04:56:18 <anssik> [showing example code of Conv + Add + Relu by TF.js WebGPU]
04:56:42 <anssik> RRSAgent, draft minutes v2
04:56:42 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
04:57:41 <anssik> [showing example of compile WebNN op for WebGPU device]
04:59:48 <anssik> [scribe sees ~30 participants, not all names recorded in minutes]
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05:00:25 <anssik> [showing example of execute WebNN's op with WebGPU op]
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05:02:16 <anssik> -> https://docs.google.com/presentation/d/1KGRc1RnnYt_1JK2Pk6r2xRkD60v4F8jc4beHMv0crng/ WebNN Interop Investigation slides
05:03:16 <junwei> junwei has joined #webmachinelearning
05:03:31 <anssik> [ningxin showing a demo on his laptop]
05:03:52 <anssik> ningxin_hu: custom build of Chromium on macOS
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05:05:20 <ningxin_hu> https://docs.google.com/presentation/d/1KGRc1RnnYt_1JK2Pk6r2xRkD60v4F8jc4beHMv0crng/edit?usp=sharing
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05:05:44 <ningxin_hu> conv input dims: [1,100,100,100] and filter dims: [3,3,100,100]  WebGPU conv2d/add/relu elapsed time: 60.81 ms WebNN conv2d interops with WebGPU add/relu via ArrayBuffer elapsed time: 39.67 ms WebNN conv2d interops with WebGPU add/relu via WebGPUBuffer elapsed time: 22.11 ms WebNN conv2d with fused add/relu elapsed time: 21.11 ms
05:06:32 <anssik> [above pasted text is an output of test case of TF.js sets backend as WebGPU]
05:07:50 <anssik> sangwhan: is the Chromium source available?
05:07:57 <anssik> ningxin_hu: that's available
05:08:58 <anssik> nikhil: how fast is the readback?
05:09:08 <anssik> ningxin_hu: not yet tested that
05:09:24 <anssik> dino: you can't use MPS, why is that?
05:09:40 <anssik> ningxin_hu: different memory layout internally
05:10:15 <anssik> dino: can you show conv operations, what they are doing?
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05:10:33 <anssik> ... I was expected to see a custom op, i.e. shader code
05:10:45 <anssik> ningxin_hu: shader code is inside TF.js
05:11:47 <anssik> ningxin_hu: subtopic, POC Implementation on MPS
05:11:57 <anssik> ... Reuse the same MTLDevice associated with WebGPUDevice.
05:12:07 <anssik> ... Get the MTLBuffer associated with input and output WebGPUBuffer.
05:12:14 <anssik> ... Allocate MPSImage for inputs with MTLDevice.
05:12:21 <anssik> ... Create MTLCommandBuffer from MTLQueue associated with WebGPUDevice.
05:12:28 <anssik> ... Encode a compute shader that copies and reorders data from MTLBuffer to MPSImage (MPSImage layout).
05:12:48 <anssik> dino: is this a custom WebGPU implementation? Where you decide you MPS?
05:13:15 <anssik> ... TF.js running on top of WebGPU
05:13:25 <anssik> ... this is an impl of WebNN, not TF on for of Chromium
05:13:44 <anssik> ... using WebGPU infra underneath it has platform implementation e.g. MPS
05:13:55 <anssik> ningxin_hu: Encode MPSNNGraph/MPSCNNKernel to MTLCommandBuffer
05:14:02 <anssik> ... Encode a compute shader that copies and reorders data from output MPSImage to output MTLBuffer.
05:14:10 <anssik> ... Commit MTLCommandBuffer.
05:14:37 <anssik>  ningxin_hu: Performance Summary
05:15:24 <anssik> ... Inference time (ms)
05:16:00 <anssik>  ... WebGPU conv/add/relu 61.31
05:16:14 <anssik> ... WebNN conv interops with WebGPU add/relu via ArrayBuffer 43.42
05:16:28 <anssik> ... WebNN conv interops with WebGPU add/relu via WebGPUBuffer 23.06
05:16:34 <anssik> ... WebNN conv with fused add/relu 21.25
05:16:52 <anssik> ningxin_hu: Copying/Reordering Optimization
05:17:01 <anssik> ... Inference time (ms)
05:17:16 <anssik>  WebGPU conv x2 112.96
05:17:22 <anssik> WebNN conv + WebGPU conv 67.33
05:17:38 <anssik> ... WebNN conv x2 with reordering 24.53
05:17:54 <anssik> s/ WebGPU conv x2 112.96/... WebGPU conv x2 112.96/
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05:18:03 <anssik> s/WebNN conv + WebGPU conv 67.33/... WebNN conv + WebGPU conv 67.33/
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05:18:45 <anssik> sangwhan: with this design, vendors targeting a single type of accelerator, what are the implications?
05:19:12 <anssik> ... if you were to implement this in a general browser, not OS bound, you'd have multiple accelerators, what's the story?
05:19:41 <anssik> ... you'd need to have compilers for every accelerator
05:19:51 <anssik> ... implementability question
05:20:20 <anssik> ... if you'd use the platform APIs, it'd be fine, but they can be limited in terms of support
05:20:48 <anssik> dino: Apple's perspective is we want to offload to the hardware as much as possible
05:21:26 <anssik> sangwhan: when testing the POC, did the inference affect the ref(?)
05:21:47 <anssik> dino: same issue with WebGL/GPU
05:22:20 <anssik> ... issue if the background task freezes the computer
05:22:44 <anssik> ... battery and perf benefit for going to ML hardware
05:23:01 <anssik> sangwhan: would be nice if everyone had these purpose-built accelerators
05:23:11 <anssik> ... curious of implications of that
05:23:24 <anssik> dino: not sure what Android devices have AI accelerators
05:23:48 <anssik> sangwhan: based on testing, could be NEON accelerated, or GPU, whatever the vendor had time to do
05:24:20 <anssik> nikhil: also good to benchmark readback times from those accelerators
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05:25:17 <anssik> [skipping slides to Summary of WebNN-WASM interop slide]
05:25:28 <anssik> ningxin_hu: WebNN ops allow to access vendor specific CPU acceleration
05:25:36 <anssik> ... Interop between WASM ops and WebNN op has overhead
05:25:42 <anssik> ... Memory copying between WASM heap and WebNN backend
05:25:49 <anssik> ... Memory reordering, e.g. MKL-DNN blocked layout
05:25:58 <anssik> ... Execute WebNN ops chain with opaque operands can avoid unnecessary overhead
05:26:24 <anssik> ningxin_hu: Proposal
05:26:43 <anssik> ... Support key ops that access hardware acceleration (#17) E.g. conv2d and matmul
05:26:57 <anssik> ... Support compiling and executing ops for devices (new issue?) CPU or GPU
05:27:09 <anssik> ... Support interop with WebAssembly and WebGPU compute shader
05:27:18 <anssik> ... Sharing ArrayBuffer with WASM op
05:27:27 <anssik> ... Sharing WebGPUBuffer with WebGPU op (new issue?)
05:28:00 <anssik> ... Support interop with WebAssembly and WebGPU compute shader
05:28:07 <anssik> ... - Sharing ArrayBuffer with WASM op
05:28:17 <anssik> ... - Sharing WebGPUBuffer with WebGPU op (new issue?)
05:28:25 <anssik> ... Support executing ops chain with opaque operands (#11)
05:28:33 <anssik> ... - Leverage device optimized memory layout and avoid unnecessary memory reordering
05:28:41 <anssik> ... Explore custom op support by DSL (new issue?)
05:30:03 <anssik> dino: how do these numbers compare with true native frameworks, CoreML, TensorFlow native?
05:31:29 <anssik> ningxin_hu: 10% WebNN overhead over native
05:31:48 <anssik> nikhil: TensorFlow/WebGL vs. CUDA, CUDA 10x faster
05:32:06 <anssik> ???: what kind of model do you use?
05:32:46 <anssik> ningxin_hu: we have multiple models for this experiment, we use conv kernels, MobileNet, Inception, ResNet50
05:33:12 <anssik> ... on our website we have bigger models, the model size constraints us
05:34:09 <anssik> nikhil: CPU and non-CPU accelerators an issue, how to consider them in the context of custom ops, understand readbacks
05:34:40 <anssik> ???: what is the focus in terms of hardware targets of this group?
05:34:58 <anssik> ningxin_hu: we have experience on Android phone with an AI accelerator, close to native perf
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05:35:59 <anssik> ???: what is the scope of this work? Recommendation to define a higher level abstraction to be flexible
05:36:25 <anssik> [hearing no concerns for the proposed tasks to investigate further]
05:36:55 <anssik> ningxin_hu: I'm willing to take "Support compiling and executing ops for devices (new issue?)" task
05:37:28 <anssik> ... maybe Kai could help with "Explore custom op support by DSL (new issue?)"
05:38:34 <anssik> dino: Apple could look at "Support key ops that access hardware acceleration (#17)" and provide feedback for that
05:39:15 <anssik> nikhil: just filed issues for conv2d and matmul
05:39:28 <anssik> https://github.com/webmachinelearning/webnn/issues/27
05:39:34 <anssik> https://github.com/webmachinelearning/webnn/issues/28
05:39:57 <anssik> ... will move forward with issues #27 and #28
05:40:45 <anssik> Topic: MLIR
05:41:07 <anssik> nikhil: disclaimer, I'm not a compiler person, but talked with Google experts on that field
05:41:22 <anssik> RRSAgent, draft minutes v2
05:41:22 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
05:42:07 <anssik> nikhil: we'll not proposing MLIR, just exploring this area
05:42:14 <jdarpinian> do you have a link to the slides?
05:43:00 <anssik> -> https://docs.google.com/presentation/d/1vv-pFsTqAVITtx3RwmEs-g7YRK1PD9APSIuice88aSI/ MLIR slides by Nikhil
05:43:20 <anssik> [nikhil presenting MLIR slides]
05:44:49 <anssik> ???: XLA compiler spits out LLVM IR already?
05:44:54 <anssik> nikhil: correct
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05:46:11 <anssik> ... Domain specific optimizations, progressive lowering
05:46:34 <anssik> ... The TensorFlow compiler ecosystem has many “Graph” IRs, each with challenges
05:47:47 <anssik> ... Domain Specific IRs, Great! High-level domain-specific optimizations; Progressive lowering encourages reuse between levels
05:48:24 <anssik> ... Not great!
05:48:29 <anssik> ... Huge expense to build this infrastructure
05:48:34 <anssik> ... Reimplementation of all the same stuff:
05:48:40 <anssik> ... pass managers, location tracking, use-def chains, inlining,
05:48:47 <anssik> ... constant folding, CSE, testing tools, ….
05:48:51 <anssik> ... Innovations in one community don’t benefit the others
05:49:19 <anssik> nikhil: let's talk about what is MLIR
05:50:21 <anssik> ... TensorFlow
05:50:21 <anssik> ... "An open source machine learning framework for everyone"
05:50:21 <anssik> ... Multi-Level Intermediate Representation
05:50:21 <anssik> ... "An open source program optimization framework for ... everyone"
05:50:21 <anssik> ... Abstraction Building Toolkit
05:50:22 <anssik> ... Reusable set of compiler passes for higher abstractions
05:50:22 <anssik> ... Targeting analysis/program optimization/code generation
05:50:22 <anssik> ... Open governance and part of LLVM
05:50:48 <anssik> nikhil: MLIR has wide support across industry
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05:51:19 <anssik> nikhil: Extensible Operations Allow Multi-Level IR
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05:52:43 <anssik> ... MLIR “Dialects”: Families of defined operations
05:53:16 <anssik> ... Example Dialects:
05:53:16 <anssik> ... TensorFlow, LLVM IR, XLA HLO, TF Lite, Swift SIL…
05:53:16 <anssik> ... Dialects can define:
05:53:16 <anssik> ... Sets of defined operations
05:53:16 <anssik> ... Entirely custom type system
05:53:16 <anssik> ... Customization hooks
05:53:16 <anssik> ... Constant folding, decoding
05:53:18 <anssik> ... Operation can define:
05:53:18 <anssik> ... Invariants on # operands, results, attributes, etc
05:53:18 <anssik> ... Custom parser, printer, verifier, …
05:53:37 <anssik> nikhil: MLIR Type System - some examples
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05:53:58 <anssik> ... Scalars:
05:53:58 <anssik> ... f16, bf16, f32, … i1, i8, i16, i32, … i3, i4, i7, i57, …
05:53:58 <anssik> ... Vectors:
05:53:58 <anssik> ... vector<4 x f32>   vector<4x4 x f16>  etc.
05:53:59 <anssik> ... Tensors, including dynamic shape and rank:
05:53:59 <anssik> ... tensor<4x4 x f32>   tensor<4x?x?x17x? x f32>     tensor<* x f32>
05:53:59 <anssik> ... Others: functions, memory buffers, quantized integers, other ... TensorFlow stuff, ...
05:53:59 <anssik> ... Extensible!!
05:55:58 <anssik> nikhil: Applications of MLIR
05:56:05 <anssik> ... TensorFlow Lite Converter
05:56:30 <anssik> ... One of the focusses: Usability
05:56:45 <anssik> ... Usability of TOCO top complaint among TFLite users
05:56:46 <anssik> ... Debugging
05:56:53 <anssik> ... Report why a model failed to convert
05:57:01 <anssik> ... Dialect types enable more checking & better reporting
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05:58:51 <anssik> ... [MLIR] for the Web?
05:59:08 <anssik> ... Some facts from MLIR investigations
05:59:14 <anssik> ... Operator expansion is about 25% YoY for TensorFlow
05:59:20 <anssik> ... Hardware vendors will implement dialects
05:59:50 <anssik> ... Open governance
06:00:29 <anssik> riju: regarding operator expansion, is there a fallback mechanism, even if with performance penalty?
06:00:37 <anssik> nikhil: we'd need to use e.g. a Wasm polyfill
06:01:36 <anssik> ... MLIR dialect on the web -- thoughts
06:02:13 <anssik> ... No backwards compatible guarantees today from MLIR
06:02:13 <anssik> ... A dialect could be invented that is backwards compatible
06:02:13 <anssik> ... What does maintaining this look like?
06:02:13 <anssik> ... Web sourcemaps => python code
06:02:13 <anssik> ... Immediately tells you whether python code will execute in browser
06:02:28 <anssik> kenneth: web needs backwards compat, and we do not really do versioning on the Web
06:02:46 <anssik> nikhil: how maintaining backwards compatibility could happen?
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06:03:42 <anssik> dino: LLVM IR is a well-suited as a web transport format
06:04:20 <yoshiaki_> yoshiaki_ has joined #webmachinelearning
06:04:20 <whsieh> ^ *not* well-suited?
06:04:31 <anssik> ... a lot of lowering, what is the improvement?
06:04:49 <anssik> s/well-suited/not well-suited/
06:05:18 <anssik> RRSAgent, draft minutes v2
06:05:18 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
06:07:05 <anssik> dino: what is the scope of the group, all models interop with all devices?
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06:08:39 <anssik> ... we could start with a set of ops everyone supports
06:09:05 <anssik> nikhil: initially we wanted to support all ops
06:09:24 <anssik> ... then understood better growing the set slowly is a better approach
06:10:14 <anssik> dino: our fear is, and I can be wrong, if the ecosystem becomes skewed toward TF models, so that those get hardware acceleration while some other models might not
06:10:31 <anssik> nikhil: as a group we can grow that set so that it does not happen
06:10:57 <anssik> dino: TF is growing fast, how's hardware adding ops?
06:11:20 <anssik> nikhil: I think hardware vendors add new ops more slowly
06:11:34 <anssik> kenneth: do any ops go away with time?
06:12:02 <anssik> riju: any kind of ranking within these ops, what are used the most?
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06:12:15 <anssik> nikhil: TF has it, not sure if can make that data public
06:13:56 <anssik> Philip: Swift for TF was good experience from usability perspecticve
06:14:13 <anssik> ... ML not a domain of data scientists for any longer, need good dev ergonomics
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06:14:43 <anssik> ningxin_hu: on which level of abstraction would the Web dialect of MLIR sit on?
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06:15:39 <anssik> nikhil: lower level things would evolve more slowly, but not sure at this point on which level the web dialect should be at
06:16:01 <anssik> dino: generally Apple's position is that a high-level abstraction works well on the Web since it allows implementations to optimize
06:16:18 <anssik> ... we don't have a huge dataset, but JS is a good example
06:16:34 <anssik> ... no enough data yet how Wasm goes
06:16:53 <anssik> ... if we did a Web dialect, it would be something like that, but we'd make it a bit more higher-level than LLVM IR
06:17:37 <anssik> nikhil: I'm wondering whether there's a level of abstraction between ops and LLVM IR we should target
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06:20:30 <anssik> anssik: what would be good next steps for the group re MLIR tasks?
06:20:48 <anssik> nikhil: talking to MLIR people, it seems a bit too early still since moving target
06:21:46 <anssik> ... concretely, I can try to figure out which ops are used, how many times an op is called
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06:22:38 <anssik> RRSAgent, draft minutes v2
06:22:38 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
06:24:01 <anssik> RRSAgent, draft minutes v2
06:24:01 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
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06:24:30 <HelloFillip> The link to Chris's talk on Swift for TensorFlow can be found here (as an example for other languages): https://www.youtube.com/watch?v=s65BigoMV_I
06:25:56 <anssik> we'll defer Day 1 3rd topic "operation set" to Day 2 on Friday
06:26:09 <anssik> thanks for attending, we'll see again on Friday!
06:26:15 <anssik> Topic: Adjourn
06:26:17 <anssik> RRSAgent, draft minutes v2
06:26:17 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
06:26:22 <belem> Thanks Anssi!
06:29:10 <anssik> Present+ Nikhil_Thorat
06:29:12 <anssik> RRSAgent, draft minutes v2
06:29:12 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
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06:32:09 <anssik> Present+ Heejin_Chung
06:32:53 <anssik> Present+ Philip_Laszkowicz
06:33:11 <anssik> Present+ Diogo_Cortiz
06:33:36 <anssik> Present+ Dean_Jackson
06:34:05 <anssik> Present+ Wooglae_Kim
06:34:25 <anssik> RRSAgent, draft minutes v2
06:34:25 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
06:36:01 <anssik> Present+ David_Marsh
06:36:25 <anssik> Present+ Kenneth_Christiansen
06:37:15 <anssik> Present+ Wenson_Hsieh
06:37:38 <anssik> Present+ A
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06:37:55 <anssik> Present+ Takio_Yamaoka
06:38:02 <anssik> s/Present+ A//
06:38:25 <anssik> Present+ Sangwhan_Moon
06:39:36 <anssik> Present+ Belem_Zhang_(remote)
06:39:54 <anssik> Present+ James_Darpinian_(remote)
06:39:59 <anssik> RRSAgent, draft minutes v2
06:39:59 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
06:41:02 <anssik> Present+ Frank_?
06:41:13 <anssik> RRSAgent, draft minutes v2
06:41:13 <RRSAgent> I have made the request to generate https://www.w3.org/2019/09/17-webmachinelearning-minutes.html anssik
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