Visit https://ollama.com to download for your system.
In the ollama library we see all kinds of available models. We'll use Llama 2 and we have a few options:
# Video https://youtube.com/shorts/MNUdPGIjMPw | |
# Python 3.10 | |
# pip install openai-whisper | |
# pip install git+https://github.com/openai/whisper.git | |
# install ffmpeg | |
# brew install ffmpeg | |
import subprocess | |
import whisper | |
model = whisper.load_model("base") |
#!/bin/bash | |
# Public gist available at: | |
# https://gist.github.com/codingforentrepreneurs/aef0968829883110e24b107f7278255f | |
# Check if an argument is provided | |
if [ "$#" -ne 1 ]; then | |
echo "Usage: $0 new_hostname" | |
exit 1 | |
fi |
Visit https://ollama.com to download for your system.
In the ollama library we see all kinds of available models. We'll use Llama 2 and we have a few options:
Use this IAM policy for the Serverless Framework with the AWS Provider for deploying Node.js apps as serverless functions on AWS Lambda.
Replace AWS_ID
with your AWS Account ID (e.g. 123456789
) which you can find under AWS IAM in the console.
The Django Celery Redis github repo shows a full Django project leveraging the results of this blog post tutorial and this sample project.
git clone https://github.com/codingforentrepreneurs/Django-Celery-Redis
cd Django-Celery-Redis
macos/linux
python3 -m venv venv
version: '3.9'
services:
db:
image: postgres
restart: always
ports:
- 5430:5432
volumes:
compose.yaml
version: '3.9'
services:
redis:
image: redis
restart: always
ports:
- 6178:6379
Python and many ways to create a Rest API endpoint. This one uses FastAP which is designed to easily create API endpoints for nearly anything.
REST APIs are here so software can talk to other software. REST APIs typically send JSON data types (instead of HTML like websites do for humans)
Create virtual environment, activate it, and install FastAPI and Uvicorn:
Export your Pandas analysis really easily to a PostgresSQL database table with this tutorial. We used Docker Compose to create the postgres database with docker compose up
and the related compose.yaml
file.
Add requirements.txt
from below.
python3 -m pip install -r requirements.txt