programming matrix

Monday, May 3, 2021

# signup/login

https://ngc.nvidia.com/signin

https://ngc.nvidia.com/setup/api-key

# Generate API Key

# Generate your own API key in order to use the NGC service through the Docker client or through NGC CLI.

https://ngc.nvidia.com/setup

https://ngc.nvidia.com/setup/installers/cli

AMD64 Linux Install

The NGC CLI binary for Linux is supported on Ubuntu 16.04 and later distributions.

Click Download CLI to download the zip file that contains the binary, then transfer the zip file to a directory where you have permissions and then unzip and execute the binary. You can also download, unzip, and install from the command line by moving to a directory where you have execute permissions and then running the following command:

wget -O ngccli_cat_linux.zip https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && unzip -o ngccli_cat_linux.zip && chmod u+x ngc

Check the binary's md5 hash to ensure the file wasn't corrupted during download:

md5sum -c ngc.md5

Add your current directory to path:

echo "export PATH=\"\$PATH:$(pwd)\"" >> ~/.bash_profile && source ~/.bash_profile

You must configure NGC CLI for your use so that you can run the commands.

Enter the following command, including your API key when prompted:

ngc config set

Linux Uninstall:

Remove the binary file:

rm `which ngc`

https://www.pugetsystems.com/labs/hpc/How-To-Setup-NVIDIA-Docker-and-NGC-Registry-on-your-Workstation---Part-4-Accessing-the-NGC-Registry-1115/

"... Or, create a file (or alias) to make that a single command."

"To make that process a little easier you can put the following into a file, (maybe call it ngc-login)

docker login -u '$oauthtoken' --password-stdin nvcr.io <<< ' your API key here between the quotes '

Then to change the permisions to make it only accessible by you,

chmod 700 ngc-login"

~/Docker/ngc_login.sh
WARNING! Your password will be stored unencrypted in /home/.../.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded

$ docker run --runtime=nvidia --rm nvcr.io/nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 nvidia-smi
Unable to find image 'nvcr.io/nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04' locally
9.0-cudnn7-devel-ubuntu16.04: Pulling from nvidia/cuda
4007a89234b4: Pull complete
c1de0f9cdfc1: Pull complete
c8ee6ca703b8: Pull complete
b39e2761d3d4: Pull complete
6fd4d5fe5247: Pull complete
a2aa5ab1b28d: Pull complete
d115189e3588: Pull complete
c208532c0f67: Pull complete
822dc7c71a4b: Pull complete
05d21eb076f4: Pull complete
b62c22f14976: Pull complete
Digest: sha256:eb0f7d58b15893c7d3d676d23e9a4abaaaa6c09019f24bce29a448a9b8a261d8
Status: Downloaded newer image for nvcr.io/nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
Tue May 4 03:20:53 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.73.01    Driver Version: 460.73.01    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap|         Memory-Usage | GPU-Util Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0 GeForce GTX 106... Off | 00000000:01:00.0 Off |                  N/A |
| 0%   36C    P8     7W / 200W |      6MiB / 6078MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
| GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+

# Setup the Docker credential store

https://docs.docker.com/engine/reference/commandline/login/#credentials-store

https://github.com/docker/docker-credential-helpers/releases

# NGC commands

https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html

=====================================================

What is the NGC registry?

The NGC registry is the main component of NGC. NGC stands for "NVIDIA GPU Cloud". That consists of an NVIDIA maintained Docker registry and an AWS machine instance (AMI) on Amazon AWS. That AMI has a setup similar to what I have gone through in first 2 posts of this series (User-namespaces are not configured).

It's the NGC Docker registry that contains all of the great container images and you can use those docker images on your own workstation. You don't need to access AWS and you don't need to have an AWS account. However, you do need to sign up for NGC in order to get a registry key to access those docker images. It's a private registry so you need an account with NVIDIA NGC and the registry key to login to it.

What's in the NGC Registry?

There are 4 section in the registry,

Deep Learning
HPC Apps
HPC Visualization
And a few "partner" docker images

Here are the GPU optimized application container images is in the repository as of this writing, (using labels as listed in the registry repository)

Repositories

nvidia

caffe
caffe2
cntk
cuda
digits
mxnet
pytorch
tensorflow
tensorrt
theano
torch

hpc

gamess
gromacs
lammps
namd
relion

nvidia-hpcvis

paraview-holodeck
paraview-index
paraview-optix

partners

chainer
h2oai-driverless
mapd
paddlepaddle

Uninstall Docker from Ubuntu

sudo systemctl unmask docker
sudo systemctl enable docker
sudo apt remove --purge docker docker.io containerd runc
sudo rm -Rf /etc/docker

Install node via NVM and then react-native

https://github.com/nvm-sh/nvm#install--update-script

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.37.2/install.sh | bash

source ~/.bash_profile

https://www.keycdn.com/blog/node-version-manager

https://medium.com/@waqqas/how-to-setup-development-environment-for-react-native-on-mac-85fb216ba0ff

AI Transformers

https://arxiv.org/pdf/1706.03762.pdf

http://jalammar.github.io/illustrated-transformer/

https://towardsdatascience.com/attention-is-all-you-need-discovering-the-transformer-paper-

https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html

http://nlp.seas.harvard.edu/2018/04/03/attention.html

https://github.com/jadore801120/attention-is-all-you-need-pytorch

https://www.youtube.com/watch?v=S27pHKBEp30&ab_channel=SeattleAppliedDeepLearning

https://gist.github.com/leopd/

https://github.com/huggingface/transformers

https://keras.io/examples/structured_data/movielens_recommendations_transformers/

https://towardsdatascience.com/attn-illustrated-attention-5ec4ad276ee3

https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a

https://towardsdatascience.com/the-definitive-guide-to-bi-directional-attention-flow-d0e96e9e666b

https://towardsdatascience.com/illustrated-guide-to-transformer-cf6969ffa067

Scala and JSON

https://stackoverflow.com/questions/40172313/scala-read-and-parse-json

https://www.lihaoyi.com/post/HowtoworkwithJSONinScala.html

https://mungingdata.com/scala/read-write-json/

https://antoine-doeraene.medium.com/5-things-that-developers-do-all-the-time-in-scala-64626c15440a

Startup Financing Documents

https://siliconhillslawyer.com/2021/03/08/the-carta-safe-seed-rounds/

https://nvca.org/model-legal-documents/

https://siliconhillslawyer.com/2021/01/04/fixing-convertible-note-and-safe-economics-in-seed-rounds/

https://www.linkedin.com/in/joseancer/

Tuesday, November 24, 2020

Ubuntu 20.4 give a user sudo permissions

sudo usermod -aG sudo <username>

Install NVDA drivers on Ubuntu 20.4

https://www.itzgeek.com/post/how-to-install-nvidia-drivers-on-ubuntu-20-04-ubuntu-18-04.html

Wednesday, November 18, 2020

Anaconda Ubuntu 20.4 setup

https://linuxize.com/post/how-to-install-anaconda-on-ubuntu-20-04/

Download the installer from:
https://repo.anaconda.com/archive/Anaconda3-5.3.1-Linux-x86_64.sh

Get the hash from:
https://docs.anaconda.com/anaconda/install/hashes/Anaconda3-5.3.1-Linux-x86_64.sh-hash/
d4c4256a8f46173b675dd6a62d12f566ed3487f932bab6bb7058f06c124bcc27

# Comand line
wget https://docs.anaconda.com/anaconda/install/hashes/Anaconda3-5.3.1-Linux-x86_64.sh-hash/

sha256sum Anaconda3-5.3.1-Linux-x86_64.sh 
#d4c4256a8f46173b675dd6a62d12f566ed3487f932bab6bb7058f06c124bcc27  Anaconda3-5.3.1-Linux-x86_64.sh

chmod +x Anaconda3-5.3.1-Linux-x86_64.sh 
./Anaconda3-5.3.1-Linux-x86_64.sh

Friday, October 2, 2020

SPARK dataframe join

https://sparkbyexamples.com/spark/spark-sql-dataframe-join/

Tuesday, September 29, 2020

SPARK Dataframes example

https://medium.com/expedia-group-tech/deep-dive-into-apache-spark-array-functions-720b8fbfa729

val initial_df = Seq(
("x", 4, 1),
("x", 6, 2),
("z", 7, 3),
("a", 3, 4),
("z", 5, 2),
("x", 7, 3),
("x", 9, 7),
("z", 1, 8),
("z", 4, 9),
("z", 7, 4),
("a", 8, 5),
("a", 5, 2),
("a", 3, 8),
("x", 2, 7),
("z", 1, 9)
).toDF("col1", "col2", "col3")
// Generate Array columns

scala> initial_df

res58: org.apache.spark.sql.DataFrame = [col1: string, col2: int ... 1 more field]

scala> initial_df.show

+----+----+----+

|col1|col2|col3|

+----+----+----+

| x| 4| 1|

| x| 6| 2|

| z| 7| 3|

| a| 3| 4|

| z| 5| 2|

| x| 7| 3|

| x| 9| 7|

| z| 1| 8|

| z| 4| 9|

| z| 7| 4|

| a| 8| 5|

| a| 5| 2|

| a| 3| 8|

| x| 2| 7|

| z| 1| 9|

+----+----+----+

scala> val full_df = (initial_df.groupBy("col1").agg(collect_list($"col2")))

full_df: org.apache.spark.sql.DataFrame = [col1: string, collect_list(col2): array<int>]

scala> val full_df = (initial_df.groupBy("col1").agg(collect_list($"col2"))).show

+----+------------------+

|col1|collect_list(col2)|

+----+------------------+

| x| [4, 6, 7, 9, 2]|

| z|[7, 5, 1, 4, 7, 1]|

| a| [3, 8, 5, 3]|

+----+------------------+

scala> initial_df.schema

res61: org.apache.spark.sql.types.StructType = StructType(StructField(col1,StringType,true), StructField(col2,IntegerType,false), StructField(col3,IntegerType,false))

scala> initial_df.toJSON

res62: org.apache.spark.sql.Dataset[String] = [value: string]

scala> initial_df.toJSON.show

+--------------------+

| value|

+--------------------+

|{"col1":"x","col2...|

|{"col1":"z","col2...|

|{"col1":"a","col2...|

|{"col1":"z","col2...|

|{"col1":"x","col2...|

|{"col1":"z","col2...|

|{"col1":"a","col2...|

|{"col1":"x","col2...|

|{"col1":"z","col2...|

+--------------------+

scala> val full_df = (initial_df.groupBy("col1")).agg(collect_list($"col2"))

full_df: org.apache.spark.sql.DataFrame = [col1: string, collect_list(col2): array<int>]

scala> val full_df = (initial_df.groupBy("col1")).agg(collect_list($"col2")).show

+----+------------------+

|col1|collect_list(col2)|

+----+------------------+

| x| [4, 6, 7, 9, 2]|

| z|[7, 5, 1, 4, 7, 1]|

| a| [3, 8, 5, 3]|

+----+------------------+

scala> val full_df = (initial_df.groupBy("col1").agg(collect_list($"col2").as("array_col1"),collect_list($"col3").as("array_col2")))

full_df: org.apache.spark.sql.DataFrame = [col1: string, array_col1: array<int> ... 1 more field]

scala> full_df.show

+----+------------------+------------------+

|col1| array_col1| array_col2|

+----+------------------+------------------+

| x| [4, 6, 7, 9, 2]| [1, 2, 3, 7, 7]|

| z|[7, 5, 1, 4, 7, 1]|[3, 2, 8, 9, 4, 9]|

| a| [3, 8, 5, 3]| [4, 5, 2, 8]|

+----+------------------+------------------+

scala> initial_df.show

+----+----+----+

|col1|col2|col3|

+----+----+----+

| x| 4| 1|

| x| 6| 2|

| z| 7| 3|

| a| 3| 4|

| z| 5| 2|

| x| 7| 3|

| x| 9| 7|

| z| 1| 8|

| z| 4| 9|

| z| 7| 4|

| a| 8| 5|

| a| 5| 2|

| a| 3| 8|

| x| 2| 7|

| z| 1| 9|

+----+----+----+

scala> val df = full_df.drop("array_col1")

df: org.apache.spark.sql.DataFrame = [col1: string, array_col2: array<int>]

scala> df.show

+----+------------------+

|col1| array_col2|

+----+------------------+

| x| [1, 2, 3, 7, 7]|

| z|[3, 2, 8, 9, 4, 9]|

| a| [4, 5, 2, 8]|

+----+------------------+

scala> val arr_contains_df = df.withColumn("result", array_contains($"array_col2", 3))

arr_contains_df: org.apache.spark.sql.DataFrame = [col1: string, array_col2: array<int> ... 1 more field]

scala>

scala> arr_contains_df.show()

+----+------------------+------+

|col1| array_col2|result|

+----+------------------+------+

| x| [1, 2, 3, 7, 7]| true|

| z|[3, 2, 8, 9, 4, 9]| true|

| a| [4, 5, 2, 8]| false|

+----+------------------+------+

scala> val arr_distinct_df = df.withColumn("result", array_distinct($"array_col2"))

arr_distinct_df: org.apache.spark.sql.DataFrame = [col1: string, array_col2: array<int> ... 1 more field]

scala>

scala> arr_distinct_df.show()

+----+------------------+---------------+

|col1| array_col2| result|

+----+------------------+---------------+

| x| [1, 2, 3, 7, 7]| [1, 2, 3, 7]|

| z|[3, 2, 8, 9, 4, 9]|[3, 2, 8, 9, 4]|

| a| [4, 5, 2, 8]| [4, 5, 2, 8]|

+----+------------------+---------------+

[cmakefileapi-parser] Code model version (2.1) of cmake-file-api is unexpected. Expecting (2.0). IntelliSense configuration may be incorrect.

https://www.codenong.com/cs107130032/

[cmakefileapi-parser] Code model version (2.1) of cmake-file-api is unexpected. Expecting (2.0). IntelliSense configuration may be incorrect.
[cmakefileapi-parser] Code model version (2.1) of cmake-file-api is unexpected. Expecting (2.0). IntelliSense configuration may be incorrect.
[build] Build finished with exit code 0

After running the CMake configuration, I opened this file -> "build\.cmake\api\v1\reply\codemodel-v2-ab6f9cacd31dc7acf0c6.json", scrolled all the way down, and changed the value of "minor" from 1 to 0. When I clicked Build again, the warnings were gone. FYI, I don't know if this breaks something else or not, I just figured it out by trail and error.

    "version" :
    {
        "major" : 2,
        "minor" : 0
    }

CMake : globbing the file list

http://derekmolloy.ie/hello-world-introductions-to-cmake/

cmake_minimum_required(VERSION 3.0.0)

project(csim_2020 VERSION 0.1.0)

include(CTest)
enable_testing()

file(GLOB SOURCES "*.cpp")
add_executable(testStudent ${SOURCES})

set(CPACK_PROJECT_NAME ${PROJECT_NAME})
set(CPACK_PROJECT_VERSION ${PROJECT_VERSION})
include(CPack)

Sunday, September 13, 2020

MLIR Toy Tutorial - How to simplify the AST so it is easier to read.

https://mlir.llvm.org/docs/Tutorials/Toy/Ch-1/

# User defined generic function that operates on unknown shaped arguments.
def multiply_transpose(a, b) {
  return transpose(a) * transpose(b);
}

def main() {
  # Define a variable `a` with shape <2, 3>, initialized with the literal value.
  var a = [[1, 2, 3], [4, 5, 6]];
  var b<2, 3> = [1, 2, 3, 4, 5, 6];

  # This call will specialize `multiply_transpose` with <2, 3> for both
  # arguments and deduce a return type of <3, 2> in initialization of `c`.
  var c = multiply_transpose(a, b);

  # A second call to `multiply_transpose` with <2, 3> for both arguments will
  # reuse the previously specialized and inferred version and return <3, 2>.
  var d = multiply_transpose(b, a);

  # A new call with <3, 2> (instead of <2, 3>) for both dimensions will
  # trigger another specialization of `multiply_transpose`.
  var e = multiply_transpose(c, d);

  # Finally, calling into `multiply_transpose` with incompatible shape will
  # trigger a shape inference error.
  var f = multiply_transpose(transpose(a), c);
}

~/llvm-project/build/bin/toyc-ch1 test/Examples/Toy/Ch1/ast.toy -emit=ast > ast.ir 2>&1

# Strip the comments

sed 's/\@test.*//g' ast.ir

# Lets get rid of the fractional floating point to reduce the "niose"

sed 's/\@test.*//g' ast.ir | sed 's/.000000e+00//g'

Module:

Function

Proto 'multiply_transpose'

Params: [a, b]

Block {

Return

BinOp: *

Call 'transpose' [

var: a

]

Call 'transpose' [

var: b

]

} // Block

Function

Proto 'main'

Params: []

Block {

VarDecl a<>

Literal: <2, 3>[ <3>[ 1, 2, 3], <3>[ 4, 5, 6]]

VarDecl b<2, 3>

Literal: <6>[ 1, 2, 3, 4, 5, 6]

VarDecl c<>

Call 'multiply_transpose' [

var: a

var: b

]

VarDecl d<>

Call 'multiply_transpose' [

var: b

var: a

]

VarDecl e<>

Call 'multiply_transpose' [

var: b

var: c

]

VarDecl f<>

Call 'multiply_transpose' [

Call 'transpose' [

var: a

]

var: c

]

} // Block

https://unix.stackexchange.com/questions/152774/how-to-remove-a-newline-character-in-a-particular-line-through-perl

-00 option turns paragraph slurp mode on, your regex now can work on multi line.

$ sed 's/\@test.*//g' ast.ir | sed 's/.000000e+00//g' | perl -00pe 's/\n[ \t]*var://gi' | perl -00pe 's/\n[ \t]*]/]/gi'

Module:

Function

Proto 'multiply_transpose'

Params: [a, b]

Block {

Return

BinOp: *

Call 'transpose' [ a ]

Call 'transpose' [ b ]

} // Block

Function

Proto 'main'

Params: []

Block {

VarDecl a<>

Literal: <2, 3>[ <3>[ 1, 2, 3], <3>[ 4, 5, 6]]

VarDecl b<2, 3>

Literal: <6>[ 1, 2, 3, 4, 5, 6]

VarDecl c<>

Call 'multiply_transpose' [ a b ]

VarDecl d<>

Call 'multiply_transpose' [ b a ]

VarDecl e<>

Call 'multiply_transpose' [ b c ]

VarDecl f<>

Call 'multiply_transpose' [

Call 'transpose' [ a ] c ]

} // Block

Exception in thread "main" org.apache.spark.sql.AnalysisException: Failed to find data source: avro.

class ProcessAvro(sparkSession: SparkSession, fileName: String) {
  val df = readAvro()
  
  def readAvro(): sql.DataFrame = {
    val df = sparkSession.read.format("avro")
      .load(fileName)

    df
  }
}

Exception in thread "main" org.apache.spark.sql.AnalysisException: Failed to find data source: avro. Avro is built-in but external data source module since Spark 2.4. Please deploy the application as per the deployment section of "Apache Avro Data Source Guide".;

Problem: SPARK does not know where to find the avro source.

Solution: https://bmwieczorek.wordpress.com/tag/avro/

scala> val df = spark.read.format("avro").load("myclass_avro")
org.apache.spark.sql.AnalysisException: Failed to find data source: avro. Please find an Avro package at http://spark.apache.org/third-party-projects.html;
at org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:634)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:190)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:174)
... 49 elided

scala> val df = spark.read.format("com.databricks.spark.avro").load("myclass_avro")
df: org.apache.spark.sql.DataFrame = [mystring: string, myboolean: boolean ... 4 more fields]

scala> df.show
+--------+---------+-----+------+----------+------------------+
|mystring|myboolean|myint|myLong| myfloat| mydouble|
+--------+---------+-----+------+----------+------------------+
| a| true| 1| 1|0.14285715|0.1428571492433548|
| b| false| 2| 2| 2.0| 2.0|
+--------+---------+-----+------+----------+------------------+

Fix: qualify the format.

 val df = sparkSession.read.format("com.databricks.spark.avro")
      .load(fileName)

package org.example

import org.apache.spark
import org.apache.spark.sql
import org.apache.spark.sql.SparkSession

class ProcessAvro(sparkSession: SparkSession, fileName: String) {
  val df = readAvro()

  def readAvro(): sql.DataFrame = {
    val df = sparkSession.read.format("com.databricks.spark.avro")
      .load(fileName)

    df
  }
}

/**
 * Hello world!
 *
 */
object Main extends App{
  println( "Hello World!" )

  val sparkSession = SparkSession.builder
    .appName("Simple Application")
    .config("spark.master", "local")
    .getOrCreate()

  val processAvro = new ProcessAvro(sparkSession,"file.avro")

  val df = processAvro.readAvro()

SBT file for SPARK 3,0, Scala 2.12.4, and JUnit

name := "reporting"


version := "0.1"

scalaVersion := "2.12.4"

resolvers += "Junit Repository" at "https://mvnrepository.com/artifact/com.novocode/junit-interface"
resolvers += "SPARK Repository" at "https://mvnrepository.com/artifact/org.apache.spark/spark-core"

// https://mvnrepository.com/artifact/com.novocode/junit-interface
libraryDependencies += "com.novocode" % "junit-interface" % "0.11" % Test
// https://mvnrepository.com/artifact/org.apache.spark/spark-core
libraryDependencies += "org.apache.spark" %% "spark-core" % "3.0.0"

https://mvnrepository.com/artifact/com.novocode/junit-interface/0.11

https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.12/3.0.0

Friday, September 11, 2020

How to negate specific word in regex?

https://stackoverflow.com/questions/1240275/how-to-negate-specific-word-in-regex

A great way to do this is to use negative lookahead:
^(?!.*bar).*$

Tuesday, September 8, 2020

How to manage Erlang and Elixir versions on Mac or Linux to fix a vscode debugger incompatibility issue with elixir-ls

VSCode issue:

Started ElixirLS debugger v0.5.0

Elixir version: "1.10.2 (compiled with Erlang/OTP 21)"

Erlang version: "22"

ElixirLS compiled with Elixir 1.7.4 and erlang 20

WARNING: Debugging is not supported on Elixir 1.10.2. Please upgrade to at least 1.10.3

more info: https://github.com/elixir-lsp/elixir-ls/issues/158

asdf Version/Package manager
https://medium.com/juq/how-to-manage-elixir-versions-on-mac-or-linux-getting-started-with-elixir-12308e7b6451

https://thinkingelixir.com/install-elixir-using-asdf/

https://alchemist.camp/episodes/asdf-language-versions

Compatible Erlang/Elixir versions

https://hexdocs.pm/elixir/compatibility-and-deprecations.html

ELIXIR VERSION SUPPORTED ERLANG/OTP VERSIONS
1.10 21 - 22 (and Erlang/OTP 23 from v1.10.3)

Linux Version/Package manager

git clone https://github.com/asdf-vm/asdf.git ~/.asdf

echo -e '\n. $HOME/.asdf/asdf.sh' >> ~/.bashrc

echo -e '\n. $HOME/.asdf/completions/asdf.bash' >> ~/.bashrc

asdf update

source ~/.bashrc

asdf update

sudo apt install automake autoconf libreadline-dev libncurses-dev libssl-dev libyaml-dev libxslt-dev libffi-dev libtool unixodbc-dev

asdf plugin-add erlang

asdf plugin-add elixir

asdf list-all erlang

asdf install erlang 22.3.4.10

asdf list-all elixir

# select the version that is compatible wit the erlang version and

# the compatible otp version for the erlang top that you selected

# above. In this case we selected with OTP 22

asdf install elixir 1.10.3-otp-22

asdf global erlang 22.3.4.10

asdf global elixir 1.10.3-otp-22

VSCode works!

Started ElixirLS debugger v0.5.0

Elixir version: "1.10.3 (compiled with Erlang/OTP 22)"

Erlang version: "22"

ElixirLS compiled with Elixir 1.7.4 and erlang 20

Compiling 1 file (.ex)

Generated pm app

PmTest

* doctest Pm.hello/0 (1)

* doctest Pm.hello/0 (1) (0.1ms)

* test greets the world

* test greets the world (0.1ms)

Finished in 0.00 seconds

1 doctest, 1 test, 0 failures

Randomized with seed 285439

Tuesday, May 12, 2020

User identity and authorization

SSI
https://www.windley.com/archives/2018/09/multi-source_and_self-sovereign_identity.shtml

Confused deputy problem
https://en.wikipedia.org/wiki/Confused_deputy_problem

Capability-Based Access Control
http://www.cis.syr.edu/~wedu/Teaching/CompSec/LectureNotes_New/Capability.pdf

Sunday, May 10, 2020

Graphviz

https://www.katrinasiegfried.com/single-post/2018/10/24/Installing-GraphViz-on-Linux-Ubuntu-1804LTS

https://www.tonyballantyne.com/graphs.html#orgheadline12

https://graphviz.gitlab.io/documentation/

https://graphs.grevian.org/example

https://graphviz.gitlab.io/_pages/pdf/dotguide.pdf

Visualizer
https://graphs.grevian.org/graph/5124187289026560

Saturday, April 18, 2020

ffmpeg: get video resolution

https://superuser.com/questions/841235/how-do-i-use-ffmpeg-to-get-the-video-resolution

Friday, April 3, 2020

JPEG

https://en.wikipedia.org/wiki/JPEG#Discrete_cosine_transform

Tuesday, February 25, 2020

B2B slaes funnel to conversions

https://medium.com/unusual-ventures/how-many-meetings-does-it-take-to-close-1m-68b38a3ac614

Thursday, February 13, 2020

Paradigms of Artificial Intelligence Programming

https://github.com/norvig/paip-lisp

A brief introduction to Haskell, and why it matters EditNew Page

https://github.com/fpinscala/fpinscala/wiki/A-brief-introduction-to-Haskell,-and-why-it-matters

Wednesday, February 12, 2020

How to install an older version of scala

https://stackoverflow.com/questions/32767204/how-to-install-an-older-version-of-scala/42908943#42908943

brew search scala

then you will find all the available versions like below

scala                     scala@2.10                scala@2.11                scalaenv                  scalariform               scalastyle
homebrew/science/scalapack                                                     Caskroom/cask/scala-ide

Then choose whichever version you want to install. say if you want to install scala 2.11 then you can do that by runnig the command like below

brew install scala@2.11

Tuesday, January 28, 2020

How to keep a git branch in sync with master?

https://stackoverflow.com/questions/16329776/how-to-keep-a-git-branch-in-sync-with-master

git checkout master
git pull
git checkout mobiledevicesupport
git merge master

Friday, January 24, 2020

ScalaTest in sbt: is there a way to run a single test without tags?

a single test can be ran by running, in sbt,

testOnly *class -- -n Tag

to run only the tests whose name includes the substring "foo".

testOnly *MySuite -- -z foo

For exact match rather than substring, use -t instead of -z.

Updating a mutable map counter value

val symTab = collection.mutable.HashMap[String, Int]()
def update0(s: String): Unit = {

if (symTab.contains(s)) {

symTab(s) = symTab(s) + 1

}

else {

symTab(s) = 1

}

// Do this instead

def update1(s: String): Unit = symTab(s) = symTab.getOrElse(s, 0) + 1

scala> update1("x")

scala> symTab("x")

res2: Int = 1

scala> update1("x")

scala> symTab("x")

res4: Int = 2

Thursday, January 2, 2020

Brand management

https://www.brandingstrategyinsider.com/30-must-read-articles-on-brand-management-2/#.Xg6eSS2ZNjU

Wednesday, January 1, 2020

Negotiation

https://www.masterclass.com/classes/chris-voss-teaches-the-art-of-negotiation?utm_source=Paid&utm_medium=Youtube&utm_campaign=CV&utm_content=Video&utm_term=Aq-Prospecting&gclid=CjwKCAiAo7HwBRBKEiwAvC_Q8T6WhSztG1zpTeiDRcazj5Ki2_UgKh-LzG2xHFWaqN97SV2bGuDHihoCnYgQAvD_BwE