Deep Learning

IoT Edge Deep Learning Neural Network Accelerators

2021-11-25. Category & Tags: Deep Learning, IoT, Edge, TPU, NCS, NCS2

Coral Edge Accelerator # 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 0.5 watts/TOPS MAX 4 TOPS (int8), 2W. TF Lite + Debian only. Not available world wide Intel NCS2 Neural Compute Stick 2 # Processor: Intel Movidius Myriad X Vision Processing Unit (VPU) USB 3. ...

AI Cloud

2019-11-01. Category & Tags: Cloud Platform, Machine Learning, ML, Deep Learning, DL

“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 Google Colab (cooperating with (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

Caffe Installation, Hello World

2017-12-03. Category & Tags: Caffe, Deep Learning, Mnist

Note: tested with Ubuntu 16.04.1 using /root, for newer Ubuntu version (>= 17.04), check here. Installation & Self-Tests # 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) # prepare data: # ./data/mnist/ # will download into . ...

TensorFlow Engineering with CUDA GPU for Deep Learning

2017-03-13. Category & Tags: Deep Learning, TensorFlow, GPU, Cuda, TF

See also: TF practical part in do deep learning How to setup Docker and Nvidia-Docker 2.0 on Ubuntu 18.04 Install # Summary: install CUDA first, then TF. ref TF 1.0 doc ref nvidia doc, until step 3 requirements # 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 # 1. ...