Dong Wang nanguoyu

Install Python env with Mamba in MacOS

Mamba是一个智能版本的Conda，用来管理Python虚拟环境。详细介绍请参考：https://mamba.readthedocs.io/en/latest/user_guide/mamba.html

curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"

yum install gcc-c++

wget https://cmake.org/files/v3.12/cmake-3.12.3.tar.gz

	import torch
	import torch.nn as nn
	import torch.optim as optim
	import matplotlib.pyplot as plt

	def compute_rgn_from_gradients_and_params(gradients, parameters):
	"""
	根据梯度值和参数值直接计算 RGN

	参数:

	"""
	@File : fast_torchvision_dataloader.py
	@Author: Dong Wang
	@Date : 2024/06/25
	@Description : a fast image dataloader for Pytorch models. It tries to use FFCV to speed up your dataloader for vision tasks.
	You need first install FFCV in your Python ENV and run prepare_ffcv_dataset.py to prepare datasets in FFCV.
	"""

	import os
	from torch.utils.data import DataLoader

	# Go to https://docs.conda.io/en/latest/miniconda.html and choose the suitable file link
	wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.12.0-Linux-x86_64.sh

	# install Miniconda
	bash Miniconda3-py37_4.12.0-Linux-x86_64.sh

	conda create -n env_name python=3.7
	conda activate env_name
	conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

	panic(cpu 1 caller 0xfffffe0026801790): watchdog timeout: no checkins from watchdogd in 93 seconds (127 total checkins since monitoring last enabled)
	Debugger message: panic
	Memory ID: 0x6
	OS release type: User
	OS version: 22C65
	Kernel version: Darwin Kernel Version 22.2.0: Fri Nov 11 02:04:44 PST 2022; root:xnu-8792.61.2~4/RELEASE_ARM64_T8103
	Fileset Kernelcache UUID: ADB288150AFF2B26A022D2179A26F30C
	Kernel UUID: D43063DF-7FAB-3E39-9807-2FC6A0C7F76A
	Boot session UUID: BD64D3CE-D999-44A9-A66B-88DAF57DAA20
	iBoot version: iBoot-8419.60.44

	hyper_output = []
	with tqdm(range(-180, 181, 10), position=0) as t:
	for x in t:
	for y in tqdm(range(-180, 181, 10), disable=True):
	for z in tqdm(range(-180, 181, 10), disable=True):

	angles = torch.tensor([x,y,z])/180*torch.pi
	Hyper_x = transform_angles(angles=angles).to(device=gpu_computation)

	hyper_output.append(model.hyper_stack(Hyper_x).cpu().detach().numpy())

	git clone https://github.com/nanguoyu/GenoCAE.git
	cd GenoCAE/
	docker build -t gcae/genocae:build -f docker/build.dockerfile .
	docker run -it --rm -v ${PWD}:/workspace gcae/genocae:build python3 run_gcae.py --help

	apt-get update && apt-get upgrade -y &&\
	apt-get install -y wget \
	build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev


	wget https://www.python.org/ftp/python/3.8.0/Python-3.8.0.tgz &&\
	tar -xf Python-3.8.0.tgz &&\
	cd Python-3.8.0 &&\
	./configure --enable-optimizations &&\
	make -j8 &&\

	sudo apt-get install qt5-default
	sudo apt-get install opencl-headers
	sudo pip install pylint
	# Install minimal prerequisites (Ubuntu 18.04 as reference)
	sudo apt update && sudo apt install -y cmake g++ wget unzip
	# Download and unpack sources
	wget -O opencv.zip https://github.com/opencv/opencv/archive/master.zip
	wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/master.zip
	unzip opencv.zip
	unzip opencv_contrib.zip