This conversation started as a symptom of my laziness but quickly grew to be some solid beginner groundwork for building Discord Bots.
The easiest way to send data to a PostgreSQL database from Python is to use the psycopg2 library. psycopg2 is a PostgreSQL database adapter for Python. It is used to connect to, query, and manage PostgreSQL databases from Python.
Here is an example of how you can use psycopg2 to connect to a PostgreSQL database and insert a row into a table:
| Active wear | |
| Swimwear | |
| Tailored clothing Suits | |
| Casual wear Sports wear | |
| Leg wear | |
| Neckwear | |
| Shawls and wraps | |
| Bridal wear | |
| Evening wear | |
| Undergarments |
| Africa/Abidjan | |
| Africa/Accra | |
| Africa/Addis_Ababa | |
| Africa/Algiers | |
| Africa/Asmara | |
| Africa/Asmera | |
| Africa/Bamako | |
| Africa/Bangui | |
| Africa/Banjul | |
| Africa/Bissau |
| <div class="hero is-fullheight is-bold is-info"> | |
| <div class="hero-body"> | |
| <div class="container"> | |
| <div class="header content"> | |
| <h2 class="subtitle is-6">Code Challenge #16</h2> | |
| <h1 class="title is-1"> | |
| Infinite Scroll Unsplash Code Challenge | |
| </h1> | |
| </div> | |
| <div v-infinite-scroll="loadMore" infinite-scroll-disabled="loading" infinite-scroll-distance="20"> |
#!/path/to/venv/python in the first line$ sudo systemctl edit --force --full name.service. In the editor complete at least these statments,[Unit]
Description=Python Service
Wants=network.target
After=network.target
[Service]
ExecStartPre=/bin/sleep 10
| import torch | |
| import numpy as np | |
| import k_diffusion as K | |
| from PIL import Image | |
| from torch import autocast | |
| from einops import rearrange, repeat | |
| def pil_img_to_torch(pil_img, half=False): | |
| image = np.array(pil_img).astype(np.float32) / 255.0 |
Credit to NotTheDroids at StackExchange
You can now execute an arbitrary command line when starting an instance by creating/editing /etc/wsl.conf (via sudo) with the following:
[boot]
| # I added all the imports by hand | |
| import torch | |
| from torchvision.datasets import ImageFolder | |
| from torchvision import transforms,models | |
| from torch import nn,optim | |
| # For all functions including this one, I wrote the name and docstring, and sometimes also the param names | |
| def finetune(folder, model): | |
| """fine tune pytorch model using images from folder and report results on validation set""" | |
| if not os.path.exists(folder): raise ValueError(f"{folder} does not exist") |
| { | |
| "timezones": [ | |
| "Africa/Abidjan", | |
| "Africa/Accra", | |
| "Africa/Addis_Ababa", | |
| "Africa/Algiers", | |
| "Africa/Asmara", | |
| "Africa/Asmera", | |
| "Africa/Bamako", | |
| "Africa/Bangui", |
An acquaintence on Discord came across the term "attention resolutions" in the code for configuring the training of an image model and couldn't find documentation on the term's meaning. I could only find it mentioned in one paper and the way it was used didn't help.
The following 'conversation' came from having Codex explain a code snippet that likely exists nowhere else because I imagined it purely to try draw an explanation.
It seems to have worked.
