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Mourad mouradmourafiq

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View polyaxon_cla
## Please sign the CLA for Polyaxon
#### Individual Contributor License Agreement ("Agreement")
Thank you for your interest in the Polyaxon. In order to clarify the intellectual property license granted with Contributions from any person or entity, Polyaxon, Inc. (Polyaxon), the current custodian of Polyaxon, must have a Contributor License Agreement ("CLA") on file that has been signed by each Contributor, indicating agreement to the license terms below. This license is for your protection as a Contributor as well as the protection of the Polyaxon user community, and Polyaxon, Inc. the current custodian of Polyaxon. It does not change your rights to use your own Contributions for any other purpose.
Please read this Agreement carefully before signing and keep a copy for your records.
You accept and agree to the following terms and conditions for Your present and future Contributions submitted to Polyaxon. In return, Polyaxon shall not use Your Contributions in a way that is contrary to the public benefit
View minikube loadbalancer and ingress
# https://github.com/lotharschulz/akkahttp-playground/releases/tag/0.0.6-minikube-lb-ing-presentation-leipzig
# prerequsites: scala, sbt, docker, minikube, kubectl, local registry, project's docker images in local registry
kubectl create -f minikube-deployment-config.yaml
kubectl expose deployment akkahttpplayground-deployment --type="LoadBalancer" --port=8181 -target-port=8181
minikube service akkahttpplayground-deployment # opens browser window
minikube addons enable ingress
kubectl create -f minikube-ingress.yaml
echo "$(minikube ip) myminikube.info" | sudo tee -a /etc/hosts
# check the service
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mouradmourafiq / akmtdfgen.py
Created Aug 5, 2017 — forked from timehaven/akmtdfgen.py
kmtdfgen: Keras multithreaded dataframe generator
View akmtdfgen.py
"""akmtdfgen: A Keras multithreaded dataframe generator.
Works with Python 2.7 and Keras 2.x.
For Python 3.x, need to fiddle with the threadsafe generator code.
Test the generator_from_df() functions by running this file:
python akmtdfgen.py
View queue_data_feeder.py
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class FeederConfig(object):
"""The FeederConfig holds information needed to create data feeders for training and evaluating.
View RLbyDavidSilverNotes.ipynb
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mouradmourafiq / labels_1024.tsv
Created Mar 2, 2017 — forked from decentralion/labels_1024.tsv
TensorBoard: TF Dev Summit Tutorial
View labels_1024.tsv
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
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mouradmourafiq / company-ownership.md
Created Jan 18, 2017 — forked from jdmaturen/company-ownership.md
Who pays when startup employees keep their equity?
View company-ownership.md

Who pays when startup employees keep their equity?

JD Maturen, 2016/07/05, San Francisco, CA

As has been much discussed, stock options as used today are not a practical or reliable way of compensating employees of fast growing startups. With an often high strike price, a large tax burden on execution due to AMT, and a 90 day execution window after leaving the company many share options are left unexecuted.

There have been a variety of proposed modifications to how equity is distributed to address these issues for individual employees. However, there hasn't been much discussion of how these modifications will change overall ownership dynamics of startups. In this post we'll dive into the situation as it stands today where there is very near 100% equity loss when employees leave companies pre-exit and then we'll look at what would happen if there were instead a 0% loss rate.

What we'll see is that employees gain nearly 3-fold, while both founders and investors – particularly early investors – get dilute

View what-i-wish-id-known-about-equity-before-joining-a-unicorn.md

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

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mouradmourafiq / docker.sock.plist
Created Dec 28, 2016 — forked from billylaing/docker.sock.plist
MacOS script to start docker sock for Jetbrains docker integration with Docker for Mac.
View docker.sock.plist
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple Computer//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>docker.sock</string>
<!-- This file should be ~/Library/LaunchAgents/docker.sock.plist -->
<!-- Start with: launchctl load ~/Library/LaunchAgents/docker.sock.plist -->
<!-- Configure jetbrains with tcp://localhost:2375 -->
View tf_scopes.py
"""Illustration for various types of namespace scopes in TensorFlow.
> python tf_scopes.py
foo_name_scoped :
v.name= v:0
v2.name= foo_name_scoped/v2:0
a.name= Variable:0
b.name= Variable_1:0
result_op.name= foo_name_scoped/Add:0
foo_op_scoped :