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branch () { | |
branches=$(git for-each-ref --sort=-committerdate refs/heads/ --format='%(color:cyan)%(refname:short)|%(color:bold green)%(committerdate:relative)|%(color:magenta)%(authorname)%(color:reset)' --color=always|column -ts'|') && | |
branch=$(echo "$branches" | fzf --multi --ansi --height=20%) | |
if [ -z "${branch}" ]; | |
then return 0 | |
else git switch $(echo "$branch" | sed "s/ .*//") | |
fi | |
} |
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venv () { | |
RED='\033[0;31m' | |
NC='\033[0m' # No Color | |
# create virtualenv | |
if [ ! -d .venv ] # if venv does not exist already | |
then | |
python -m venv .venv --system-site-packages | |
else | |
echo -e "${RED}.venv${NC} already exists!" | |
return 0 |
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def train_test_split( | |
df: pl.DataFrame, train_fraction: float = 0.75 | |
) -> Tuple[pl.DataFrame, pl.DataFrame]: | |
"""Split polars dataframe into two sets. | |
Args: | |
df (pl.DataFrame): Dataframe to split | |
train_fraction (float, optional): Fraction that goes to train. Defaults to 0.75. |
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module load CUDA/11.0 CMake/3.21.1-GCCcore-11.2.0 Boost/1.74.0-GCC-10.2.0 | |
pip install lightgbm --install-option=--gpu | |
# If running from notebook, ensure that the following contains Boost: | |
# import os | |
# os.environ["LD_LIBRARY_PATH"].split(":") |
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# must not use gcc version > 10 | |
module load CUDA/11.0 CMake/3.21.1-GCCcore-11.2.0 | |
# Clone with recursive! Otherwise you get dmlc error later on! | |
git clone --recursive https://github.com/dmlc/xgboost | |
cd xgboost/python-package | |
# clean out any old build files that may break things if try to recompile | |
rm -rf build |
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# From a directory with a Dockerfile | |
# Example Dockerfile with "Dockerfile" as filename | |
FROM python:3.10-slim-buster | |
RUN pip install numpy | |
# Then, run the following | |
docker build -t your_image_name . |
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from matplotlib.colors import LinearSegmentedColormap | |
def black_to_color(high_color: tuple, low_color: tuple = (0,0,0), name: str = "my_color", steps=256): | |
"Get linear colormap from one rgb color to another, defaulting from black" | |
r1,g1,b1 = low_color | |
r2,g2,b2 = high_color | |
cdict = { | |
'red': [(0.0, r1, r1), |
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cat /root/.jupyter/lab/user-settings/@jupyterlab/notebook-extension/tracker.jupyterlab-settings | |
{ | |
// Notebook | |
// @jupyterlab/notebook-extension:tracker | |
// Notebook settings. | |
// ************************************** | |
// Code Cell Configuration | |
// The configuration for all code cells. | |
"codeCellConfig": { |
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if docker info > /dev/null 2>&1; then | |
: # pass | |
else | |
echo "Docker Daemon is not running. Please open it and retry." | |
exit -1 | |
fi |
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import numpy as np | |
import dask.array as da | |
x = da.random.random((120,500,500), chunks=(40, 500, 500)) # 80 MB chunks | |
X = x.compute() | |
@da.as_gufunc(signature="(n,n)->(n,n)", output_dtypes=float, vectorize=True) | |
def gufunc(x): | |
return np.linalg.inv(x) |