alpine(Dockerコンテナ)上でTensorflow.jsを利用する 〜 ラズパイへの導入編

            FROM arm32v7/node:12.18-alpine3.11

RUN apk update && apk upgrade && apk add --no-cache \
    openssh curl bash

WORKDIR /usr/src/app
        

            version: "3"

services:
  app:
    image: tfjs-arm-dkcr:3.11
    build: .
    user: "node:node"
    environment:
      NODE_ENV: development
    volumes:
      - ./:/usr/src/app
        

            $ docker-compose build
Building app
Step 1/7 : FROM arm32v7/node:12.18-alpine3.11
12.18-alpine3.11: Pulling from arm32v7/node
3cfb62949d9d: Pull complete
9f88341ca4dc: Pull complete
5765f8fb68cd: Pull complete
ed6b46590934: Pull complete
Digest: sha256:ab58814a31480be66de2f73d233d74f9d9a2761c3ed30ea4445e01f034dd2f58
Status: Downloaded newer image for arm32v7/node:12.18-alpine3.11
 ---> 7d07d27963b2
Step 2/7 : ENV NODE_ENV development
 ---> Running in 7eaef5b163ef
Removing intermediate container 7eaef5b163ef
 ---> b890256ee944
Step 3/7 : RUN apk update && apk upgrade && apk add --no-cache openssh curl sh
 ---> Running in 4133f832fea6
#...中略
Successfully built e08f1549a457
Successfully tagged tfjs-arm-dkcr:3.12
        

            $ docker images
REPOSITORY                   TAG                     IMAGE ID            CREATED             SIZE
tfjs-arm-dkcr                3.11                    e08f1549a457        5 minutes ago       89.4MB
arm32v7/node                 12.18-alpine3.11        7d07d27963b2        2 weeks ago         80.4MB
#...
        

            $ cat /etc/group
root:x:0:root
bin:x:1:root,bin,daemon
daemon:x:2:root,bin,daemon
sys:x:3:root,bin,adm
adm:x:4:root,adm,daemon
tty:x:5:
disk:x:6:root,adm
lp:x:7:lp
mem:x:8:
kmem:x:9:
wheel:x:10:root
floppy:x:11:root
mail:x:12:mail
news:x:13:news
uucp:x:14:uucp
man:x:15:man
cron:x:16:cron
console:x:17:
audio:x:18:
cdrom:x:19:
dialout:x:20:root
ftp:x:21:
sshd:x:22:
input:x:23:
at:x:25:at
tape:x:26:root
video:x:27:root
netdev:x:28:
readproc:x:30:
squid:x:31:squid
xfs:x:33:xfs
kvm:x:34:kvm
games:x:35:
shadow:x:42:
cdrw:x:80:
usb:x:85:
vpopmail:x:89:
users:x:100:games
ntp:x:123:
nofiles:x:200:
smmsp:x:209:smmsp
locate:x:245:
abuild:x:300:
utmp:x:406:
ping:x:999:
nogroup:x:65533:
nobody:x:65534:
node:x:1000:node
        

            $ docker-compose run --rm app bash
Creating network "tfjs-arm-dckr_default" with the default driver
$ node -v
v12.18.3
$ npm --version
6.14.6
$ yarn --version
1.22.4
        

            {
    "name": "tfjs-dckr-raspi",
    "version": "0.0.1",
    "description": "It enables RaspberryPi to use tensorflow.js via alpine:node on Docker.",
    "main": "dist/index.js",
    "scripts": {
        "build": "tsc",
        "tap": "babel-node dist/index.js",
    },
    "dependencies": {
        "@babel/core": "^7.10.5",
        "@babel/node": "^7.10.5",
        "@tensorflow/tfjs": "^2.0.1",
        "argparse": "^1.0.10",
        "ts-node": "^8.10.2",
        "typescript": "3.5.3"
    },
    "devDependencies": {
        "@types/argparse": "^1.0.38",
        "@types/node": "^13.7.1",
        "@types/shelljs": "^0.8.8",
        "shelljs": "^0.8.4",
        "tmp": "^0.2.1"
    }
}
        

            $ yarn install
yarn install v1.22.4
[1/4] Resolving packages...
[2/4] Fetching packages...
info fsevents@2.1.3: The platform "linux" is incompatible with this module.
info "fsevents@2.1.3" is an optional dependency and failed compatibility check. Excluding it from installation.
[3/4] Linking dependencies...
warning "@tensorflow/tfjs > @tensorflow/tfjs-data@2.0.1" has unmet peer dependency "seedrandom@~2.4.3".
[4/4] Building fresh packages...
Done in 296.66s.
        

            $ ./node_modules/.bin/tsc --version
Version 3.5.3
        

            $ ./node_modules/.bin/tsc --init
        

            {
    "compilerOptions": {
      "target": "es6",
      "module": "commonjs",
      "declaration": true,
      "sourceMap": true,
      "outDir": "./dist",
      "moduleResolution": "node",
      "esModuleInterop": true
    },
    "include": [
      "./index.ts",
      "./src/*",
    ],
    "exclude": [
      "./test/*.spec.ts"
    ],
    "compileOnSave": false
}
        

            import * as tf from '@tensorflow/tfjs';

const primitiveOps = () => {
    const x = tf.tensor1d([1,2,3]);
    const a = tf.scalar(4);
    console.log('Four arithmetic operation with scalar:');
    x.add(a).print();
    x.sub(a).print();
    x.mul(a).print();
    x.div(a).print();
}

const arrayOps = () => {
    const x1 = tf.tensor1d([1,2,3])
    const x2 = tf.tensor1d([2,3,4])
    console.log('Four arithmetic operation with tensor1d:');

    console.log('add')
    x1.add(x2).print();

    console.log('sub')
    x1.sub(x2).print();

    console.log('mul')
    x1.mul(x2).print();

    console.log('div')
    x1.div(x2).print();
}

const tensor2array = () => {
    const b = tf.tensor1d([1,2,3])
    console.log('Convert tensor1d to Array:');

    console.log('tensor1d:')
    b.print()

    const b_value = b.dataSync();
    console.log('--> Array:')
    console.log(b_value)
}

primitiveOps();
arrayOps();
tensor2array();
        

            $ yarn build
$ babel-node dist/index.js
#もしくは yarn tap
Overriding the gradient for 'Max'
Overriding the gradient for 'OneHot'
Overriding the gradient for 'PadV2'
Overriding the gradient for 'SpaceToBatchND'
Overriding the gradient for 'SplitV'
============================
Hi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================
Four arithmetic operation with scalar:
Tensor
    [5, 6, 7]
Tensor
    [-3, -2, -1]
Tensor
    [4, 8, 12]
Tensor
    [0.25, 0.5, 0.75]
Four arithmetic operation with tensor1d:
add
Tensor
    [3, 5, 7]
sub
Tensor
    [-1, -1, -1]
mul
Tensor
    [2, 6, 12]
div
Tensor
    [0.5, 0.6666667, 0.75]
Convert tensor1d to Array:
tensor1d:
Tensor
    [1, 2, 3]
--> Array:
Float32Array(3) [ 1, 2, 3 ]
    ✓