├── .gitignore ├── CONTRIBUTING.md ├── LICENSE.txt ├── Makefile ├── README.md ├── docs ├── .nojekyll ├── README.md ├── _navbar.md ├── _sidebar.md ├── about.md ├── assets │ ├── arch-sm.jpg │ ├── docsify.min.js │ ├── ga.min.js │ ├── graphpipe.css │ ├── graphpipe.js │ ├── logo.png │ ├── prism-bash.min.js │ ├── prism-c.min.js │ ├── prism-cpp.min.js │ ├── prism-go.min.js │ ├── prism-java.min.js │ ├── prism-latex.min.js │ ├── prism-python.min.js │ ├── search.min.js │ ├── toaster-js.css │ ├── toaster-js.js │ ├── vue-material.min.css │ ├── vue-material@beta │ ├── vue.css │ └── vue.min.js ├── favicon.ico ├── guide │ ├── _sidebar.md │ ├── clients │ │ ├── _media │ │ │ └── client_interaction.png │ │ ├── installation.md │ │ ├── overview.md │ │ └── usage.md │ ├── examples │ │ └── overview.md │ ├── servers │ │ ├── _media │ │ │ └── server_flow.png │ │ ├── converting.md │ │ ├── graphpipe-onnx.md │ │ ├── graphpipe-tf.md │ │ ├── installation.md │ │ ├── overview.md │ │ └── serving.md │ └── user-guide │ │ ├── _examples │ │ ├── _metadata_1.py │ │ └── _squeezenet_req.python │ │ ├── _media │ │ ├── gparch.001.jpg │ │ ├── gparch.002.jpg │ │ ├── logo.png │ │ ├── mug.png │ │ ├── mug227.png │ │ ├── perf.001.jpg │ │ └── perf.002.jpg │ │ ├── get_involved.md │ │ ├── overview.md │ │ ├── performance.md │ │ ├── quickstart.md │ │ └── spec.md ├── index.html └── models │ ├── squeezenet.h5 │ ├── squeezenet.init_net.pb │ ├── squeezenet.onnx │ ├── squeezenet.pb │ ├── squeezenet.predict_net.pb │ ├── squeezenet.value_inputs.json │ └── squeezenet.value_inputs_caffe2.json └── graphpipe.fbs /.gitignore: -------------------------------------------------------------------------------- 1 | go 2 | python 3 | cpp 4 | *.sw* 5 | .idea 6 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to Graphpipe # 2 | 3 | *Copyright (c) 2018, Oracle and/or its affiliates. All rights reserved.* 4 | 5 | Pull requests can be made under 6 | [The Oracle Contributor Agreement](https://www.oracle.com/technetwork/community/oca-486395.html) (OCA). 7 | 8 | For pull requests to be accepted, the bottom of 9 | your commit message must have the following line using your name and 10 | e-mail address as it appears in the OCA Signatories list. 11 | 12 | ``` 13 | Signed-off-by: Your Name 14 | ``` 15 | 16 | This can be automatically added to pull requests by committing with: 17 | 18 | ``` 19 | git commit --signoff 20 | ``` 21 | 22 | Only pull requests from committers that can be verified as having 23 | signed the OCA can be accepted. 24 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2018, Oracle and/or its affiliates. All rights reserved. 2 | 3 | This software is licensed to you under the Universal Permissive License (UPL). See below for license terms. 4 | ____________________________ 5 | The Universal Permissive License (UPL), Version 1.0 6 | Copyright (c) 2018, Oracle and/or its affiliates. All rights reserved. 7 | 8 | Subject to the condition set forth below, permission is hereby granted to any person obtaining a copy of this software, associated documentation and/or data (collectively the "Software"), free of charge and under any and all copyright rights in the Software, and any and all patent rights owned or freely licensable by each licensor hereunder covering either (i) the unmodified Software as contributed to or provided by such licensor, or (ii) the Larger Works (as defined below), to deal in both 9 | 10 | (a) the Software, and 11 | (b) any piece of software and/or hardware listed in the lrgrwrks.txt file if one is included with the Software (each a "Larger Work" to which the Software is contributed by such licensors), 12 | 13 | without restriction, including without limitation the rights to copy, create derivative works of, display, perform, and distribute the Software and make, use, sell, offer for sale, import, export, have made, and have sold the Software and the Larger Work(s), and to sublicense the foregoing rights on either these or other terms. 14 | 15 | This license is subject to the following condition: 16 | 17 | The above copyright notice and either this complete permission notice or at a minimum a reference to the UPL must be included in all copies or substantial portions of the Software. 18 | 19 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 20 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, Oracle and/or its affiliates. All rights reserved. 2 | # 3 | # Licensed under the Universal Permissive License v 1.0 as shown at 4 | # http://oss.oracle.com/licenses/upl. 5 | 6 | FLATC=flatc 7 | ROOT=. 8 | 9 | ifeq ('$(USE_DOCKER)', '1') 10 | ROOT=/src 11 | FLATC=docker run --rm -it \ 12 | --user $$(id -u):$$(id -g) \ 13 | -v $(PWD):/src neomantra/flatbuffers:v1.9.0 flatc 14 | endif 15 | 16 | all: cpp/graphpipe_generated.h go/graphpipe python/graphpipe 17 | 18 | .PHONY: go python cpp 19 | 20 | go: go/graphpipe 21 | 22 | cpp: cpp/graphpipe_generated.h 23 | 24 | python: python/graphpipe 25 | 26 | cpp/graphpipe_generated.h: 27 | $(FLATC) --cpp -o $(ROOT)/cpp $(ROOT)/graphpipe.fbs 28 | 29 | go/graphpipe: 30 | $(FLATC) --go -o $(ROOT)/go $(ROOT)/graphpipe.fbs 31 | 32 | python/graphpipe: 33 | $(FLATC) --python -o $(ROOT)/python $(ROOT)/graphpipe.fbs 34 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 6 | 7 | # GraphPipe 8 | 9 | > Machine Learning Model Deployment Made Simple 10 | 11 | # What is it? 12 | 13 | GraphPipe is a protocol and collection of software designed to simplify machine 14 | learning model deployment and decouple it from framework-specific model 15 | implementations. 16 | 17 | The existing solutions for model serving are inconsistent and/or inefficient. 18 | There is no consistent protocol for communicating with these model servers so 19 | it is often necessary to build custom clients for each workload. GraphPipe 20 | solves these problems by standardizing on an efficient communication protocol 21 | and providing simple model servers for the major ML frameworks. 22 | 23 | We hope that open sourcing GraphPipe makes the model serving landscape a 24 | friendlier place. See more about why we built it 25 | [here](https://oracle.github.io/graphpipe/#/guide/user-guide/overview). 26 | 27 | Or browse the rest of the [documentation](https://oracle.github.io/graphpipe). 28 | 29 | # Features 30 | 31 | * A minimalist machine learning transport specification based on [flatbuffers] 32 | * Simple, efficient reference model servers for [Tensorflow], [Caffe2], and [ONNX]. 33 | * Efficient client implementations in Go, Python, and Java. 34 | 35 | [flatbuffers]: https://google.github.io/flatbuffers/ 36 | [Tensorflow]: https://www.tensorflow.org 37 | [Caffe2]: https://caffe2.ai 38 | [ONNX]: https://onnx.ai 39 | 40 | # What is in this repo? 41 | This repo contains documentation as well as the flatubuffer definition files 42 | that are used by other language specific repos. If you are looking for 43 | GraphPipe clients, servers, and example code, check out our other GraphPipe 44 | repos: 45 | 46 | - https://github.com/oracle/graphpipe-go - the GraphPipe go client library 47 | - https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-tf - 48 | Go implementation of a GraphPipe TensorFlow model server 49 | - https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-onnx - 50 | a Go implementation a GraphPipe ONNX/Caffe2 model server 51 | - https://github.com/oracle/graphpipe-py - the GraphPipe client library for 52 | Python 53 | - https://github.com/oracle/graphpipe-tf-py - a Python implementation of 54 | a remote operation client for TensorFlow, as well as some example server 55 | implementations 56 | 57 | ## Building flatbuffer definitions 58 | 59 | If you've got flatc installed you can just `make all` but if you don't want 60 | to install it, you can `export USE_DOCKER=1` and then `make all`. (Remember, 61 | make needs vars exported, not just on the command-line where you run make). 62 | 63 | This will produce the go, c, and python libraries, which can then be copied 64 | into their projects graphpipe-go, graphpipe-tf-py, and graphpipe-py, 65 | respectively. 66 | 67 | ## Contributing 68 | 69 | All of the GraphPipe projects are open source. To find out how to contribute 70 | see [CONTRIBUTING.md](CONTRIBUTING.md) 71 | 72 | You can also chat us up on our [Slack Channel](https://join.slack.com/t/graphpipe/shared_invite/enQtNDE4MTUyODk2NzQzLTUwODlkZDRiYTI4NmE1OTA5NzRmNjk5MGZiY2M0ZDRiYzNiMTQ0ZmIxODYzZjY2NzRmNzM4NTI0OGVlZGYzZTA). 73 | -------------------------------------------------------------------------------- /docs/.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/.nojekyll -------------------------------------------------------------------------------- /docs/README.md: -------------------------------------------------------------------------------- 1 | 6 | 7 |
__Machine Learning Model Deployment Made Simple__
8 | 9 | 14 | 15 | # What is it? 16 | 17 | GraphPipe is a protocol and collection of software designed to simplify machine 18 | learning model deployment and decouple it from framework-specific model 19 | implementations. 20 | 21 | # Why did we make it? 22 | 23 | We found existing solutions for model serving to be inconsistent and/or inefficient. 24 | Without a consistent protocol for communicating with different model servers, 25 | it is often necessary to build custom clients for each workload. GraphPipe 26 | solves these problems by standardizing on an efficient communication protocol 27 | and providing simple model servers for the major ML frameworks. 28 | 29 | We hope that open sourcing GraphPipe makes the model serving landscape a 30 | friendlier place. See more about why we built it 31 | [here](guide/user-guide/overview). 32 | 33 | # Features 34 | 35 | * A minimalist machine learning transport specification based on [flatbuffers] 36 | * Simple, efficient reference model servers for [Tensorflow], [Caffe2], and [ONNX]. 37 | * Efficient client implementations in Go, Python, and Java. 38 | 39 | [flatbuffers]: https://google.github.io/flatbuffers/ 40 | [Tensorflow]: https://www.tensorflow.org 41 | [Caffe2]: https://caffe2.ai 42 | [ONNX]: https://onnx.ai 43 | 44 | 45 | ### Quick Navigation 46 | 47 | - [Project Overview](guide/user-guide/overview.md) 48 | - [Getting Started](guide/user-guide/quickstart.md) 49 | - [GraphPipe Specification](guide/user-guide/spec.md) 50 | - [Performance Measurements](guide/user-guide/performance.md) 51 | - [Running a GraphPipe Server](guide/servers/overview.md) 52 | - [Using GraphPipe Clients](guide/clients/overview.md) 53 | -------------------------------------------------------------------------------- /docs/_navbar.md: -------------------------------------------------------------------------------- 1 | - [Home](/)     2 | 3 | - Documentation ▾ 4 | - [Overview](/guide/user-guide/overview) 5 | - [Quick Start](/guide/user-guide/quickstart) 6 | - [Spec](/guide/user-guide/spec) 7 | - [Performance](/guide/user-guide/performance) 8 | - [Serving Models](/guide/servers/overview) 9 | - [Using GraphPipe Clients](/guide/clients/overview) 10 | -------------------------------------------------------------------------------- /docs/_sidebar.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/_sidebar.md -------------------------------------------------------------------------------- /docs/about.md: -------------------------------------------------------------------------------- 1 | about us! 2 | -------------------------------------------------------------------------------- /docs/assets/arch-sm.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/assets/arch-sm.jpg -------------------------------------------------------------------------------- /docs/assets/docsify.min.js: -------------------------------------------------------------------------------- 1 | !function(){function e(e){var t=Object.create(null);return function(n){var r=i(n)?n:JSON.stringify(n);return t[r]||(t[r]=e(n))}}var t=e(function(e){return e.replace(/([A-Z])/g,function(e){return"-"+e.toLowerCase()})}),n=Object.prototype.hasOwnProperty,r=Object.assign||function(e){for(var t=arguments,r=1;r=i.length)r(n);else if("function"==typeof t)if(2===t.length)t(n,function(t){n=t,o(e+1)});else{var a=t(n);n=void 0===a?n:a,o(e+1)}else o(e+1)};o(0)}var l=!0,c=l&&document.body.clientWidth<=600,u=l&&window.history&&window.history.pushState&&window.history.replaceState&&!navigator.userAgent.match(/((iPod|iPhone|iPad).+\bOS\s+[1-4]\D|WebApps\/.+CFNetwork)/),h={};function p(e,t){if(void 0===t&&(t=!1),"string"==typeof e){if(void 0!==window.Vue)return m(e);e=t?m(e):h[e]||(h[e]=m(e))}return e}var d=l&&document,g=l&&d.body,f=l&&d.head;function m(e,t){return t?e.querySelector(t):d.querySelector(e)}function v(e,t){return[].slice.call(t?e.querySelectorAll(t):d.querySelectorAll(e))}function b(e,t){return e=d.createElement(e),t&&(e.innerHTML=t),e}function y(e,t){return e.appendChild(t)}function k(e,t){return e.insertBefore(t,e.children[0])}function w(e,t,n){o(t)?window.addEventListener(e,t):e.addEventListener(t,n)}function x(e,t,n){o(t)?window.removeEventListener(e,t):e.removeEventListener(t,n)}function _(e,t,n){e&&e.classList[n?t:"toggle"](n||t)}var S=Object.freeze({getNode:p,$:d,body:g,head:f,find:m,findAll:v,create:b,appendTo:y,before:k,on:w,off:x,toggleClass:_,style:function(e){y(f,b("style",e))}});function C(e,t){return void 0===t&&(t=""),e&&e.length?(e.forEach(function(e){t+='
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4 | margin-left: 1em; 5 | padding-left: 1em; 6 | border-left: 1px solid #ededed; 7 | } 8 | /*====================================*/ 9 | 10 | /*---[ Header & Footer ]--------------*/ 11 | #footer { 12 | color: #B2B3BA; 13 | line-height: 1.7em; 14 | margin-top: 2em; 15 | } 16 | 17 | #footer > hr { 18 | margin-bottom: 0.5em; 19 | } 20 | 21 | #footer a { 22 | color: #B2B3BA; 23 | font-weight: 400; 24 | } 25 | 26 | #footer .docsify-text { 27 | font-weight: 600; 28 | } 29 | 30 | #footer .heart { 31 | color: #F99F9F; 32 | } 33 | 34 | #main { 35 | padding-top: 50px !important; 36 | } 37 | 38 | .markdown-section h1:not(:first-of-type) { 39 | margin-top: 1em; 40 | } 41 | 42 | a:not(.md-button):hover { 43 | text-decoration-color: #F7D096; 44 | } 45 | /*====================================*/ 46 | 47 | 48 | /*---[ Navbar ]----------------------*/ 49 | .app-nav { 50 | margin-right: 100px; 51 | } 52 | 53 | .app-nav .arrow { 54 | color: #CFD3D9; 55 | margin-left: 3px; 56 | } 57 | 58 | .app-nav > ul { 59 | padding-left: 0px !important; 60 | margin-right: 50px; 61 | } 62 | 63 | .app-nav > ul > li > a { 64 | color: inherit; 65 | text-decoration: none; 66 | transition: color .3s; 67 | } 68 | 69 | .app-nav > ul > li > p > a { 70 | font-size: 15px; 71 | } 72 | 73 | .app-nav a:hover { 74 | text-decoration: none !important; 75 | } 76 | /*====================================*/ 77 | 78 | 79 | /*---[ Sidebar ]----------------------*/ 80 | /* |---[ Reorder sidebar ]-----------*/ 81 | .search { 82 | position: absolute; 83 | width: 100%; 84 | top: 70px; 85 | border-top: 1px solid #EEEEEE; 86 | z-index: 10; 87 | background-color: #FFFFFF; 88 | } 89 | 90 | .sidebar { 91 | overflow: hidden; 92 | } 93 | 94 | .sidebar > h1 { 95 | position: absolute; 96 | top: 20px; 97 | left: 0; 98 | width: 100%; 99 | } 100 | 101 | .sidebar-nav { 102 | position: absolute; 103 | width: 100%; 104 | top: 130px; 105 | /* Make only the nav scrollable */ 106 | overflow-y: auto; 107 | height: calc(100% - 168px); /* top (130px) + toggle (38px) */ 108 | box-sizing: content-box; 109 | padding-right: 17px; 110 | } 111 | /* |=================================*/ 112 | 113 | .sidebar > h1 > a.app-name-link { 114 | color: #2B7FB9; 115 | } 116 | 117 | .app-sub-sidebar li:before { 118 | display: none; 119 | } 120 | 121 | body.no-sidebar .sidebar, 122 | body.no-sidebar .sidebar-toggle { 123 | display: none !important; 124 | } 125 | 126 | body.no-sidebar .content { 127 | left: 0px !important; 128 | } 129 | 130 | .github-corner { 131 | display: block !important; 132 | border-bottom: 0 !important; 133 | position: fixed !important; 134 | right: 0 !important; 135 | text-decoration: none !important; 136 | top: 0 !important; 137 | z-index: 1 !important; 138 | } 139 | 140 | .github-corner svg { 141 | color: #FFFFFF !important; 142 | fill: #00748f !important; 143 | } 144 | /*====================================*/ 145 | 146 | 147 | /*---[ Tabs ]-------------------------*/ 148 | .md-tabs .md-tabs-content { 149 | margin-bottom: -1em; 150 | } 151 | 152 | .md-button.md-focused:before, 153 | .md-button:active:before, 154 | .md-button:hover:before { 155 | background-color: #00748f; 156 | opacity: .12; 157 | } 158 | 159 | .md-tabs.md-theme-default .md-tabs-navigation .md-button.md-active { 160 | color: #00748f; 161 | } 162 | 163 | .md-tabs-indicator { 164 | background-color: #00748f; 165 | } 166 | 167 | .md-ripple .md-ripple-wave { 168 | background-color: #00748f; 169 | } 170 | 171 | /* Code Blocks */ 172 | .md-tabs pre:not(:first-of-type) { 173 | margin-top: 1em; 174 | } 175 | 176 | .md-tabs pre { 177 | margin-top: 0; 178 | margin-bottom: 0; 179 | } 180 | 181 | .markdown-section pre { 182 | margin: 0 !important; 183 | padding: 1.5em !important; 184 | } 185 | 186 | .markdown-section pre:not(:first-child) { 187 | margin-top: 1em !important; 188 | } 189 | 190 | pre:after { 191 | font-size: 0.8em !important; 192 | font-family: Source Sans Pro, Helvetica Neue, Arial, sans-serif; 193 | font-weight: 400; 194 | } 195 | 196 | .markdown-section code { 197 | margin: 0 !important; 198 | padding: 0 !important; 199 | background-color: transparent !important; 200 | } 201 | 202 | blockquote pre { 203 | border: 1px solid #F0B794 !important; 204 | } 205 | /*====================================*/ 206 | 207 | 208 | /*---[ Markdown ]---------------------*/ 209 | p.warn, p.tip, blockquote { 210 | position: relative !important; 211 | } 212 | 213 | p.warn:before, 214 | p.tip:before, 215 | blockquote:before { 216 | border-radius: 100% !important; 217 | font-family: Dosis,Source Sans Pro,Helvetica Neue,Arial,sans-serif !important; 218 | font-size: 14px !important; 219 | font-weight: 700 !important; 220 | left: -12px !important; 221 | line-height: 20px !important; 222 | position: absolute !important; 223 | height: 20px !important; 224 | width: 20px !important; 225 | text-align: center !important; 226 | top: 17px !important; 227 | } 228 | 229 | .markdown-section p.warn { 230 | background-color: transparent; 231 | border: 1px solid #E9692F; 232 | padding: 1.5rem 1rem; 233 | } 234 | 235 | .markdown-section p.tip { 236 | background-color: #F8F8F8; 237 | border-left: 3px solid #FF6666; 238 | padding: 1.5rem 1rem !important; 239 | } 240 | 241 | .markdown-section p.tip:before { 242 | top: 27px !important; 243 | } 244 | 245 | .markdown-section blockquote { 246 | padding: 1rem !important; 247 | background-color: #F8F8F8; 248 | } 249 | 250 | .markdown-section blockquote > p { 251 | margin: 0 !important; 252 | font-weight: normal !important; 253 | color: #34495E !important; 254 | } 255 | 256 | .markdown-section p > code { 257 | background-color: transparent !important; 258 | font-size: 0.85em !important; 259 | } 260 | 261 | .markdown-section p.warn > code { 262 | color: #F54D4D !important; 263 | } 264 | /*====================================*/ 265 | 266 | 267 | /*---[ Attributes ]-------------------*/ 268 | code .token.annotation { 269 | color: #00748f; 270 | } 271 | 272 | .transform-arrow { 273 | display: block; 274 | width: 1em; 275 | margin: -0.3em auto; 276 | color: #00748f; 277 | } 278 | /*====================================*/ 279 | 280 | 281 | /*---[ API ]--------------------------*/ 282 | .api-container h2 { 283 | margin-top: 0.5em; 284 | } 285 | 286 | .api-container h2 a { 287 | text-decoration: none; 288 | } 289 | 290 | .api-container h2 a span { 291 | font-size: 1rem; 292 | font-family: Roboto Mono,Monaco,courier,monospace; 293 | font-weight: lighter; 294 | margin-left: 1.2em; 295 | } 296 | 297 | .api-container h2 a span:before { 298 | content: "#"; 299 | color: #00748f; 300 | position: absolute; 301 | left: 1em; 302 | margin-top: 0.25em; 303 | font-size: 1em; 304 | } 305 | 306 | .api-container hr { 307 | margin: 5px 0; 308 | } 309 | 310 | .api-container ul { 311 | list-style-type: none; 312 | } 313 | 314 | .api-container li p:first-child { 315 | margin-bottom: 0.3em; 316 | } 317 | 318 | .api-container li p:nth-child(2) { 319 | margin-top: 0.3em; 320 | } 321 | /*====================================*/ 322 | 323 | 324 | /*---[ Team ]-------------------------*/ 325 | .member { 326 | display: flex; 327 | margin-top: 2em; 328 | } 329 | 330 | .member .avatar { 331 | flex: 0 0 80px; 332 | } 333 | 334 | .member .avatar .md-avatar { 335 | width: 80px; 336 | height: 80px; 337 | } 338 | 339 | .member .profile { 340 | padding-left: 26px; 341 | flex: 1; 342 | } 343 | 344 | .member .profile h3 { 345 | margin-top: 0; 346 | } 347 | 348 | .member .profile dl { 349 | margin-top: 0.6em; 350 | } 351 | 352 | .member .profile dt { 353 | font-size: 0.84em; 354 | } 355 | 356 | .member .profile dt, 357 | .member .profile dd { 358 | display: inline; 359 | padding: 0; 360 | margin: 0; 361 | line-height: 1.3; 362 | font-weight: 600; 363 | } 364 | 365 | .member .profile dt::after { 366 | content: ""; 367 | margin-right: 7px; 368 | } 369 | 370 | .member .profile dd::after { 371 | display: block; 372 | content: " "; 373 | } 374 | 375 | .member .profile dt i { 376 | width: 14px; 377 | text-align: center; 378 | } 379 | 380 | .member .profile dt i.fa-map-marker { 381 | font-size: 1.15em; 382 | } 383 | 384 | .member footer { 385 | margin-top: 0.5em; 386 | } 387 | 388 | .member footer a:not(:first-child) { 389 | margin-left: 5px; 390 | } 391 | 392 | .fa-github { 393 | color: #2C3E50; 394 | } 395 | 396 | .fa-twitter { 397 | color: #1DA1F3; 398 | } 399 | 400 | .fa-google { 401 | color: #4285f4; 402 | } 403 | 404 | .team-header-gap { 405 | height: 1px; 406 | } 407 | /*====================================*/ 408 | 409 | 410 | /*---[ Gallery ]----------------------*/ 411 | .gallery-entry { 412 | display: flex; 413 | margin-top: 3em; 414 | } 415 | 416 | .gallery-entry .image { 417 | flex: 0 0 100px; 418 | height: 100px; 419 | border-radius: 1000px; 420 | overflow: hidden; 421 | border: 5px solid white; 422 | 423 | box-shadow: 424 | 0px 1px 5px 0px rgba(0, 0, 0, 0.2), 425 | 0px 2px 2px 0px rgba(0, 0, 0, 0.14), 426 | 0px 3px 1px -2px rgba(0, 0, 0, 0.12); 427 | } 428 | 429 | .gallery-entry .image:hover { 430 | box-shadow: 431 | 0px 2px 4px -1px rgba(0, 0, 0, 0.2), 432 | 0px 4px 5px 0px rgba(0, 0, 0, 0.14), 433 | 0px 1px 10px 0px rgba(0, 0, 0, 0.12); 434 | } 435 | 436 | .gallery-entry .image img { 437 | width: 90px; 438 | height: 90px; 439 | } 440 | 441 | .gallery-entry .at { 442 | font-weight: 100; 443 | color: #7A8796; 444 | } 445 | 446 | .gallery-entry .description { 447 | padding-left: 26px; 448 | padding-top: 0.3em; 449 | flex: 1; 450 | } 451 | 452 | .gallery-entry .description h3 { 453 | margin-top: 0; 454 | } 455 | /*====================================*/ 456 | 457 | 458 | /*---[ Publications ]-----------------*/ 459 | .authors { 460 | margin-top: -0.9em; 461 | font-style: italic; 462 | } 463 | 464 | .authors a { 465 | font-weight: 400; 466 | } 467 | /*====================================*/ 468 | 469 | 470 | /*---[ Print ]------------------------*/ 471 | @media print { 472 | .sidebar { 473 | display: none, 474 | } 475 | .content { 476 | left: 0; 477 | } 478 | } 479 | /*====================================*/ 480 | 481 | .app-nav ul li p { 482 | cursor: pointer; 483 | } 484 | -------------------------------------------------------------------------------- /docs/assets/graphpipe.js: -------------------------------------------------------------------------------- 1 | var graphpipe = graphpipe || {}; 2 | 3 | graphpipe.languageLabels = { 4 | cpp: 'C++', 5 | okl: 'OKL', 6 | }; 7 | 8 | graphpipe.getLanguageLabel = (language) => ( 9 | graphpipe.languageLabels[language] || language.toUpperCase() 10 | ); 11 | 12 | //---[ Header & Footer ]---------------- 13 | graphpipe.addFooter = (content) => ( 14 | content 15 | + '\n\n' 16 | + '\n' 25 | ); 26 | //====================================== 27 | 28 | //---[ Indent ]------------------------- 29 | graphpipe.parseIndent = (content) => { 30 | const parts = marked.lexer(content); 31 | const mdContent = graphpipe.tokensToHTML(parts); 32 | return ( 33 | '
      \n' 34 | + mdContent 35 | + '
      \n' 36 | ); 37 | } 38 | 39 | graphpipe.addIndents = (content) => { 40 | const re = /\n::: indent\n([\s\S]*?)\n:::(\n|$)/g; 41 | const parts = []; 42 | var lastIndex = 0; 43 | while ((match = re.exec(content)) != null) { 44 | const [fullMatch, indentContent] = match; 45 | 46 | parts.push(content.substring(lastIndex, match.index)); 47 | parts.push(graphpipe.parseIndent(indentContent)); 48 | 49 | lastIndex = match.index + fullMatch.length; 50 | } 51 | parts.push(content.substring(lastIndex)); 52 | 53 | return parts.join('\n'); 54 | }; 55 | //====================================== 56 | 57 | //---[ Tabs ]--------------------------- 58 | graphpipe.markdown = { 59 | space: () => ( 60 | '' 61 | ), 62 | text: ({ text }) => ( 63 | `

      ${text}

      ` 64 | ), 65 | paragraph: ({ text }) => ( 66 | `

      ${text}

      ` 67 | ), 68 | list_start: () => ( 69 | '
        ' 70 | ), 71 | list_end: () => ( 72 | '
      ' 73 | ), 74 | list_item_start: () => ( 75 | '
    • ' 76 | ), 77 | list_item_end: () => ( 78 | '
      • ' 79 | ), 80 | html: ({ text }) => ( 81 | text 82 | ), 83 | }; 84 | 85 | graphpipe.markdown.code = ({ lang, text }) => { 86 | // Remove indentation 87 | const initIndent = text.match(/^\s*/)[0]; 88 | if (initIndent.length) { 89 | const lines = text .split(/\r?\n/); 90 | const isIndented = lines.every((line) => ( 91 | !line.length 92 | || line.startsWith(initIndent) 93 | )); 94 | 95 | if (isIndented) { 96 | text = lines.map((line) => ( 97 | line.substring(initIndent.length) 98 | )).join('\n'); 99 | } 100 | } 101 | 102 | // Generate highlighted HTML 103 | const styledCode = Prism.highlight(text, 104 | Prism.languages[lang], 105 | lang); 106 | 107 | // Wrap around pre + code 108 | return ( 109 | ( 110 | `
      `
111 |         + ``
112 |         + `${styledCode}\n`
113 |         + ''
114 |         + '
      ' 115 | ) 116 | .replace(/[*]/g, '*') 117 | .replace(/[_]/g, '_') 118 | ); 119 | } 120 | 121 | graphpipe.tokenToMarkdown = (token) => { 122 | const { type } = token; 123 | if (type in graphpipe.markdown) { 124 | return graphpipe.markdown[token.type](token); 125 | } 126 | console.error(`Missing token format for: ${token.type}`, token); 127 | return ''; 128 | }; 129 | 130 | graphpipe.mergeTextTokens = (tokens) => { 131 | const newTokens = []; 132 | let texts = []; 133 | for (var i = 0; i < tokens.length; ++i) { 134 | const token = tokens[i]; 135 | if (token.type === 'text') { 136 | texts.push(token.text); 137 | continue; 138 | } 139 | if (texts.length) { 140 | newTokens.push({ 141 | type: 'text', 142 | text: texts.join(' '), 143 | }); 144 | texts = []; 145 | } 146 | newTokens.push(token); 147 | } 148 | // Join the tail texts 149 | if (texts.length) { 150 | newTokens.push({ 151 | type: 'text', 152 | text: texts.join(' '), 153 | }); 154 | } 155 | return newTokens; 156 | }; 157 | 158 | graphpipe.tokensToHTML = (tokens) => { 159 | tokens = graphpipe.mergeTextTokens(tokens); 160 | return ( 161 | tokens 162 | .map(graphpipe.tokenToMarkdown) 163 | .join('\n') 164 | ); 165 | }; 166 | 167 | graphpipe.getTab = ({ tab, content }) => ( 168 | ` \n` 169 | + graphpipe.tokensToHTML(content) 170 | + ' ' 171 | ); 172 | 173 | graphpipe.getTabs = (namespace, tabs) => { 174 | const content = tabs.map(graphpipe.getTab).join('\n'); 175 | 176 | const tab = `vm.$data.tabNamespaces['${namespace}']`; 177 | const onClick = `(tab) => vm.onTabChange('${namespace}', tab)`; 178 | 179 | return ( 180 | '\n' 191 | ); 192 | }; 193 | 194 | graphpipe.parseTabs = (namespace, content) => { 195 | const parts = marked.lexer(content); 196 | const newParts = []; 197 | 198 | // Skip begin/end of list 199 | for (var i = 1; i < (parts.length - 1); ++i) { 200 | var stackSize = 1; 201 | 202 | // Skip loose_item_start; 203 | ++i; 204 | const tab = parts[i++].text; 205 | const start = i++; 206 | 207 | while ((i < (parts.length - 1)) && (stackSize > 0)) { 208 | switch (parts[i].type) { 209 | case 'list_item_start': 210 | ++stackSize; 211 | break; 212 | case 'list_item_end': 213 | --stackSize; 214 | break; 215 | } 216 | ++i; 217 | } 218 | 219 | // Don't take the token after list_item_end 220 | --i; 221 | 222 | newParts.push({ 223 | tab, 224 | content: parts.slice(start, i), 225 | }); 226 | } 227 | 228 | if (!newParts.length) { 229 | return []; 230 | } 231 | 232 | if (!(namespace in vm.$data.tabNamespaces)) { 233 | Vue.set(vm.tabNamespaces, namespace, newParts[0].tab); 234 | } 235 | 236 | return graphpipe.getTabs(namespace, newParts); 237 | }; 238 | 239 | graphpipe.addTabs = (content) => { 240 | const re = /\n::: tabs (.*)\n([\s\S]*?)\n:::(\n|$)/g; 241 | const parts = []; 242 | var lastIndex = 0; 243 | while ((match = re.exec(content)) != null) { 244 | const [fullMatch, namespace, tabContent] = match; 245 | 246 | parts.push(content.substring(lastIndex, match.index)); 247 | parts.push(graphpipe.parseTabs(namespace, tabContent)); 248 | 249 | lastIndex = match.index + fullMatch.length; 250 | } 251 | parts.push(content.substring(lastIndex)); 252 | 253 | return parts.join('\n'); 254 | }; 255 | //====================================== 256 | 257 | // Root-level markdowns don't have a sidebar 258 | graphpipe.hasSidebar = (file) => ( 259 | !file.match(/^[^/]*\.md$/) 260 | ) 261 | 262 | graphpipe.docsifyPlugin = (hook, vm) => { 263 | hook.init(() => { 264 | Prism.languages.okl = Prism.languages.extend('cpp', { 265 | annotation: { 266 | pattern: /@[a-zA-Z][a-zA-Z0-9_]*/, 267 | greedy: true, 268 | }, 269 | }); 270 | Prism.languages.bibtex = Prism.languages.extend('latex'); 271 | }); 272 | 273 | hook.beforeEach((content) => { 274 | // No \n means the last line turns into a header 275 | if (!content.endsWith('\n')) { 276 | content += '\n'; 277 | } 278 | content = graphpipe.addIndents(content); 279 | content = graphpipe.addTabs(content); 280 | content = graphpipe.addFooter(content); 281 | return content; 282 | }); 283 | 284 | hook.doneEach(() => { 285 | const dom = document.querySelector('body'); 286 | const file = vm.route.file; 287 | // Add API styling 288 | if (!file.startsWith('api/')) { 289 | dom.classList.remove('api-container'); 290 | } else { 291 | dom.classList.add('api-container'); 292 | } 293 | // Close sidebar 294 | if (graphpipe.hasSidebar(file)) { 295 | dom.classList.remove('no-sidebar'); 296 | } else { 297 | dom.classList.add('no-sidebar'); 298 | } 299 | }); 300 | }; 301 | -------------------------------------------------------------------------------- /docs/assets/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/assets/logo.png -------------------------------------------------------------------------------- /docs/assets/prism-bash.min.js: -------------------------------------------------------------------------------- 1 | !function(e){var t={variable:[{pattern:/\$?\(\([\s\S]+?\)\)/,inside:{variable:[{pattern:/(^\$\(\([\s\S]+)\)\)/,lookbehind:!0},/^\$\(\(/],number:/\b0x[\dA-Fa-f]+\b|(?:\b\d+\.?\d*|\B\.\d+)(?:[Ee]-?\d+)?/,operator:/--?|-=|\+\+?|\+=|!=?|~|\*\*?|\*=|\/=?|%=?|<<=?|>>=?|<=?|>=?|==?|&&?|&=|\^=?|\|\|?|\|=|\?|:/,punctuation:/\(\(?|\)\)?|,|;/}},{pattern:/\$\([^)]+\)|`[^`]+`/,greedy:!0,inside:{variable:/^\$\(|^`|\)$|`$/}},/\$(?:[\w#?*!@]+|\{[^}]+\})/i]};e.languages.bash={shebang:{pattern:/^#!\s*\/bin\/bash|^#!\s*\/bin\/sh/,alias:"important"},comment:{pattern:/(^|[^"{\\])#.*/,lookbehind:!0},string:[{pattern:/((?:^|[^<])<<\s*)["']?(\w+?)["']?\s*\r?\n(?:[\s\S])*?\r?\n\2/,lookbehind:!0,greedy:!0,inside:t},{pattern:/(["'])(?:\\[\s\S]|\$\([^)]+\)|`[^`]+`|(?!\1)[^\\])*\1/,greedy:!0,inside:t}],variable:t.variable,"function":{pattern:/(^|[\s;|&])(?:alias|apropos|apt-get|aptitude|aspell|awk|basename|bash|bc|bg|builtin|bzip2|cal|cat|cd|cfdisk|chgrp|chmod|chown|chroot|chkconfig|cksum|clear|cmp|comm|command|cp|cron|crontab|csplit|cut|date|dc|dd|ddrescue|df|diff|diff3|dig|dir|dircolors|dirname|dirs|dmesg|du|egrep|eject|enable|env|ethtool|eval|exec|expand|expect|export|expr|fdformat|fdisk|fg|fgrep|file|find|fmt|fold|format|free|fsck|ftp|fuser|gawk|getopts|git|grep|groupadd|groupdel|groupmod|groups|gzip|hash|head|help|hg|history|hostname|htop|iconv|id|ifconfig|ifdown|ifup|import|install|jobs|join|kill|killall|less|link|ln|locate|logname|logout|look|lpc|lpr|lprint|lprintd|lprintq|lprm|ls|lsof|make|man|mkdir|mkfifo|mkisofs|mknod|more|most|mount|mtools|mtr|mv|mmv|nano|netstat|nice|nl|nohup|notify-send|npm|nslookup|open|op|passwd|paste|pathchk|ping|pkill|popd|pr|printcap|printenv|printf|ps|pushd|pv|pwd|quota|quotacheck|quotactl|ram|rar|rcp|read|readarray|readonly|reboot|rename|renice|remsync|rev|rm|rmdir|rsync|screen|scp|sdiff|sed|seq|service|sftp|shift|shopt|shutdown|sleep|slocate|sort|source|split|ssh|stat|strace|su|sudo|sum|suspend|sync|tail|tar|tee|test|time|timeout|times|touch|top|traceroute|trap|tr|tsort|tty|type|ulimit|umask|umount|unalias|uname|unexpand|uniq|units|unrar|unshar|uptime|useradd|userdel|usermod|users|uuencode|uudecode|v|vdir|vi|vmstat|wait|watch|wc|wget|whereis|which|who|whoami|write|xargs|xdg-open|yes|zip)(?=$|[\s;|&])/,lookbehind:!0},keyword:{pattern:/(^|[\s;|&])(?:let|:|\.|if|then|else|elif|fi|for|break|continue|while|in|case|function|select|do|done|until|echo|exit|return|set|declare)(?=$|[\s;|&])/,lookbehind:!0},"boolean":{pattern:/(^|[\s;|&])(?:true|false)(?=$|[\s;|&])/,lookbehind:!0},operator:/&&?|\|\|?|==?|!=?|<<>|<=?|>=?|=~/,punctuation:/\$?\(\(?|\)\)?|\.\.|[{}[\];]/};var a=t.variable[1].inside;a.string=e.languages.bash.string,a["function"]=e.languages.bash["function"],a.keyword=e.languages.bash.keyword,a.boolean=e.languages.bash.boolean,a.operator=e.languages.bash.operator,a.punctuation=e.languages.bash.punctuation,e.languages.shell=e.languages.bash}(Prism); -------------------------------------------------------------------------------- /docs/assets/prism-c.min.js: -------------------------------------------------------------------------------- 1 | Prism.languages.c=Prism.languages.extend("clike",{keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|asm|typeof|inline|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|union|unsigned|void|volatile|while)\b/,operator:/-[>-]?|\+\+?|!=?|<>?=?|==?|&&?|\|\|?|[~^%?*\/]/,number:/(?:\b0x[\da-f]+|(?:\b\d+\.?\d*|\B\.\d+)(?:e[+-]?\d+)?)[ful]*/i}),Prism.languages.insertBefore("c","string",{macro:{pattern:/(^\s*)#\s*[a-z]+(?:[^\r\n\\]|\\(?:\r\n|[\s\S]))*/im,lookbehind:!0,alias:"property",inside:{string:{pattern:/(#\s*include\s*)(?:<.+?>|("|')(?:\\?.)+?\2)/,lookbehind:!0},directive:{pattern:/(#\s*)\b(?:define|defined|elif|else|endif|error|ifdef|ifndef|if|import|include|line|pragma|undef|using)\b/,lookbehind:!0,alias:"keyword"}}},constant:/\b(?:__FILE__|__LINE__|__DATE__|__TIME__|__TIMESTAMP__|__func__|EOF|NULL|SEEK_CUR|SEEK_END|SEEK_SET|stdin|stdout|stderr)\b/}),delete Prism.languages.c["class-name"],delete Prism.languages.c["boolean"]; -------------------------------------------------------------------------------- /docs/assets/prism-cpp.min.js: -------------------------------------------------------------------------------- 1 | Prism.languages.cpp=Prism.languages.extend("c",{keyword:/\b(?:alignas|alignof|asm|auto|bool|break|case|catch|char|char16_t|char32_t|class|compl|const|constexpr|const_cast|continue|decltype|default|delete|do|double|dynamic_cast|else|enum|explicit|export|extern|float|for|friend|goto|if|inline|int|int8_t|int16_t|int32_t|int64_t|uint8_t|uint16_t|uint32_t|uint64_t|long|mutable|namespace|new|noexcept|nullptr|operator|private|protected|public|register|reinterpret_cast|return|short|signed|sizeof|static|static_assert|static_cast|struct|switch|template|this|thread_local|throw|try|typedef|typeid|typename|union|unsigned|using|virtual|void|volatile|wchar_t|while)\b/,"boolean":/\b(?:true|false)\b/,operator:/--?|\+\+?|!=?|<{1,2}=?|>{1,2}=?|->|:{1,2}|={1,2}|\^|~|%|&{1,2}|\|\|?|\?|\*|\/|\b(?:and|and_eq|bitand|bitor|not|not_eq|or|or_eq|xor|xor_eq)\b/}),Prism.languages.insertBefore("cpp","keyword",{"class-name":{pattern:/(class\s+)\w+/i,lookbehind:!0}}),Prism.languages.insertBefore("cpp","string",{"raw-string":{pattern:/R"([^()\\ ]{0,16})\([\s\S]*?\)\1"/,alias:"string",greedy:!0}}); 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.token{-moz-osx-font-smoothing:initial;-webkit-font-smoothing:initial;min-height:1.5rem}pre:after{color:#ccc;content:attr(data-lang);font-size:.6rem;font-weight:600;height:15px;line-height:15px;padding:5px 10px 0;position:absolute;right:0;text-align:right;top:0} -------------------------------------------------------------------------------- /docs/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/favicon.ico -------------------------------------------------------------------------------- /docs/guide/_sidebar.md: -------------------------------------------------------------------------------- 1 | - [**User Guide**](/guide/user-guide/overview) 2 | - [Overview](/guide/user-guide/overview) 3 | - [Quick Start](/guide/user-guide/quickstart) 4 | - [Specification](/guide/user-guide/spec) 5 | - [Performance](/guide/user-guide/performance) 6 | - [Get Involved](/guide/user-guide/get_involved) 7 | 8 | - [**Serving Models**](/guide/servers/overview.md) 9 | - [Overview](/guide/servers/overview.md) 10 | - [Installation](/guide/servers/installation) 11 | - [Supported Model Formats](/guide/servers/converting) 12 | - [Running GraphPipe Servers](/guide/servers/serving) 13 | - [`graphpipe-tf` Reference](/guide/servers/graphpipe-tf) 14 | - [`graphpipe-onnx` Reference](/guide/servers/graphpipe-onnx) 15 | 16 | - [**Using GraphPipe Clients**](/guide/clients/overview.md) 17 | - [Overview](/guide/clients/overview.md) 18 | - [Installation](/guide/clients/installation.md) 19 | - [Usage](/guide/clients/usage) 20 | 21 | - [**Examples**](/guide/examples/overview.md) 22 | -------------------------------------------------------------------------------- /docs/guide/clients/_media/client_interaction.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/clients/_media/client_interaction.png -------------------------------------------------------------------------------- /docs/guide/clients/installation.md: -------------------------------------------------------------------------------- 1 | # Client Installation 2 | 3 | Before making inference requests against a GraphPipe server, you need to 4 | install the appropriate GraphPipe client libraries for your language of choice. 5 | 6 | Don't see your favorite language? Please open an 7 | [issue](https://github.com/oracle/graphpipe/issues/) so that we can gauge which 8 | languages to support next! 9 | 10 | 11 | ::: tabs install 12 | 13 | - python 14 | 15 | ```bash 16 | pip install graphpipe 17 | ``` 18 | 19 | - go 20 | 21 | ```bash 22 | go get github.com/oracle/graphpipe-go 23 | cd $GOPATH/src/github.com/oracle/graphpipe-go 24 | make install-govendor 25 | make deps 26 | ``` 27 | 28 | - java 29 | 30 | 72 | Coming Soon! 73 | 74 | ::: 75 | 76 | Once you've installed your client, continue to the 77 | [next section](guide/clients/usage.md) to see an example of how 78 | to make a basic request. 79 | -------------------------------------------------------------------------------- /docs/guide/clients/overview.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | In order to make it as easy as possible to integrate GraphPipe into your 4 | application, we provide efficient client implementations for major programming 5 | languages. 6 | 7 | In general, the machine learning client's work looks something like this: 8 | 9 | ![image](_media/client_interaction.png) 10 | 11 | It is important for each of the steps in the dotted box to be implemented as 12 | efficiently as possible. GraphPipe is implemented with this efficiency in 13 | mind. 14 | 15 | At present, we provide client support for python, go, and java. 16 | -------------------------------------------------------------------------------- /docs/guide/clients/usage.md: -------------------------------------------------------------------------------- 1 | # Usage 2 | 3 | Here, we will briefly explore how to access a GraphPipe server 4 | from various clients. To keep things simple, we will begin by launching 5 | [`graphpipe-echo`](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-echo), 6 | a server that simply echoes back any GraphPipe Tensor that is sent to it. 7 | 8 | To start the server, use the following docker command. 9 | 10 | ```bash 11 | docker run -it --rm \ 12 | -p 9000:9000 \ 13 | sleepsonthefloor/graphpipe-echo \ 14 | --listen=0.0.0.0:9000 15 | ``` 16 | 17 | The server will now be accessible over port 9000 on your local machine. Now, 18 | you can make a request against the model using the following languages: 19 | 20 | ::: tabs remoteinf 21 | 22 | - python 23 | 24 | ```python 25 | from graphpipe import remote 26 | import numpy as np 27 | request = np.array([[0.0, 1.0], [2.0, 3.0]]) 28 | result = remote.execute("http://127.0.0.1:9000", request) 29 | print(result) 30 | 31 | ``` 32 | 33 | - go 34 | 35 | ```go 36 | package main 37 | 38 | import ( 39 | "fmt" 40 | "net/http" 41 | 42 | graphpipe "github.com/oracle/graphpipe-go" 43 | ) 44 | 45 | func main() { 46 | uri := "http://127.0.0.1:9000" 47 | request := [][]float32{{0.0, 1.0}, {2.0, 3.0}} 48 | result, err := graphpipe.Remote(uri, request) 49 | if err != nil { 50 | panic(err) 51 | } 52 | fmt.Println(result) 53 | } 54 | ``` 55 | 56 | - java 57 | 72 | Coming Soon! 73 | 74 | ::: 75 | 76 | -------------------------------------------------------------------------------- /docs/guide/examples/overview.md: -------------------------------------------------------------------------------- 1 | # Examples 2 | Here is a collection of links for example code related to GraphPipe: 3 | 4 | * Basic Serving Examples 5 | * [Simple Golang Identity Server](https://github.com/oracle/graphpipe-go) 6 | * [Simple Python Identity Server](https://github.com/oracle/graphpipe-py/tree/master/examples/identity_example) 7 | * [Simple Python SKLearn Server](https://github.com/oracle/graphpipe-py/tree/master/examples/sklearn_example) 8 | * [Simple Python Tensorflow Model Server](https://github.com/oracle/graphpipe-tf-py/blob/master/examples/model_server.py) 9 | * Production GraphPipe Server Source Code 10 | * [graphpipe-tf Source Code](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-tf) 11 | * [graphpipe-onnx Source Code](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-onnx) 12 | * Model Conversion Examples 13 | * [Keras to Graphdef Conversion Script](https://github.com/oracle/graphpipe-tf-py/blob/master/examples/convert.py) 14 | * [Tensorflow Graph to GraphDef](https://github.com/oracle/graphpipe-tf-py/blob/master/examples/tf_graph.py) 15 | * [Saving Pytorch Model as ONNX](https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html) 16 | * End-to-end examples 17 | * [Jupyter Notebook: serving and querying VGG with GraphPipe](https://github.com/oracle/graphpipe-tf-py/blob/master/examples/RemoteModelWithGraphPipe.ipynb) 18 | * [Python Complete client/server example](https://github.com/oracle/graphpipe-tf-py/blob/master/examples/simple_request.py) 19 | -------------------------------------------------------------------------------- /docs/guide/servers/_media/server_flow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/servers/_media/server_flow.png -------------------------------------------------------------------------------- /docs/guide/servers/converting.md: -------------------------------------------------------------------------------- 1 | # Supported Model Formats 2 | 3 | In order to serve models using our example GraphPipe servers, your model must 4 | be in a supported format. 5 | 6 | # Tensorflow Model Formats 7 | 8 | If you are using Tensorflow, we support the two most common model formats: 9 | 10 | * SavedModel format - the tensorflow-serving directory format ([see 11 | here](https://www.tensorflow.org/serving/serving_basic)) 12 | * GraphDef (.pb) format. This is simply the graphdef serialized as a protobuf. 13 | If you are trying to productionize a Keras model, this is the easiest format 14 | to generate. 15 | 16 | #### Keras model conversion 17 | 18 | It is very common for data scientists to first develop models using Keras 19 | before attempting to deploy them. The graphpipe-tf-py repository provides a 20 | simple tool to convert your keras .h5 model into GraphDef .pb format. It has 21 | been packaged into a docker container, which you can use like this: 22 | 23 | ```bash 24 | curl https://oracle.github.io/graphpipe/models/squeezenet.h5 > squeezenet.h5 25 | docker run -v $PWD:/tmp/ sleepsonthefloor/graphpipe-h5topb:latest squeezenet.h5 converted_squeezenet.pb 26 | ``` 27 | 28 | # Caffe2/ONNX/Pytorch model formats 29 | 30 | If you are using Caffe2 or Pytorch, you will likely be using one of the 31 | following formats: 32 | 33 | * ONNX (.onnx) + value_inputs.json 34 | * Caffe2 NetDef + value_inputs.json - in this case, your model has three files: 35 | init_net.pb, predict_net.pb, and value_inputs.json 36 | 37 | #### Pytorch model conversion 38 | 39 | If you are using Pytorch, see [this 40 | guide](https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html) 41 | on how to convert your model into ONNX or caffe2 format. 42 | 43 | #### Caffe2 model conversion 44 | 45 | You can find some tips on saving caffe2 models to NetDef format (init_net.pb + 46 | predict_net.pb + value_inputs.json) 47 | [here](https://github.com/caffe2/caffe2/issues/642). 48 | -------------------------------------------------------------------------------- /docs/guide/servers/graphpipe-onnx.md: -------------------------------------------------------------------------------- 1 | # graphpipe-onnx Reference 2 | 3 | ## Overview ## 4 | 5 | `graphpipe-onnx` is a simple onnx/caffe2 model server written in Go. It 6 | supports both the ONNX model format as well as the caffe2 NetDef format. 7 | 8 | ## Building From Source ## 9 | 10 | Instructions for building `graphpipe-onnx` can be found on 11 | [github](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-onnx). 12 | 13 | ## Server Options ## 14 | 15 | The `graphpipe-onnx` binary has the following runtime flags: 16 | 17 | ```bash 18 | Required Flags for ONNX Models: 19 | -m, --model string ONNX model to load. Accepts local file or http(s) url. 20 | --value-inputs string value_inputs.json for the model. Accepts local file or http(s) url. 21 | 22 | Required Flags for Caffe2 Models: 23 | --init-net string init_net file to load 24 | --predict-net string predict_net file to load. Accepts local file or http(s) url. 25 | --value-inputs string value_inputs.json for the model. Accepts local file or http(s) url. 26 | 27 | Optional Flags: 28 | --cache enable results caching 29 | --cache-dir string directory for local cache state (default "~/.graphpipe") 30 | --disable-cuda disable Cuda 31 | --engine-count int number of caffe2 graph engines to create (default 1) 32 | -h, --help help for graphpipe-caffe2 33 | -l, --listen string listen string (default "127.0.0.1:9000") 34 | --profile string profile and write profiling output to this file 35 | -v, --verbose enable verbose output 36 | -V, --version show version 37 | ``` 38 | 39 | Additionally, you may use the following environment variables: 40 | 41 | ```bash 42 | GP_OUTPUTS comma seprated default inputs 43 | GP_INPUTS comma seprated default outputs 44 | GP_MODEL ONNX model to load. Accepts local file or http(s) url. 45 | GP_CACHE enable results caching 46 | GP_INIT_NET init_net file to load. Accepts local file or http(s) url. 47 | GP_PREDICT_NET predict_net file to load. Accepts local file or http(s) url. 48 | GP_VALUE_INPUTS value_inputs.json file to load. Accepts local file or http(s) url. 49 | ``` 50 | -------------------------------------------------------------------------------- /docs/guide/servers/graphpipe-tf.md: -------------------------------------------------------------------------------- 1 | # graphpipe-tf Reference 2 | 3 | ## Overview ## 4 | 5 | `graphpipe-tf` is a simple tensorflow model server written in Go. It 6 | supports both the tensorflow SavedModel format as well as the GraphDef format. 7 | 8 | ## Building From Source ## 9 | 10 | Instructions for building `graphpipe-tf` can be found on 11 | [github](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-tf). 12 | 13 | ## Server Options ## 14 | 15 | The `graphpipe-tf` binary has the following runtime flags: 16 | 17 | ```bash 18 | Required Flags: 19 | -m, --model string tensorflow model to load. Accepts local file or http(s) url. 20 | 21 | Optional Flags: 22 | -c, --cache enable results caching 23 | -d, --cache-dir string directory for local cache state (default "~/.graphpipe") 24 | -h, --help help for graphpipe-tf 25 | -i, --inputs string comma seprated default inputs 26 | -l, --listen string listen string (default "127.0.0.1:9000") 27 | -o, --outputs string comma separated default outputs 28 | -v, --verbose verbose output 29 | -V, --version show version 30 | ``` 31 | 32 | Additionally, you may use the following environment variables: 33 | 34 | ```bash 35 | GP_OUTPUTS comma seprated default inputs 36 | GP_INPUTS comma seprated default outputs 37 | GP_MODEL tensorflow model to load. Accepts local file or http(s) url. 38 | GP_CACHE enable results caching 39 | ``` 40 | -------------------------------------------------------------------------------- /docs/guide/servers/installation.md: -------------------------------------------------------------------------------- 1 | # Server installation 2 | While we provide full source code for our servers, such that you can build your 3 | own binaries for your preferred platform, we make it easy to use our published 4 | Docker images out-of-the-box. Note that while "official" images are coming 5 | soon, the following community images are available. 6 | 7 | ## Installation of CPU server 8 | 9 | ::: tabs installcpu 10 | 11 | - Tensorflow 12 | 13 | ```bash 14 | docker pull sleepsonthefloor/graphpipe-tf:cpu 15 | ``` 16 | 17 | - ONNX/Caffe2 18 | 19 | ```bash 20 | docker pull sleepsonthefloor/graphpipe-onnx:cpu 21 | ``` 22 | 23 | - Tensorflow + Oracle Linux 24 | 25 | ```bash 26 | docker pull sleepsonthefloor/graphpipe-tf:oraclelinux-cpu 27 | ``` 28 | 29 | - ONNX/Caffe2 + Oracle Linux 30 | 31 | ```bash 32 | docker pull sleepsonthefloor/graphpipe-onnx:oraclelinux-cpu 33 | ``` 34 | ::: 35 | 36 | 37 | ## Installation of GPU server 38 | ::: tabs installgpu 39 | 40 | - Tensorflow 41 | 42 | ```bash 43 | docker pull sleepsonthefloor/graphpipe-tf:gpu 44 | ``` 45 | 46 | - ONNX/Caffe2 47 | 48 | ```bash 49 | docker pull sleepsonthefloor/graphpipe-onnx:gpu 50 | ``` 51 | 52 | ::: 53 | -------------------------------------------------------------------------------- /docs/guide/servers/overview.md: -------------------------------------------------------------------------------- 1 | # Serving Models with GraphPipe 2 | 3 | In addition to an efficient protocol, GraphPipe offers reference model servers 4 | written in go to simplify the process of deploying machine learning models. In 5 | our experience, converting existing models into a common format can involve 6 | lots of pitfalls. We therefore provide model servers that can natively run the 7 | most common ML model formats. 8 | 9 | The servers can be found in the cmd subdirectory of the 10 | [graphpipe-go](https://github.com/oracle/graphpipe-go) 11 | repository: 12 | 13 | * [`graphpipe-tf`](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-tf) 14 | uses libtensorflow to serve tensorflow models. 15 | * [`graphpipe-onnx`](https://github.com/oracle/graphpipe-go/tree/master/cmd/graphpipe-onnx) 16 | uses libcaffe2 to serve caffe2 and onnx models. 17 | 18 | ## Goals 19 | 20 | In designing the model servers we aimed to achieve the following: 21 | 22 | * Excellent performance 23 | * Simple, documented build process 24 | * Flexible code that is easy to work with 25 | * Support for the most common ML Frameworks 26 | * Optimized cpu and gpu support 27 | 28 | ## Overview of server functionality 29 | The basic mechanics of what our reference servers do is fairly straightforward. 30 | 31 | ![image](_media/server_flow.png) 32 | 33 | In order to minimize overhead, we have attempted to make the above illustrated 34 | steps as efficient as possible. For example, because we use flatbuffers as a 35 | network transport format, de-serialization is effectively a pointer cast. 36 | 37 | ## Language Choice 38 | 39 | While go isn't a common language in the ML space, it is ideal for creating 40 | efficient servers. It is also an explicit and friendly language without many 41 | surprises, which makes it an excellent language for collaboration. These 42 | qualities make it an ideal choice for GraphPipe's model servers. 43 | 44 | The one drawback of go is that because existing ML Frameworks are written 45 | in C/C++, there is a slight overhead when context switching from go code into 46 | the framework backend. In practice, however, we have seen very little 47 | real-world performance impact due to this. During performance testing, our go 48 | code achieved virtually the same performance as an optimized build of 49 | tensorflow-serving, which is written in pure C++. 50 | 51 | ## CUDA GPU Acceleration 52 | For each of our reference servers, we provide docker images and source code for 53 | building binaries optimized to run with CUDA acceleration. 54 | 55 | ## MKL CPU acceleration 56 | In order to provide maximize compatibility between our various server builds, and 57 | also to maximize performance, the CPU builds of our servers are compiled with 58 | [MKL](https://software.intel.com/en-us/mkl). When using Tensorflow, MKL 59 | provides the added benefit of supporting channels_first dimension ordering for 60 | convolutional operations, which is generally the preferred ordering when using GPU. 61 | Thus, when using the default configurations of GraphPipe servers, CPU and GPU 62 | versions can serve the same models and accept the same inputs. 63 | 64 | Of course, it could be that your application requires different optimizations 65 | or channel ordering; if this is the case, it should be straightforward to tweak 66 | the source code to build a custom version for your needs. 67 | 68 | ## Caching 69 | 70 | One of the advantages of a language like go is easy access to simple and 71 | powerful libraries. We were therefore able to add some shared code to create a 72 | row-level cache for both of the model servers. This optional row-level cache 73 | is based on [boltdb](https://github.com/coreos/bbolt) and can be very useful 74 | for models that receive many requests for the same data. The cache can often 75 | return data in a matter of microseconds, whereas a large model can take 76 | hundreds of milliseconds to process a request. 77 | -------------------------------------------------------------------------------- /docs/guide/servers/serving.md: -------------------------------------------------------------------------------- 1 | # Running GraphPipe Servers 2 | 3 | This page provides an overview on how to serve models using our reference 4 | model servers, `graphpipe-tf` and `graphpipe-onnx`. 5 | 6 | ## Remote vs. Local Models 7 | 8 | Unless you are using the Tensorflow SavedModel format, where the model is 9 | stored in a directory instead of an individual file, you have the option to 10 | load your models over a remote http/https url. Here is an example of serving a 11 | model that is accessible from a remote url: 12 | 13 | ```bash 14 | docker run -it --rm \ 15 | -p 9000:9000 \ 16 | sleepsonthefloor/graphpipe-tf:cpu \ 17 | --model=https://oracle.github.io/graphpipe/models/squeezenet.pb \ 18 | --listen=0.0.0.0:9000 19 | ``` 20 | 21 | Because our recommended configuration runs the server from a docker image, you 22 | will need to use a volume mount in order to serve local models. Here is an 23 | example of serving a local .pb model: 24 | 25 | ```bash 26 | curl https://oracle.github.io/graphpipe/models/squeezenet.pb > squeezenet.pb 27 | docker run -it --rm \ 28 | -v "$PWD:/models/" \ 29 | -p 9000:9000 \ 30 | sleepsonthefloor/graphpipe-tf:cpu \ 31 | --model=/models/squeezenet.pb \ 32 | --listen=0.0.0.0:9000 33 | ``` 34 | 35 | ## Serving Different Model Types 36 | 37 | ::: tabs cpu 38 | 39 | - Tensorflow 40 | 41 | ```bash 42 | docker run -it --rm \ 43 | -e https_proxy=${https_proxy} \ 44 | -p 9000:9000 \ 45 | sleepsonthefloor/graphpipe-tf:cpu \ 46 | --model=https://oracle.github.io/graphpipe/models/squeezenet.pb \ 47 | --listen=0.0.0.0:9000 48 | ``` 49 | 50 | - ONNX 51 | 52 | ```bash 53 | docker run -it --rm \ 54 | -e https_proxy=${https_proxy} \ 55 | -p 9000:9000 \ 56 | sleepsonthefloor/graphpipe-onnx:cpu \ 57 | --value-inputs=https://oracle.github.io/graphpipe/models/squeezenet.value_inputs.json \ 58 | --model=https://oracle.github.io/graphpipe/models/squeezenet.onnx \ 59 | --listen=0.0.0.0:9000 60 | ``` 61 | 62 | - Caffe2 63 | 64 | ```bash 65 | docker run -it --rm \ 66 | -e https_proxy=${https_proxy} \ 67 | -p 9000:9000 \ 68 | graphpipe-onnx:cpu \ 69 | --value-inputs=https://oracle.github.io/graphpipe/models/squeezenet.value_inputs_caffe2.json \ 70 | --init-net=https://oracle.github.io/graphpipe/models/squeezenet.init_net.pb \ 71 | --predict-net=https://oracle.github.io/graphpipe/models/squeezenet.predict_net.pb \ 72 | --listen=0.0.0.0:9000 73 | ``` 74 | 75 | ::: 76 | 77 | ## GPU Acceleration 78 | 79 | For those wishing to deploy on GPU-accelerated CUDA hardware, we provide gpu 80 | builds for each of our servers. In order to make this work correctly, you need 81 | to install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), as well as 82 | cuda-9.0+cudnn7. 83 | 84 | ::: tabs gpu 85 | 86 | - Tensorflow 87 | ```bash 88 | nvidia-docker run -it --rm \ 89 | -e https_proxy=${https_proxy} \ 90 | -p 9000:9000 \ 91 | sleepsonthefloor/graphpipe-tf:gpu \ 92 | --model=https://oracle.github.io/graphpipe/models/squeezenet.pb \ 93 | --listen=0.0.0.0:9000 94 | ``` 95 | 96 | - ONNX 97 | ```bash 98 | nvidia-docker run -it --rm \ 99 | -e https_proxy=${https_proxy} \ 100 | -p 9000:9000 \ 101 | sleepsonthefloor/graphpipe-onnx:gpu \ 102 | --value-inputs=https://oracle.github.io/graphpipe/models/squeezenet.value_inputs.json \ 103 | --model=https://oracle.github.io/graphpipe/models/squeezenet.onnx \ 104 | --listen=0.0.0.0:9000 105 | ``` 106 | 107 | - CAFFE2 108 | ```bash 109 | nvidia-docker run -it --rm \ 110 | -e https_proxy=${https_proxy} \ 111 | -p 9000:9000 \ 112 | sleepsonthefloor/graphpipe-onnx:gpu \ 113 | --value-inputs=https://oracle.github.io/graphpipe/models/squeezenet.value_inputs_caffe2.json \ 114 | --init-net=https://oracle.github.io/graphpipe/models/squeezenet.init_net.pb \ 115 | --predict-net=https://oracle.github.io/graphpipe/models/squeezenet.predict_net.pb \ 116 | --listen=0.0.0.0:9000 117 | ``` 118 | 119 | ::: 120 | -------------------------------------------------------------------------------- /docs/guide/user-guide/_examples/_metadata_1.py: -------------------------------------------------------------------------------- 1 | from graphpipe import remote 2 | 3 | url = "http://localhost:9000" 4 | metadata = remote.metadata(url) 5 | print(metadata.Outputs) 6 | -------------------------------------------------------------------------------- /docs/guide/user-guide/_examples/_squeezenet_req.python: -------------------------------------------------------------------------------- 1 | from io import BytesIO 2 | from PIL import Image, ImageOps 3 | import numpy as np 4 | import requests 5 | 6 | from graphpipe import remote 7 | 8 | data = np.array(Image.open("mug227.png")) 9 | data = data.reshape([1] + list(data.shape)) 10 | data = np.rollaxis(data, 3, 1).astype(np.float32) # channels first 11 | print(data.shape) 12 | 13 | pred = remote.execute("http://127.0.0.1:9000", data) 14 | print("Expected 504 (Coffee mug), got: %s", np.argmax(pred, axis=1)) 15 | -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/gparch.001.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/gparch.001.jpg -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/gparch.002.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/gparch.002.jpg -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/logo.png -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/mug.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/mug.png -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/mug227.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/mug227.png -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/perf.001.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/perf.001.jpg -------------------------------------------------------------------------------- /docs/guide/user-guide/_media/perf.002.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/guide/user-guide/_media/perf.002.jpg -------------------------------------------------------------------------------- /docs/guide/user-guide/get_involved.md: -------------------------------------------------------------------------------- 1 | # Getting Involved 2 | 3 | ## Repositories 4 | 5 | The GraphPipe code is divided into a few different projects: 6 | 7 | * [graphpipe](https://github.com/oracle/graphpipe) contains the flatbuffer 8 | spec and the documentation. 9 | * [graphpipe-go](https://github.com/oracle/graphpipe-go) contains go client 10 | and server code, as well as the reference servers. 11 | * [graphpipe-py](https://github.com/oracle/graphpipe-py) contains python 12 | client and server code. 13 | * [graphpipe-tf-py](https://github.com/oracle/graphpipe-tf-py) contains a 14 | GraphPipe remote operation for TensorFlow as well as some example code. 15 | * [graphpipe-java](https://github.com/oracle/graphpipe-java) __(COMING SOON)__ 16 | contains the java client library. 17 | 18 | If you have a question or issue related to the code or one of our projects, 19 | please send us an issue in the relevant repo, or talk to us on our [Slack 20 | Channel](https://join.slack.com/t/graphpipe/shared_invite/enQtNDE4MTUyODk2NzQzLTUwODlkZDRiYTI4NmE1OTA5NzRmNjk5MGZiY2M0ZDRiYzNiMTQ0ZmIxODYzZjY2NzRmNzM4NTI0OGVlZGYzZTA). 21 | 22 | ## Contributing 23 | 24 | All of the GraphPipe projects are licensed under the [UPL]. We welcome 25 | contributions. Pull requests can be made on github under the [Oracle 26 | Contributor Agreement] (OCA). If you find any bugs or have feature requests, 27 | please add them to our [issue 28 | tracker](https://github.com/oracle/graphpipe/issues). 29 | 30 | [UPL]: https://opensource.org/licenses/UPL 31 | [Oracle Contributor Agreement]: https://www.oracle.com/technetwork/community/oca-486395.html 32 | 33 | If you are unfamiliar with the UPL, it is a very permissive license that is 34 | similar to the MIT license. You can read more about it 35 | [here](https://oss.oracle.com/licenses/upl/). 36 | 37 | For pull requests to be accepted, the bottom of your commit message must have 38 | the following line using your name and e-mail address as it appears in the OCA 39 | Signatories list. 40 | 41 | Signed-off-by: Your Name 42 | 43 | This can be automatically added to pull requests by committing with: 44 | 45 | git commit --signoff 46 | 47 | Only pull requests from committers that can be verified as having signed the 48 | OCA can be accepted. 49 | -------------------------------------------------------------------------------- /docs/guide/user-guide/overview.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | While surveying the existing landscape of Machine Learning model serving, we 4 | discovered: 5 | 6 | * Model serving network protocols are tied to underlying model implementations. 7 | If you have a Tensorflow model, for example, you need to use tensorflow's 8 | protocol buffer server (tensorflow-serving) to perform remote inference. 9 | * Pytorch and Caffe2, on the other hand, do not provide an efficient model 10 | server in their codebase, but rely on tools like mxnet-model-server for 11 | remote inference. mxnet-model-server is written in python and provides a json 12 | api without batch support. While this is good for simple use cases, it is 13 | not suitable for back-end infrastructure. 14 | * ONNX exists, but tackles the vendor-coupling problem by standardizing model 15 | formats rather than protocol formats. This is useful but challenging, as not 16 | all backend model formats have fully equivalent operations. This means a 17 | simple conversion doesn't always work, and sometimes a model rewrite is 18 | necessary. 19 | * For operators looking to sanely maintain infrastructure, having a standard 20 | way for front-end clients to talk to back-end machine-learning models, 21 | irrespective of model implementation, is important. 22 | 23 | In other words, serving models without GraphPipe is a huge pain: 24 | 25 | ![image](_media/gparch.001.jpg) 26 | 27 | In the code we are releasing we provide: 28 | 29 | * A minimalist machine learning transport specification based on [flatbuffers] 30 | * Simple, efficient reference model servers for [Tensorflow], [Caffe2], and [ONNX]. 31 | * Efficient client implementations in Go, Python, and Java. 32 | 33 | [flatbuffers]: https://google.github.io/flatbuffers/ 34 | [Tensorflow]: https://www.tensorflow.org 35 | [Caffe2]: https://caffe2.ai 36 | [ONNX]: https://onnx.ai 37 | 38 | With these tools, communication is streamlined and serving is simpler: 39 | 40 | ![image](_media/gparch.002.jpg) 41 | -------------------------------------------------------------------------------- /docs/guide/user-guide/performance.md: -------------------------------------------------------------------------------- 1 | # Performance 2 | 3 | First, we compare serialization and deserialization speed of float tensor data 4 | in python using a custom ujson API, protocol buffers using a TensorFlow-serving 5 | predict request, and a GraphPipe remote request. The request consisted of about 6 | 19 million floating-point values (consisting of 128 224x224x3 images) and the 7 | response was approximately 3.2 million floating point values (consisting of 128 8 | 7x7x512 convolutional outputs). The units on the left are in seconds. 9 | 10 | ![image](_media/perf.001.jpg) 11 | 12 | _GraphPipe is especially performant on the deserialize side, because 13 | flatbuffers provide access to underlying data without a memory copy._ 14 | 15 | Second, we compare end-to-end throughput using a Python-JSON TensorFlow model 16 | server, TensorFlow-serving, and the GraphPipe-go TensorFlow model server. In 17 | each case the backend model was the same. Large requests were made to the 18 | server using 1 thread and then again with 5 threads. The units on the left are 19 | rows calculated by the model per second. 20 | 21 | ![image](_media/perf.002.jpg) 22 | 23 | _Note that this test uses the recommended parameters for building 24 | Tensorflow-serving. Although the recommended build parameters for 25 | TensorFlow-serving do not perform well, we were ultimately able to discover 26 | compilation parameters that allow it to perform on par with our GraphPipe 27 | implementation. In other words, an optimized TensorFlow-serving performs 28 | similarly to GraphPipe, although building TensorFlow-serving to perform 29 | optimally is not documented nor easy._ 30 | 31 | -------------------------------------------------------------------------------- /docs/guide/user-guide/quickstart.md: -------------------------------------------------------------------------------- 1 | # Getting Started 2 | 3 | To get started, let's see how to deploy a tensorflow model and communicate with 4 | it via python. 5 | 6 | First, launch a simple GraphPipe model server that is serving squeezenet on 7 | CPU: 8 | 9 | ```bash 10 | docker run -it --rm \ 11 | -e https_proxy=${https_proxy} \ 12 | -p 9000:9000 \ 13 | sleepsonthefloor/graphpipe-tf:cpu \ 14 | --model=https://oracle.github.io/graphpipe/models/squeezenet.pb \ 15 | --listen=0.0.0.0:9000 16 | ``` 17 | 18 | To see metadata about this model: 19 | 20 | ```bash 21 | curl localhost:9000 22 | ``` 23 | 24 | To make a request, install the python client and dependencies: 25 | 26 | ```bash 27 | pip install graphpipe 28 | pip install pillow # needed for image manipulation 29 | ``` 30 | 31 | Finally, let's test an image against the model. Right click and download the 32 | [following image](https://oracle.github.io/graphpipe/guide/user-guide/_media/mug227.png): 33 | 34 | ![image](_media/mug227.png) 35 | 36 | After you download the image, run this script: 37 | 38 | [filename](_examples/_squeezenet_req.python ':include :type=code') 39 | 40 | You should see that your image was correctly classified as a Coffee mug. 41 | Congratulations! 42 | -------------------------------------------------------------------------------- /docs/guide/user-guide/spec.md: -------------------------------------------------------------------------------- 1 | # Specification 2 | 3 | ## 1. Introduction 4 | 5 | The GraphPipe specification describes a transport protocol used to exchange 6 | tensors between a client and a model server. A client uses the GraphPipe 7 | protocol to send one or more tensors to a GraphPipe-compatible server and 8 | recevies one or more tensors in response. A standard protocol allows client and 9 | server implementations to be completely decoupled. 10 | 11 | The GraphPipe protocol employs a low-overhead data format based on 12 | [flatbuffers], which implements highly efficient serialization/deserialization 13 | operations. 14 | 15 | [flatbuffers]: https://github.com/google/flatbuffers 16 | 17 | ## 2. Terminology 18 | 19 | __Model__ Any machine learning entity or function that accepts input data and 20 | returns output data. 21 | 22 | __Server__ Software that provides access to a model by implementing the 23 | GraphPipe spec as detailed in [Section 4](guide/user-guide/spec?id=_4-server) 24 | 25 | __Tensor__ A multi-dimensional array of data with a specific shape and type. 26 | 27 | __Input__ Tensor data transmitted in a request to the server. 28 | 29 | __Output__ Tensor data transmitted in the response from the server. 30 | 31 | 32 | ## 3. Messages 33 | 34 | A flatbuffer specification includes a nested set of _tables_ or messages. These 35 | are analogous to classes or types in a programming language. The flatbuffer 36 | definitions include a number of messages to create a simple API around model 37 | inference. The messages are outlined below. Note that the `string` type in 38 | flatbuffers can hold any variable length binary data, including null, so it 39 | can be thought of as an arbitrarily-sized byte-array. 40 | 41 | ### 3.1 Tensor 42 | 43 | The `Tensor` message holds a tensor. The tensor has a type, which is an 44 | enumeration that is defined in the next section. It has a list of integers 45 | representing the shape of the tensor and two fields for storing data, depending 46 | on the type of the tensor. One of `data` and `string_val` MUST be empty. 47 | 48 | ``` 49 | table Tensor { 50 | type:Type; 51 | shape:[int64]; 52 | data:[uint8]; 53 | string_val:[string]; 54 | } 55 | ``` 56 | 57 | | Field | Explanation | 58 | |------------|-------------| 59 | | type | The type of each element of the tensor. | 60 | | shape | Array of int64 values representing the size of each dimension of the tensor | 61 | | data | The raw data of the tensor in little-endian row-major order | 62 | | string_val | An array of raw byte tensors (only used for the `String` type | 63 | 64 | #### 3.1.1 Tensor Type 65 | 66 | GraphPipe currently supports the following tensor types: 67 | 68 | | Type | Id | Bytes | Description | 69 | |---------|----|-------|-------------| 70 | | Null | 0 | ??? | Type is unknown or not supported | 71 | | Uint8 | 1 | 1 | Unsigned 8-bit integer | 72 | | Int8 | 2 | 1 | Signed 8-bit integer | 73 | | Uint16 | 3 | 2 | Unsigned 16-bit integer | 74 | | Int16 | 4 | 2 | Signed 16-bit integer | 75 | | Uint32 | 5 | 4 | Unsigned 32-bit integer | 76 | | Int32 | 6 | 4 | Signed 32-bit integer | 77 | | Uint64 | 7 | 8 | Unsigned 64-bit integer | 78 | | Int64 | 8 | 8 | Signed 64-bit integer | 79 | | Float16 | 9 | 2 | Half-precision (16-bit) floating point number | 80 | | Float32 | 10 | 4 | Full-precision (32-bit) floating point number | 81 | | Float64 | 11 | 8 | Double-precision (64-bit) floating point number | 82 | | String | 12 | var | Raw bytes. Note that this can hold irregular data | 83 | 84 | ### 3.2 Request 85 | 86 | The GraphPipe spec requires the server to respond to a `Request` 87 | message. This request message contains a union representing two different 88 | request types. The request is either an `InferRequest` or a `MetadataRequest`. 89 | 90 | 91 | ``` 92 | union Req {InferRequest, MetadataRequest} 93 | 94 | table Request { 95 | req:Req; 96 | } 97 | ``` 98 | 99 | #### 3.2.1 InferRequest 100 | 101 | The `InferRequest` accepts one or more tensors as well as a list of unicode 102 | strings representing the input names and a list of unicode strings representing 103 | the output names. The model server MAY include default input and output names 104 | that are used if none are specified. If input names are specified, the length 105 | of input_names and input_tensors MUST be the same. 106 | 107 | The model server MAY support arbitrary config data to change its behavior. This 108 | config data is sent to the server using the config field. This could be used to 109 | turn on debugging, for example. The format of the config field is be defined by 110 | the model server. It could be a json-encoded string or any other binary data. 111 | 112 | ``` 113 | table InferRequest { 114 | config:string; // optional 115 | input_names:[string]; // optional 116 | input_tensors:[Tensor]; 117 | output_names:[string]; // optional 118 | } 119 | ``` 120 | 121 | | Field | Explanation | 122 | |----------------|-------------| 123 | | config | Config data for the model server | 124 | | input_names | List of unicode strings representing inputs | 125 | | input_tensors | List of input tensors | 126 | | output_names | List of unicode strings representing outputs | 127 | 128 | #### 3.2.2 MetadataRequest 129 | 130 | The `MetadataRequest` allows a client to request information about the server and 131 | the model. A `MetadataRequest` has no fields. 132 | 133 | ``` 134 | table MetadataRequest {} 135 | ``` 136 | 137 | ### 3.3 Responses 138 | 139 | The server MUST respond to a `InferRequest` with an `InferResponse` and a 140 | `MetadataRequest` with a `MetadataResponse`. In the case of a badly formatted 141 | Request with an invalid type, the server SHOULD respond with an `InferResponse` 142 | containing an `Error`. Note that there is no union response type because the 143 | response type can be determined from the request type. 144 | 145 | #### 3.3.1 InferResponse 146 | 147 | The `InferResponse` contains one output tensor per requested output name, or an 148 | error. If no output names are requested, the server may return one or more 149 | default output tensors. If no input names are included, the server may apply 150 | the input tensors to default inputs. The server MUST include data in only one 151 | of`output_tensors` and `errors`. 152 | 153 | ``` 154 | table InferResponse { 155 | output_tensors:[Tensor]; 156 | errors:[Error]; 157 | } 158 | ``` 159 | 160 | | Field | Explanation | 161 | |----------------|-------------| 162 | | output_tensors | The type of each element of the tensor. | 163 | | errors | Array of `Error` messages (defined below) | 164 | 165 | ##### 3.3.1.1 Error 166 | 167 | The `Error` type contains a representation of the error. The 168 | server SHOULD include a numeric code for each error so they can be 169 | differentiated without parsing the error string 170 | 171 | ``` 172 | table Error { 173 | code:int64; 174 | message:string; 175 | } 176 | ``` 177 | | Field | Explanation | 178 | |------------|-------------| 179 | | code | A unique error code for this particular problem | 180 | | message | A human-friendly unicode error message describing the problem | 181 | 182 | #### 3.3.2 MetadataResponse 183 | 184 | The `MetadataResponse` contains information describing the types and shapes of 185 | the supported inputs and outputs. It allows a client to validate input and 186 | output data without sending a request through the model. 187 | 188 | Input and output names are unicode identifiers representing inputs and outputs 189 | to the model. It is not required for the server to support multiple inputs and 190 | outputs. For example, it is perfectly acceptable to have a simple server that 191 | accepts and returns a single tensor and requires no input or output names. On 192 | the other hand, you could have a complex convolutional model that accepts 193 | multiple inputs and allows the client to request output at one or more of the 194 | convolutional layers. 195 | 196 | ``` 197 | table MetadataResponse { 198 | name:string; // optional 199 | version:string; // optional 200 | server:string; // optional 201 | description:string; //optional 202 | inputs:[IOMetadata]; // required 203 | outputs:[IOMetadata]; // required 204 | } 205 | ``` 206 | 207 | | Field | Explanation | 208 | |-------------|-------------| 209 | | name | Name of the model being served | 210 | | version | Version of the model server | 211 | | server | Name of the model server | 212 | | description | Description of the model being served | 213 | | inputs | Array of `IOMetadata` (defined below) about the inputs | 214 | | outputs | Array of `IOMetadata` (defined below) about the outputs | 215 | 216 | ##### 3.3.2.1 IOMetadata 217 | 218 | `IOMetadata` contains information about a given input or output. The name is 219 | the unique unicode identifier for this particular input or output. This name is 220 | used when specifying `input_names` and `output_names` as part of an 221 | `InferRequest`. A negative one (-1) in the shape means the model accepts an 222 | arbitrary size input in that dimension. This is generally useful if the model 223 | supports a batch dimension. 224 | 225 | ``` 226 | table IOMetadata { 227 | name:string; // required 228 | description:string; // optional 229 | shape:[int64]; // required 230 | type:Type; // required 231 | } 232 | ``` 233 | 234 | | Field | Explanation | 235 | |-------------|-------------| 236 | | name | Name of the input or output | 237 | | description | Description of the input or output | 238 | | shape | Shape of the input or output (-1 represents any size) | 239 | | type | Type of the input or output | 240 | 241 | 242 | ## 4. Server 243 | 244 | A GraphPipe server MUST accept flatbuffer-encoded `Request`s over a binary 245 | protocol and return an `InferResponse` or a `MetadataResponse`. This can be a 246 | standard tcp or unix socket, or it can be served over a higher level protocol 247 | like HTTP/HTTPS. If the server is serving over HTTP, it MUST accept the request 248 | as a POST where the body of the http request contains only the 249 | flatbuffer-encoded requests. It SHOULD also provide helpful information when 250 | the serving endpoint is queried with a GET. One method would be to serialize 251 | the `MetadataResponse` into json and return it in response to a GET. 252 | -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | GraphPipe -- Machine Learning Model Deployment Made Simple 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 25 | 26 | 27 | 28 |
      29 | 30 | 31 | 32 | 33 | 34 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 104 | 125 | 126 | 127 | -------------------------------------------------------------------------------- /docs/models/squeezenet.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/models/squeezenet.h5 -------------------------------------------------------------------------------- /docs/models/squeezenet.init_net.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/models/squeezenet.init_net.pb -------------------------------------------------------------------------------- /docs/models/squeezenet.onnx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/models/squeezenet.onnx -------------------------------------------------------------------------------- /docs/models/squeezenet.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/oracle/graphpipe/224c32130006d652a9c3bf205abd700ad67940b3/docs/models/squeezenet.pb -------------------------------------------------------------------------------- /docs/models/squeezenet.predict_net.pb: -------------------------------------------------------------------------------- 1 | 2 | F 3 | data 4 | conv1_w 5 | conv1_bconv1"Conv* 6 | 7 | stride* 8 | pad* 9 | 10 | kernel 11 | conv1conv1"ReluW 12 | conv1pool1"MaxPool* 13 | 14 | stride* 15 | pad* 16 | 17 | kernel* 18 | order"NCHW* 19 | 20 | legacy_padh 21 | pool1 22 | fire2/squeeze1x1_w 23 | fire2/squeeze1x1_bfire2/squeeze1x1"Conv* 24 | 25 | stride* 26 | pad* 27 | 28 | kernel* 29 | fire2/squeeze1x1fire2/squeeze1x1"Relup 30 | fire2/squeeze1x1 31 | fire2/expand1x1_w 32 | fire2/expand1x1_bfire2/expand1x1"Conv* 33 | 34 | stride* 35 | pad* 36 | 37 | kernel( 38 | fire2/expand1x1fire2/expand1x1"Relup 39 | fire2/squeeze1x1 40 | fire2/expand3x3_w 41 | fire2/expand3x3_bfire2/expand3x3"Conv* 42 | 43 | stride* 44 | pad* 45 | 46 | kernel( 47 | fire2/expand3x3fire2/expand3x3"Relu[ 48 | fire2/expand1x1 49 | fire2/expand3x3 fire2/concat_fire2/concat_dims"Concat* 50 | order"NCHWo 51 | fire2/concat 52 | fire3/squeeze1x1_w 53 | fire3/squeeze1x1_bfire3/squeeze1x1"Conv* 54 | 55 | stride* 56 | pad* 57 | 58 | kernel* 59 | fire3/squeeze1x1fire3/squeeze1x1"Relup 60 | fire3/squeeze1x1 61 | fire3/expand1x1_w 62 | fire3/expand1x1_bfire3/expand1x1"Conv* 63 | 64 | stride* 65 | pad* 66 | 67 | kernel( 68 | fire3/expand1x1fire3/expand1x1"Relup 69 | fire3/squeeze1x1 70 | fire3/expand3x3_w 71 | fire3/expand3x3_bfire3/expand3x3"Conv* 72 | 73 | stride* 74 | pad* 75 | 76 | kernel( 77 | fire3/expand3x3fire3/expand3x3"Relu[ 78 | fire3/expand1x1 79 | fire3/expand3x3 fire3/concat_fire3/concat_dims"Concat* 80 | order"NCHW^ 81 | fire3/concatpool3"MaxPool* 82 | 83 | stride* 84 | pad* 85 | 86 | kernel* 87 | order"NCHW* 88 | 89 | legacy_padh 90 | pool3 91 | fire4/squeeze1x1_w 92 | fire4/squeeze1x1_bfire4/squeeze1x1"Conv* 93 | 94 | stride* 95 | pad* 96 | 97 | kernel* 98 | fire4/squeeze1x1fire4/squeeze1x1"Relup 99 | fire4/squeeze1x1 100 | fire4/expand1x1_w 101 | fire4/expand1x1_bfire4/expand1x1"Conv* 102 | 103 | stride* 104 | pad* 105 | 106 | kernel( 107 | fire4/expand1x1fire4/expand1x1"Relup 108 | fire4/squeeze1x1 109 | fire4/expand3x3_w 110 | fire4/expand3x3_bfire4/expand3x3"Conv* 111 | 112 | stride* 113 | pad* 114 | 115 | kernel( 116 | fire4/expand3x3fire4/expand3x3"Relu[ 117 | fire4/expand1x1 118 | fire4/expand3x3 fire4/concat_fire4/concat_dims"Concat* 119 | order"NCHWo 120 | fire4/concat 121 | fire5/squeeze1x1_w 122 | fire5/squeeze1x1_bfire5/squeeze1x1"Conv* 123 | 124 | stride* 125 | pad* 126 | 127 | kernel* 128 | fire5/squeeze1x1fire5/squeeze1x1"Relup 129 | fire5/squeeze1x1 130 | fire5/expand1x1_w 131 | fire5/expand1x1_bfire5/expand1x1"Conv* 132 | 133 | stride* 134 | pad* 135 | 136 | kernel( 137 | fire5/expand1x1fire5/expand1x1"Relup 138 | fire5/squeeze1x1 139 | fire5/expand3x3_w 140 | fire5/expand3x3_bfire5/expand3x3"Conv* 141 | 142 | stride* 143 | pad* 144 | 145 | kernel( 146 | fire5/expand3x3fire5/expand3x3"Relu[ 147 | fire5/expand1x1 148 | fire5/expand3x3 fire5/concat_fire5/concat_dims"Concat* 149 | order"NCHW^ 150 | fire5/concatpool5"MaxPool* 151 | 152 | stride* 153 | pad* 154 | 155 | kernel* 156 | order"NCHW* 157 | 158 | legacy_padh 159 | pool5 160 | fire6/squeeze1x1_w 161 | fire6/squeeze1x1_bfire6/squeeze1x1"Conv* 162 | 163 | stride* 164 | pad* 165 | 166 | kernel* 167 | fire6/squeeze1x1fire6/squeeze1x1"Relup 168 | fire6/squeeze1x1 169 | fire6/expand1x1_w 170 | fire6/expand1x1_bfire6/expand1x1"Conv* 171 | 172 | stride* 173 | pad* 174 | 175 | kernel( 176 | fire6/expand1x1fire6/expand1x1"Relup 177 | fire6/squeeze1x1 178 | fire6/expand3x3_w 179 | fire6/expand3x3_bfire6/expand3x3"Conv* 180 | 181 | stride* 182 | pad* 183 | 184 | kernel( 185 | fire6/expand3x3fire6/expand3x3"Relu[ 186 | fire6/expand1x1 187 | fire6/expand3x3 fire6/concat_fire6/concat_dims"Concat* 188 | order"NCHWo 189 | fire6/concat 190 | fire7/squeeze1x1_w 191 | fire7/squeeze1x1_bfire7/squeeze1x1"Conv* 192 | 193 | stride* 194 | pad* 195 | 196 | kernel* 197 | fire7/squeeze1x1fire7/squeeze1x1"Relup 198 | fire7/squeeze1x1 199 | fire7/expand1x1_w 200 | fire7/expand1x1_bfire7/expand1x1"Conv* 201 | 202 | stride* 203 | pad* 204 | 205 | kernel( 206 | fire7/expand1x1fire7/expand1x1"Relup 207 | fire7/squeeze1x1 208 | fire7/expand3x3_w 209 | fire7/expand3x3_bfire7/expand3x3"Conv* 210 | 211 | stride* 212 | pad* 213 | 214 | kernel( 215 | fire7/expand3x3fire7/expand3x3"Relu[ 216 | fire7/expand1x1 217 | fire7/expand3x3 fire7/concat_fire7/concat_dims"Concat* 218 | order"NCHWo 219 | fire7/concat 220 | fire8/squeeze1x1_w 221 | fire8/squeeze1x1_bfire8/squeeze1x1"Conv* 222 | 223 | stride* 224 | pad* 225 | 226 | kernel* 227 | fire8/squeeze1x1fire8/squeeze1x1"Relup 228 | fire8/squeeze1x1 229 | fire8/expand1x1_w 230 | fire8/expand1x1_bfire8/expand1x1"Conv* 231 | 232 | stride* 233 | pad* 234 | 235 | kernel( 236 | fire8/expand1x1fire8/expand1x1"Relup 237 | fire8/squeeze1x1 238 | fire8/expand3x3_w 239 | fire8/expand3x3_bfire8/expand3x3"Conv* 240 | 241 | stride* 242 | pad* 243 | 244 | kernel( 245 | fire8/expand3x3fire8/expand3x3"Relu[ 246 | fire8/expand1x1 247 | fire8/expand3x3 fire8/concat_fire8/concat_dims"Concat* 248 | order"NCHWo 249 | fire8/concat 250 | fire9/squeeze1x1_w 251 | fire9/squeeze1x1_bfire9/squeeze1x1"Conv* 252 | 253 | stride* 254 | pad* 255 | 256 | kernel* 257 | fire9/squeeze1x1fire9/squeeze1x1"Relup 258 | fire9/squeeze1x1 259 | fire9/expand1x1_w 260 | fire9/expand1x1_bfire9/expand1x1"Conv* 261 | 262 | stride* 263 | pad* 264 | 265 | kernel( 266 | fire9/expand1x1fire9/expand1x1"Relup 267 | fire9/squeeze1x1 268 | fire9/expand3x3_w 269 | fire9/expand3x3_bfire9/expand3x3"Conv* 270 | 271 | stride* 272 | pad* 273 | 274 | kernel( 275 | fire9/expand3x3fire9/expand3x3"Relu[ 276 | fire9/expand1x1 277 | fire9/expand3x3 fire9/concat_fire9/concat_dims"Concat* 278 | order"NCHWT 279 | fire9/concat fire9/concat_fire9/concat_mask"Dropout* 280 | ratio?* 281 | is_testQ 282 | fire9/concat 283 | conv10_w 284 | conv10_bconv10"Conv* 285 | 286 | stride* 287 | pad* 288 | 289 | kernel 290 | conv10conv10"Reluq 291 | conv10pool10" AveragePool* 292 | 293 | stride* 294 | pad* 295 | 296 | kernel* 297 | order"NCHW* 298 | 299 | legacy_pad* 300 | global_pooling 301 | pool10 302 | softmaxout"Softmax:data:conv1_w:conv1_b:fire2/squeeze1x1_w:fire2/squeeze1x1_b:fire2/expand1x1_w:fire2/expand1x1_b:fire2/expand3x3_w:fire2/expand3x3_b:fire3/squeeze1x1_w:fire3/squeeze1x1_b:fire3/expand1x1_w:fire3/expand1x1_b:fire3/expand3x3_w:fire3/expand3x3_b:fire4/squeeze1x1_w:fire4/squeeze1x1_b:fire4/expand1x1_w:fire4/expand1x1_b:fire4/expand3x3_w:fire4/expand3x3_b:fire5/squeeze1x1_w:fire5/squeeze1x1_b:fire5/expand1x1_w:fire5/expand1x1_b:fire5/expand3x3_w:fire5/expand3x3_b:fire6/squeeze1x1_w:fire6/squeeze1x1_b:fire6/expand1x1_w:fire6/expand1x1_b:fire6/expand3x3_w:fire6/expand3x3_b:fire7/squeeze1x1_w:fire7/squeeze1x1_b:fire7/expand1x1_w:fire7/expand1x1_b:fire7/expand3x3_w:fire7/expand3x3_b:fire8/squeeze1x1_w:fire8/squeeze1x1_b:fire8/expand1x1_w:fire8/expand1x1_b:fire8/expand3x3_w:fire8/expand3x3_b:fire9/squeeze1x1_w:fire9/squeeze1x1_b:fire9/expand1x1_w:fire9/expand1x1_b:fire9/expand3x3_w:fire9/expand3x3_b:conv10_w:conv10_bB 303 | softmaxout -------------------------------------------------------------------------------- /docs/models/squeezenet.value_inputs.json: -------------------------------------------------------------------------------- 1 | {"data_0": [1, [1, 3, 227, 227]]} 2 | -------------------------------------------------------------------------------- /docs/models/squeezenet.value_inputs_caffe2.json: -------------------------------------------------------------------------------- 1 | {"data": [1, [1, 3, 227, 227]]} 2 | -------------------------------------------------------------------------------- /graphpipe.fbs: -------------------------------------------------------------------------------- 1 | /* 2 | ** Copyright © 2018, Oracle and/or its affiliates. All rights reserved. 3 | ** 4 | ** Licensed under the Universal Permissive License v 1.0 as shown at 5 | ** http://oss.oracle.com/licenses/upl. 6 | */ 7 | namespace graphpipe; 8 | 9 | /* 10 | Enumeration of supported types. 11 | */ 12 | enum Type:uint8 { 13 | Null, 14 | Uint8, 15 | Int8, 16 | Uint16, 17 | Int16, 18 | Uint32, 19 | Int32, 20 | Uint64, 21 | Int64, 22 | Float16, 23 | Float32, 24 | Float64, 25 | String, 26 | } 27 | 28 | /* 29 | Tensor definition. 30 | 31 | type: defines what type of data this Tensor holds 32 | shape: an array that describes the shape of the Tensor (like [10, 3, 224, 224]) 33 | data: stores the actual tensor data for all types but String 34 | string_val: holds the data for tensors of type String 35 | */ 36 | table Tensor { 37 | type:Type; 38 | shape:[int64]; 39 | data:[uint8]; 40 | string_val:[string]; 41 | } 42 | 43 | /* 44 | Req definition, which is a union of an InferRequest and a a MetadataRequest. 45 | */ 46 | union Req {InferRequest, MetadataRequest} 47 | 48 | /* 49 | Request definition, which is a container for one of the two allowed request types. 50 | req: a union representing an InferRequest or a MetadataRequest. 51 | */ 52 | table Request { 53 | req:Req; 54 | } 55 | 56 | /* 57 | Infer Request definition, which is used to request data from a remote model. 58 | config: application-specific string used for request-specific model configuration. 59 | input_names: a list of input names 60 | input_tensors: a list of input tensors, associated with input_names 61 | output_names: a list of outputs to return in response to the provided inputs 62 | */ 63 | table InferRequest { 64 | config:string; // optional 65 | input_names:[string]; // optional 66 | input_tensors:[Tensor]; 67 | output_names:[string]; // optional 68 | } 69 | 70 | /* 71 | Error definition 72 | code: integer representation of the error 73 | message: Human-readable message description 74 | */ 75 | table Error { 76 | code:int64; 77 | message:string; 78 | } 79 | 80 | /* 81 | InferResponse definition. Should contain either a list of tensors or a list 82 | of errors, but not both. 83 | output_tensors: a list of output_tensors, in the order requested by InferRequest.output_names 84 | errors: A list of errors that occurred during processing, if any 85 | */ 86 | table InferResponse { 87 | output_tensors:[Tensor]; 88 | errors:[Error]; 89 | } 90 | 91 | /* 92 | MetadatRequest. Used to request metadata about a graphpipe model server. 93 | */ 94 | table MetadataRequest {} 95 | 96 | /* 97 | IOMetadata definition. Provides info about inputs and outputs of a model. 98 | name: name of the input/output 99 | description: description of the input/output 100 | shape: input or output shape 101 | type: Type of the input/output 102 | */ 103 | table IOMetadata { 104 | name:string; // required 105 | description:string; // optional 106 | shape:[int64]; // required 107 | type:Type; // required 108 | } 109 | 110 | /* 111 | MetadataResponse definition. Describes characteristics of the server and the model being served. 112 | name: name of the model being served 113 | version: version of the server 114 | server: name of the server 115 | description: description of the model being served 116 | inputs: metadata about the model's inputs 117 | outputs: metadata about the model's outputs 118 | */ 119 | table MetadataResponse { 120 | name:string; // optional 121 | version:string; // optional 122 | server:string; // optional 123 | description:string; //optional 124 | inputs:[IOMetadata]; // required 125 | outputs:[IOMetadata]; // required 126 | } 127 | 128 | root_type Request; 129 | --------------------------------------------------------------------------------