Originally posted on: https://matheustguimaraes.com/blog/cuda-cudnn-ubuntu-installation
Update package lists, download and install NVIDIA driver
sudo apt-get update
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt install nvidia-410
torch.manual_seed(42) | |
x_tensor = torch.from_numpy(x).float() | |
y_tensor = torch.from_numpy(y).float() | |
# Builds dataset with ALL data | |
dataset = TensorDataset(x_tensor, y_tensor) | |
# Splits randomly into train and validation datasets | |
train_dataset, val_dataset = random_split(dataset, [80, 20]) |
Update package lists, download and install NVIDIA driver
sudo apt-get update
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt install nvidia-410
knowledge dump on container runtimes | |
KataContainers | |
- image coupled with kernel | |
- light vm layer | |
- can run in nested virturalization environments if hardware supports and you can enable it in bios (ex. only bare metal EC2 instances, limits many cloud providers) | |
- slower startup time | |
- OCI compliant | |
- previously known as ClearContainers by Intel |
variables: | |
VERSION_ID: '1.0.$CI_PIPELINE_ID' | |
stages: | |
- build | |
build: | |
image: slauta93/electron-builder-win | |
stage: build | |
artifacts: |
# openocd -f stm32f429-disco.cfg -c "program FILE_NAME.elf verify reset exit" | |
# for: http://www.st.com/web/catalog/tools/FM116/SC959/SS1532/PF259090 | |
interface hla | |
hla_layout stlink | |
hla_device_desc "ST-LINK/V2" | |
# stm32f429 discovery 0483:374b | |
hla_vid_pid 0x0483 0x374b |
#Send image data to v4l2loopback using python | |
#Remember to do sudo modprobe v4l2loopback first! | |
#Released under CC0 by Tim Sheerman-Chase, 2013 | |
import fcntl, sys, os | |
from v4l2 import * | |
import time | |
import scipy.misc as misc | |
import numpy as np |