| # .github/workflows/mlops-pipeline.yml | |
| name: MLOps Pipeline | |
| on: | |
| workflow_dispatch: | |
| inputs: | |
| run_all: | |
| description: 'Run all jobs' | |
| required: false | |
| default: 'true' |
| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| """ | |
| citation: | |
| @misc{brown2025grpodemo, | |
| title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models}, | |
| author={Brown, William}, |
Get the UUID of the default profile
Substitute that value in to the second command to enable transparency level
$ dconf read /org/gnome/Ptyxis/default-profile-uuid
'3aae5a177777aa966b1fd63467153e2d'
$ dconf write /org/gnome/Ptyxis/Profiles/3aae5a177777aa966b1fd63467153e2d/opacity 0.85
| ## install uv on OS | |
| curl -LsSf https://astral.sh/uv/install.sh | sh | |
| ## create new project | |
| uv init myproj | |
| ## install packages | |
| uv add django requests "pandas>=2.3" | |
| ## remove package |
| # make sure you have Selenium installed: https://pypi.org/project/selenium/ | |
| # make sure you have the latest chromedriver installed: https://googlechromelabs.github.io/chrome-for-testing/ | |
| # save this file somewhere, change the paths as you need and make it autostart. enjoy. | |
| from selenium import webdriver | |
| from selenium.webdriver.chrome.options import Options | |
| from selenium.webdriver.chrome.service import Service | |
| import os | |
| import time |
Useful links:
As some of these sites are a bit sketchy, make sure to keep privacy & security measures. Learn more here: https://www.privacyguides.org/en/desktop-browsers/
UPDATE - I now just use https://5sim.net and choose England as the country (it works the best for me)!
See the new site: https://postgresisenough.dev
| # The link of our dataset | |
| medical_charges_url = 'https://raw.githubusercontent.com/JovianML/opendatasets/master/data/medical-charges.csv' | |
| # The urlretrieve function to put data in 'medical.csv' format | |
| urlretrieve(medical_charges_url, 'medical.csv') | |
| # Here, we're using pd.read_csv function to pull or read our data | |
| medical_df = pd.read_csv('medical.csv') |
As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.
(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))
**1.
