GitHub Demo Days, 7 Nov 2020

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From YouTube: Codespaces - Start working on software projects with zero setup time

Description

Every time developers start working on a new project, they waste precious time and energy setting up a development environment before they can start doing what they truly love: writing code. Join Dr. Diego Assencio (Solutions Engineer at GitHub) on an awesome ride through GitHub Codespaces and learn how to eliminate this problem. Hop on and have fun!

You an learn more about Codespaces here: https://github.com/features/codespaces

A

Welcome to this demo, um I'm really happy to be here, especially because this topic is so exciting for me. So you don't know me, my name is diego sencio. I am a solutions engineer here at github and I have peter murray with me. He's gonna be uh helping me with with the chat and assisting me throughout the call. So thanks a lot peter for being there as well um before.

A

I start, though I would be really happy if you could uh drop me a message to stating where, where are you from or uh what do you do? Are you a developer? Are you a manager? Are you an enthusiast so if you're willing to share that, if you're comfortable sharing that with us, it would be really great, so I actually know have an idea. What is the type of audience that we have today?

A

As I said, I am a solutions engineer at github and uh I am I did not start my career as a solutions engineer and also not in I.t.

A

um I was actually a physicist and I did a phd in physics, and I thought I'm gonna be a professor. That was my career dream and uh over time I discovered that actually, my my my greatest passion and professionally speaking, is with software development. So over time I switched to the to the it industry. I have been software engineer.

A

I took roles as project manager, engineering manager and I really love software development, not only from a code perspective, not only actually writing the code, but also managing projects and managing people and helping keep people engaged throughout a project, for example. This is these are things that I'm truly passionate about and that eventually led me to github right.

A

This is a this is really a place where you can have such a strong impact on the software development community, in terms of adding tools uh and and and providing technical knowledge, and also providing guidance on how to how to best write software.

A

I think most of you have already heard of the github flow, but in case you haven't, I mean the github flows basically essentially captures the the key steps when you are developing software and trying to put this into a production environment. So um we we usually start with a feature request or a bug, bug fix and by the way, you're welcome to send os messages on the chat.

A

If you wanna ask questions uh peter's gonna, let me know if there's anybody saying something um yeah so feel free to ask me at any point, and also, if you want me to try something on live by by all means, do it? Okay, so going back, we start with a feature or preacher request or a bug fix, and then we start working right. We create some commits at this point if you are actually using a a tool like github.

A

If you push this commits, you may even have some ci pipelines, for example, kicking in and testing your code and give you early and quick feedback. Am I going the right direction? Am I breaking something? Am I breaking functionality?

A

Am I breaking my unit tests and throughout this process you develop your code at some point, you feel like this is a good moment to share it with the rest of of my team. uh It's a very good moment to actually start getting feedback and then ask people. What do you think about it? Is this implementation making sense? Do you have contributions to make here, and at this point you would make a pull request right. So pull request is basically you stating all right guys. This is my. This is my work.

A

This is what I've been working on. What do you think share your your thoughts with me and and this uh stakeholders here don't have to be only developers. It can also be your product manager. It can be a project manager. It can be. I don't know a qa engineer. Anybody in the organization can actually jump with the step and start contributing and adding comments and making suggestions being collaborative and, at some point, you're gonna get to. Hopefully you'll get to the stage where you feel like okay. This is really good.

A

uh Maybe we can deploy this now um and this deployment does not have to be to production. It can be, for example, for a test environment where you're gonna wanna carry out the further testing.

A

uh Maybe you're, going to forward this to some some specialists. That will actually look, maybe there's some ui aspects that need to be taken into consideration on the development on the on the uh on an actual environment where the application runs and everything and when everybody's happy, maybe you deploy this to production, and you merge this back to your default branch. So this is basically the github flow in a nutshell, and the nice thing about this is that this is not how software was initially developed.

A

This is the type of process that we have uh invented over time. There has been so much innovation on software development practices and with this practices also there was. uh There was also progress on the uh on the actual tooling that you need for for managing to develop software. This way, so there's so much automation. Nowadays, that is possible. Throughout this process. You can have automation while you're committing before you even create a pull request by the time you create a pull request. You can have all sorts of tests running uh even with comments.

A

You can have automation, for example, github when somebody writes a comment that this comment gets automatically parsed in a certain action is taken. Maybe you can even deploy your system uh through a comment on an issue uh on a pull request. I'm sorry and deployment. Also a great deal of automation has been done there even after you merge your contribution back to your default branch, you can have additional automation, for example, for code scanning uh checking your code for vulnerabilities right. If vulnerabilities are discovered, your code gets scanned and maybe you find out.

A

Oh actually, this code that we have in production is a problem that needs to be addressed. Maybe you have monitoring tools that will either take corrective action if your production system is is failing for whatever reason or they can notify you. But the message here is that there's so much automation throughout this process, but then what about this?

A

We always take for granted that the development already starts and you can just start working right, but everybody was a developer knows that this is not like that. Usually you have to set up a development environment.

A

You get a laptop, so let's say you join a new company, they give you a laptop and they say well now, uh good luck, you're gonna have to install all of these libraries and packages and tools that you need in order to develop code here, and you may even have a wiki or something like this help you with that and have a buddy who will assist you throughout the way, but it can be super painful.

A

I mean I I've been at projects in the past, where it would take one to two weeks before you would really have your your development environment fully set up, and if you switch projects again, you may have to start from scratch and to make things even things even harder for a project the dependencies in the development environment change over time. It's not like you set up your development environment at some point and you say: I'm good forever.

A

There's always new dependencies being added new libraries, new system tools that you have to install in order to develop and all of this costs time, and it's quite frustrating when it happens a lot.

A

So is there a possibility for automating things at this level right? So much effort has been put into automated things in this entire chain of events, but this is the final frontier on software development, automation, so code spaces is an answer from github to this particular problem, and I guess I've already spoken enough about it and I think you understand the pain. You will definitely sympathize with it if you have written code before so, let's actually see what this looks like. What does a code space? Look like okay!

A

I have this repo here where um there are three code spaces and a code. Space is basically development environment which you specify as code, so it lives within the repository. Many for each repository continue different project. You can create a code space, a development environment which is optimal for that particular project.

B

Are you sharing your screen.

A

uh I am not one second, I forgot to start it. uh My apologies.

A

Okay, do you see it now.

A

Yes, we did perfect. Thank you. I forgot to click the button so great, so uh this repository here is what I was talking about. uh It has the three code spaces which I mentioned and, as I said uh code space is basically development environment, just code, uh I'm gonna show you what it looks like uh for a code space which has no configuration as in this is just a vanilla code space which uh you're gonna get on a repository at github.

A

If you don't add any config, so I'm gonna go to default here and you can click code open with code spaces, new code.

A

Space, this is going to take a couple of seconds. So what is happening in the background is you'll. Have a virtual machine uh managed by github and a code space operates on a docker container.

A

You can even see the the okay. This was a red a little bit quick, but there was a docker container being created here and the code space runs on top of this docker container, and this container contains the entire environment, where we're gonna we're gonna work on and because it's a container, you can do a great deal of things with it. You can install your packages inside of it or you can add your dependencies environment variables and so on.

A

As I said, this code space is very, very basic and actually minimize this.

A

So this code space is very basic and it doesn't have much, but even then, if you're familiar with visual studio code, you're you're going to recognize that this is basically visual studio code in the browser right. So everything you can do with videos to the code that you're familiar with uh yeah, but at least most of the functionality should be available here. For example, you could install extensions, uh you could edit files, you even you have source control.

A

So if you make a change in this in this files, you can actually commit them back to github uh from this code space and so on and in case you're curious. Are there any questions by the way, peter? If there's anything on the chat, please let me know.

B

There's nothing at the moment: okay,.

A

Okay, so let me quickly show you: what is the operating system that we're based on so the docker container is for debian 9.

A

and in case you're, wondering oops in case you're wondering what kind of virtual machine we have. So we have a 2 core virtual machine um cool. So again you could start this. uh You could have uh edit your files already inside of this environment and even start creating a code space from within the code, space and bootstrap it, but we're gonna go back and show you something more interesting than that.

A

Okay, so now I'm going back to my repository and I'm going to show you a second code space, so the second code space lives on a second branch which I'm calling simple, and this is already somewhat more complex. uh I actually created a code space here. I added some configuration to it, which I'm going to show you in a moment uh and it I even have a python script which we're going to execute inside of the code space. So let me start it in the background.

A

Okay, this may take a couple of minutes and I don't want to bore you. Let me quickly go back to the and show you what is on that branch.

A

Okay, so the as I mentioned before- and I really want to highlight this- a code space is a developed environment as code. So instead of this dot dev container directory, we have two files. One is the dockerfile and the devcontainer.json. This one gives you the the high level description of the code space. So if you go inside, we will see, for example, it specifies uh how the docker container is supposed to be built, and it's based on the docker file, which is also in that directory a context directory for docker.

A

We specify an extension to be installed on the vs code instance, so, for example, this is going to install a python extension and we have a bunch of settings which are injected directly on vs code. So, for example, uh I want to use black as my code formatter, and I personally like black, because it's highly opinionated, I don't have to worry about the details about indication and so on.

A

Black basically says I wanted to write code like this, and it said: okay, fine, uh I trust your opinion and it also has a launch task and the launch task is sort of registered code. It is can be used, for example, to start a debugger and in this case it's going to run a script called octocat.pipe in a debugging session, and it will stop the debugger as soon as the script begins.

A

Okay, let's see if the code space is already up and running almost done. Okay, we can go back and I want to show you a second file here, which is the docker file.

A

So remember: a code space is based on the docker container, it's running on top of a docker container and you can specify you can build this docker container in any way you want. So in this case, I'm starting from ubuntu. uh I update packages and I install python 3 and python 3 pip.

A

I also have some requirements for this project. Some python modules, which I need to install, which I copy to the docker container and I install the the packages already in advance, which means when the code space starts, and I think now should be done- yeah great when it starts everything is already in there for me.

A

So, for instance, if I go to.

A

Plugins so yeah this is already installed for me. I didn't have to do this so if you're a person joining a project or if you, if you're working on a project, never worked on before you can start this code space, and in this case you would already have python for you meaning, for example, if I go to my octocat pi script, uh I will have a great deal of support, for example, for for syntax, highlighting apologies, uh syntax highlighting or I can, for example, jump to the definition of this module right.

A

Everyone who's familiar is going to know the very likelihood of the sys module and so on and uh yeah. I have already a pretty nice development environment in here. um What this script does is it will call the octocat api I'm going to copy and open it on a new tab. So you can see what it looks like.

A

So the octa cad api is a github provided api which, when you call it returns, an ascii version of the octocat which our mascot uh was. This was a sentence which belongs to the zen of github, and the zen of github is basically a set of values that we believe in as an organization, so speak like a human meaning. Don't talk too complicated talk to people, don't don't overuse technical jargon, always try to communicate with people in a way that they understand, which is hopefully what I'm doing today.

A

So, let's go back to octocat.pai. As I told you before, I have a requirements file in which this case is quite simple. It just says black and pilot, uh and my script inside. I can also see the dev container, which I've already showed, so I'm not going to show it again, but uh what I'm going to show you here is.

A

If I go to the run view I can remember. I had this launch task already inside of my my dev container.json file. So I can execute the script directly from here and when the debugger starts, it will stop at the very beginning of the script. As I said before, we can set, for example, breakpoint here. uh Okay, I'm going to execute up to that step and on the left side you can even see variables are being shown, for example, html. This is already the result of this.

A

This api call to the uh to the autocad api. You cannot see it here, but if you hover, for example, on top of html, you can see what it looks like it looks kind of cryptic, because all the new lines are not being interpreted by visual by visual studio code, but on the terminal.

A

If I click play you can see now we have the autocad right. Are there any questions? Anything you would like to talk about or would like me to try.

B

People seem to be very impressed by the debugger there.

A

Are there any comments or questions are.

B

uh No they're all answered at the moment. Okay doing doing a great job of uh hitting them heading off the questions as they come in.

A

Well, perfect, uh so yeah I mean it. This is extremely powerful by the way I'm doing python, but you could run it, for example, for c plus plus java, it doesn't have to be even a non-compiled language compiled languages would also work perfectly well here, um but with all that said, I want to jump to the last code space, which is the coolest one. um Let me go back to the demo. um I would not start the the code space directly from here, but I want to show you.

A

um I want to show that this is a much more complica, that's significantly more complex project, so this is going to have a code space with a lot of tools inside the darker. The darker container is going to be much richer, actually quite heavy in a way. That's why I'm not starting now. It will take a couple of minutes to actually start, even though a couple of minutes in the grand scheme of things is very little compared to what people usually spend on setting up a development environment and this code space.

A

I have already started for you it's in here.

A

uh So, let's see what we have out of the box, uh we let me go back to go to the dev container, as you can see a lot more stuff, this code space. I didn't create this by hand. What I did was I copied this from the microsoft. This microsoft repository called vs code, dev containers and, as you can see, there are lots of code space definitions in here for various programming languages. Various projects- and I picked this one here- python 3 anaconda. So this code space contains python, of course, and it also contains anaconda.

A

So you can manage code environments directly inside of this code. Spacing you don't even have to go to the process of installing conda, so you'd say yeah. I can use this locally on my projects and I can start condo, but you're still gonna have to install conda. What, if you didn't, have to do that? uh And here you don't this code space already, has it for you and not only that, but it has already installed python and other tools like auto pap, pylind and so on. It has installed the python plugin for vs code, etc.

A

Let's take a quick look at the docker file: this is a docker image provided by microsoft, already containing most of the things we're going to need uh and we're it. As I said, I, I literally copy pasted it this, this all the stuff from the microsoft ripple to my ripple and have made no changes, and that already gives me a very nice environment where I can develop python code.

A

They were kind enough uh to even give us some test code, so you can actually test if everything is okay. Before I jump to that, though, I wanted to point out that there's a file here called environment.yaml and if you're familiar with with conda, you will know that this this is a way to actually specify, for example, your actual environment, which do you need, for example, additional python packages, and I do in this case uh this is a very simple file which I created, and I say I need things for flowing keras.

A

So if you are familiar with one of these projects, you're going to know well we're very likely going to have a script running here. That is going to do some. Some machine learning work for us and that's exactly what we're going to do, but before we jump into that stuff, I need to go to the test project and show you a few things before.

A

So I didn't write this code. This also came on the on the um on the on the code, space definition from microsoft and, as you can see, we've already used matplotlib numpy and we're defining a set of x and y values here and we plot it here and at the end I store the the plot as a png file.

A

Let me execute this or the run view, there's even already a a job for us, so in the process of running the script, it's also activating the code environment which you could be could, by the way, also have put automated uh in the in the docker container, but in this in this particular code space, it's done when you run the script using this visual studio uh code launch task and it has created a plot.png file.

A

So let's see what it looks like: okay awesome. We have a sine wave amazing. uh So again, if you look at this and thinking okay, you have plotted a sine wave that the point is not about the the the plot itself. uh What is really interesting about this is that I haven't done any configuration of the development environment right.

A

I managed to have a script running with non-trivial dependencies uh without any setup effort, and if you're not satisfied with this, then let's go to a much more complex example, which is uh a jupiter notebook which I have created to analyze the titanic data set. So the titanic dataset is a pretty famous one. I have this. This data, such as a csv file and what it contains, is a list of people who are of passengers and data such as which class they were at what is their gender age? Have they survived? Have they not survived?

A

Who were they with and so on? So this list is what we're going to analyze and we're going to create a predictor so that when you have, for example, the information about a single person, this particular will tell you: has this person survived or not, and then we can see how accurate the predictor is.

A

So let me go to my python, I'm sorry to my jupyter notebook. It's going to take a couple seconds to start: okay cool! So uh if you look at this, you will see uh that uh it. It already recognized that this is a jupiter notebook for us. uh It's asking us to trust it. So let's go ahead and trust it. The jupiter server is already initiated, uh and with that said, let's look at the actual uh notebook and see what's inside uh I, but in case you're wondering yes, I have written all of this stuff.

A

This analysis is basically uh from the microsoft web page where they give you an example of how you can use visual studio code for data science. So, let's look at what's going on here and it's okay, if you don't understand all the steps, uh because really what matters here is actually that you see how much you can do within visual studio code and also this case with jupiter notebook which already came pre-installed for me. I didn't have to do anything and and not actually what the exact steps are doing right.

A

This is just for for the fun of it, so we use pandas to load this data set and create a data frame uh after that we're going to do a little bit of magic. For example, we have a bunch of question marks in this data set whenever the data is not known. So let's clean that up and let's make sure the types of the columns are correct, so age and fair, we want to make sure that they are.

A

They are floating point numbers uh and so on, I'm going to execute and explain the steps so executing the step, loading, pandas and no pie, okay, good we're done, and some data cleaning and now we're going to plot stuff we're going to plot.

A

For example, the survival rate of over various uh features that are given in this data set it's going to take a couple of seconds and it's coming. Okay, great, oh, what happened to my ai there we go! So uh if you double click on this, you can actually visualize these things in a better way and even you know, navigate around these plots. And let's look at uh okay this one here.

A

So this is the survival rate uh per class and also per gender, and, as you can see, for example, uh the survival rate for men was much lower than the survival rate for women even across all classes. But if you're in the first class- and you are female- your chances of survival were the highest right. uh Let me go back now to my notebook, so we do we we converge, maybe in female, into binary variables, so zero and one we compute.

A

A few correlations so, for example, we see various correlations with the survival rate, and you can see that the gender here is one of the best ones right and, as you can see, we just saw on that plot. That was really the case. uh Ginger is a very good predictor of of whether somebody survived or not in the titanic, and we add a little bit more data here.

A

For example, maybe if you have relatives how, if you have relatives, does that increase or decrease your survival rate, but as it turns out, it's not a very good predictor. If you were, if you were with your relatives on the on the titanic, the correlation value is rather low, so we're just going to keep whatever the best attributes and we're going to create a model again, the particular details of this model.

A

It doesn't matter this if you know we're talking about here, it's naive base and I'm gonna start the model uh split the data into test entering data uh and, at the very end, I'm gonna use the test data to see if this model is accurate or not.

A

So if it was a simple model like knife base, is a it's a pretty basic algorithm, but even then you can see that it has already a 74.6, approximately 75 accuracy uh rate, which is not bad at all. uh Given given how basic this model is, it's actually not a bad predictor for for survival, um but what, if you can, can we do better nah?

A

That's that's really the question here, for example, I added this tensorflow, stuff and and and keras- and let's make good use of this so here we're going to create a second model, and this time it's going to be a layered neural network, and let me start it it's going to take a few seconds uh and after that, as I said, we're gonna create three layers in this model: we're gonna train the model and then we're gonna check.

A

If this is a good predictor or not, and how does it compare with the previous one, this is taking a little bit longer. Okay, cool so create the.

B

A

It should be quick, summarize it so if you're familiar with this, you can actually check the details of your model. Does it you know that? Do you think it would be a good model for this, for the data set or not we're going to fit the model?

A

So it's going to take a little while, okay, excellent?

A

Is it done perfect? This was pretty quick and now the question is: is a neural network based model better or worse, in knife base, so.

A

It is better, I mean it's not a lot better. This is a significantly more complex model than the face, but even then the accuracy jumped to 81 right. So it's great and it's not it's not fantastic, but it's already better and with that said, I hope you get the message here that really all I've done was writing this python notebook this uh the jupyter notebook here and everything was in there for me.

A

If you would be if you're data scientist and you jump into this project, you can actually write your own draw notebook and pretty much all of the tools you need are in there and if they're, not, you can come to the environment file, install your additional dependencies and you're good to go.

A

I think with that said, that was a lot of information and uh yeah. I will open for questions if you have any.

B

So diego you, you showed examples there of a single container. What if we have a more complex uh setup, so maybe like a database container, maybe a caching layer or something like that in their application. How would we, how would we solve that in code, spaces.

A

So, in that case, uh you can specify service containers uh with with a database, for example, and uh peter. I hope you don't mind if I show you the uh the octodemo quickly, at least.

B

On the high level.

A

That's fine, okay, so this is something we have been working on as a team, and this is basically uh it's a bookstore application. But what the whole point of this is to show uh that you can have pretty complex uh development environments. So, for example, in this case, I'm not only using docker, I'm actually using docker compose, because there are a bunch of containers that we're gonna spawn inside of our code space, and in this case, for example, let's see what we have we have postgres.

A

uh We do even some binding of of okay. This is not what what really matters here, but, for example, you can set up volumes uh and you have a progress, uh postgres darker container running in in the background, and this is going to be accessible from within the code space. So actually, for example, if you have an application that has a radis uh cache and a postgres database, and you need to when you, for example, you know system, we would have to install these things and and and activate them.

A

So you can actually test your application. How does it actually run with the code space? You can have this this, this services, as docker containers and they're, going to be part of your entire development environment. They can be started automatically within your code space right.

A

I hope that answers your question. Peter.

B

It does thank you.

A

And just for curiosity, uh inside of the dev container.json, we're actually specifying docker compose.yaml here and we even do. This is a significantly more complex project. So there's a lot of java plugins that we're installing inside of this container. We are also doing some port forwarding so also very important if you're developing something that is a web application inside of a code space, you can do use port forwarding and open a port for your web application, which is running local in your codespace within the browser.

A

So you can actually have this exposed to the external world. That's also possible.

A

Are there more questions.

B

There's comments more questions at the moment. I'm just waiting to see if anybody raises some more.

B

Diego might it be possible that you would actually show us what what the portfolio actually looks like on a on a container.

A

uh Quickly speaking, let me see, do you have, do you think they have a good example? On top of your mind, what would this one be a good one.

B

Yeah, let's spin that container up okay. But while we wait for some other questions to come in, okay, just just to show what external access looks like.

A

Let's do it, then. This is going to take a couple of seconds to initialize, though, and in the meantime I unfortunately forgot to start sharing the screen at the beginning. I just wanted to show you quickly the the github flow, which is what I was talking about, because this is such a nice image and, as I said, it captures the the phases of software development so nicely. So that's what I meant by adding commits uh opening a pull request, uh the phase in which you're collaborating with others deployment and merge.

A

So throughout this we have a great deal of automation and, as I said before, code space really is an answer to this step here, which is very often overlooked, because we very often uh uh assume that developing something can start doing it right away. But there's a there's a lot of costs involved here in getting people to set up their development environments and often having to do a lot of a lot of work that has little value to the project right.

A

It would be much better if you could get people started on the project right away and and have developers doing what they like most, which is writing code without having to fight against a development environment installing dependencies and trying to figure out. Why does it work on your laptop, but not on my laptop, because you have by accident, I don't know node.js versus 11, but I have 12 and so on, and I'm sure this speaks to the heart of every developer out there who has been through this right.

A

So, let's see oh code, space is almost done. This was nice.

A

So this actually loaded up quite fast because peter the docker image he has already hosted it on on uh on the github github container registry, which means the entire loading process happens a lot faster right. If you build the docker image from scratch, it takes a lot longer, but nothing stops you from getting that image and push into, for example, a container registry and use that inside of your code space. So let me quickly show that uh no, not here, okay, so in this case you can see.

A

For example, there is a container which is stored on github on our container registry and, as I said, this was this was created by by uh by us, uh and it's already, it already contains pretty much everything that we need to fire up this container uh this code space and this this speeds up things significantly so.

A

So better remind me on top of my head: how do I start the.

B

Just just there forever to start the f5.

A

Okay, so let's start.

B

A

Server, okay, almost done and port forwarding was.

A

The uh I hear no.

B

The the one with the number two one.

A

Exactly perfect, uh okay! This is not something I do typically by the way this, but this is a pretty nice example. Let me open this externally.

A

Okay, cool. That is not what I was expecting, though, did I open the wrong one.

A

Not this one, this one.

A

Okay, so, as you can see, we've we started a web server locally within the code space and we did port forwarding here. So you can access this this web server from uh from from an external device and with port forwarding, you even get a link that when you click, we have this bookstore application, and this this is the magic here right. If I make changes on the code and I recompile the bookstore, you will see the changes reflected in here, so you can actually use this for development.

A

Even if you have various services versus containers that you need to have at the same time, you can still put them all inside of a code. Space have them automatically started for you and do your development, so really it's a very, very flexible, very powerful development environment for applications of various types.

A

This has nothing to do with python anymore, though I mean this is a java application, but I guess this was a very good question. It's it's always a a nice thing to highlight that you can also use uh port forwarding for accessing services externally.

A

Are there more questions.

B

The chat is quiet at the moment.

A

I'll take I'll take that, as everyone is quite.

A

B

There's a question here about live share.

A

Live share. We can do that. uh Do you want to try peter.

B

We could give it a go.

A

That's right, we have time uh live share so.

A

A

Trying to get to that.

A

A

B

Live shows the second to bottom icon, underneath the docker icon on the left, hand, side and the toolbar. If you want to click on that.

A

This here ah perfect, that's much easier, awesome so start collaboration session and okay. So I will copy this link and send it to peter.

A

Let me quickly stop sharing my screen just for a second I'm going to check up this link to peter.

A

And now we'll go back to screen sharing.

A

Okay, peter, can you try that I'm.

B

A

Okay, cool so live sharing. Is this feature from from codespace? Where you can? You can really work collaboratively with people in real time and I've used it many times, uh for example, peter- and I very often work together on creating docker files and and and and working on, developing projects together and, uh for example, peter was teaching me a few things about docker, uh which I didn't know at the time, and this was really great because we were working together.

A

You could see me writing code in real time and you could also make changes in the code in real time uh so as a not only as a as a collaboration tool, but also as a learning tool. It's really fantastic. So I will open some file here. Maybe you can jump in on the dev container.json, so people can see that you are yeah, so you can already see peter murray in there and if you make changes we you know we're we're really live together on the same file.

A

uh One question which you may also be interested in is uh how long lived is this code space? So if you don't do anything in the code space for a very long time, like I mean very long, it's probably 30 minutes to an hour. I remember on top of my head.

A

It will be automatically disconnected and it will be turned off and when you, when you want, you can actually reignite this code space and you will go back to the same session where you were, but not only that you on top of that, you can also by the way, if you don't like work inside of the browser, you can use visual studio code locally and connect to a code space.

A

So let me see if I can get this done now, uh so I have this plugin here, which is called uh what is the name again a remote explorer. So this is already connected to my github account and in case you're wondering this is a pretty easy thing to set up. uh You install the the extension and then you you say I wanna authenticate with github vs code is going to automatically uh recognize which code spaces you have running on github and you can see them here.

A

I want to just check what is the name of it so for vx6 this one here and I can go and connect.

A

So this is also a good alternative for you, because here the only thing you have to install locally is visual studio code, but everything else, the development environment, docker. You don't have to set up any of that. This is all running, as I said, on a virtual machine which is managed by github, but now you can already see all the files. Let me see if I see devcontainer.json okay, there we go. uh Let me just make a change here: let's see how it reflects there, so you can see real time.

A

Of course, I wouldn't be writing code from two sources on the same device at the same time, but uh if you, for example, are working on the project and- uh and uh you know you want to move to another location- uh you want to work on a second device, you can also go to the same code. Space and continue from there. Everything is is, is live on a virtual machine, so you you, don't have to actually close this save and reload it somewhere else.

A

It's basically open and you are at the same spot where you were when you started or when you left.

B

If you, if you want to jump back to the live, show session um on line 34, I've added a comment there, you can see the dot and the.

A

Cool okay, pretty awesome.

B

Yeah, so the power of life share is actually quite quite impressive. It allows you to have those collaborative environments. You know compared programming type aspects. The ability to you know discuss your code before you even push it into into a commit and ends up on your git reaper. So it's really a nice way to tap out and now get some extra extra help and support.

A

And again, if you want to start with code spaces- and you and you don't know where would you start you're? Always it's always a good idea to come to this ripple and and go to the examples which already provided uh by microsoft. So I think it was containers yeah, so there's so much good stuff in here, so you don't have to start from scratch and and then bang your head against the wall until until things work, uh you know bootstrap your code space from something that is already done for you and uh customize from there.

A

That's gonna save you a lot of time and I think in most cases, you're very likely going to be lucky enough that the code space, which is provided by microsoft, you may already have everything you need, so you won't even have to add new things, but if you do again it's much easier to start from something already pre-made for you than than from z from scratch. Thank you very much for coming. uh This was this was a lot of fun.

A

uh If you have questions about this, uh if you're interested in the topic, you can always send me an email. My my email is diasensi, which is my github handle at github.com.

A

So if you'd like to you, have a question after this demo uh that just came to mind after we're done, and you still want to have you know you want to learn something about this you're welcome to send me an email I'll do my best to reply as quickly as possible. Other than that, I wish you a lot of fun with this feature. It's currently better we're about to make a ga and yeah have fun. Thank you for.

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