| ; /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 |
This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software
on an Ubuntu Linux system, in particular Ubuntu 20.04.
| 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 |
I often train machine learning/deep learning models and it takes a very long time to finish. Even an epoch in a moderately complex model takes near to half an hour to train. So, I constantly need to check (baby sit) the training process.
To help reduce the pain, I need a way to notify me on the training metrics. The idea is, we will send the training metrics (messages) as notifications on mobile using PyTorch Callbacks.
I have written some Python code snippets that helps me send my training metrics log as mobile push notifications using Pushover service. They have a limit of 7500 requests per month per user—which is fine for my usecase.
Those who'd like to have something like this, you can grab those little hacky scripts.
| conda uninstall --force pillow -y | |
| # install libjpeg-turbo to $HOME/turbojpeg | |
| git clone https://github.com/libjpeg-turbo/libjpeg-turbo | |
| pushd libjpeg-turbo | |
| mkdir build | |
| cd build | |
| cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/turbojpeg | |
| make | |
| make install |
| #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. |
| import datetime | |
| import linecache | |
| import os | |
| import pynvml3 | |
| import torch | |
| print_tensor_sizes = True | |
| last_tensor_sizes = set() | |
| gpu_profile_fn = f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_prof.txt' |
| """ | |
| 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: |
| 'use strict'; | |
| ////////////////////////////////// | |
| // How to use? | |
| // 1. Create `sequelize-schema-file-generator.js` in your app root | |
| // 2. Make sure you've ran the `sequelize init` before (It should create `config`,`seeders`,`migrations` folders). | |
| // 3. Update `DATABASE_DSN` below to match your connection string (works with any database adapter that Sequelize supports) | |
| // 4. Run it with `node sequelize-schema-file-generator.js` | |
| // 5. Review the generated migrations inside of the `migrations` folder. | |
| ////////////////////////////////// |
| Host *+* | |
| ProxyCommand ssh $(echo %h | sed 's/+[^+]*$//;s/\([^+%%]*\)%%\([^+]*\)$/\2 -l \1/;s/:/ -p /') exec nc -w1 $(echo %h | sed 's/^.*+//;/:/!s/$/ %p/;s/:/ /') |
