Skip to content

Latest commit

 

History

History
61 lines (42 loc) · 2.25 KB

README.md

File metadata and controls

61 lines (42 loc) · 2.25 KB

Talos

Talos is a dataflow analysis and scheduling tool for deep learning applications. It has already be accepted by ASAP 21

Yuanjia XU, Heng WU*, Wenbo ZHANG, Tao WANG, Chen YANG, Heran GAO. Talos: A Weighted Speedup-Aware Device Placement of Deep Learning Models. The 32nd IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP 2021). 101-108

Talos paper can be found here.

This work is supported in part by National Key R&D Program of China (No. 2018YFB1402503), National Natural Science Foundation of China (No. 61872344) and Youth Innovation Promotion Association of Chinese Academy of Sciences Fund (No. 2018144).

operator speedup analysis for pytorch

click here to see pytorch operator chrome tracing

operator speedup analysis for TensorFlow

click here to see tensorflow operator chrome tracing

operator speedup aware scheduling

click here to see operator speedup awareness scheudling

problems

code completion

ctrl+shift+p: open user settings

copy and change path:

{
    "python.autoComplete.addBrackets": true,
    "python.autoComplete.extraPaths": [
        // "/root/anaconda3/envs/xyj_pytorch",
        // "/root/anaconda3/envs/xyj_pytorch/lib/python38.zip",
        // "/root/anaconda3/envs/xyj_pytorch/lib/python3.8",
        // "/root/anaconda3/envs/xyj_pytorch/lib/python3.8/lib-dynload",
        // "/root/anaconda3/envs/xyj_pytorch/lib/python3.8/site-packages",
        "/root/anaconda3/envs/d2l",
        "/root/anaconda3/envs/d2l/lib/python38.zip",
        "/root/anaconda3/envs/d2l/lib/python3.8",
        "/root/anaconda3/envs/d2l/lib/python3.8/lib-dynload",
        "/root/anaconda3/envs/d2l/lib/python3.8/site-packages"
    ]
}

d2l has some dependencies:

export PYTHONPATH=/root/d2l-en/mxnet 或 pip install d2l==0.13.2 -f https://d2l.ai/whl.html
pip install ipython
pip install pandas
pip install ipykernel

comments on dive to deep learning

  1. 机器学习的过程:优化和泛化
  2. 计算图可以标记常量,避免全部微分,也能够控制微分分支