Value | Color |
---|---|
\e[0;30m | Black |
\e[0;31m | Red |
\e[0;32m | Green |
\e[0;33m | Yellow |
\e[0;34m | Blue |
\e[0;35m | Purple |
Discover gists
python examples/persona_chatbot/pytorch/train.py \ | |
--experiment-name persona-bot-6xMI100 \ | |
--num-dataloader-workers 2 \ | |
--use-mixed-precision \ | |
--batch-size 30 \ | |
--batch-chunk-size 10 \ | |
--num-choices 8 \ | |
--sequence-length-outlier-threshold 0.05 \ | |
--learning-rate 6.25e-5 \ | |
--lr-warmup-schedule \ |
{ config, ... }: | |
let | |
immichHost = "immich.example.com"; # TODO: put your immich domain name here | |
immichRoot = "/tank/immich"; # TODO: Tweak these to your desired storage locations | |
immichPhotos = "${immichRoot}/photos"; | |
immichAppdataRoot = "${immichRoot}/appdata"; | |
immichVersion = "release"; | |
immichExternalVolume1 = "/tank/BackupData/Google Photos/someone@example.com"; # TODO: if external volumes are desired |
/* | |
Twitch chat browsersource CSS for OBS | |
Original by twitch.tv/starvingpoet modified by github.com/Bluscream | |
Just set the URL as either one of | |
- https://www.twitch.tv/%%TWITCHCHANNEL%%/chat?popout=true | |
- https://www.twitch.tv/popout/%%TWITCHCHANNEL%%/chat | |
- https://www.twitch.tv/embed/%%TWITCHCHANNEL%%/chat?parent=localhost | |
And paste this entire file into the CSS box or paste direct import css like |
# See examples here: https://twitter.com/martinsohndk/status/1783470845119152340 | |
# Add the below to your PowerShell profile | |
# 1. In PowerShell, run: Set-ExecutionPolicy -ExecutionPolicy Bypass -Scope CurrentUser -Force | |
# 2. In PowerShell, run: if(!(Test-Path $PROFILE)){New-Item $PROFILE -ItemType File -Force}; notepad.exe $PROFILE | |
# 3. Add the function to your PowerShell profile | |
# 4. (Optional) Change the default behaviour from Clipboard to some other in 'DefaultParameterSetName' | |
# 5. Start a new PowerShell instance | |
# 6. Export JSON from BloodHound | |
# 7. Convert the JSON with 'ConvertFrom-BHJSON' or the alias 'cfb' |
{ | |
// for filter panel | |
page: 'Страница', | |
more: 'ещё', | |
to: 'к', | |
of: 'из', | |
next: 'Следующая', | |
last: 'Последняя', | |
first: 'Первая', | |
previous: 'Предыдущая', |
" </div>" | |
" })();\r\n" | |
" && " | |
" & " | |
" " | |
" ''The " | |
" ("" | |
" (199" | |
" (200" | |
" (e.g." |
In most of deep learning projects, the training scripts always start with lines to load in data, which can easily take a handful minutes. Only after data ready can start testing my buggy code. It is so frustratingly often that I wait for ten minutes just to find I made a stupid typo, then I have to restart and wait for another ten minutes hoping no other typos are made.
In order to make my life easy, I devote lots of effort to reduce the overhead of I/O loading. Here I list some useful tricks I found and hope they also save you some time.
-
use Numpy Memmap to load array and say goodbye to HDF5.
I used to relay on HDF5 to read/write data, especially when loading only sub-part of all data. Yet that was before I realized how fast and charming Numpy Memmapfile is. In short, Memmapfile does not load in the whole array at open, and only later "lazily" load in the parts that are required for real operations.
Sometimes I may want to copy the full array to memory at once, as it makes later operations