Linux Environment Variables

 

Working with Environment Variables

To list all environment variables and their values use:

$ env

To display the value of some particular env var use echo $ENV_VAR_NAME. Example:

$ echo $GOPATH
/home/test_user/dev/go

To set environment variables for the single command:

Example:

$ env GOOS=linux GOARCH=amd64 go build cmd/main.go

From the executable's point of view, the same would have been achieved without using env:

$ GOOS=linux GOARCH=amd64 go build cmd/main.go

To set environment variables for the current terminal session:

$ export GOPATH=/mnt/c/dev/go

export is a bash builtin. export key=value is extended syntax and should not be used in portable scripts (i.e. #! /bin/sh)

What's the difference between set, export and env and when should I use each?
What is the difference between set, env, declare and export when setting a variable in a Linux shell?

If some bash script calls executable which requires some env variables, we also need to use export.Example:

demo.sh:

#!/bin/bash
...
echo
echo Env variables:
go env
export CGO_ENABLED=0
export GOOS=linux
export GOARCH=amd64 
echo
echo Env variables:
go env
go build -o './bin/myapp' -v './cmd/main.go'

...gives the output:

Env variables:
[16:33:25][Step 4/7] GOARCH="amd64"
[16:33:25][Step 4/7] GOOS="linux"
[16:33:25][Step 4/7] CGO_ENABLED="1"
...
[16:33:25][Step 4/7] 
[16:33:25][Step 4/7] Env variables:
[16:33:25][Step 4/7] GOARCH="amd64"
[16:33:25][Step 4/7] GOOS="linux"
[16:33:25][Step 4/7] CGO_ENABLED="0"
...

How do I add environment variables?
How to set an environment variable only for the duration of the script?

To add en environment variable during the session of a particular user (and also make them available in terminal) append the desired var name and its value to ~/.profile file. Example:

export GOROOT=/usr/local/go
export GOPATH=$HOME/go
export PATH=$GOPATH/bin:$GOROOT/bin:$PATH

We'd need to restart the terminal in order to get these changes visible but to make terminal fetch them in the current session, we can update the current shell session with:

source ~/.profile

Alternatively we could have used a dot command:

. ~/.profile 

What's the meaning of a dot before a command in shell? - Unix & Linux Stack Exchange

How and When to Use the Dot Command in Bash?

To add a new or modify existing environment variable permanently (to make environment variable persistent) (for non-root user) we need to change ~/.bashrc:

Example of ~/.bashrc snippet:

# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples
...
export GOPATH=$HOME/dev/go
export PATH=$PATH:$GOPATH/bin


To do the same for root user, open /etc/environment:

$ sudo gedit /etc/environment

...and add desired path:

PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games"
GOPATH="/home/test_user/dev/go"

 

 

Linux Default Environment Variables

user@host:~/$ echo $HOSTNAME
host

 

 

How to upgrade docker-compose on Linux

Let's check the version of currently installed docker-compose:

 

$ docker-compose --version
docker-compose version 1.27.4, build 40524192
 

Let's verify the path to executable:

$ which docker-compose
/usr/local/bin/docker-compose

To upgrade docker-compose, we first need to remove the old version:

$ sudo rm /usr/local/bin/docker-compose
[sudo] password for user: 

 

We can then download the binary of the latest stable version (1.29.2 at time of writing)

 

$ sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   664  100   664    0     0   2114      0 --:--:-- --:--:-- --:--:--  2121
100 12.1M  100 12.1M    0     0  2544k      0  0:00:04  0:00:04 --:--:-- 2723k
 

Add permissions to execute the newly downloaded binary: 

$ sudo chmod +x /usr/local/bin/docker-compose
 

Let's verify the new version:

 

$ docker-compose --version
docker-compose version 1.29.2, build 5becea4c

References

Install Docker Compose | Docker Documentation

When you need to run "nvm install N/A"...

I noticed in my Terminal (within VSCode) the following:

nvm is Node Version Manager and nvm install installs desired version of Node.

nvm uses the version aliases, some of which are:

• default - version set to be default in shell
• node, stable - point to the latest stable version
• unstable - points to the latest unstable version
  
• lts/* - points to the latest long term support line
• lts/xyz - points to the latest node in xyz line (major release) e.g. erbium, fermium, gallium

 

To Node versions pointed to by aliases, it is possible to use nvm alias command. If alias is pointing to Node version that is not installed locally, N/A is shown:

$ nvm alias 

default -> 11.13.0 (-> N/A)
node -> stable (-> v16.13.1) (default)
stable -> 16.13 (-> v16.13.1) (default)
iojs -> N/A (default)
unstable -> N/A (default)
lts/* -> lts/gallium (-> v16.13.1)
lts/argon -> v4.9.1 (-> N/A)
lts/boron -> v6.17.1 (-> N/A)
lts/carbon -> v8.17.0 (-> N/A)
lts/dubnium -> v10.24.1 (-> N/A)
lts/erbium -> v12.22.7 (-> N/A)
lts/fermium -> v14.18.2 (-> N/A)
lts/gallium -> v16.13.1

 

nvm ls lists all locally installed Node versions, system version of Node, together with aliases.

 

$ nvm ls
       v16.13.1
->       system
default -> 11.13.0 (-> N/A)
node -> stable (-> v16.13.1) (default)
stable -> 16.13 (-> v16.13.1) (default)
iojs -> N/A (default)
unstable -> N/A (default)
lts/* -> lts/gallium (-> v16.13.1)
lts/argon -> v4.9.1 (-> N/A)
lts/boron -> v6.17.1 (-> N/A)
lts/carbon -> v8.17.0 (-> N/A)
lts/dubnium -> v10.24.1 (-> N/A)
lts/erbium -> v12.22.7 (-> N/A)
lts/fermium -> v14.18.2 (-> N/A)
lts/gallium -> v16.13.1

Path to system-installed Node:

$ nvm which system 

/usr/bin/node
 

...and it's version:

 

$ /usr/bin/node --version
v16.13.1

 

In our case  default shell version is set to 11.13.0 which is not installed locally (I might have uninstalled it some time ago). That's why I got this in my Terminal:

N/A: version "N/A -> N/A" is not yet installed.
You need to run "nvm install N/A" to install it before using it.

 

To rectify this, I set default shell version to node:

$ nvm alias default node
default -> node (-> v16.13.1)

 

 

After this, default alias was showing the installed version:

$ nvm ls
->     v16.13.1
         system
default -> node (-> v16.13.1)
node -> stable (-> v16.13.1) (default)
stable -> 16.13 (-> v16.13.1) (default)
iojs -> N/A (default)
unstable -> N/A (default)
lts/* -> lts/gallium (-> v16.13.1)
lts/argon -> v4.9.1 (-> N/A)
lts/boron -> v6.17.1 (-> N/A)
lts/carbon -> v8.17.0 (-> N/A)
lts/dubnium -> v10.24.1 (-> N/A)
lts/erbium -> v12.22.7 (-> N/A)
lts/fermium -> v14.18.2 (-> N/A)
lts/gallium -> v16.13.1

Unsupervised Machine Learning

Data is, by default, unlabeled. Labeling data is manual (or somewhat automated) process, thus timely and expensive. Unsupervised machine learning uses unlabeled data (raw, cheap, widely available) for model training. Nevertheless, this comes with the cost of unsupervised learning requiring higher volumes of data for the training if comparing to supervised learning.

Typical use cases for unsupervised ML:

• Clustering
• Anomaly Detection
• Dimensionality Reduction

Clustering

Unsupervised learning algorithms extract features and patterns from unlabeled data which can then be used to label and group together data points that share same or similar features. This is known as clustering and is one of typical problems solved by unsupervised learning.

image source: KDNuggets

Clustering algorithms:

• k-means clustering
• neural networks
• hypothesis function is a mapping from input space back into this input space
• the goal of an unsupervised learning loss function is to measure the difference between the hypothesis function and the input itself

Example: Image set clustering

Each cluster contains images which have the same object in them. Model does not know the name of that object, that it is e.g. bird. It only knows (learns) that objects in each cluster share the same/similar features. We might only need to set in advance the number of clusters we want to get.

In supervised learning, if we have a labeled dataset which contains images of birds, fish and mammals, our model will learn to identify if the image contains a bird, a fish or a mammal. In unsupervised learning, model will learn to distinguish and separate images that share same/similar features and it would group them in three clusters but it would not know that in one cluster are birds and in another fish for example, it would just know that there are three (or maybe even more) types of objects. 

image credits: Devin Pickell, g2.com

Example: Customer segmentation

Each cluster contains customers of some differentiable profile. This helps in e.g. targeted marketing.

image source: data-flair.training

Example: Spam detection

Unsupervised learning algorithm can analyze huge volumes of emails and uncover the features and patterns that indicate spam (and keep getting better at flagging spam over time).

Anomaly Detection

Another type of problems solved by unsupervised learning is anomaly detection. The goal here is to find abnormal data points. Model is trained to detect if data point has some unusual features.

Example: Fraud detection (Anomaly Detection)

Fraudulent transactions tend to involve larger sums of money. Fraud only occurs with transfers and cash-out transactions.

image credit: Shirin Elsinghorst, codecentric.de

Class 0 is normal transaction. Class 1 is fraudulent transaction.

Dimensionality Reduction

Data dimensionality refers to feature space. Each data point can be defined as a vector in N-dimensional space where N is number of features. Some features are more and some less important, in a way how much do they contribute in differentiating data points. The more features, the more complex model is, the more time and storage is required for its training and inference. The idea here is to reduce number of features without losing the semantic meaning of the data. E.g. bird can still be distinguished from other animals by recognising that it has features like beak, wings and a tail but eye color or feather color pattern is not important.

Some dimensionality reduction techniques:

• Independent Components Analysis (ICA)
• Principal Components Analysis (PCA)

Sometimes, before applying k-means clustering, a dimensionality reduction is applied on data.

Principal Component Analysis (PCA)

Transforms data from d-dimensional to p-dimensional feature space where p < d. It first finds the dimension of the highest variance (e.g. direction where the data is most spread out) - principal component. Data points are then projected onto this dimension. Small amount of information gets lost but overall data integrity is not changed.

PCA is based on reducing correlation (linear dependence) between features. If two features are linearly dependent, we can derive value of one feature if value of the another one is known. PCA removes this redundancy by projecting a set of linearly dependent features into a smaller set of new, uncorrelated features. 

Original data points are in 2-dimensional feature space. Features are denoted as x and y.
 

PCA finds the direction along which values have the highest variance. It is a red diagonal in our case.

Data points are projected onto component which carries the highest variance. That principal component becomes a newly derived feature. The next principal component (pc2) which carries the most variance is the one defined by the line perpendicular to the direction of pc1.

As pc2 exhibits low variance, this component does not carry much information (that helps differentiating data points). It can be ignored (small amount of information is lost) thus reducing the feature space to a single dimension.

image credits: V. Powell, setosa.io

Example: Solution to “Cocktail Party” problem

Dimension Reduction via Independent Components Analysis (ICA) is used to extract independent sources of audio signal from a recording which contains mixed signals.

image source: 2014, J. Shlens: "A Tutorial on Independent Component Analysis"

References

Unsupervised learning - Wikipedia

Supervised vs Unsupervised Learning – What's the Difference?

Data Science Project - Customer Segmentation using Machine Learning in R - DataFlair

Data Science for Fraud Detection - codecentric AG Blog

[PDF] A Tutorial on Independent Component Analysis | Semantic Scholar

Unsupervised learning

Commonly Used Machine Learning Algorithms | Data Science

Unsupervised Learning Definition | DeepAI

(203) Can deep convolutional neural network be trained via unsupervised learning? - Quora

Principal Component Analysis (PCA)— Part 1 — Fundamentals and Applications | by Nitish Kumar Thakur | Analytics Vidhya | Medium

Principal Component Analysis explained visually

(206) What is principal component analysis and how many variables can be used for PCA? - Quora

Introduction to Principal Component Analysis(PCA) - AI ASPIRANT

Installing Python3 on Mac Big Sur

Mac comes with Python 2 by default and I wanted to install and use Python 3. I installed it by using brew: 

% brew install python3

Running `brew update --preinstall`...

==> Auto-updated Homebrew!

Updated 2 taps (homebrew/core and homebrew/cask).

==> New Formulae

abi-compliance-checker     gotify                     pam-reattach

abi-dumper                 hurl                       payload-dumper-go

biber                      imap-backup                pip-audit

brigade-cli                isa-l                      pocsuite3

chroma                     jsonschema                 rpki-client

coursier                   kubernetes-cli@1.22        salt-lint

djhtml                     lua-language-server        sevenzip

dynomite                   mcfly                      statix

fastp                      mist                       tsduck

goawk                      openliberty-jakartaee9     vtable-dumper

goplus                     openliberty-webprofile9

==> Updated Formulae

Updated 1431 formulae.

==> Deleted Formulae

ape             es              jerasure        makepp          swiftplate

balance         eventlog        kakasi          marst           torrentcheck

bbcolors        flasm           l-smash         mboxgrep        udns

colorsvn        fondu           libbind         md              whitedb

contacts        gconf           liberasurecode  namazu          xidel

csv-fix         gcore           libmill         postmark        xtail

dlite           gf-complete     libopendkim     redsocks        zdelta

dnsrend         git-hooks       libpuzzle       sdhash

drip            git-sh          libvbucket      shorten

dshb            henplus         m2c             srmio

eject           httptunnel      magnetix        svdlibc

==> New Casks

appflowy            finalshell          projector           teamspeak-client

appium-inspector    folder-colorizer    schildichat         tidgi

centered            grammarly-desktop   sitala              volley

citrix-workspace    handyprintpro       soundtoys           wolai

cron                linearmouse         spaceid             xstation5

emmetapp            ludwig              supermjograph

equinox             macrorecorder       tablecruncher

==> Updated Casks

Updated 648 casks.

==> Deleted Casks

air-connect                              lelivrescolairefr

aja-system-test                          napari

anka-build-cloud-registry                octoscreen

asc-timetables                           platelet

avast-secureline-vpn                     pullover

chameleon-ssd-optimizer                  punto-switcher

chocolat                                 qit

domainbrain                              river-sparkle

drama                                    scrutiny

everweb                                  tmnotifier

freeter                                  unity-linux-support-for-editor

gitbook                                  unity-lumin-support-for-editor

inboard                                  visicut

python@3.9 3.9.6 is already installed but outdated (so it will be upgraded).

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==> Upgrading python3

  3.9.6 -> 3.9.9 

==> Installing dependencies for python@3.9: gdbm, ca-certificates, openssl@1.1, readline and sqlite

==> Installing python@3.9 dependency: gdbm

==> Pouring gdbm--1.22.big_sur.bottle.tar.gz

🍺  /usr/local/Cellar/gdbm/1.22: 24 files, 957.9KB

==> Installing python@3.9 dependency: ca-certificates

==> Pouring ca-certificates--2021-10-26.all.bottle.tar.gz

==> Regenerating CA certificate bundle from keychain, this may take a while...

🍺  /usr/local/Cellar/ca-certificates/2021-10-26: 3 files, 208.5KB

==> Installing python@3.9 dependency: openssl@1.1

==> Pouring openssl@1.1--1.1.1m.big_sur.bottle.tar.gz

🍺  /usr/local/Cellar/openssl@1.1/1.1.1m: 8,081 files, 18.5MB

==> Installing python@3.9 dependency: readline

==> Pouring readline--8.1.1.big_sur.bottle.tar.gz

🍺  /usr/local/Cellar/readline/8.1.1: 48 files, 1.6MB

==> Installing python@3.9 dependency: sqlite

==> Pouring sqlite--3.37.0.big_sur.bottle.tar.gz

🍺  /usr/local/Cellar/sqlite/3.37.0: 11 files, 4.3MB

==> Installing python@3.9

==> Pouring python@3.9--3.9.9.big_sur.bottle.tar.gz

==> /usr/local/Cellar/python@3.9/3.9.9/bin/python3 -m ensurepip

==> /usr/local/Cellar/python@3.9/3.9.9/bin/python3 -m pip install -v --no-deps -

==> Caveats

Python has been installed as

  /usr/local/bin/python3

Unversioned symlinks `python`, `python-config`, `pip` etc. pointing to

`python3`, `python3-config`, `pip3` etc., respectively, have been installed into

  /usr/local/opt/python@3.9/libexec/bin

You can install Python packages with

  pip3 install <package>

They will install into the site-package directory

  /usr/local/lib/python3.9/site-packages

tkinter is no longer included with this formula, but it is available separately:

  brew install python-tk@3.9

See: https://docs.brew.sh/Homebrew-and-Python

==> Summary

🍺  /usr/local/Cellar/python@3.9/3.9.9: 3,080 files, 55.0MB

==> `brew cleanup` has not been run in the last 30 days, running now...

Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP.

Hide these hints with HOMEBREW_NO_ENV_HINTS (see `man brew`).

Removing: /usr/local/Cellar/gdbm/1.20... (24 files, 825.0KB)

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Pruned 0 symbolic links and 2 directories from /usr/local

==> Upgrading 1 dependent:

Disable this behaviour by setting HOMEBREW_NO_INSTALLED_DEPENDENTS_CHECK.

Hide these hints with HOMEBREW_NO_ENV_HINTS (see `man brew`).

you-get 0.4.1536 -> 0.4.1555

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==> Downloading from https://pkg-containers.githubusercontent.com/ghcr1/blobs/sh

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==> Downloading https://ghcr.io/v2/homebrew/core/you-get/manifests/0.4.1555

######################################################################## 100.0%

==> Downloading https://ghcr.io/v2/homebrew/core/you-get/blobs/sha256:df0dc12c74

==> Downloading from https://pkg-containers.githubusercontent.com/ghcr1/blobs/sh

######################################################################## 100.0%

==> Upgrading you-get

  0.4.1536 -> 0.4.1555 

==> Installing dependencies for you-get: python@3.10

==> Installing you-get dependency: python@3.10

==> Pouring python@3.10--3.10.1.big_sur.bottle.tar.gz

==> /usr/local/Cellar/python@3.10/3.10.1/bin/python3 -m ensurepip

==> /usr/local/Cellar/python@3.10/3.10.1/bin/python3 -m pip install -v --no-deps

🍺  /usr/local/Cellar/python@3.10/3.10.1: 3,132 files, 56MB

==> Installing you-get

==> Pouring you-get--0.4.1555.big_sur.bottle.tar.gz

==> Caveats

To use post-processing options, run `brew install ffmpeg` or `brew install libav`.

==> Summary

🍺  /usr/local/Cellar/you-get/0.4.1555: 736 files, 8.5MB

==> Running `brew cleanup you-get`...

Removing: /usr/local/Cellar/you-get/0.4.1536... (845 files, 8.6MB)

==> Checking for dependents of upgraded formulae...

==> No broken dependents found!

==> Caveats

==> python@3.9

Python has been installed as

  /usr/local/bin/python3

Unversioned symlinks `python`, `python-config`, `pip` etc. pointing to

`python3`, `python3-config`, `pip3` etc., respectively, have been installed into

  /usr/local/opt/python@3.9/libexec/bin

You can install Python packages with

  pip3 install <package>

They will install into the site-package directory

  /usr/local/lib/python3.9/site-packages

tkinter is no longer included with this formula, but it is available separately:

  brew install python-tk@3.9

See: https://docs.brew.sh/Homebrew-and-Python

==> you-get

To use post-processing options, run `brew install ffmpeg` or `brew install libav`.

% 

If zsh configuration file does not exist, create it and open it:

% touch ~/.zshrc

% vi ~/.zshrc

Type in it:

export PATH=/usr/local/opt/python@3.9/libexec/bin:$PATH

Restart the terminal.

% which python

/usr/local/opt/python@3.9/libexec/bin/python

% python --version

Python 3.9.9

If using VSCode, restart it and it will also pick up this version of Python in its Terminal.

Installing Go on Mac Big Sur

 I followed instructions listed here. I downloaded go1.17.3.darwin-amd64.pkg and run it.

Upon installation I checked that Go bin path is indeed added to $PATH and also that go command works fine:

~ % echo $PATH
/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/go/bin

~ % go version
go version go1.17.3 darwin/amd64

How to copy files from Ubuntu to Mac

If remote login is not enabled on Mac, SSH connection will fail:

~/Videos$ scp -r MyDir/ user@192.168.0.30:/Users/user/Movies

ssh: connect to host 192.168.0.30 port 22: Connection refused

lost connection

We need to enable remote login on Mac (System Preferences >>  Sharing):

~/Videos$ scp -r MyDir/ user@192.168.0.30:/Users/user/Movies

The authenticity of host '192.168.0.30 (192.168.0.30)' can't be established.

ECDSA key fingerprint is SHA256:fvlF0KH/l1N/fHmg4etTM/OR9DJYl1mnzD2iD43R+j5.

Are you sure you want to continue connecting (yes/no/[fingerprint])? yes

Warning: Permanently added '192.168.0.30' (ECDSA) to the list of known hosts.

Password:

file1.m4v         100%  137MB   1.6MB/s   01:28    

file2.m4v         100%  151MB   1.5MB/s   01:40    

Files are copied to /Users/user/Movies/MyDir/.