---
title: nvidea
updated: 2022-04-03 11:44:16Z
created: 2021-05-04 14:58:11Z
---

# NVIDIA

## show installed video drivers

nvidia-smi

[Latest drivers](https://www.nvidia.com/Download/index.aspx?lang=en-us)

---

## list installed hw

lspci | grep -i nvidia
sudo lshw -numeric -C display

## find NVIDIA modules

find /usr/lib/modules -name nvidia.ko

## Settings

nvidia-settings

## run
```bash
nvidia-smi                               nvidia-smi -L
nvidia-smi -l n   # run every n seconds
```


## monitoring nvidia
https://github.com/fbcotter/py3nvml

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## successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero  => error; Modify in host and set the -1 to 0
/sys/bus/pci/devices/0000:2b:00.0/numa_node

for a in /sys/bus/pci/devices/*; do echo 0 | sudo tee -a $a/numa_node; done

https://stackoverflow.com/questions/44232898/memoryerror-in-tensorflow-and-successful-numa-node-read-from-sysfs-had-negativ

---

## set numa value at start computer

```bash
sudo crontab -e
# Add the following line
@reboot (echo 0 | tee -a "/sys/bus/pci/devices/<PCI_ID>/numa_node")
```
[Source](https://askubuntu.com/questions/1379119/how-to-set-the-numa-node-for-an-nvidia-gpu-persistently)

---

## start docker with --gpus=all every time, otherwise error
### failed call to cuInit: UNKNOWN ERROR (-1
### no NVIDIA GPU device is present: /dev/nvidia0 does not exist
docker run -it -p 8888:8888 --gpus=all tensorflow/tensorflow:latest-gpu-jupyter

---

## update nvidea drivers
ubuntu-drivers autoinstall