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PowerIK For Unreal Engine Hands-On

Today we look at PowerIK for Unreal Engine, a full body IK solver. PowerIK was recently released as part of the December monthly UE giveaways as part of the free forever category. On the Unreal Engine marketplace, PowerIK was described as:

Power IK is a full-body IK solver that lets animators push and pull any skeleton with any number of effectors.

Use Power IK to easily align creatures to uneven terrain, or dynamically modify their pose at run-time. Power IK is a robust and efficient solver that produces remarkably natural poses even under extreme circumstances.

PowerIK has the following features:

  • Unique proprietary full-body IK solver
  • Power IK Solver AnimGraph node
  • Built-in ground alignment
  • Power IK Rig Actor Component for making interactive rigs
  • Bonus! Procedural animation example blueprints
  • Bonus! 6 sample skeletal meshes with fully documented blueprints

While it supports Unreal Engine 4.26, the current install will give you an error when you try to run PowerIK. If this occurs, on Windows the fix is fairly simple. Navigate to your install directory for UE 4.26, then navigate to:

\Engine\Plugins\Marketplace\PowerIK\Source\PowerIKRuntime\sdk\lib\Win64

Copy the file POWERIK.DLL. Next paste it to the directory

\Engine\Plugins\Marketplace\PowerIK\Binaries\Win64

Now it should work just fine. In the video below we go hands-on with PowerIK using the example project currently available for download here. If you run into some trouble, the documentation is available here.

[youtube https://www.youtube.com/watch?v=VXL2k2SbBms?feature=oembed&w=1500&h=844]
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Choose between Btrfs and LVM-ext4

Fedora 33 introduced a new default filesystem in desktop variants, Btrfs. After years of Fedora using ext4 on top of Logical Volume Manager (LVM) volumes, this is a big shift. Changing the default file system requires compelling reasons. While Btrfs is an exciting next-generation file system, ext4 on LVM is well established and stable. This guide aims to explore the high-level features of each and make it easier to choose between Btrfs and LVM-ext4.

In summary

The simplest advice is to stick with the defaults. A fresh Fedora 33 install defaults to Btrfs and upgrading a previous Fedora release continues to use whatever was initially installed, typically LVM-ext4. For an existing Fedora user, the cleanest way to get Btrfs is with a fresh install. However, a fresh install is much more disruptive than a simple upgrade. Unless there is a specific need, this disruption could be unnecessary. The Fedora development team carefully considered both defaults, so be confident with either choice.

What about all the other file systems?

There are a large number of file systems for Linux systems. The number explodes after adding in combinations of volume managers, encryption methods, and storage mechanisms . So why focus on Btrfs and LVM-ext4? For the Fedora audience these two setups are likely to be the most common. Ext4 on top of LVM became the default disk layout in Fedora 11, and ext3 on top of LVM came before that.

Now that Btrfs is the default for Fedora 33, the vast majority of existing users will be looking at whether they should stay where they are or make the jump forward. Faced with a fresh Fedora 33 install, experienced Linux users may wonder whether to use this new file system or fall back to what they are familiar with. So out of the wide field of possible storage options, many Fedora users will wonder how to choose between Btrfs and LVM-ext4.

Commonalities

Despite core differences between the two setups, Btrfs and LVM-ext4 actually have a lot in common. Both are mature and well-tested storage technologies. LVM has been in continuous use since the early days of Fedora Core and ext4 became the default in 2009 with Fedora 11. Btrfs merged into the mainline Linux kernel in 2009 and Facebook uses it widely. SUSE Linux Enterprise 12 made it the default in 2014. So there is plenty of production run time there as well.

Both systems do a great job preventing file system corruption due to unexpected power outages, even though the way they accomplish it is different. Supported configurations include single drive setups as well as spanning multiple devices, and both are capable of creating nearly instant snapshots. A variety of tools exist to help manage either system, both with the command line and graphical interfaces. Either solution works equally well on home desktops and on high-end servers.

Advantages of LVM-ext4

Show the relationship of LVM-ext4 filesystem to hard-drive partitions and mounted directories.
Structure of ext4 on LVM

The ext4 file system focuses on high-performance and scalability, without a lot of extra frills. It is effective at preventing fragmentation over extended periods of time and provides nice tools for when it does happen. Ext4 is rock solid because it built on the previous ext3 file system, bringing with it all the years of in-system testing and bug fixes.

Most of the advanced capabilities in the LVM-ext4 setup come from LVM itself. LVM sits “below” the file system, which means it supports any file system. Logical volumes (LV) are generic block devices so virtual machines can use them directly. This flexibility allows each logical volume to use the right file system, with the right options, for a variety of situations. This layered approach also honors the Unix philosophy of small tools working together.

The volume group (VG) abstraction from the hardware allows LVM to create flexible logical volumes. Each LV pulls from the same storage pool but has its own configuration. Resizing volumes is a lot easier than resizing physical partitions as there are no limitation of ordered placement of the data. LVM physical volumes (PV) can be any number of partitions and can even move between devices while the system is running.

LVM supports read-only and read-write snapshots, which make it easy to create consistent backups from active systems. Each snapshot has a defined size, and a change to the source or snapshot volume use space from there. Alternately, logical volumes can also be part of a thinly provisioned pool. This allows snapshots to automatically use data from a pool instead of consuming fixed sized chunks defined at volume creation.

Multiple devices with LVM

LVM really shines when there are multiple devices. It has native support for most RAID levels and each logical volume can have a different RAID level. LVM will automatically choose appropriate physical devices for the RAID configuration or the user can specify it directly. Basic RAID support includes data striping for performance (RAID0) and mirroring for redundancy (RAID1). Logical volumes can also use advanced setups like RAID5, RAID6, and RAID10. LVM RAID support is mature because under the hood LVM uses the same device-mapper (dm) and multiple-device (md) kernel support used by mdadm.

Logical volumes can also be cached volumes for systems with both fast and slow drives. A classic example is a combination of SSD and spinning-disk drives. Cached volumes use faster drives for more frequently accessed data (or as a write cache), and the slower drive for bulk data.

The large number of stable features in LVM and the reliable performance of ext4 are a testament to how long they have been in use. Of course, with more features comes complexity. It can be challenging to find the right options for the right feature when configuring LVM. For single drive desktop systems, features of LVM like RAID and cache volumes don’t apply. However, logical volumes are more flexible than physical partitions and snapshots are useful. For normal desktop use, the complexity of LVM can also be a barrier to recovering from issues a typical user might encounter.

Advantages of Btrfs

Show the relationship of Btrfs filesystem to hard-drive partitions and mounted directories.
Btrfs Structure

Lessons learned from previous generations guided the features built into Btrfs. Unlike ext4, it can directly span multiple devices, so it brings along features typically found only in volume managers. It also has features that are unique in the Linux file system space (ZFS has a similar feature set, but don’t expect it in the Linux kernel).

Key Btrfs features

Perhaps the most important feature is the checksumming of all data. Checksumming, along with copy-on-write, provides the key method of ensuring file system integrity after unexpected power loss. More uniquely, checksumming can detect errors in the data itself. Silent data corruption, sometimes referred to as bitrot, is more common that most people realize. Without active validation, corruption can end up propagating to all available backups. This leaves the user with no valid copies. By transparently checksumming all data, Btrfs is able to immediately detect any such corruption. Enabling the right dup or raid option allows the file system to transparently fix the corruption as well.

Copy-on-write (COW) is also a fundamental feature of Btrfs, as it is critical in providing file system integrity and instant subvolume snapshots. Snapshots automatically share underlying data when created from common subvolumes. Additionally, after-the-fact deduplication uses the same technology to eliminate identical data blocks. Individual files can use COW features by calling cp with the reflink option. Reflink copies are especially useful for copying large files, such as virtual machine images, that tend to have mostly identical data over time.

Btrfs supports spanning multiple devices with no volume manager required. Multiple device support unlocks data mirroring for redundancy and striping for performance. There is also experimental support for more advanced RAID levels, such as RAID5 and RAID6. Unlike standard RAID setups, the Btrfs raid1 option actually allows an odd number of devices. For example, it can use 3 devices, even if they are are different sizes.

All RAID and dup options are specified at the file system level. As a consequence, individual subvolumes cannot use different options. Note that using the RAID1 option with multiple devices means that all data in the volume is available even if one device fails and the checksum feature maintains the integrity of the data itself. That is beyond what current typical RAID setups can provide.

Additional features

Btrfs also enables quick and easy remote backups. Subvolume snapshots can be sent to a remote system for storage. By leveraging the inherent COW meta-data in the file system, these transfers are efficient by only sending incremental changes from previously sent snapshots. User applications such as snapper make it easy to manage these snapshots.

Additionally, a Btrfs volume can have transparent compression and chattr +c will mark individual files or directories for compression. Not only does compression reduce the space consumed by data, but it helps extend the life of SSDs by reducing the volume of write operations. Compression certainly introduces additional CPU overhead, but a lot of options are available to dial in the right trade-offs.

The integration of file system and volume manager functions by Btrfs means that overall maintenance is simpler than LVM-ext4. Certainly this integration comes with less flexibility, but for most desktop, and even server, setups it is more than sufficient.

Btrfs on LVM

Btrfs can convert an ext3/ext4 file system in place. In-place conversion means no data to copy out and then back in. The data blocks themselves are not even modified. As a result, one option for an existing LVM-ext4 systems is to leave LVM in place and simply convert ext4 over to Btrfs. While doable and supported, there are reasons why this isn’t the best option.

Some of the appeal of Btrfs is the easier management that comes with a file system integrated with a volume manager. By running on top of LVM, there is still some other volume manager in play for any system maintenance. Also, LVM setups typically have multiple fixed sized logical volumes with independent file systems. While Btrfs supports multiple volumes in a given computer, many of the nice features expect a single volume with multiple subvolumes. The user is still stuck manually managing fixed sized LVM volumes if each one has an independent Btrfs volume. Though, the ability to shrink mounted Btrfs filesystems does make working with fixed sized volumes less painful. With online shrink there is no need to boot a live image.

The physical locations of logical volumes must be carefully considered when using the multiple device support of Btrfs. To Btrfs, each LV is a separate physical device and if that is not actually the case, then certain data availability features might make the wrong decision. For example, using raid1 for data typically provides protection if a single drive fails. If the actual logical volumes are on the same physical device, then there is no redundancy.

If there is a strong need for some particular LVM feature, such as raw block devices or cached logical volumes, then running Btrfs on top of LVM makes sense. In this configuration, Btrfs still provides most of its advantages such as checksumming and easy sending of incremental snapshots. While LVM has some operational overhead when used, it is no more so with Btrfs than with any other file system.

Wrap up

When trying to choose between Btrfs and LVM-ext4 there is no single right answer. Each user has unique requirements, and the same user may have different systems with different needs. Take a look at the feature set of each configuration, and decide if there is something compelling about one over the other. If not, there is nothing wrong with sticking with the defaults. There are excellent reasons to choose either setup.

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Easy Anime Character Creation with VRoid Studio and Blender

Creating anime characters for game development has never been easier with tools like VRoid Studio and Blender. In this tutorial we showcase using VRoid Studio, a free tool for creating textured and animated anime avatars. If VRoid Studio sounds familiar, we featured this tool as recently as 2019.

In the video below we walk through the following processes:

  • Using VRoid Studio
  • Exporting VRM files
  • Importing VRM into Blender
  • Creating a simple animation
  • Exporting from Blender in GLB/GLTF format
  • Importing GLB formats into the Godot game engine
  • Exporting VRoid characters to Mixamo for animting

In addition to VRoid Studio you need the VRM importer for Blender. If you are using the Unity game engine, there is a Unity importer for VRM files available as well, although we wont be covering it in the video below.

One area of importance with any tool, especially free tools, are what the license terms are. You can see the list of appropriate uses here, which specifically includes “Selling video games and other products featuring characters created with VRoid Studio”. Once you have all the appropriate tools, check out the video below for step by step instruction son how to create an animated anime character for use in Godot using VRoid Studio and Blender.

[youtube https://www.youtube.com/watch?v=dAW6ovhENs8?feature=oembed&w=1500&h=844]
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Contribute at the Fedora Test Week for Kernel 5.10

The kernel team is working on final integration for kernel 5.10. This version was just recently released, and will arrive soon in Fedora. As a result, the Fedora kernel and QA teams have organized a test week from Monday, January 04, 2021 through Monday, January 11, 2021. Refer to the wiki page for links to the test images you’ll need to participate. Read below for details.

How does a test week work?

A test week is an event where anyone can help make sure changes in Fedora work well in an upcoming release. Fedora community members often participate, and the public is welcome at these events. If you’ve never contributed before, this is a perfect way to get started.

To contribute, you only need to be able to do the following things:

  • Download test materials, which include some large files
  • Read and follow directions step by step

The wiki page for the kernel test day has a lot of good information on what and how to test. After you’ve done some testing, you can log your results in the test day web application. If you’re available on or around the day of the event, please do some testing and report your results. We have a document which provides all the steps written.

Happy testing, and we hope to see you on test day.

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Raylib 3.5 Released

Eight months after the release of Raylib 3.0, Raylib 3.5 was just released. Raylib is an open source cross platform C/C++ game framework. Raylib runs on a ton of different platforms and has bindings available for more than 50 different programming languages. The Raylib 3.5 release brings the following new features.

  • NEW Platform supported: Raspberry Pi 4 native mode (no X11 windows) through DRM subsystem and GBM API. Actually this is a really interesting improvement because it opens the door to raylib to support other embedded platforms (Odroid, GameShell, NanoPi…). Also worth mentioning the un-official homebrew ports of raylib for PS4 and PSVita.
  • NEW configuration options exposed: For custom raylib builds, config.h now exposes more than 150 flags and defines to build raylib with only the desired features, for example, it allows to build a minimal raylib library in just some KB removing all external data filetypes supported, very useful to generate small executables or embedded devices.
  • NEW automatic GIF recording feature: Actually, automatic GIF recording (CTRL+F12) for any raylib application has been available for some versions but this feature was really slow and low-performant using an old gif library with many file-accesses. It has been replaced by a high-performant alternative (msf_gif.h) that operates directly on memory… and actually works very well! Try it out!
  • NEW RenderBatch system: rlgl module has been redesigned to support custom render batches to allow grouping draw calls as desired, previous implementation just had one default render batch. This feature has not been exposed to raylib API yet but it can be used by advance users dealing with rlgl directly. For example, multiple RenderBatch can be created for 2D sprites and 3D geometry independently.
  • NEW Framebuffer system: rlgl module now exposes an API for custom Framebuffer attachments (including cubemaps!). raylib RenderTexture is a basic use-case, just allowing color and depth textures, but this new API allows the creation of more advance Framebuffers with multiple attachments, like the G-BuffersGenTexture*() functions have been redesigned to use this new API.
  • Improved software rendering: raylib Image*() API is intended for software rendering, for those cases when no GPU or no Window is available. Those functions operate directly with multi-format pixel data on RAM and they have been completely redesigned to be way faster, specially for small resolutions and retro-gaming. Low-end embedded devices like microcontrollers with custom displays could benefit of this raylib functionality!
  • File loading from memory: Multiple functions have been redesigned to load data from memory buffers instead of directly accessing the files, now all raylib file loading/saving goes through a couple of functions that load data into memory. This feature allows custom virtual-file-systems and it gives more control to the user to access data already loaded in memory (i.e. images, fonts, sounds…).
  • NEW Window states management system: raylib core module has been redesigned to support Window state check and setup more easily and also before/after Window initializationSetConfigFlags() has been reviewed and SetWindowState() has been added to control Window minification, maximization, hidding, focusing, topmost and more.
  • NEW GitHub Actions CI/CD system: Previous CI implementation has been reviewed and improved a lot to support multiple build configurations (platforms, compilers, static/shared build) and also an automatic deploy system has been implemented to automatically attach the diferent generated artifacts to every new release. As the system seems to work very good, previous CI platforms (AppVeyor/TravisCI) have been removed.

Release notes are available here and a complete change log is available here. Binary versions of Raylib are available on Raylib.com while the source code is hosted under the ZLib license on GitHub. If you are interested in learning Raylib you can check out their community on Discord. You can also download Raylib via vcpkg on Visual Studio with step by step instructions available here. You can learn more about Raylib and the 3.5 release in the video below.

[youtube https://www.youtube.com/watch?v=RZJ-Z–6uxY?feature=oembed&w=1500&h=844]
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Waterways & Terrain Add-Ons For Godot

Today we are going hands on with two powerful Godot plugins, Waterways and Heightmap Terrain for Godot. Both are open source add-ons that work in Godot 3.2.x and both are hosted on GitHub. In the video below we showcase using easy add-on and show how they work well together.

Waterways

Formally known as WaterGenGodot on GitHub, Waterways enables you to quickly create rivers using spline controls. You have full control over the path the river follows, the look of the water and even have fine tuned control over the foam generated by collisions with other objects in the scene.

Heightmap Terrain

This add-on adds terrain creation tools to Godot. Either import and existing heightmap or create your own from scratch. You get full sculpting tools for raising and lower terrain, simulating erosion, etc. You also get tools for painting the texture layer on your newly created terrain. You also get the ability to export as a mesh or heightmap for use in other applications or engines.

Getting Started Tutorial

Installing the plugins is a straight forward exercise. Clone each project from GitHub to a directory of choice. You can get the git url on GitHub here:

Get GitHub URL for Godot Addon

Assuming you have a git client installed, from a command line run the command git clone then the copied url. For example:

git clone https://github.com/Arnklit/WaterGenGodot.git

Now in your Godot project (or create one if you dont have one already), create a folder called addons then copy the addons directory from the two just cloned projects. In your project you now simply need to enable each addon. In Godot go to Project->Project Settings menu. Now switch to the Plugins tab and make sure both are enabled:

Enabling Godot Waterways & Heightmap Terrain Plugins

Now you’re ready to go! Be sure to check the video below to see both Water Ways & Heightmap Terrain for Godot add-ons in action.

[youtube https://www.youtube.com/watch?v=jYVO0-_sXZs?feature=oembed&w=1500&h=844]
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Deploy Fedora CoreOS servers with Terraform

Fedora CoreOS is a lightweight, secure operating system optimized for running containerized workloads. A YAML document is all you need to describe the workload you’d like to run on a Fedora CoreOS server.

This is wonderful for a single server, but how would you describe a fleet of cooperating Fedora CoreOS servers? For example, what if you wanted a set of servers running load balancers, others running a database cluster and others running a web application? How can you get them all configured and provisioned? How can you configure them to communicate with each other? This article looks at how Terraform solves this problem.

Getting started

Before you start, decide whether you need to review the basics of Fedora CoreOS. Check out this previous article on the Fedora Magazine:

Terraform is an open source tool for defining and provisioning infrastructure. Terraform defines infrastructure as code in files. It provisions infrastructure by calculating the difference between the desired state in code and observed state and applying changes to remove the difference.

HashiCorp, the company that created and maintains Terraform, offers an RPM repository to install Terraform.

sudo dnf config-manager --add-repo \ https://rpm.releases.hashicorp.com/fedora/hashicorp.repo
sudo dnf install terraform

To get yourself familiar with the tools, start with a simple example. You’re going to create a single Fedora CoreOS server in AWS. To follow along, you need to install awscli and have an AWS account. awscli can be installed from the Fedora repositories and configured using the aws configure command

sudo dnf install -y awscli
aws configure

Please note, AWS is a paid service. If executed correctly, participants should expect less than $1 USD in charges, but mistakes may lead to unexpected charges.

Configuring Terraform

In a new directory, create a file named config.yaml. This file will hold the contents of your Fedore CoreOS configuration. The configuration simply adds an SSH key for the core user. Modify the authorized_ssh_key section to use your own.

variant: fcos
version: 1.2.0
passwd: users: - name: core authorized_ssh_keys: - "ssh-ed25519 AAAAC3....... user@hostname"

Next, create a file main.tf to contain your Terraform specification. Take a look at the contents section by section. It begins with a block to specify the versions of your providers.

terraform { required_providers { ct = { source = "poseidon/ct" version = "0.7.1" } aws = { source = "hashicorp/aws" version = "~> 3.0" } }
}

Terraform uses providers to control infrastructure. Here it uses the AWS provider to provision EC2 servers, but it can provision any kind of AWS infrastructure. The ct provider from Poseidon Labs stands for config transpiler. This provider will transpile Fedora CoreOS configurations into Ignition configurations. As a result, you do not need to use fcct to transpile your configurations. Now that your provider versions are specified, initialize them.

provider "aws" { region = "us-west-2"
} provider "ct" {}

The AWS region is set to us-west-2 and the ct provider requires no configuration. With the providers configured, you’re ready to define some infrastructure. Use a data source block to read the configuration.

data "ct_config" "config" { content = file("config.yaml") strict = true
}

With this data block defined, you can now access the transpiled Ignition output as data.ct_config.config.rendered. To create an EC2 server, use a resource block, and pass the Ignition output as the user_data attribute.

resource "aws_instance" "server" { ami = "ami-0699a4456969d8650" instance_type = "t3.micro" user_data = data.ct_config.config.rendered
}

This configuration hard-codes the virtual machine image (AMI) to the latest stable image of Fedora CoreOS in the us-west-2 region at time of writing. If you would like to use a different region or stream, you can discover the correct AMI on the Fedora CoreOS downloads page.

Finally, you’d like to know the public IP address of the server once it’s created. Use an output block to define the outputs to be displayed once Terraform completes its provisioning.

output "instance_ip_addr" { value = aws_instance.server.public_ip
}

Alright! You’re ready to create some infrastructure. To deploy the server simply run:

terraform init # Installs the provider dependencies
terraform apply # Displays the proposed changes and applies them

Once completed, Terraform prints the public IP address of the server, and you can SSH to the server by running ssh core@{public ip here}. Congratulations — you’ve provisioned your first Fedora CoreOS server using Terraform!

Updates and immutability

At this point you can modify the configuration in config.yaml however you like. To deploy your change simply run terraform apply again. Notice that each time you change the configuration, when you run terraform apply it destroys the server and creates a new one. This aligns well with the Fedora CoreOS philosophy: Configuration can only happen once. Want to change that configuration? Create a new server. This can feel pretty alien if you’re accustomed to provisioning your servers once and continuously re-configuring them with tools like Ansible, Puppet or Chef.

The benefit of always creating new servers is that it is significantly easier to test that newly provisioned servers will act as expected. It can be much more difficult to account for all of the possible ways in which updating a system in place may break. Tooling that adheres to this philosophy typically falls under the heading of Immutable Infrastructure. This approach to infrastructure has some of the same benefits seen in functional programming techniques, namely that mutable state is often a source of error.

Using variables

You can use Terraform input variables to parameterize your infrastructure. In the previous example, you might like to parameterize the AWS region or instance type. This would let you deploy several instances of the same configuration with differing parameters. What if you want to parameterize the Fedora CoreOS configuration? Do so using the templatefile function.

As an example, try parameterizing the username of your user. To do this, add a username variable to the main.tf file:

variable "username" { type = string description = "Fedora CoreOS user" default = "core"
}

Next, modify the config.yaml file to turn it into a template. When rendered, the ${username} will be replaced.

variant: fcos
version: 1.2.0
passwd: users: - name: ${username} authorized_ssh_keys: - "ssh-ed25519 AAAAC3....... user@hostname"

Finally, modify the data block to render the template using the templatefile function.

data "ct_config" "config" { content = templatefile( "config.yaml", { username = var.username } ) strict = true
}

To deploy with username set to jane, run terraform apply -var=”username=jane”. To verify, try to SSH into the server with ssh jane@{public ip address}.

Leveraging the dependency graph

Passing variables from Terraform into Fedora CoreOS configuration is quite useful. But you can go one step further and pass infrastructure data into the server configuration. This is where Terraform and Fedora CoreOS start to really shine.

Terraform creates a dependency graph to model the state of infrastructure and to plan updates. If the output of one resource (e.g the public IP address of a server) is passed as the input of another service (e.g the destination in a firewall rule), Terraform understands that changes in the former require recreating or modifying the later. If you pass infrastructure data into a Fedora CoreOS configuration, it will participate in the dependency graph. Updates to the inputs will trigger creation of a new server with the new configuration.

Consider a system of one load balancer and three web servers as an example.

The goal is to configure the load balancer with the IP address of each web server so that it can forward traffic to them.

Web server configuration

First, create a file web.yaml and add a simple Nginx configuration with a templated message.

variant: fcos
version: 1.2.0
systemd: units: - name: nginx.service enabled: true contents: | [Unit] Description=Nginx Web Server After=network-online.target Wants=network-online.target [Service] ExecStartPre=-/bin/podman kill nginx ExecStartPre=-/bin/podman rm nginx ExecStartPre=/bin/podman pull nginx ExecStart=/bin/podman run --name nginx -p 80:80 -v /etc/nginx/index.html:/usr/share/nginx/html/index.html:z nginx [Install] WantedBy=multi-user.target
storage: directories: - path: /etc/nginx files: - path: /etc/nginx/index.html mode: 0444 contents: inline: | <html> <h1>Hello from Server ${count}</h1> </html>

In main.tf, you can create three web servers using this template with the following blocks:

data "ct_config" "web" { count = 3 content = templatefile( "web.yaml", { count = count.index } ) strict = true
} resource "aws_instance" "web" { count = 3 ami = "ami-0699a4456969d8650" instance_type = "t3.micro" user_data = data.ct_config.web[count.index].rendered
}

Notice the use of count = 3 and the count.index variable. You can use count to make many copies of a resource. Here, it creates three configurations and three web servers. The count.index variable is used to pass the first configuration to the first web server and so on.

Load balancer configuration

The load balancer will be a basic HAProxy load balancer that forwards to each server. Place the configuration in a file named lb.yaml:

variant: fcos
version: 1.2.0
systemd: units: - name: haproxy.service enabled: true contents: | [Unit] Description=Haproxy Load Balancer After=network-online.target Wants=network-online.target [Service] ExecStartPre=-/bin/podman kill haproxy ExecStartPre=-/bin/podman rm haproxy ExecStartPre=/bin/podman pull haproxy ExecStart=/bin/podman run --name haproxy -p 80:8080 -v /etc/haproxy/haproxy.cfg:/usr/local/etc/haproxy/haproxy.cfg:ro haproxy [Install] WantedBy=multi-user.target
storage: directories: - path: /etc/haproxy files: - path: /etc/haproxy/haproxy.cfg mode: 0444 contents: inline: | global log stdout format raw local0 defaults mode tcp log global option tcplog frontend http bind *:8080 default_backend http backend http balance roundrobin
%{ for name, addr in servers ~} server ${name} ${addr}:80 check
%{ endfor ~}

The template expects a map with server names as keys and IP addresses as values. You can create that using the zipmap function. Use the ID of the web servers as keys and the public IP addresses as values.

data "ct_config" "lb" { content = templatefile( "lb.yaml", { servers = zipmap( aws_instance.web.*.id, aws_instance.web.*.public_ip ) } ) strict = true
} resource "aws_instance" "lb" { ami = "ami-0699a4456969d8650" instance_type = "t3.micro user_data = data.ct_config.lb.rendered
}

Finally, add an output block to display the IP address of the load balancer.

output "load_balancer_ip" { value = aws_instance.lb.public_ip
}

All right! Run terraform apply and the IP address of the load balancer displays on completion. You should be able to make requests to the load balancer and get responses from each web server.

$ export LB={{load balancer IP here}}
$ curl $LB
<html> <h1>Hello from Server 0</h1>
</html>
$ curl $LB
<html> <h1>Hello from Server 1</h1>
</html>
$ curl $LB
<html> <h1>Hello from Server 2</h1>
</html>

Now you can modify the configuration of the web servers or load balancer. Any changes can be realized by running terraform apply once again. Note in particular that any change to the web server IP addresses will cause Terraform to recreate the load balancer (changing the count from 3 to 4 is a simple test). Hopefully this emphasizes that the load balancer configuration is indeed a part of the Terraform dependency graph.

Clean up

You can destroy all the infrastructure using the terraform destroy command. Simply navigate to the folder where you created main.tf and run terraform destroy.

Where next?

Code for this tutorial can be found at this GitHub repository. Feel free to play with examples and contribute more if you find something you’d love to share with the world. To learn more about all the amazing things Fedora CoreOS can do, dive into the docs or come chat with the community. To learn more about Terraform, you can rummage through the docs, checkout #terraform on freenode, or contribute on GitHub.

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Unreal Engine As A 3D Modeling & Sculpting Application

The most recent releases of Unreal Engine now include new beta Unreal Modeling Tools Editor Mode enabling you to create, sculpt and even texture entirely in Unreal Engine. If you want to check out the new features, you need to enable the plugin. Don’t worry, there are step by step instructions available below

In Unreal Engine, select Edit->Plugins.

In the Plugins dialog, filter by Model and locate and select Modeling Tools Editor Mode and click the Enabled checkbox.

Modeling Tools Editor Mode in Unreal Engine

This will first prompt you if you want to continue due to it being an experimental feature. Allow this, then it will prompt you to restart Unreal Engine, click Restart Now.

New plugin restart Unreal Engine

Once your project has restarted, you can access the new modeling tools in the Modes menu by selecting Modeling.

Modeling Mode in Unreal Engine

Once enabled a new toolbar will be available with options for creating new geometry from primitives or other creation modes, tools for modifying and deforming as well as sculpting geometry and much more.

Go hands-on with the Unreal Modeling Tools Editor Mode plugin in the video below.

[youtube https://www.youtube.com/watch?v=9SgD17vqU3A?feature=oembed&w=1500&h=844]
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4 cool new projects to try in COPR from December 2020

COPR is a collection of personal repositories for software that isn’t carried in Fedora. Some software doesn’t conform to standards that allow easy packaging. Or it may not meet other Fedora standards, despite being free and open-source. COPR can offer these projects outside the Fedora set of packages. Software in COPR isn’t supported by Fedora infrastructure or signed by the project. However, it can be a neat way to try new or experimental software.

This article presents a few new and interesting projects in COPR. If you’re new to using COPR, see the COPR User Documentation for how to get started.

Blanket

Blanket is an application for playing background sounds, which may potentially improve your focus and increase your productivity. Alternatively, it may help you relax and fall asleep in a noisy environment. No matter what time it is or where you are, Blanket allows you to wake up while birds are chirping, work surrounded by friendly coffee shop chatter or distant city traffic, and then sleep like a log next to a fireplace while it is raining outside. Other popular choices for background sounds such as pink and white noise are also available.

Blanket

Installation instructions

The repo currently provides Blanket for Fedora 32 and 33. To install it, use these commands:

sudo dnf copr enable tuxino/blanket
sudo dnf install blanket

k9s

k9s is a command-line tool for managing Kubernetes clusters. It allows you to list and interact with running pods, read their logs, dig through used resources, and overall make the Kubernetes life easier. With its extensibility through plugins and customizable UI, k9s is welcoming to power-users.

k9s

For many more preview screenshots, please see the project page.

Installation instructions

The repo currently provides k9s for Fedora 32, 33, and Fedora Rawhide as well as EPEL 7, 8, Centos Stream, and others. To install it, use these commands:

sudo dnf copr enable luminoso/k9s
sudo dnf install k9s

rhbzquery

rhbzquery is a simple tool for querying the Fedora Bugzilla instance. It provides an interface for specifying the search query but it doesn’t list results in the command-line. Instead, rhbzquery generates a Bugzilla URL and opens it in a web browser.

rhbzquery

Installation instructions

The repo currently provides rhbzquery for Fedora 32, 33, and Fedora Rawhide. To install it, use these commands:

sudo dnf copr enable petersen/rhbzquery
sudo dnf install rhbzquery

gping

gping is a more visually intriguing alternative to the standard ping command, as it shows results in a graph. It is also possible to ping multiple hosts at the same time to easily compare their response times.

gping

Installation instructions

The repo currently provides gping for Fedora 32, 33, and Fedora Rawhide as well as for EPEL 7 and 8. To install it, use these commands:

sudo dnf copr enable atim/gping
sudo dnf install gping
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Flax Engine Released

The Flax Engine game engine has just seen it’s 1.0 release. We’ve had our eyes on this engine since it’s first public beta in 2018, which was then followed by a few years of radio silence. The in July of 2020 we got the 0.7 release which added several new features including C++ live scripting support. With today’s release the Flax Engine is now available to everyone.

Key features include:

  • Seamless C# and C++ scripting
  • Automatic draw calls batching and instancing
  • Every asset is using async content streaming by default
  • Cross-platform support (Windows, Linux, Android, PS4. Xbox One, Xbox Series X/S, UWP…)
  • GPU Lightmaps Baking
  • Visual Scripting
  • VFX tools
  • Nested prefabs
  • Gameplay Globals for technical artists
  • Open World Tools (terrain, foliage, fog, levels streaming)
  • Hot-reloading C#/C++ in Editor
  • Full source-code available
  • Direct communication and help from engine devs
  • Lightweight development (full repo clone + compilation in less than 3 min)

Flax is available for Windows and Linux developers with the source code available on GitHub. Flax is a commercial game engine, but under fairly liberal terms. Commercial license terms are:

Use Flax for free, pay 4% when you release (above first $25k per quarter). Flax Engine and all related tools, all features, all supported platforms, all source code, all complete projects and Flax Samples with regular updates can be used for free.

If you want to learn more about Flax Engine, be sure to check out the following links:

You can learn more about the game engine and see it in action in the video below. Stay tuned for a more in-depth technical video on Flax Engine in the future.

[youtube https://www.youtube.com/watch?v=R4M4Yp7CjM0?feature=oembed&w=1500&h=844]