A fun little CLI tool to gather and disiplay GPU utilization for nodes in a cluster.
Inspired by this LinkedIn post.
A fun little CLI tool to gather and disiplay GPU utilization for nodes in a cluster.
Inspired by this LinkedIn post.
channels: | |
- rapidsai | |
- conda-forge | |
- nvidia | |
dependencies: | |
- cudf=24.10 | |
- python=3.12 | |
- cuda-version>=12.0,<=12.5 | |
- s3fs | |
- dask |
awk 'NR==FNR {map[$1]=$2; next} {print ($1 in map ? map[$1] : $1)}' <(alias | sed -E "s/^([^=]*)='?([^ ]*).*/\1 \2/") <(echo "SHELL HISTORY UNWRAPPED" `date +%Y` && history | gawk '{gsub(/^\s*[0-9]+\*?(\s*[0-9/T:]+)\s+/, "", $0); print $0}' | gawk '{gsub(/ \| /, "\n", $0); print $0}' | gawk ' { i=2; while ($1 ~ /^[A-Z0-9_]+=/) { $1=$i; i++ }; print $1 }') | sort | uniq -c | sort -n | tail -n 10 |
The RAPIDS cudf.pandas
accelerator allows you to leverage the power of NVIDIA GPU acceleration in your pandas
workflows.
Scripts that use pandas
can be run via the cudf.pandas
module to accelerate your code with zero-code change.
python my_code.py # Uses the CPU
python -m cudf.pandas my_code.py # Same pandas code uses the GPU
### Apple Specific ### | |
# ignore OS X hidden meta files | |
.DS_Store | |
.AppleDouble | |
.LSOverride | |
# Icon must end with two \r | |
Icon |
import contextlib | |
import codecs | |
import subprocess | |
import pandas as pd | |
# Load list of global nameservers and country code information | |
print("Loading data sources...") | |
nameservers = pd.read_csv("https://public-dns.info/nameservers.csv") | |
countries = pd.read_csv("https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes/raw/refs/heads/master/all/all.csv") |
When using LocalCUDACluster
on a single node it is possible to scale your work out on a SLURM based HPC with a few small tweaks.
First install the Dask Runners package. (Note: this is a prototype and will be merged into dask-jobqueue
in the future)
pip install git+https://github.com/jacobtomlinson/dask-hpc-runner.git
Then replace LocalCUDACluster
with the SLURMRunner
class.
station | mean_temp | |
---|---|---|
Abha | 18.0 | |
Abidjan | 26.0 | |
Abéché | 29.4 | |
Accra | 26.4 | |
Addis Ababa | 16.0 | |
Adelaide | 17.3 | |
Aden | 29.1 | |
Ahvaz | 25.4 | |
Albuquerque | 14.0 |
import os | |
import subprocess | |
import time | |
import socket | |
DB_IS_DRIVER = os.getenv('DB_IS_DRIVER') | |
DB_DRIVER_IP = os.getenv('DB_DRIVER_IP') | |
if DB_IS_DRIVER == "TRUE": | |
print("This node is the Dask scheduler.") |