Tutorial on the usage of Sumatra (http://neuralensemble.org/sumatra/) with git. Presented at the Bernstein Center for Computational Neuroscience in May 2015.
CraftCamp for Students - Introduction to gitcraftworkz
CraftCamp for Students - Introduction to git
Git is a distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows. Git was initially designed and developed by Linus Torvalds for Linux kernel development in 2005, and has since become the most widely adopted version control system for software development.
Introduction to Gitlab | Gitlab 101 | Training SessionAnwarul Islam
I actually described in this slide how to use Gitlab with git. I explained what is git, push, pull, clone, commit etc. so, you can use this slide to learn or tech someone.
CraftCamp for Students - Introduction to gitcraftworkz
CraftCamp for Students - Introduction to git
Git is a distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows. Git was initially designed and developed by Linus Torvalds for Linux kernel development in 2005, and has since become the most widely adopted version control system for software development.
Introduction to Gitlab | Gitlab 101 | Training SessionAnwarul Islam
I actually described in this slide how to use Gitlab with git. I explained what is git, push, pull, clone, commit etc. so, you can use this slide to learn or tech someone.
GLV OnAir Ottobre 2019
In questa introduzione a GitHub Actions: vedremo gli elementi base, cosa è possibile fare, cosa invece si rivela complicato o impossibile da fare, come trovare informazioni ed esempi.
It is the material that I use for artificial intelligence exercise.
From Linux installation to tensor flow practice environment.
1. Prepare the virtual development environment.
2. Installing Linux (ubuntu)
3. Installing the Python Development Environment (Python, Pyenv + Virtualenv)
4. Installing the Python web development environment (Jupyter notebook, Numpy, Matplot, BS4 ...)
5. Tensor flow installation (TensorFlow)
6. Tensor Flow Practice (Python & Tensorflow tutorial)
EuroPython 2014 - How we switched our 800+ projects from Apache to uWSGIMax Tepkeev
During the last 7 years the company I am working for developed more than 800 projects in PHP and Python. All this time we were using Apache+nginx for hosting this projects. In this talk I will explain why we decided to switch all our projects from Apache+nginx to uWSGI+nginx and how we did that.
GLV OnAir Ottobre 2019
In questa introduzione a GitHub Actions: vedremo gli elementi base, cosa è possibile fare, cosa invece si rivela complicato o impossibile da fare, come trovare informazioni ed esempi.
It is the material that I use for artificial intelligence exercise.
From Linux installation to tensor flow practice environment.
1. Prepare the virtual development environment.
2. Installing Linux (ubuntu)
3. Installing the Python Development Environment (Python, Pyenv + Virtualenv)
4. Installing the Python web development environment (Jupyter notebook, Numpy, Matplot, BS4 ...)
5. Tensor flow installation (TensorFlow)
6. Tensor Flow Practice (Python & Tensorflow tutorial)
EuroPython 2014 - How we switched our 800+ projects from Apache to uWSGIMax Tepkeev
During the last 7 years the company I am working for developed more than 800 projects in PHP and Python. All this time we were using Apache+nginx for hosting this projects. In this talk I will explain why we decided to switch all our projects from Apache+nginx to uWSGI+nginx and how we did that.
"Git Tutorial" a hands-on session on Git presented at Theoretical Neuroscience Lab, IISER Pune.
Very brief overview of Git commands.
Github: https://github.com/pranavcode/git-tutorial
An easy way to get started with git. I use these slides with my talk highlighting why we need git, a small command list to follow, and some more trivia and a ton of useful links.
In this tutorial, we will explain how to get your own GitHub instance running on your own Ubuntu 12.04 VPS. Ubuntu 12.04 is recommended because of some incompatibilities between Python and Ruby on other Linux distributions. Also, make sure you have at least 1GB RAM memory on your VPS. Our first step is to install some required packages and dependencies.
Presented by VEXXHOST, provider of Openstack based Public and Private Cloud Infrastructure
https://vexxhost.com/
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
1. Introduction to
Presented by
Felix Hoffmann
felix11h.github.io/
Slides
Slideshare:
tiny.cc/smt-present
Source:
tiny.cc/smt-source
Resources on Sumatra
Website: neuralensemble.org/sumatra/
Getting started:
packages.python.org/Sumatra/
Repository:
bitbucket.org/apdavison/sumatra/
Mailinglist:
tiny.cc/smt-user
Maintainer: Andrew Davison
This work is licensed under a Creative Commons Attribution 4.0 International License.
2. Git Primer I
Initialization:
1 mkdir new_dir
2 cd new_dir
3 # new_dir is empty
4 ls -a
5 git init
6 # after initialization now has .git folder
7 ls -a
3. Git Primer II
Adding files and committing changes:
1 # write "hello world" in a file
2 echo "hello world" >> hello_world.txt
3 # stages the file
4 git add hello_world.txt
5 # commit (-a)ll staged changes with a (-m)essage
6 git commit -am ’added hello world’
7 # show the commit in the log
8 git log
4. Git Primer III
Going back to previous versions:
1 # first change the file
2 echo "goodbye world" >> hello_world.txt
3 # content has changed indeed
4 more hello_world.txt
5 # commit the changes
6 git commit hello_world.txt -m ’goodbye world’
7 # new commit appears in log
8 git log
9
10 # now use first 4 digits of commit ID shown in log
11 git checkout XXXX
12 # hello_world.txt was reverted to old version:
13 more hello_world.txt
14 # finlly, go back to latest commit
15 git checkout master
5. Git Primer IV
Other helpful commands:
1 # show the status of all files
2 git status -s
Resources
- Software Carpentry
http://swcarpentry.github.io/git-novice/
- GitHub, Bitbucket
https://github.com/
http://bitbucket.com/
- git documentation
http://git-scm.com/doc
6. Sumatra Installation (Version 0.6.0)
Install globally (Ubuntu):
1 sudo apt-get install python-pip
2 sudo pip install django==1.6
3 sudo pip install sumatra
4 sudo apt-get install git
5 sudo pip install gitpython==0.3.7
... or install in a virtualenv:
1 virtualenv smt_0.6.0
2 source smt_0.6.0/bin/activate
3 pip install django==1.6
4 pip install sumatra
5 pip install gitpython==0.3.7
7. Sumatra Installation (Version 0.6.0) II
Manually test your setup:
1 mkdir smt_test/
2 cd smt_test/
3 git init
4 smt init Test
5 echo "print ’hello’" >> hello_world.py
6 git add hello_world.py
7 git commit -am ’test’
8 smt run --main=hello_world.py
9 smtweb
8. Setting up your first Sumatra project...
1 mkdir new_project/
2 cd new_project/
3 git init
4 # configure for relative input paths and custon output
5 smt init PROJECT_NAME --input=. --datapath=MyData/
6 # show the configuration
7 info
9. ... and running the first tracked computation
1 echo "open(’MyData/out1.dat’,’a’).close()" > my_scrpt.py
2 git add my_scrpt.py
3 git commit -am ’added my_scrpt’
4 echo "a = 1
5 b = 2" > params.py
6 # create some example input file
7 echo "123,456,789
8 987,654,321" > input1.dat
9 # input & parameter files need not be (shouldn’t be?)
10 # under version control
11 smt run --main=my_scrpt.py input1.dat params.py
12 # inspect new simulation record in the terminal...
13 smt list --long
14 # ... and the web interface
15 smtweb
10. Things Sumatra can’t do
- track parallel computations
- is not easily transferable to other systems or even
local directories
- misses some implementations (rerun when code has
been changed, display code from web interface, ...)
Alternatives to Sumatra
- pypet
https://pypet.readthedocs.org/en/latest/
- Manual tracking of computations (file names,
spreadsheets, ...)
- custom tools?
11. Have fun with Sumatra!
Presented by
Felix Hoffmann
felix11h.github.io/
Slides
Slideshare:
tiny.cc/smt-present
Source:
tiny.cc/smt-source
Resources on Sumatra
Website: neuralensemble.org/sumatra/
Getting started:
packages.python.org/Sumatra/
Repository:
bitbucket.org/apdavison/sumatra/
Mailinglist:
tiny.cc/smt-user
Maintainer: Andrew Davison
This work is licensed under a Creative Commons Attribution 4.0 International License.