sudo nvpmodel -m 0
Mode Mode Name Denver 2 Frequency ARM A57 Frequency GPU Frequency
0 Max-N 2 2.0 GHz 4 2.0 GHz 1.30 Ghz
{ | |
"graph": "/data/docker", | |
"storage-driver": "overlay2", | |
"runtimes": { | |
"nvidia": { | |
"path": "nvidia-container-runtime", | |
"runtimeArgs": [] | |
} | |
}, | |
"features": { |
sudo nvpmodel -m 0
Mode Mode Name Denver 2 Frequency ARM A57 Frequency GPU Frequency
0 Max-N 2 2.0 GHz 4 2.0 GHz 1.30 Ghz
def iter_batches(iterable, batch_size): | |
"""Iterates over the given iterable in batches. | |
Args: | |
iterable: an iterable | |
batch_size: the desired batch size, or None to return the contents in | |
a single batch | |
Returns: | |
a generator that emits tuples of elements of the requested batch size |
import os
import platform
import subprocess
import warnings
from distutils import spawn
def get_gpu_model_name() -> str:
if platform.system() == "Windows":
If you only want to change for current notebook, add the following code to cell:
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
jupyter nbconvert results.ipynb --no-input --to html
docker rmi $(docker images | grep stuff_ | tr -s ' ' | cut -d ' ' -f 3)
python3 -c "import torch; print(torch.tensor([0.], device='cuda:0'))"
sudo umount /dev/mmcblk0
sudo badblocks -n -v /dev/mmcblk0
A flash based medium should normally never give errors while using badblocks to the OS/application. If it does it means that:
It is worn out to the point the wear-leveling doesn't have enough room anymore. (part of) the flash memory itself is faulty.