Farhood FeryET

Project Ideas

Ok. I'm going to list off some ideas for projects. You will have to determine if any particular idea is good enough to include in a portfolio. These aren't creative ideas. They likely already exist. Some are way too advanced while others are simplistic.

I will recommend to post any project you make to github and make a github project page for it. Explain in as much detail as possible how you made it, how it can be improved etc. Document it.

If you pick an advanced idea, setup a development roadmap and follow it. This will show some project management skills.

Another piece of advice for those who are design challenged. Use different front end frameworks and use different themes for those frameworks to provide appealing designs without looking like yet another bootstrap site.

A step-by-step guide how to use Python with Tor and Privoxy

Latest revision: 2025-07-24.

Tested on Ubuntu 24.04 Docker container. The Dockerfile is a single line FROM ubuntu:24.04. Alternatively, you can simply run docker run -it ubuntu:24.04 bash.

NOTE: stopping services didn't work for me for some reason. That's why there is kill $(pidof <service name>) after each failed service <service name> stop to kill it.

References

CNs are important!!! -days 3650

Make PEM containig a public key certificate and its associated private key

openssl req -newkey rsa:2048 -new -x509 -days 3650 -nodes -subj '/C=US/ST=Massachusetts/L=Bedford/O=Personal/OU=Personal/[email protected]/CN=localhost' -out mongodb-cert.crt -keyout mongodb-cert.key

cat mongodb-cert.key mongodb-cert.crt > mongodb.pem

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

Test a Local Jenkins Instance on MacOS: Download, Setup, and Run Groovy Scripts Locally

About

There are many ways to test Jenkins...

Jenkins Pipeline Unit is great but it's generally recommended to keep all of your logic with stages and not get too crazy with custom Groovy in your pipeline. Though, it's a great option if you have a very advanced use-case and want to make sure your code is reliable.

The Replay Pipeline Run Option is awesome if you want to verify a quick change or iterate quickly on a previously setup pipeline. But it only allows for altering Jenkinsfile Code and runs against the production Jenkins Instance.

A case for `pytest` in PyTorch

This is a short post about why I think it would be beneficial for PyTorch to not only use pytest as test runner, but also rely on the other features it provides.

Disclaimer

My experience with the PyTorch test suite is limited as of now. Thus, it might very well be that my view on things is too naive. In that case I'm happy to hear about examples where and adoption of pytest would make a use case significantly harder or outright impossible.

	# https://gist.github.com/althonos/6914b896789d3f2078d1e6237642c35c

	[metadata]
	name = {name}
	version = file: {name}/_version.txt
	author = Martin Larralde
	author_email = [email protected]
	url = https://github.com/althonos/{name}
	description = {description}
	long_description = file: README.md

	#!/usr/bin/env python
	# -- coding:UTF-8 --

	import torch
	import torch.nn as nn
	import torch.nn.init as init


	def weight_init(m):
	'''

	I ran into the battle of running all of my VMs and the host node under a single public IP address. Luckily, the host is just pure Debian, and ships with iptables.

	What needs to be done is essentially to run all the VMs on a private internal network. Outbound internet access is done via NAT. Inbound access is via port forwarding.

	Network configuration

	Here’s how it’s done:

	Create a virtual interface that serves as the gateway for your VMs:

	build-container:
	stage: build
	image:
	name: moby/buildkit:rootless
	entrypoint: [ "sh", "-c" ]
	variables:
	BUILDKITD_FLAGS: --oci-worker-no-process-sandbox
	before_script:
	- \|
	mkdir ~/.docker