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 @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 # 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) # win10 # 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:
pip source: # pip install pytorch torchvision torchaudio cudatoolkit=11.1 pip binary: # pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html Tip: the torch...whl file is > 3GB, which can be pre-downloaded via IDM etc.
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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/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 # 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.
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