From c4d81449c2aa417b0a551cac34db01e1e6ce747d Mon Sep 17 00:00:00 2001 From: Yuduo Wu Date: Fri, 14 Dec 2018 14:04:46 -0800 Subject: [PATCH] add project ray --- README.md | 65 ++++++++++++++++++++++++-------- images/intel-distiller-arch.png | Bin 0 -> 224802 bytes images/logo.png | Bin 0 -> 10264 bytes images/ucb-ray-arch.png | Bin 0 -> 192306 bytes 4 files changed, 50 insertions(+), 15 deletions(-) create mode 100644 images/intel-distiller-arch.png create mode 100644 images/logo.png create mode 100644 images/ucb-ray-arch.png diff --git a/README.md b/README.md index 827d79f..3e4288b 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,6 @@ -*** +

-

-:tent: Awesome AI Infrastructures :tent: -

+***

:orange_book: List of real-world AI infrastructures (a.k.a., machine @@ -17,7 +15,7 @@ recommendations and suggestions are welcome :tada:. *** -# Introduction +## Introduction This list contains some popular actively-maintained AI infrastructures that focus on one or more of the following topics: @@ -33,7 +31,7 @@ inference frameworks. My learning goals are: understand the workflows and principles of how to build (large-scale) systems that can enable machine learning in production. -# Machine Learning Platforms +## Machine Learning Platforms ### [TFX](https://www.tensorflow.org/tfx/) - TensorFlow Extended ([Google](https://www.google.com/about/)) @@ -85,7 +83,7 @@ learning in production. #### Architecture: -

+

#### Components: @@ -167,7 +165,7 @@ up for easy, fast, and scalable distributed training. #### Architecture: -

+

#### Components: @@ -272,9 +270,27 @@ allows them to upload and browse the code assets, submit distributed jobs, and q | [__h2o__](https://www.h2o.ai/products/h2o/) | [__h2o4gpu__](https://www.h2o.ai/products/h2o4gpu/) | -

+

+ +### Project Ray ([RISELab](https://rise.cs.berkeley.edu/projects/ray/)) + +> Ray is a high-performance distributed execution framework targeted at large-scale machine learning and reinforcement learning applications. It achieves scalability and fault tolerance by abstracting the control state of the system in a global control store and keeping all other components stateless. It uses a shared-memory distributed object store to efficiently handle large data through shared memory, and it uses a bottom-up hierarchical scheduling architecture to achieve low-latency and high-throughput scheduling. It uses a lightweight API based on dynamic task graphs and actors to express a wide range of applications in a flexible manner. + +| [__homepage__](https://ray.readthedocs.io/en/latest/) | [__github__](https://github.com/ray-project/ray) | [__blog__](https://ray-project.github.io/) | [__design overview__](https://ray.readthedocs.io/en/latest/internals-overview.html) | [__paper__](https://arxiv.org/abs/1712.05889) | + +#### Architecture: + +

+ +#### Components: + +- **Tune**: Scalable hyper-parameter search. + +- **RLlib**: Scalable reinforcement learning. -# Model Inference Deployment +- **Distributed** training. + +## Model Inference Deployment ### CoreML ([Apple](https://www.apple.com/)) @@ -284,7 +300,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q | [__homepage__](https://developer.apple.com/machine-learning/) | [__documentation__](https://developer.apple.com/documentation/coreml) | [__resources__](https://developer.apple.com/machine-learning/build-run-models/) | -

+

### TensorFlow Lite @@ -341,7 +357,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q #### Architecture: -

+

#### Features: @@ -351,7 +367,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q - Efficient client implementations in Go, Python, and Java. -# Model Training / Inference Optimizations +## Model Training / Inference Optimizations ### TensorFlow XLA (Accelerated Linear Algebra) @@ -492,6 +508,25 @@ to any order. - **Hardware Optimizations**: ONNX makes it easier for optimizations to reach more developers. Any tools exporting ONNX models can benefit ONNX-compatible runtimes and libraries designed to maximize performance on some of the best hardware in the industry. +### Neural Network Distiller (Intel AI Lab) + +> Distiller is an open-source Python package for neural network compression research. +Network compression can reduce the footprint of a neural network, increase its inference speed and save energy. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low precision arithmetic. + +| [__homepage__](https://nervanasystems.github.io/distiller/index.html) | [__github__](https://github.com/NervanaSystems/distiller/) | [__documentation__](https://nervanasystems.github.io/distiller/usage/index.html) | + +#### Workflow: + +

+ +#### Components: + +- A framework for integrating pruning, regularization and quantization algorithms. + +- A set of tools for analyzing and evaluating compression performance. + +- Example implementations of state-of-the-art compression algorithms. + ### AMC - AutoML for Model Compression engine > We propose AutoML for Model Compression (AMC) which leverage [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning) to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor. @@ -570,7 +605,7 @@ batch size during training - Input pipeline optimization: dataset sharding and caching, prefetch, fused JPEG decoding and cropping, parallel data parsing - Communication: 2D gradient summation -# AI Infrastructures / Machine Learning Systems Online Lectures +## AI Infrastructures / Machine Learning Systems Lectures #### CSE 599W Systems for ML (University of Washington) @@ -586,7 +621,7 @@ batch size during training | [__link__](https://pooyanjamshidi.github.io/mls/) | [__github__](https://github.com/pooyanjamshidi/mls) | [__materials__](https://pooyanjamshidi.github.io/mls/lectures/) | -# AI Infrastructures / Machine Learning Systems Conferences +## AI Infrastructures / Machine Learning Systems Conferences #### [SysML - Conference on Systems and Machine Learning @ Stanford](https://www.sysml.cc/) diff --git a/images/intel-distiller-arch.png b/images/intel-distiller-arch.png new file mode 100644 index 0000000000000000000000000000000000000000..de4a3bb64413cdaa89659d78379eaffbbc4ab066 GIT binary patch literal 224802 zcmeFZbyQSs*f$CYQc4JjG?LN+0@9&$cY_LuG|~($64H&*Db3JGW6>eqgHi)S3^jC| zP5C_U`rhaKdDi*kb1i3$d-lHXD}Ptq*Y<;olJs3HQY<7Sq`R^*&()BS&|pYNs1+Eu zfh(P=QvpawcbF}nJyVf=_KaG^!S1z%wHXqU%!hbwbRG51`)T?yQBl88GG#EEJ`5HO z|DwT=K_`2NNf(Dl@GgXch)4&U`F`emi(%(1%|cu1I0$oR|lU)0{d zP*9-r<=Q>VYivNqJ3KD*pC0Q!J=c8V?#V=qM0b16DJ?D;d+Q;Ry=$pX3@%b8I;=Nu zG4vsWM1q(;3Z{hh!}d>dYfSAca^wYNA0o}fQ*QfW#ea`BMaf`*{$&0AT0+#)3{Nn` zO#=T%FW&K6G;+!x=?^JCB`2eo*WdlYyE1#&c@osW`!bzeY3Z@nQWjqPi%gA_@1M4O z66Ss6Z6mB71@c>K_|XYzrBycCPPpScC|G#&pN>;P+;Z3Shi5>GNAo zx9qhqGLqcl+jq3oyzj>q;s}WN^Y^>)0xKQ{R!h$Z9Im-7>cla2hEnWCDnaF+%>_P| z&I+clWr{XqM8!FpX8m0m6a`N(?GD=3pRV$8Q+DaEinw#E%}@9pf*B*J$g_H| zS>{6T-99u~oIA-F7eA($OZh17QT!>LfMG0Ie43dpn2dBP8J97c%a}ZP$7C*k+J2q2 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