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first pass at server-side sockets (+ client comm changes) (#5)
* first pass at server-side sockets * update client comm * very hacky polyfill for fetch in node * load mnist-transfer-cnn model, send weights to client * changes based on PR feedback * make loss fun a scalar * avoid using export default * spacing and license fixes for polyfill * convert more serialization methods to async * test coverage for serialization * tests for API between server and client * refactors based on feedback * fix lint errors
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| /** | ||
| * @license | ||
| * Copyright 2018 Google LLC. All Rights Reserved. | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| * ============================================================================= | ||
| */ | ||
|
|
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| import * as tf from '@tensorflow/tfjs'; | ||
| import {Scalar, Tensor} from '@tensorflow/tfjs'; | ||
| import {FederatedModel, ModelDict} from '../src/index'; | ||
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| // https://github.com/tensorflow/tfjs-examples/tree/master/mnist-transfer-cnn | ||
| const mnistTransferLearningModelURL = | ||
| // tslint:disable-next-line:max-line-length | ||
| 'https://storage.googleapis.com/tfjs-models/tfjs/mnist_transfer_cnn_v1/model.json'; | ||
|
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| export class MnistTransferLearningModel implements FederatedModel { | ||
| async setup(): Promise<ModelDict> { | ||
| const model = await tf.loadModel(mnistTransferLearningModelURL); | ||
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| for (let i = 0; i < 7; ++i) { | ||
| model.layers[i].trainable = false; // freeze conv layers | ||
| } | ||
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| const loss = (inputs: Tensor, labels: Tensor) => { | ||
| const logits = model.predict(inputs) as Tensor; | ||
| const losses = tf.losses.softmaxCrossEntropy(logits, labels); | ||
| return losses.mean() as Scalar; | ||
| }; | ||
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| return {predict: model.predict, vars: model.trainableWeights, loss}; | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| /** | ||
| * * @license | ||
| * Copyright 2018 Google LLC. All Rights Reserved. | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| * ============================================================================= | ||
| */ | ||
|
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| import * as tf from '@tensorflow/tfjs'; | ||
| import {test_util, Variable} from '@tensorflow/tfjs'; | ||
| import * as fs from 'fs'; | ||
| import * as http from 'http'; | ||
| import * as path from 'path'; | ||
| import * as rimraf from 'rimraf'; | ||
| import * as serverSocket from 'socket.io'; | ||
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| import {VariableSynchroniser} from './client/comm'; | ||
| import {tensorToJson} from './serialization'; | ||
| import {SocketAPI} from './server/comm'; | ||
| import {ModelDB} from './server/model_db'; | ||
|
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| const modelId = '1528400733553'; | ||
| const batchSize = 42; | ||
| const FIT_CONFIG = {batchSize}; | ||
| const PORT = 3000; | ||
| const socketURL = `http://0.0.0.0:${PORT}`; | ||
| const initWeights = | ||
| [tf.tensor([1, 1, 1, 1], [2, 2]), tf.tensor([1, 2, 3, 4], [1, 4])]; | ||
| const updateThreshold = 2; | ||
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| function waitUntil(done: () => boolean, then: () => void, timeout?: number) { | ||
| const moveOn = () => { | ||
| clearInterval(moveOnIfDone); | ||
| clearTimeout(moveOnAnyway); | ||
| then(); | ||
| }; | ||
| const moveOnAnyway = setTimeout(moveOn, timeout || 100); | ||
| const moveOnIfDone = setInterval(() => { | ||
| if (done()) { | ||
| moveOn(); | ||
| } | ||
| }, 1); | ||
| } | ||
|
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| describe('Socket API', () => { | ||
| let dataDir: string; | ||
| let modelDir: string; | ||
| let modelPath: string; | ||
| let modelDB: ModelDB; | ||
| let serverAPI: SocketAPI; | ||
| let clientAPI: VariableSynchroniser; | ||
| let clientVars: Variable[]; | ||
| let httpServer: http.Server; | ||
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| beforeEach(async () => { | ||
| // Set up model database with our initial weights | ||
| dataDir = fs.mkdtempSync('/tmp/modeldb_test'); | ||
| modelDir = path.join(dataDir, modelId); | ||
| modelPath = path.join(dataDir, modelId + '.json'); | ||
| fs.mkdirSync(modelDir); | ||
| const modelJSON = await Promise.all(initWeights.map(tensorToJson)); | ||
| fs.writeFileSync(modelPath, JSON.stringify({'vars': modelJSON})); | ||
| modelDB = new ModelDB(dataDir, updateThreshold); | ||
| await modelDB.setup(); | ||
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| // Set up the server exposing our upload/download API | ||
| httpServer = http.createServer(); | ||
| serverAPI = new SocketAPI(modelDB, FIT_CONFIG, serverSocket(httpServer)); | ||
| await serverAPI.setup(); | ||
| await httpServer.listen(PORT); | ||
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| // Set up the API client with zeroed out weights | ||
| clientVars = initWeights.map(t => tf.variable(tf.zerosLike(t))); | ||
| clientAPI = new VariableSynchroniser(clientVars); | ||
| await clientAPI.initialise(socketURL); | ||
| }); | ||
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| afterEach(async () => { | ||
| rimraf.sync(dataDir); | ||
| await httpServer.close(); | ||
| }); | ||
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| it('transmits fit config on startup', () => { | ||
| expect(clientAPI.fitConfig.batchSize).toBe(batchSize); | ||
| }); | ||
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| it('transmits model version on startup', () => { | ||
| expect(clientAPI.modelId).toBe(modelId); | ||
| }); | ||
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| it('transmits model parameters on startup', () => { | ||
| test_util.expectArraysClose(clientVars[0], initWeights[0]); | ||
| test_util.expectArraysClose(clientVars[1], initWeights[1]); | ||
| }); | ||
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| it('transmits updates', async () => { | ||
| let updateFiles = await modelDB.listUpdateFiles(); | ||
| expect(updateFiles.length).toBe(0); | ||
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| clientVars[0].assign(tf.tensor([2, 2, 2, 2], [2, 2])); | ||
| clientAPI.numExamples = 1; | ||
| await clientAPI.uploadVars(); | ||
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| updateFiles = await modelDB.listUpdateFiles(); | ||
| expect(updateFiles.length).toBe(1); | ||
| }); | ||
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| it('triggers a download after enough uploads', async (done) => { | ||
| clientVars[0].assign(tf.tensor([2, 2, 2, 2], [2, 2])); | ||
| clientAPI.numExamples = 1; | ||
| await clientAPI.uploadVars(); | ||
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| clientVars[0].assign(tf.tensor([1, 1, 1, 1], [2, 2])); | ||
| clientVars[1].assign(tf.tensor([4, 3, 2, 1], [1, 4])); | ||
| clientAPI.numExamples = 3; | ||
| await clientAPI.uploadVars(); | ||
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| waitUntil(() => clientAPI.modelId !== modelId, () => { | ||
| test_util.expectArraysClose( | ||
| clientVars[0], tf.tensor([1.25, 1.25, 1.25, 1.25], [2, 2])); | ||
| test_util.expectArraysClose( | ||
| clientVars[1], tf.tensor([3.25, 2.75, 2.25, 1.75], [1, 4])); | ||
| expect(clientAPI.numExamples).toBe(0); | ||
| expect(clientAPI.modelId).toBe(modelDB.modelId); | ||
| done(); | ||
| }); | ||
| }); | ||
| }); |
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