I have a Yocto based OS on which I have everything installed to start the network.
Nevertheless, at each boot I need to do systemctl start networking to start it. Initially the service was even masked. I found out how to unmask it but I can't find a way to start it automatically.
I don't know much about systemd but the networking.service is located in generator.late folder. From what I understood, it's generated afterward.
How can I enable it?
It depends if you want to enable the service only on one particular device. If yes, it is simple:
systemctl enable networking
Append the parameter --now if you also want to start the service just now.
If you want to enable the service on all your devices (i.e. it will be automatically enabled in all your images coming from build), the best way is to extend the recipe, but please see below for other ways how to handle the network. The process is describe at NXP support for example.
Some notes about networking.service itself: I assume that your networking.service comes from init-ifupdown recipe. If yes, is there any reason to handle network configuration using old SysV init script in system with systemd? The service is generated from SysV init script by systemd-sysv-generator. So I would suggest to try other networking services like systemd's native "systemd-networkd", "NetworkManager" or "connman". The best choice depends on type of your embedded systemd. These services are integrated with systemd much better.
Some more information on activating or enabling the services: https://unix.stackexchange.com/questions/302261/systemd-unit-activate-vs-enable
Related
We have a CentOS 8 (tried 7 as well) image and I am adding some config to act as a router.
The issue is, for some reason, the first time the instance is created, cloud init doesn't read the network config we pass using the user-data metadata
#cloud-config
network
version: 1
etc...
We configure eth1 to use dhcp and get cloud-init to manage it, as well as add a route.
Works perfectly every time after the initial boot up (and stop>start again).
To me it feels like cloud-init is not aware of the config, but when I go in the machine and do cloud-init query userdata i can see the data, and even then if I do cloud-init clean && cloud-init init it doesn't do anything. The same commands work fine if the machine was rebooted
Try running cloud-init analyze show both times (instance creation and consecutive reboot) and check for any differences.
Sadly, cloud providers kind-of abuse the abilities of cloud-init, not to a complete fault. cloud-init allows for customization of vendor/user provided configuration (who overrides what), changing the order of boot stages, etc.
This is done mostly because different cloud providers need network/provisioning/storage at different times. For example, AWS attaches storage after network (EBS only), Azure provides VM only after storage is attached and it's natively provided as NTFS (they really format the drive if you need anything else), etc.
These shenanigans, while understandable (datacenter infrastructure defines user availability) make cloud-init's documentation merely a suggestion for the user to investigate.
From my experience, Azure is the closest to original implementation. Possibly they haven't learned yet how to utilize the potential in their favor.
My general suggestion for any instance customization (almost always works) is to write a script with write_files and execute them with bootcmd/runcmd, because these run at the final stage, and provide for best override opportunity. Edit hosts, change firewall rules - most of the stuff will not require reboot.
I have a python app that builds a dataset for a machine learning task on GCP.
Currently I have to start an instance of a VM that we have, and then SSH in, and run the app, which will complete in 2-24 hours depending on the size of the dataset requested.
Once the dataset is complete the VM needs to be shutdown so we don't incur additional charges.
I am looking to streamline this process as much as possible, so that we have a "1 click" or "1 command" solution, but I'm not sure the best way to go about it.
From what I've read about so far it seems like containers might be a good way to go, but I'm inexperienced with docker.
Can I setup a container that will pip install the latest app from our private GitHub and execute the dataset build before shutting down? How would I pass information to the container such as where to get the config file etc? It's conceivable that we will have multiple datasets being generated at the same time based on different config files.
Is there a better gcloud feature that suits our purpose more effectively than containers?
I'm struggling to get information regarding these basic questions, it seems like container tutorials are dominated by web apps.
It would be useful to have a batch-like container service that runs a container until its process completes. I'm unsure whether such a service exists. I'm most familiar with Google Cloud Platform and this provides a wealth of compute and container services. However -- to your point -- these predominantly scale by (HTTP) requests.
One possibility may be Cloud Run and to trigger jobs using Cloud Pub/Sub. I see there's async capabilities too and this may be interesting (I've not explored).
Another runtime for you to consider is Kubernetes itself. While Kubernetes requires some overhead in having Google, AWS or Azure manage a cluster for you (I strongly recommend you don't run Kubernetes yourself) and some inertia in the capacity of the cluster's nodes vs. the needs of your jobs, as you scale the number of jobs, you will smooth these needs. A big advantage with Kubernetes is that it will scale (nodes|pods) as you need them. You tell Kubernetes to run X container jobs, it does it (and cleans-up) without much additional management on your part.
I'm biased and approach the container vs image question mostly from a perspective of defaulting to container-first. In this case, you'd receive several benefits from containerizing your solution:
reproducible: the same image is more probable to produce the same results
deployability: container run vs. manage OS, app stack, test for consistency etc.
maintainable: smaller image representing your app, less work to maintain it
One (beneficial!?) workflow change if you choose to use containers is that you will need to build your images before using them. Something like Knative combines these steps but, I'd stick with doing-this-yourself initially. A common solution is to trigger builds (Docker, GitHub Actions, Cloud Build) from your source code repo. Commonly you would run tests against the images that are built but you may also run your machine-learning tasks this way too.
Your containers would container only your code. When you build your container images, you would pip install, perhaps pip install --requirement requirements.txt to pull the appropriate packages. Your data (models?) are better kept separate from your code when this makes sense. When your runtime platform runs containers for you, you provide configuration information (environment variables and|or flags) to the container.
The use of a startup script seems to better fit the bill compared to containers. The instance always executes startup scripts as root, thus you can do anything you like, as the command will be executed as root.
A startup script will perform automated tasks every time your instance boots up. Startup scripts can perform many actions, such as installing software, performing updates, turning on services, and any other tasks defined in the script.
Keep in mind that a startup script cannot stop an instance but you can stop an instance through the guest operating system.
This would be the ideal solution for the question you posed. This would require you to make a small change in your Python app where the Operating system shuts off when the dataset is complete.
Q1) Can I setup a container that will pip install the latest app from our private GitHub and execute the dataset build before shutting down?
A1) Medium has a great article on installing a package from a private git repo inside a container. You can execute the dataset build before shutting down.
Q2) How would I pass information to the container such as where to get the config file etc?
A2) You can use ENV to set an environment variable. These will be available within the container.
You may consider looking into Docker for more information about container.
I'm starting to play around with NixOS deployments. To that end, I have a repo with some packages defined, and a configuration.nix for the server.
It seems like I should then be able to test this configuration locally (I'm also running NixOS). I imagine it's a bad idea to change my global configuration.nix to point to the deployment server's configuration.nix (who knows what that will break); but is there a safe and convenient way to "try out" the server locally - i.e. build it and either boot into it or, better, start it as a separate process?
I can see docker being one way, of course; maybe there's nothing else. But I have this vague sense Nix could be capable of doing it alone.
There is a fairly standard way of doing this that is built into the default system.
Namely nixos-rebuild build-vm. This will take your current configuration file (by default /etc/nixos/configuration.nix, build it and create a script allowing you to boot the configuration into a virtualmachine.
once the script has finished, it will leave a symlink in the current directory. You can then boot by running ./result/bin/run-$HOSTNAME-vm which will start a boot of your virtualmachine for you to play around with.
TLDR;
nixos-rebuild build-vm
./result/bin/run-$HOSTNAME-vm
nixos-rebuild build-vm is the easiest way to do this, however; you could also import the configuration into a NixOS container (see Chapter 47. Container Management in the NixOS manual and the nixos-container command).
This would be done with something like:
containers.mydeploy = {
privateNetwork = true;
config = import ../mydeploy-configuration.nix;
};
Note that you would not want to specify the network configuration in mydeploy-configuration.nix if it's static as that could cause conflicts with the network subnet created for the container.
As you may already know, system configurations can coexist without any problems in the Nix store. The problem here is running more than one system at once. For this, you need an isolation or virtualization tools like Docker, VirtualBox, etc.
NixOS Containers
NixOS provides an efficient implementation of the container concept, backed by systemd-nspawn instead of an image-based container runtime.
These can be specified declaratively in configuration.nix or imperatively with the nixos-container command if you need more flexibility.
Docker
Docker was not designed to run an entire operating system inside a container, so it may not be the best fit for testing NixOS-based deployments, which expect and provide systemd and some services inside their units of deployment. While you won't get a good NixOS experience with Docker, Nix and Docker are a good fit.
UPDATE: Both 'raw' Nix packages and NixOS run in Docker. For example, Arion supports images from plain Nix, NixOS modules and 'normal' Docker images.
NixOps
To deploy NixOS inside NixOS it is best to use a technology that is designed to run a full Linux system inside.
It helps to have a program that manages the integration for you. In the Nix ecosystem, NixOps is the first candidate for this. You can use NixOps with its multiple backends, such as QEMU/KVM, VirtualBox, the (currently experimental) NixOS container backend, or you can use the none backend to deploy to machines that you have created using another tool.
Here's a complete example of using NixOps with QEMU/KVM.
Tests
If the your goal is to run automated integration tests, you can make use of the NixOS VM testing framework. This uses Linux KVM virtualization (expose /dev/kvm in sandbox) to run integrations test on networks of virtual machines, and it runs them as a derivation. It is quite efficient because it does not have to create virtual machine images because it mounts the Nix store in the VM. These tests are "built" like any other derivation, making them easy to run.
Nix store optimization
A unique feature of Nix is that you can often reuse the host Nix store, so being able to mount a host filesystem in the container/vm is a nice feature to have in your solution. If you are creating your own solutions, depending on you needs, you may want to postpone this optimization, because it becomes a bit more involved if you want the container/vm to be able to modify the store. NixOS tests solve this with an overlay file system in the VM. Another approach may be to bind mount the Nix store forward the Nix daemon socket.
I have a sailsjs app running in cluster mode with pm2 and two instances. One of the main reasons for wanting the two instances was so I could restart/update the app without having to bring the entire app down.
However, in the middle of a restart of one instance pm2 restart 4, the site is all wonky (that's the technical term) if I refresh it. I'm assuming this is because grunt is doing it's thing and the .tmp folder gets destroyed for both instances?
Is the only real approach with sailsjs to have two complete instances running on different ports and use something like nginx as the load balancer, or am I missing something with PM2 that would allow for staged restarts without any downtime or hiccups in the resources being available?
There are a few issues here.
You need to provide what versions of sails.js/node.js/pm2 you're
running. In short, describe your environment as completely as
possible.
Describing your issue more completely helps people write more concise and clear answers.
node.js cluster mode may change (as of v0.12.4) and is still considered "Unstable": https://nodejs.org/api/cluster.html#cluster_cluster
In the following thread, "mikermcneil commented on Dec 3, 2014" says to disable Grunt for production with pm2: https://github.com/balderdashy/sails/pull/1716
Let me clarify by saying I've used pm2 until just recently. In addition to Grunt, it has issues with socket connections while nginx handles it just fine. Trust me, chasing down that bug was not fun. Here's a link to the thread: https://github.com/Unitech/PM2/issues/389
As an alternate solution I chose to use nginx with parallel sails.js apps, using redis for sockets and sessions. Use forever to keep the apps running and disable grunt. Point nginx to the assets folder to serve static files quickly, bypassing sails.js and add caching to those assets.
Hope this helps!
I'm currently dealing with CoreOS, and so far I think I got the overall idea and concept. One thing that I did not yet get is execution of cloud-init.
I understand that cloud-init is a process that does some configuration for CoreOS. What I do not yet understand is…
When does CoreOS run cloud-init? On first boot? On each boot? …?
How does cloud-init know where to find its configuration data? I've seen that there is config-drive and that totally makes sense, but is this the only way? What exactly is the role of the user-data file? …?
CoreOS runs cloudinit a few times during the boot process. Right now this happens at each boot, but that functionality may change in the future.
The first pass is the OEM cloud-init, which is baked into the image to set up networking and other features required for that provider. This is done for EC2, Rackspace, Google Compute Engine, etc since they all have different requirements. You can see these files on Github.
The second pass is the user-data pass, which is handled differently per provider. For example, EC2 allows the user to input free-form text in their UI, which is stored in their metadata service. The EC2 OEM has a unit that reads this metadata and passes it to the second cloud-init run. On Rackspace/Openstack, config-drive is used to mount a read-only filesystem that contains the user-data. The Rackspace and Openstack OEMs know to mount and look for the user-data file at that location.
The latest version of CoreOS also has a flag to fetch a remote file to be evaluated for use with PXE booting.
The CoreOS distribution docs have a few more details as well.