YoungJoong Kwon
youngjoong@cs.unc.edu
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Pytorch kill processes distributed on multiple Gpus
kill $(ps aux | grep “train.py” | grep -v grep | awk ‘{print $2}’)
From frankang’s answer
Anaconda virtual environment export/import 아나콘다 가상환경 export/import 가이드
- Export virtual environment (e.g. virtual environment name is ‘test’)
conda env export -n test > test_environment.yml - Import virtual environment
conda env create -f test_environment.yml
Pytorch Data Augmentation
Useful UE Blog
Conda 가상환경에서 jupyter notebook 사용하기
conda 가상환경에서 jupyter notebook을 열었는데, 지금껏 열심히 설치한 패키지들이 import되지 않는 난감한 상황이 발생할 때가 있다. 가상환경을 활성화시켰고, 가상환경 안에서 jupyter notebook까지 잘 켰는데 왜 import error가 뜨는걸까?
이는 jupyter notebook은 conda 가상환경에 따로 설치해주지 않아도 사용할수 있는 데에서 발생한다. 그냥 jupyter 패키지를 설치해주면 간단하게 해결된다. 다음의 명령어 두개를 쳐보자.
conda install jupyter
jupyter notebook
이제 노트북이 제대로 실행이 되고, 가상환경에 설치한 패키지들도 문제없이 import 될것이다.
conda로 pytorch와 tensorflow를 같이 설치할때 from torch._C import * ImportError: numpy.core.multiarray failed to import 에러 해결하기
from torch._C import * ImportError: numpy.core.multiarray failed to import 는 conda를 이용하여 pytorch와 tensorflow를 같이 설치할때 흔히 겪을 수 있는 오류이다.
이 오류는 tensorflow 가 python 3.7과 잘 맞지 않음에서 발생한다. (참고: mjahmad님의 답변)
해결방법은 다음과 같다.
- python 3.6 의 conda 가상환경을 만든다
- conda create -n 가상환경이름 python=3.6
- 방금 만든 가상환경을 활성화
- conda activate 가상환경이름
- 개발환경에 맞는 pytorch 설치
- 설치 명령어는 이곳을 참조
- tensorflow 를 설치
- conda install tensorflow
이제 제대로 해결되었는지 test 해보자. command line 에 다음의 명령어를 순서대로 쳐보자.
python
import torch
import tensorflow as tf
import numpy as np
UE4 When You Can’t Compile C++ Source Code or Can’t Add New C++ Class inside the Editor
- Shut down the Unreal Engine.
- Navigate to your project folder, where there are files like Visual Studio solution file, and Unreal Engine project file.
- Right click your Unreal Engine project file.
- Select “Generate Visual Studio Project Files”. If there is no option like this, follow the steps that are described in this link.
- Now double click the Unreal Engine project file, and the Unreal Engine editor will open up.
- Now you can add new C++ source code and then compile it inside the editor.
Stability of SVD and Eigen Decomposition
Hi, today I want to talk about the stability of SVD and Eigen Decomposition in numerical linear algebra perspective.
The purpose of SVD and Eigen Decomposition is to simplify the original matrix (say, matrix A) so that we can easily study the properties of the matrix. But as we try to use computers to do the calculation, there comes the difference between SVD and Eigen Decomposition.
No matter what the matrix is, we can always trust the result of SVD. This means SVD is stable. And SVD always guarantees the source matrix can be diagonalized.
For Eigen Decomposition, if the source matrix is symmetric then we can trust the computed eigen values and we can say that the source matrix can be always diagonalized. But, if the source matrix is any arbitrary matrix which is not symmetric, we cannot say for sure the eigen values computed are correct. It depends on the condition number of the source matrix, so we should first check the condition number. Also, we cannot guarantee that the source matrix can always be diagonalized.
Should we not use the Eigen Decomposition of math libraries out there? I would recommend keep using the math libraries. They are the best thing we can do for now. But we should remember that we cannot always trust the Eigen Decomposition since it is already ill-posed problem (the method is good). If the result is not what you want, it could be because the Eigen Decomposition is not stable for arbitrary matrix.
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