TensorFlow Dev Summit 2020 Recap

# What's this?
2020/3/11に開催されたTensorFlow Dev Summit 2020（オンラインイベント）のストリーミングを見て自分の気になったところをまとめたメモ

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# TensorFlow Dev Summit とは
2017年から毎年開催されているTensorFlowの開発者向けイベント

TensorFlowの新しいプロダクトはだいたい毎年このイベントで発表されることが多い

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# 全動画
- [Keynote](https://youtu.be/_lsjCH3fd00)
- [Learning to read with TensorFlow and Keras](https://youtu.be/aNrqaOAt5P4)
- [TensorFlow Hub: Making Model Discovery Easy](https://youtu.be/3seWxHGnDqM)
- [Collaborative ML with TensorBoard.dev](https://youtu.be/v9a240kjAx4)
- [TF2.x on Kaggle](https://youtu.be/IraU2xyAoKc)
- [Performance Profiling in TensorFlow 2](https://youtu.be/OTip0L8clKo)
- [Research with TensorFlow](https://youtu.be/51YtxSH-U3Y)
- [Differentiable convex optimization layers](https://youtu.be/NrcaNnEXkT8)
- [Scaling TensorFlow data processing with tf.data](https://youtu.be/n7byMbl2VUQ)
- [Scaling TensorFlow 2 models to multi-worker GPUs](https://youtu.be/6ovfZW8pepo)
- [Making the most of Colab](https://youtu.be/pnClcwTCyc0)
- [MLIR: Accelerating TF with compilers](https://youtu.be/Hx3A0A2lM84)
- [TFRT: A new TensorFlow Runtime](https://youtu.be/15tiQoPpuZ8)
- [TFX: Production ML with TensorFlow in 2020](https://youtu.be/I3MjuFGmJrg)
- [TensorFlow Enterprise: Productionizing TensorFlow with Google Cloud](https://youtu.be/l75buiXdW6I)
- [TensorFlow.js: ML for the Web and beyond](https://youtu.be/iH9CS-QYmZs)
- [TensorFlow Lite: ML for mobile and IoT devices](https://youtu.be/27Zx-4GOQA8)
- [TensorFlow and ML from the trenches: The innovation experience center at JPL](https://youtu.be/WMsYupTzkKw)
- [Getting involved in the TF community](https://youtu.be/kWI1fvC60bY)
- [Responsible AI with TensorFlow](https://youtu.be/UEECKh6PLhI)
- [TensorFlow Quantum: A software platform for hybrid quantum-classical ML](https://youtu.be/-o9AhIz1uvo)
- [全てを見たい人 (7:50:12)](https://youtu.be/HlBGYxO8RaU)

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# The TensorFlow Ecosystem

![eco2.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/57c97618-6da6-6bb1-5e11-567f0f4f693e.png)

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# TensorFlow Quantum
![tf_quantum_circle.jpg](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/b0849fc1-8049-cc3b-a1f6-c25acb4b9964.jpeg)

TensorFlow Dev Summitに先立って公開されたライブラリ
https://www.tensorflow.org/quantum
https://github.com/tensorflow/quantum

- 「[Cirq](https://ai.googleblog.com/2018/07/announcing-cirq-open-source-framework.html)」を、既存のTensorFlowライブラリに統合した
- TensorFlow APIと互換性をもつ量子コンピューティングを実現


紹介記事
https://gigazine.net/news/20200310-google-tensorflow-quantum/

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# TensorFlow 2.2
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# Performance Profiler

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# Overview Page 
<img width="659" alt="Screen Shot 2020-03-17 at 13.13.03.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/10f99ba5-46cc-4d17-89c5-e781ecb6e9d0.png">

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# Input Pipeline Analyzer
<img width="658" alt="Screen Shot 2020-03-17 at 13.13.48.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/e3fe7a4b-fa9d-aeae-a27f-6dfddc463c64.png">

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# Trace Viewer
<img width="660" alt="Screen Shot 2020-03-17 at 13.15.25.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/4bd114e8-7908-50ce-2c04-ccafd3693ee8.png">

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# TensorFlow Stats
<img width="660" alt="Screen Shot 2020-03-17 at 13.14.51.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/1b6e868d-6f4c-ecc2-6b2b-d48a616d0ed8.png">

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# Profiling

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# Step 1: Keras TensorBoard callback
<img width="659" alt="Screen Shot 2020-03-17 at 13.16.18.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/3d74ebe4-d0c3-c0cf-d8a0-5f06ea777755.png">

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# Step 2: Launch TensorBoard
<img width="660" alt="Screen Shot 2020-03-17 at 13.17.18.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/1f6cf3ea-2515-7137-ac11-d6bfa407ec0c.png">

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# Step 3: Check Overall Analysis
<img width="661" alt="Screen Shot 2020-03-17 at 13.17.25.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/634259f4-7de3-72fe-1426-5f08749069f0.png">

<img width="657" alt="Screen Shot 2020-03-17 at 13.18.10.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/a00632b0-c6ae-2aa3-dcc1-006797b0e889.png">


 <img width="661" alt="Screen Shot 2020-03-17 at 13.17.50.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/ef1691ac-3afb-54bc-22e2-402ef5861989.png">

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# Step 4: Input Pipeline Recommendation
<img width="657" alt="Screen Shot 2020-03-17 at 13.21.46.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/07841637-7df4-8709-2c61-d3f1e26176b8.png">

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# Step 5: Optimize the "Data Preprocessing"
<img width="659" alt="Screen Shot 2020-03-17 at 13.22.41.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/f91cb9ff-bc81-a11f-6e9c-3b85f4c54092.png">

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# Step 6: Capture a new profile
<img width="658" alt="Screen Shot 2020-03-17 at 13.23.32.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/74abce5b-bbc6-d15b-ca24-c95c39f57861.png">

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# Next Steps
<img width="656" alt="Screen Shot 2020-03-17 at 13.24.47.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/39685c3f-5eb1-cb4c-0d5f-70e818952759.png">

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# TensorFlow Developer Certificate

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# TensorFlow Developer Certificate
https://www.tensorflow.org/certificate

- 5時間のテストで5つのモデルを作る
- 全体で100点満点中90点で合格

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# カテゴリ

1. ベーシック/シンプル
2. データセットからのモデル
3. 画像データからCNNモデル
4. テキストデータからNLPテキスト分類
5. 数値データセットからシーケンスモデル

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# 受講者に必要なスキルセット

1. ML/DLの基礎的な知識
2. TensorFlowを用いた機械学習モデルの実装
3. DNNとCNNを使った画像認識アルゴリズムの実装
    - 異なる画像サイズの扱い
    - 畳み込みレイヤーの視覚化
    - lossやaccuracyのプロット
    - オーバーフィッティングを防ぐ方法
4. 画像認識、物体検出、テキスト認識
5. 自然言語処理へのニューラルネットワークの適用


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# 環境

- PyCharm﻿
- インターネット接続環境
- TensorFlow Examプラグイン
- テスト中のドキュメント参照は可能

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# コスト

-  $100 USD


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# 再受験

- 一回目から14日後に再受験
- 二回目から2ヶ月後に再受験
- 三回目から1年後に再受験（それ以降は同じ）



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# 有効期限

- 36ヶ月
- 更新不可、要再受験


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# Google Cloud AI Platform Pipelines

- ベータ版をリリース
- Notebookでの課題解決はシンプル
- ワークフローの構築やスケールは複雑
- データの準備と分析、トレーニング、評価、展開


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# TensorFlow.js 

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# Facemesh
![facemesh_final_3.gif](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/b243f41f-34b1-3c38-fd0a-b407be2e8947.gif)!

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![facemesh_3d_demo.gif](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/b84a6cc9-7d4b-063c-0a2b-d3819aa4121b.gif)

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# demo 
https://storage.googleapis.com/tfjs-models/demos/facemesh/index.html

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# Handpose
<img width="500" alt="Screen Shot 2020-03-17 at 11.27.47.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/942f29cf-5192-2dda-a6e0-808737a70a24.png">

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# demo
https://storage.googleapis.com/tfjs-models/demos/handpose/index.html

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# 実行速度
![Screen Shot 2020-03-08 at 2.53.22 PM.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/86972273-3122-0e14-cc7a-1cdf657ad460.png)

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#　MobileBERT

<img width="658" alt="Screen Shot 2020-03-17 at 12.17.08.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/41b152f5-4029-44d6-1b34-1689131ed6b3.png">

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# Use cases
<img width="659" alt="Screen Shot 2020-03-17 at 12.20.34.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/540d13c5-9866-6c34-ea27-d8c5f4fd73e6.png">

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# AR Beauty
<img width="664" alt="Screen Shot 2020-03-17 at 12.22.17.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/f2f6dd11-2634-aa93-654e-945c9657f435.png">

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# Multiple Backends 
<img width="657" alt="Screen Shot 2020-03-17 at 12.24.00.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/96aada57-a135-b1c4-4c00-894f883b6b9a.png">

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# TensorBoard.dev
![tensorboard.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/95f45ef1-745a-e6c9-5ac7-b7a9c2aec063.png)

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# TensorFlow Hub
<img width="660" alt="Screen Shot 2020-03-17 at 12.55.31.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/07c5dac3-c3e5-afe7-3890-095e70c42ad9.png">

https://tfhub.dev/

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# TensorFlow Hub
<img width="660" alt="Screen Shot 2020-03-17 at 12.55.59.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/f1c87c81-cb6c-3411-c255-230ad5d5a612.png">

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# Ready to use
<img width="657" alt="Screen Shot 2020-03-17 at 12.58.22.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/5df5fd10-627b-d22f-3144-1281ae57c96e.png">

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# Easy to Search
<img width="658" alt="Screen Shot 2020-03-17 at 12.58.59.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/aebd5f40-cdd7-ffab-ffab-266a6335e25f.png">

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# With Notebook
<img width="658" alt="Screen Shot 2020-03-17 at 12.59.14.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/b700b64b-af61-eac1-ba14-132f1ba34503.png">

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# Style transfer
<img width="657" alt="Screen Shot 2020-03-17 at 13.01.34.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/9d36e2fb-9662-43bb-92bb-39c3307aaeb4.png">

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# Text Classification
<img width="659" alt="Screen Shot 2020-03-17 at 13.02.21.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/b018fb9d-053b-c32f-1e83-e01719fefa7d.png">

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# Audio
<img width="658" alt="Screen Shot 2020-03-17 at 13.03.13.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/c2b141bb-1870-a501-55b2-5a236c564ef7.png">

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# Improved Search & Discovery
<img width="659" alt="Screen Shot 2020-03-17 at 13.04.36.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/cad626a4-7ebc-edba-bc9f-12028cdd5649.png">

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# Expanded Support for TF Formats
<img width="658" alt="Screen Shot 2020-03-17 at 13.06.04.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/274643a2-2b08-8562-2b91-988ac38d03ba.png">

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# Colab
<img width="656" alt="Screen Shot 2020-03-17 at 12.38.31.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/3dee2eeb-440e-531e-f3aa-c033080d6d9d.png">

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# Top 10 Colab Tricks for TensorFlow Users
<img width="653" alt="10" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/f95fd4b6-d9c9-6525-20f0-33b58192b30b.png">

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# 10: Specify TensorFlow version
<img width="656" alt="Screen Shot 2020-03-17 at 12.39.22.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/c4c6b850-858c-4d9a-3bb4-a123c070d136.png">

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# 9: Use TensorBoard right in Colab
<img width="659" alt="Screen Shot 2020-03-17 at 12.39.41.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/3cd3a35c-ed10-8407-e6f0-25622c5a6f34.png">

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# 8: TFLite? No problem!
<img width="654" alt="Screen Shot 2020-03-17 at 12.45.59.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/a2c05550-b8cd-6457-c918-a7af087b079c.png">

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# 7: Use TPUs
<img width="658" alt="Screen Shot 2020-03-17 at 12.41.21.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/0060d05e-bf4b-b0d0-6685-dca4b1494a1f.png">

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# 6: Use local runtimes
<img width="660" alt="Screen Shot 2020-03-17 at 12.41.43.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/e4ad9ae3-33e6-0996-e412-eed94f34bc73.png">

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# 5: The Colab scratchpad
<img width="659" alt="Screen Shot 2020-03-17 at 12.42.02.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/52c1d807-c8f4-1586-0492-4fd7e511ba40.png">

https://colab.research.google.com/notebooks/empty.ipynb

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# 4: Copy data to Colab VMs
<img width="661" alt="Screen Shot 2020-03-17 at 12.42.43.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/506671a1-9312-c7e0-0c88-4d74d78f1adc.png">

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# 3: Mind your memory
<img width="657" alt="Screen Shot 2020-03-17 at 12.43.00.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/e7deafaf-fe47-25c2-6be9-c8b05651bb27.png">
 
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# 2: Close tabs when done
<img width="658" alt="Screen Shot 2020-03-17 at 12.43.40.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/349a5567-f430-6ab6-4bf3-f56081cc631d.png">

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# 1: Only use GPUs when needed
<img width="658" alt="Screen Shot 2020-03-17 at 12.43.58.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/52defa3d-6bcb-cfea-9d77-a5d13af31dff.png">

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# Colab Pro

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# Colab Pro
<img width="659" alt="Screen Shot 2020-03-17 at 12.53.55.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/f11d2103-9a80-fff6-f777-00430fa1f8a1.png">

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# Colab Pro
<img width="657" alt="Screen Shot 2020-03-17 at 12.52.56.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/e0d13569-a24c-577f-4b92-fd70036c7c49.png">

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# Kaggle x TensorFlow 2.x
<img width="648" alt="Screen Shot 2020-03-17 at 13.09.04.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/e2612c59-6b84-503b-1f13-41f16236dd21.png">

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# TensorFlow Lite

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# Android Studio Integration
<img width="654" alt="Screen Shot 2020-03-17 at 12.30.06.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/dfd7f1ab-6da9-28f1-ff95-f700099c96df.png">

# Profiler for Android
<img width="656" alt="Screen Shot 2020-03-17 at 12.33.15.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/da897dd2-ba1d-47b6-2459-f84c45e1bdf7.png">

# iOS Core ML support
<img width="660" alt="Screen Shot 2020-03-17 at 12.31.48.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/f4e397d8-5879-6829-9c46-6e305f529126.png">

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# Language support
<img width="660" alt="Screen Shot 2020-03-17 at 12.34.46.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/59ee8bc2-bb18-f522-7956-45c177989bbb.png">

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# Hardware support
<img width="656" alt="Screen Shot 2020-03-17 at 12.36.02.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/3eeffb52-0ee3-f923-3f79-ec9ee484c429.png">

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# Arduino Suppoort
<img width="659" alt="Screen Shot 2020-03-17 at 12.36.16.png" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/129712/66dc0a0c-25b7-9323-d3da-06f39cc187c7.png">


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# Thank you