tuffacton

thomwolf

import os; import psutil; import timeit
from datasets import load_dataset

mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20
wiki = load_dataset("wikipedia", "20200501.en", split='train')
mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20
print(f"RAM memory used: {(mem_after - mem_before)} MB")

s = """batch_size = 1000
for i in range(0, len(wiki), batch_size):

sugatoray

Install Tensorflow-GPU with Conda

I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit. Miniconda has a much smaller footprint than Anaconda. I would suggest you to install Miniconda if you do not have conda already.

Quick Installtion

MarcSkovMadsen

# pip install panel==0.12.4 bokeh==2.4.0 holoviews==1.14.6 hvplot==0.7.3 shapely==1.7.1
# panel serve holoviz_linked_brushing.py --autoreload --show
import hvplot.pandas
import holoviews as hv
import panel as pn
from bokeh.sampledata.iris import flowers

pn.extension(sizing_mode="stretch_width")
hv.extension("bokeh")

sugatoray

Example badge: open-in-overleaf

[Overleaf][#overleaf] is an online service that allows you to create/edit/collaborate on LaTeX documents. For details on how the badge must be used, see the Example below.

• Sample badge:

sugatoray

Using VS Code with SSH + Proxy (Jump Box)

source: https://code.visualstudio.com/blogs/2019/10/03/remote-ssh-tips-and-tricks

marcosci

library(LaCroixColoR)
library(sf)
library(fasterize)
library(rayshader)
library(raster)
library(exactextractr)
library(rayrender)

# load raster into R
elevation <- raster("Olympus_Mons_ortho-image.tif")

sugatoray

# https://gist.github.com/althonos/6914b896789d3f2078d1e6237642c35c

[metadata]
name = {name}
version = file: {name}/_version.txt
author = Martin Larralde
author_email = [email protected]
url = https://github.com/althonos/{name}
description = {description}
long_description = file: README.md

rasbt

# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

pdtgct

The LLaMA model weights may be converted from Huggingface PyTorch format back to GGML in two steps:

1. download from decapoda-research/llama-7b-hf and save as pytorch .pth
2. use the ggerganov/llama.cpp script, convert-pth-to-ggml.py to convert from pytorch .pth to GGML

This process will result in ggml model with float16 (fp16) precision.

Prerequisite