zhanwenchen

"""
Extract the contents of a `run-{id}.wandb` database file.

These database files are stored in a custom binary format. Namely, the database
is a sequence of wandb 'records' (each representing a logging event of some
type, including compute stats, program outputs, experimental metrics, wandb
telemetry, and various other things). Within these records, some data values
are encoded with json. Each record is encoded with protobuf and stored over one
or more blocks in a LevelDB log. The result is the binary .wandb database file.

zhanwenchen

#!/bin/bash

# Downloads and applies a patch from Drupal.org.
if [ -z "$1" ] 
then
  echo "You need to supply a URL to a patch file."
  exit
fi

URL=$1;

zhanwenchen

; /usr/share/pulseaudio/alsa-mixer/profile-sets/astro-a50-gen4.conf

[General]
auto-profiles = yes

[Mapping analog-voice]
description = Voice
device-strings = hw:%f,0,0
channel-map = left,right
paths-output = steelseries-arctis-output-chat-common

zhanwenchen

# Modify apt sources lists
cd /etc/apt/sources.list.d/
sudo rm gds-11-7.conf cuda-12-3.conf cuda-12-2.conf cuda-12-1.conf 989_cuda-11.conf cuda-ubuntu2004-11-7-local.list cuda-ubuntu2004-11-7-local.list

# Modify apt preferences
cd /etc/apt/preferences.d
sudo rm cuda-repository-pin-600 nvidia-fabricmanager

# Startup shell environment variables
sudo vim /etc/profile.d/dlami.sh # comment out both

zhanwenchen

git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers.git
cd nv-codec-headers
vi Makefile   # change the first line to PREFIX = ${CONDA_PREFIX}
make install
cd ..

git clone https://git.ffmpeg.org/ffmpeg.git
cd ffmpeg
git checkout n4.2.2
conda install nasm

zhanwenchen

I wrote these instructions as part of "installing PyTorch with CUDA 12.1.1".

Anyway, if you still need to compile from source… here's how:

Build Magma from source

This is a dependency of PyTorch, which is sensitive to CUDA version.

Clone Magma:

zhanwenchen

"""
Creates an HDF5 file with a single dataset of shape (channels, n),
filled with random numbers.

Writing to the different channels (rows) is parallelized using MPI.

Usage:
  mpirun -np 8 python demo.py

Small shell script to run timings with different numbers of MPI processes:

zhanwenchen

# https://github.com/HDFGroup/hdf5/blob/hdf5-1_13_1/release_docs/INSTALL_parallel
# https://docs.olcf.ornl.gov/software/python/parallel_h5py.html
# https://www.pism.io/docs/installation/parallel-io-libraries.html
# using ~/local/build/hdf5 as the build directory.
# Install HDF5 1.13.1 with parallel I/O in ~/local/hdf5,

version=1.13.1
prefix=$HOME/local/hdf5
build_dir=~/local/build/hdf5
hdf5_site=https://support.hdfgroup.org/ftp/HDF5/releases/hdf5-1.13

zhanwenchen

#One workaround is to create clone environment, and then remove original one:
#(remember about deactivating current environment with deactivate on Windows and source deactivate on macOS/Linux)

conda create --name new_name --clone old_name --offline #use --offline flag to disable the redownload of all your packages
conda remove --name old_name --all # or its alias: `conda env remove --name old_name`

#There are several drawbacks of this method:
# time consumed on copying environment's files,
# temporary double disk usage.

zhanwenchen

# -*- coding: utf-8 -*-
import os
import sys
import logging
from flask import Flask, request, jsonify
from flask_cors import CORS, cross_origin


from translate_client import Server