az group create --name <rg_name> --location <location>
Where you replace <location>with the region where you want to create the resource group. You may have a region in mind (South Central US perhaps) or you may be wondering which regions are available. Either way, it is helpful to see the list of all possible values for <location> in commands like the one above.
Let’s get the output in a more human readable table format.
az account list-locations -o table
DisplayName Latitude Longitude Name
-------------------- ---------- ----------- ------------------
East Asia 22.267 114.188 eastasia
Southeast Asia 1.283 103.833 southeastasia
Central US 41.5908 -93.6208 centralus
East US 37.3719 -79.8164 eastus
East US 2 36.6681 -78.3889 eastus2
West US 37.783 -122.417 westus
North Central US 41.8819 -87.6278 northcentralus
South Central US 29.4167 -98.5 southcentralus
North Europe 53.3478 -6.2597 northeurope
West Europe 52.3667 4.9 westeurope
Japan West 34.6939 135.5022 japanwest
Japan East 35.68 139.77 japaneast
Brazil South -23.55 -46.633 brazilsouth
Australia East -33.86 151.2094 australiaeast
Australia Southeast -37.8136 144.9631 australiasoutheast
South India 12.9822 80.1636 southindia
Central India 18.5822 73.9197 centralindia
West India 19.088 72.868 westindia
Canada Central 43.653 -79.383 canadacentral
Canada East 46.817 -71.217 canadaeast
UK South 50.941 -0.799 uksouth
UK West 53.427 -3.084 ukwest
West Central US 40.890 -110.234 westcentralus
West US 2 47.233 -119.852 westus2
Korea Central 37.5665 126.9780 koreacentral
Korea South 35.1796 129.0756 koreasouth
France Central 46.3772 2.3730 francecentral
France South 43.8345 2.1972 francesouth
Australia Central -35.3075 149.1244 australiacentral
Australia Central 2 -35.3075 149.1244 australiacentral2
UAE Central 24.466667 54.366669 uaecentral
UAE North 25.266666 55.316666 uaenorth
South Africa North -25.731340 28.218370 southafricanorth
South Africa West -34.075691 18.843266 southafricawest
Switzerland North 47.451542 8.564572 switzerlandnorth
Switzerland West 46.204391 6.143158 switzerlandwest
Germany North 53.073635 8.806422 germanynorth
Germany West Central 50.110924 8.682127 germanywestcentral
Norway West 58.969975 5.733107 norwaywest
Norway East 59.913868 10.752245 norwayeast
The entries in the Name column are valid options for 安卓上推特教程 in the command we started out with.
Many people suggest that you should use version control as part of your scientifc workflow. This is usually quickly followed up by recommendations to learn git and to put your project on GitHub. Learning and doing all of this for the first time takes a lot of effort. Alongside all of the recommendations to learn these technologies are horror stories telling how difficult it can be and memes saying that no one really knows what they are doing!
There are a lot of reasons to not embrace the git but there are even more to go ahead and do it. This is an attempt to convince you that it’s all going to be worth it alongside a bunch of resources that make it easy to get started and academic papers discussing the issues that version control can help resolve.
This document will not address how to do version control but will instead try to answer the questions what you can do with it and why you should bother. It was inspired by a conversation on twitter.
For many people, this is just the beginning. For a project that has existed long enough there might be dozens or even hundreds of these simple scripts that somehow define all of part of your computational workflow. Version control isn’t being used because ‘The code is just a simple script developed by one person’ and yet this situation is already becoming the breeding ground for future problems.
Which one of these files is the most up to date?
Which one produced the results in your latest paper or report?
Which one contains the new work that will lead to your next paper?
Which ones contain deep flaws that should never be used as part of the research?
Which ones contain possibly useful ideas that have since been removed from the most recent version?
Applying version control to this situation would lead you to a folder containing just one file
mycode.py
All of the other versions will still be available via the commit history. Nothing is ever lost and you’ll be able to effectively go back in time to any version of mycode.py you like.
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I’ve even seen folders like the one above passed down generations of PhD students like some sort of family heirloom. I’ve seen labs where multple such folders exist across a dozen machines, each one with a mixture of duplicated and unique files. That is, not only is there a confusing mess of files in a folder but there is a confusing mess of these folders!
This can even be true when only one person is working on a project. Perhaps you have one version of your folder on your University HPC cluster, one on your home laptop and one on your work machine. Perhaps you email zipped versions to yourself from time to time. There are many everyday events that can lead to this state of affairs.
By using a GitHub repository you have a single point of truth for your project. The latest version is there. All old versions are there. All discussion about it is there.
Everything…one place.
The power of this simple idea cannot be overstated. Whenever you (or anyone else) wants to use or continue working on your project, it is always obvious where to go. Never again will you waste several days work only to realise that you weren’t working on the latest version.
It is possible to compare the differences between any two commits, not just two consecutive ones which allows you to track the evolution of your project over time.
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Ever noticed how your collaborator turns up unnanounced just as you are in the middle of hacking on your code. They want you to show them your simulation running but right now its broken! You frantically try some of the other files in your folder but none of them seem to be the version that was working last week when you sent the report that moved your collaborator to come to see you.
If you were using version control you could easily stash your current work, revert to the last good commit and show off your work.
Tracking down what went wrong
You are always changing that script and you test it as much as you can but the fact is that the version from last year is giving correct results in some edge case while your current version is not. There are 100 versions between the two and there’s a lot of code in each version! When did this edge case start to go wrong?
With git you can use git bisect to help you track down which commit started causing the problem which is the first step towards fixing it.
Providing a back up of your project
Try this thought experiment: Your laptop/PC has gone! Fire, theft, dead hard disk or crazed panda attack.
It, and all of it’s contents have vanished forever. How do you feel? What’s running through your mind? If you feel the icy cold fingers of dread crawling up your spine as you realise Everything related to my PhD/project/life’s work is lost then you have made bad life choices. In particular, you made a terrible choice when you neglected to take back ups.
Of course there are many ways to back up a project but if you are using the standard version control workflow, your code is automatically backed up as a matter of course. You don’t have to remember to back things up, back-ups happen as a natural result of your everyday way of doing things.
Making your project easier to find and install
There are dozens of ways to distribute your software to someone else. You could (HORRORS!) email the latest version to a colleuage or you could have a .zip file on your web site and so on.
Each of these methods has a small cognitive load for both recipient and sender. You need to make sure that you remember to update that .zip file on your website and your user needs to find it. I don’t want to talk about the email case, it makes me too sad. If you and your collaborator are emailing code to each other, please stop. Think of the children!
One great thing about using GitHub is that it is a standardised way of obtaining software. When someone asks for your code, you send them the URL of the repo. Assuming that the world is a better place and everyone knows how to use git, you don’t need to do anything else since the repo URL is all they need to get your code. a git clone later and they are in business.
In addition to this, some popular computational environments now allow you to install packages directly from GitHub. If, for example, you are following standard good practice for building an R package then a user can install it directly from your GitHub repo from within R using the ios上推特教程 function.
Automatically run all of your tests
You’ve sipped of the KoolAid and you’ve been writing unit tests like a pro. GitHub allows you to link your repo with something called Continuous Integration (CI) that helps maximise the utility of those tests.
Once its all set up the CI service runs every time you, or anyone else, makes a commit to your project. Every time the CI service runs, a virtual machine is created from scratch, your project is installed into it and all of your tests are run with any failures reported.
This gives you increased confidence that everything is OK with your latest version and you can choose to only accept commits that do not break your testing framework.
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How git and GitHub can make it easier to collaborate with others on computational projects.
Every GitHub repo comes with an Issues section which is effectively a discussion forum for the project. You can use it to keep track of your project To-Do list, bugs, documentation discussions and so on. The issues log can also be integrated with your commit history. This allows you to do things like git commit -m "Improve the foo algorithm according to the discussion in #34" where #34 refers to the Issue discussion where your collaborator pointed out
I start with the above statement because I’ve found that when explaining how easy it is to collaborate on GitHub, the first question is almost always ‘How do I keep control of all of this?’
What happens is that anyone can ‘fork’ your project into their account. That is, they have an independent copy of your work that is clearly linked back to your original. They can happily work away on their copy as much as they like – with no involvement from you. If and when they want to suggest that some of their modifications should go into your original version, they make a ‘Pull Request’.
I emphasised the word ‘Request’ because that’s exactly what it is. You can completely ignore it if you want and your project will remain unchanged. Alternatively you might choose to discuss it with the contributor and make modifications of your own before accepting it. At the other end of the spectrum you might simply say ‘looks cool’ and accept it immediately.
Congratulations, you’ve just found a contributing collaborator.
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How git and GitHub can contribute to improved reproducible research.
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A paper published without the supporting software and data is (much!) harder to reproduce than one that has both.
Making your software citable
Most modern research cannot be done without some software element. Even if all you did was run a simple statistical test on 20 small samples, your paper has a data and software dependency. Organisations such as the Software Sustainability Institute and the UK Research Software Engineering Association (among many others) have been arguing for many years that such software and data dependencies should be part of the scholarly record alongside the papers that discuss them. That is, they should be archived and referenced with a permanent Digital Object Identifier (DOI).
Once your code is in GitHub, it is straightforward to archive the version that goes with your latest paper and get it its own DOI using services such as Zenodo. Your University may also have its own archival system. For example, The University of Sheffield in the UK has built a system called ORDA which is based on an institutional Figshare instance which allows Sheffield academics to deposit code and data for long term archival.
Anyone who has worked with software long enough knows that simply stating the name of the software you used is often insufficient to ensure that someone else could reproduce your results. To help improve the odds, you should state exactly which version of the software you used and one way to do this is to refer to the git commit hash. Alternatively, you could go one step better and make a GitHub release of the version of your project used for your latest paper, get it a DOI and cite it.
This doesn’t guarentee reproducibility but its a step in the right direction. For extra points, you may consider making the computational environment reproducible too (e.g. all of the dependencies used by your script – Python modules, R packages and so on) using technologies such as Docker, Conda and MRAN but further discussion of these is out of scope for this article.
Building a computational environment based on your repository
Once your project is on GitHub, it is possible to integrate it with many other online services. One such service is mybinder which allows the generation of an executable environment based on the contents of your repository. This makes your code immediately reproducible by anyone, anywhere.
Similar projects are popping up elsewhere such as The Littlest JupyterHub deploy to Azure button which allows you to add a button to your GitHub repo that, when pressed by a user, builds a server in their Azure cloud account complete with your code and a computational environment specified by you along with a JupterHub instance that allows them to run Jupyter notebooks. This allows you to write interactive papers based on your software and data that can be used by anyone.
Complying with funding and journal guidelines
When I started teaching and advocating the use of technologies such as git I used to make a prediction These practices are so obviously good for computational research that they will one day be mandated by journal editors and funding providers. As such, you may as well get ahead of the curve and start using them now before the day comes when your funding is cut off because you don’t. The resulting debate was usually good fun.
Put your presentations on GitHub. I use reveal.js combined with GitHub pages to build and serve my presentations. That way, whenever I turn up at an event to speak I can use whatever computer is plugged into the projector. No more ‘I don’t have the right adaptor’ hell for me.
Write your next grant proposal. Use Markdown, LaTex or some other git-friendly text format and use git and GitHub to collaboratively write your next grant proposal
The movie below is a visualisation showing how a large H2020 grant proposal called OpenDreamKit was built on GitHub. Can you guess when the deadline was based on the activity?
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Further discussions from scientific computing practitioners that discuss using version control as part of a healthy approach to scientific computing
Is Your Research Software Correct? – A presentation from Mike Croucher discussing what can go wrong in computational research and what practices can be adopted to do help us do better
The Turing Way A handbook of good practice in data science brought to you from the Alan Turning Institute
My stepchildren are pretty good at mathematics for their age and have recently learned about Pythagora’s theorem
$c=\sqrt{a^2+b^2}$
The fact that they have learned about this so early in their mathematical lives is testament to its importance. Pythagoras is everywhere in computational science and it may well be the case that you’ll need to compute the hypotenuse to a triangle some day.
Fortunately for you, this important computation is implemented in every computational environment I can think of!
It’s almost always called hypot so it will be easy to find.
Here it is in action using Python’s numpy module
import numpy as np
a = 3
b = 4
np.hypot(3,4)
5
When I’m out and about giving talks and tutorials about Research Software Engineering, High Performance Computing and so on, I often get the chance to mention the hypot function and it turns out that fewer people know about this routine than you might expect.
Trivial Calculation? Do it Yourself!
Such a trivial calculation, so easy to code up yourself! Here’s a one-line implementation
def mike_hypot(a,b):
return(np.sqrt(a*a+b*b))
In use it looks fine
mike_hypot(3,4)
5.0
Overflow and Underflow
I could probably work for quite some time before I found that my implementation was flawed in several places. Here’s one
mike_hypot(1e154,1e154)
inf
You would, of course, expect the result to be large but not infinity. Numpy doesn’t have this problem
np.hypot(1e154,1e154)
1.414213562373095e+154
My function also doesn’t do well when things are small.
a = mike_hypot(1e-200,1e-200)
0.0
but again, the more carefully implemented hypot function in numpy does fine.
np.hypot(1e-200,1e-200)
1.414213562373095e-200
Standards Compliance
Next up — standards compliance. It turns out that there is a an official standard for how hypot implementations should behave in certain edge cases. The IEEE-754 standard for floating point arithmetic has something to say about how any implementation of hypot handles NaNs (Not a Number) and inf (Infinity).
That’s a lot of mistakes for one line of code! Of course, we can do better with a small number of extra lines of code as John D Cook demonstrates in the blog post What’s so hard about finding a hypotenuse?
Hypot implementations in production
Production versions of the hypot function, however, are much more complex than you might imagine. The source code for the implementation used in openlibm (used by Julia for example) was 132 lines long last time I checked. Here’s a screenshot of part of the implementation I saw for prosterity. At the time of writing the code is at http://github.com/JuliaMath/openlibm/blob/master/src/e_hypot.c
That’s what bullet-proof, bug checked, has been compiled on every platform you can imagine and survived code looks like.
There’s more!
Active Research
When I learned how complex production versions of hypot could be, I shouted out about it on twitter and learned that the story of hypot was far from over!
The implementation of the hypot function is still a matter of active research! See the paper here http://arxiv.org/abs/1904.09481
Is Your Research Software Correct?
Given that such a ‘simple’ computation is so complicated to implement well, consider your own code and ask Is Your Research Software Correct?.
In many introductions to numpy, one gets taught about np.ones, np.zeros and np.empty. The accepted wisdom is that np.empty will be faster than np.ones because it doesn’t have to waste time doing all that initialisation. A quick test in a Jupyter notebook shows that this seems to be true!
20 times faster may well be useful in production when using really big matrices. Might even be worth the risk of dealing with uninitialised variables even though they are scary!
However…..on my machine (Windows 10, Microsoft Surface Book 2 with 16Gb RAM), we see the following behaviour with a larger matrix size (1000 x 1000).
import numpy as np
N = 1000
zero_time = %timeit -o some_zeros = np.zeros((N,N))
empty_time = %timeit -o empty_matrix = np.empty((N,N))
print('np.empty is {0} times faster than np.zeros'.format(zero_time.average/empty_time.average))
113 µs ± 2.97 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
112 µs ± 1.01 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
np.empty is 1.0094651980894993 times faster than np.zeros
A large scale SOCP solver was one of the highlights of the Mark 27 release of the NAG library (See here for a poster about its performance). Those who have used the NAG library for years will expect this solver to have interfaces in Fortranand C and, of course, they are there. In addition to this is the fact that Mark 27 of the NAG Library for Python was released at the same time as the Fortran and C interfaces which reflects the importance of Python in today’s numerical computing landscape.
Here’s a quick demo of how the new SOCP solver works in Python. The code is based on a notebook in NAG’s PythonExamples GitHub repository.
NAG’s handle_solve_socp_ipm function (also known as e04pt) is a solver from the NAG optimization modelling suite for large-scale second-order cone programming (SOCP) problems based on an interior point method (IPM).
where $\mathcal{K} = \mathcal{K}^{n_{1}} \times \cdots \times \mathcal{K}^{n_{r}} \times \mathbb{R}^{n_{l}}$ is a Cartesian product of quadratic (second-order type) cones and $n_{l}$-dimensional real space, and $n = \sum_{i = 1}^{r}n_{i} + n_{l}$ is the number of decision variables. Here $c$, $x$, $l_x$ and $u_x$ are $n$-dimensional vectors.
$A$ is an $m$ by $n$ sparse matrix, and $l_A$ and $u_A$ and are $m$-dimensional vectors. Note that $x \in \mathcal{K}$ partitions subsets of variables into quadratic cones and each $\mathcal{K}^{n_{i}}$ can be either a quadratic cone or a rotated quadratic cone. These are defined as follows:
This example, derived from the documentation for the handle_set_group function solves the following SOCP problem
minimize $${10.0x_{1} + 20.0x_{2} + x_{3}}$$
fromnaginterfaces.baseimport国内上twitter教程fromnaginterfaces.libraryimportopt# The problem size:n=3# Create the problem handle:handle=opt.handle_init(nvar=n)苹果手机上推特教程opt.handle_set_linobj(handle,cvec=[10.0,20.0,1.0])
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# Call SOCP interior point solverresult=opt.handle_solve_socp_ipm(handle,io_manager=iom)
------------------------------------------------
E04PT, Interior point method for SOCP problems
------------------------------------------------
Status: converged, an optimal solution found
Final primal objective value -1.951817E+01
Final dual objective value -1.951817E+01
The optimal solution is
result.x
array([-1.26819151, -0.4084294 , 1.3323379 ])
and the objective function value is
result.国内iphone怎么上推特[0]
-19.51816515094211
Finally, we clean up after ourselves by destroying the handle
# Destroy the handle:opt.国内苹果怎么上twitter(handle)
As you can see, the way to use the NAG Library for Python interface follows the mathematics quite closely.
NAG also recently added support for the popular cvxpy modelling language that I’ll discuss another time.
I am a huge user of Anaconda Python and the way I usually get access to the Anaconda Prompt is to start typing ‘Anaconda’ in the Windows search box and click on the link as soon as it pops up. Easy and convenient. Earlier today, however, the Windows 10 menu shortcuts for the Anaconda command line vanished from my machine!
I’m not sure exactly what triggered this but I was heavily messing around with various environments, including the base one, and also installed Visual Studio 2019 Community Edition with all the Python extensions before I noticed that the menu shortcuts had gone missing. No idea what the root cause was.
Fortunately, getting my Anaconda Prompt back was very straightforward:
launch Anaconda Navigator
Click on Environments
Selected base (root)
Choose Not installed from the drop down list
Type for console_ in the search box
Check the console_shortcut package
Click Apply and follow the instructions to install the package
Over the course of my career I have been involved with the provision of High Performance Computing (HPC) at almost every level. As a researcher and research software engineer I have been, and continue to be, a user of many large scale systems. As a member of research computing support, I was involved in service development and delivery, user-support, system administration and HPC training. Finally, as a member of senior leadership teams, I have been involved in the financial planning and strategic development of institution-wide HPC services.
In recent years, the question that pops up at every level of involvement with HPC is ‘Should we do this with our own equipment or should we do this in the cloud?’
This is an extremely complex, multi-dimensional question where the parameters are constantly shifting. The short, answer is always ‘well…it depends’ and it always does ‘depend’…on many things! What do you want to do? What is the state of the software that you have available? How much money do you have? How much time do you have? What support do you have? What equipment do you currently have and who else are you working with and so on.
Today, I want to focus on just one of these factors. Cost. I’m not saying that cost is necessarily the most important consideration in making HPC-related decisions but given that most of us have fixed budgets, it is impossible to ignore.
The value of independence
I have been involved in many discussions of HPC costs over the years and have seen several attempts at making the cloud vs on-premise cost-comparison. Such attempts are often biased in one direction or the other. It’s difficult not to be when you have a vested interest in the outcome.
So, NAG are more interested in helping clients solve their technical computing problems than they are in driving them to any given solution. Cloud or on-premise? They don’t mind…they just want to help you make the decision.
Explore HPC cost models yourself
NAG’s VP for HPC services and consulting, Andrew Jones (@hpcnotes), has been teaching seminars on Total Cost Of Ownership models for several years at events like the SC Supercomputing conference series. To support these tutorials, Andrew created a simple, generic TCO model spreadsheet to illustrate the concepts and to show how even a simple TCO model can guide decisions.
Many people asked if NAG could send them the TCO spreadsheet from the tutorial but they decided to go one better and converted it into a web-form where you can start exploring some of the concepts yourself.
If you want more, get in touch with either me (@walkingrandomly) or Andrew (@hpcnotes) on twitter or email hpc@nag.com. We’ll also be teaching this type of thing at ISC 2019 so please do join us there too.
My preferred workflow for writing technical documents these days is to write in Markdown (Or Jupyter Notebooks) and then use Pandoc to convert to PDF, Microsoft Word or whatever format is required by the end client.
While working with a markdown file recently, the pandoc conversion to PDF failed with the following error message
! Undefined control sequence.
l.117 \[ \coloneqq
This happens because Pandoc first converts the Markdown file to LaTeX which then gets compiled to PDF and the command \coloneqq isn’t included in any of the LaTeX packages that Pandoc uses by default.
The coloneqq command is in the mathtools package which, on Ubuntu, can be installed using
apt-get install texlive-latex-recommended
Once we have the package installed, we need to tell Pandoc to make use of it. The way I did this was to create a Pandoc metadata file that contained the following
---
header-includes: |
\usepackage{mathtools}
---
I called this file pandoc_latex.yml and passed it to Pandoc as follows
which suggests that the –metadata-file option is a relatively recent addition to Pandoc. I have no idea when this option was added but if this happens to you, you could try installing the latest version from http://github.com/jgm/pandoc/
I used 2.7.1.1 and it was fine so I guess anything later than this should also be OK.
While basking in some geek nostalgia on twitter, I discovered that my first ever microcomputer, the Sinclair Spectrum, once had a Fortran compiler
However, that compiler was seemingly lost to history and was declared Missing in Action on World of Spectrum.
A few of us on Twitter enjoyed reading the 1987 review of this Fortran Compiler but since no one had ever uploaded an image of it to the internet, it seemed that we’d never get the chance to play with it ourselves.
I never thought it would come to this
One of the benefits of 5000+ followers on Twitter is that there’s usually someone who knows something interesting about whatever you happen to tweet about and in this instance, that somebody was my fellow Fellow of the Software Sustainability Institute, Barry Rowlingson. Barry was fairly sure that he’d recently packed a copy of the Mira Fortran Compiler away in his loft and was blissfully unaware of the fact that he was sitting on a missing piece of microcomputing history!
As Barry mentioned in his tweet, converting a 40 year old cassette to an archivable .tzx format is a process that could result in permanent failure. The attempt on side 1 of the cassette didn’t work but fortunately, side 2 is where the action was!
It turns out that everything worked perfectly. On loading it into a Spectrum emulator, Barry could enter and compile Fortran on this platform for the first time in decades! Here is the source code for a program that computes prime numbers
Here it is running
and here we have Barry giving the sales pitch on the advanced functionality of this compiler :)
