Coral Edge Accelerator
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ML accelerator: Edge TPU ASIC (application-specific integrated circuit) designed by Google. Provides high-performance ML inferencing for TensorFlow Lite Models. USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb/s transfer speed) Dimensions: 30 x 65 x 8mm Price: USD74.99 in 2019, USD ＄ 60 in 2021, ￥ 740 @JD.com 0.5 watts/TOPS MAX 4 TOPS (int8), 2W. TF Lite + Debian only. Not available world wide Intel NCS2 Neural Compute Stick 2
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Processor: Intel Movidius Myriad X Vision Processing Unit (VPU) USB 3.
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“if you plan to use deep learning extensively (>150 hrs/mo), building your own deep learning workstation might be the right move.” [medium]
Baidu AI Studio (only for PaddlePaddle) Paperspace (cooperating with fast.ai) Google Colab (cooperating with fast.ai) vast.ai (C2C/P2P sharing, very cheap, a lot of time to init/load/unload) Kaggle (max 6h, good GPU but complex steps to use) MS Azure Amazon FloydHub (special CLI interface) ref:
CN intro: Paperspace vs. Colab, 2019 Best Deals in Deep Learning Cloud Providers, 2018 比较云GPU平台
Install (w/ GPU)
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Install NV GPU driver and compatible CUDA version first, or install using pip together.
See PyTorch doc’s selector to find a compatible CUDA version.
Then use the cmd given by the selector to install PyTorch:
Tip: slow, tmux is suggested.
pip source:
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pip install pytorch torchvision torchaudio cudatoolkit=11.1 pip binary:
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Tip: the torch...whl file is > 3GB, which can be pre-downloaded using IDM/FDM etc.
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see also: /dl-theory
Data
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不得不赞！一个国内（可能）最好的海量 CV 数据集获取网站 Tutorials
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图像分类任务中的 tricks 总结 (bak) 邱锡鹏 教授 2020 神经网络与深度学习
Keywords: CNN, RNN, Attention, Gaussian Mix., RBM, DBN, GAN, RL, TF, PyTorch: equation, pseudocode, exercise …
PDF, PPT, code, exercise & solution, etc. A collection of various DL deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks 2019 《TensorFlow 2.0 深度学习算法实战 2019》 (pdf version via Python 数据科学) ICCV 2019 教程: DL SOTA by MXNET & GluonCV, [github] [cn intor] CS294-158 Deep Unsupervised Learning, 14 weeks, slides & youtube, Spring 2019 (overview in CN) fast.
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Note: tested with Ubuntu 16.04.1 using /root, for newer Ubuntu version (>= 17.04), check here.
Installation & Self-Tests
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Use the installation script here.
//(Sunny only added conditional USE_CUDNN=1, the rest is the same as: ref. You may wanna set USE_CUDNN to 0, if no GPU is used).
Timing: 15min if everything goes well, while downloading speed 1~8MB/s.
Hello World (Mnist)
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prepare data:
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./data/mnist/get_mnist.sh # will download into .
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See also:
TF practical part in do deep learning How to setup Docker and Nvidia-Docker 2.0 on Ubuntu 18.04 Install
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Summary: install CUDA first, then TF.
ref TF 1.0 doc
ref nvidia doc, until step 3
requirements
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64-bit Linux Python 2.7 or 3.3+ (3.5 used in this blog) NVIDIA CUDA 7.5 (8.0 if Pascal GPU) NVIDIA cuDNN >v4.0 (v5.1 recommended) NVIDIA GPU with compute capability >3.0 steps
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1.
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see also: /dl-do-it
ML Interpratation, Comprehensibility & Causality MIXED TUTORIAL + TRIKCS
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Google: TF, DL, Tutorial, Tricks, etc The Ultimate Course and Book list to be an expert in Mathematics and Programming (Content: Discrete Mathematics, Linear Algebra, Calculus, Probability, Cryptography, Geometry and Statistics. 45 Mathematics Courses) Deep Learning and Reinforcement Learning Summer School 2018 - Yoshua Bengio etc. 深度学习课程笔记 by Tess Ferrandez, recommended by Andrew Ng PAPERS, TERMS & DEFINATIONS
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一文读懂自注意力机制：8 大步骤图解+代码 ref: Illustrated: Self-Attention – Step-by-step guide to self-attention with illustrations and code [典型/经典 DL/NLP 模型] 读完这 45 篇论文，“没人比我更懂 AI 了” 如何开启深度学习之旅？这三大类 125 篇论文为你导航 Github DL papers DeepLearningBook/DeepLearningPapers.
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