I am developing a autoscaling program for Bosh running Concourse, what is the language/process to writing the program and then implementing it in the pipeline?
Any language you want! That's the beauty of Concourse. As long as you can create a runC-compatible image (like a Docker image) that can run your program, you can use it in Concourse. I've personally written tasks in bash, python, ruby, and go.
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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 am a beginner in kubernetes and have just started playing around with it. I have a use case to run commands that I take in through a UI(can be a server running inside or outside the cluster) in the kubernetes cluster. Let's say the commands are python scripts like HelloWorld.py etc. When I enter the command the server should launch a container which runs the command and exits. How do I go about this in Kubernetes? What should the scheduler look like?
You can try the training classes on katacoda.com as follows.
https://learn.openshift.com/
https://www.katacoda.com/courses/kubernetes
It's interactive handson, so it's interesting and easy to make sense of OpenShift and Kubernetes.
I hope it help you. ;)
I would like to run a sequence of Kubernetes jobs one after another. It's okay if they are run on different nodes, but it's important that each one run to completion before the next one starts. Is there anything built into Kubernetes to facilitate this? Other architecture recommendations also welcome!
This requirement to add control flow, even if it's a simple sequential flow, is outside the scope of Kubernetes native entities as far as I know.
There are many workflow engine implementations for Kubernetes, most of them are focusing on solving CI/CD but are generic enough for you to use however you want.
Argo: https://applatix.com/open-source/argo/
Added a custom resource deginition in Kubernetes entity for Workflow
Brigade: https://brigade.sh/
Takes a more serverless like approach and is built on Javascript which is very flexible
Codefresh: https://codefresh.io
Has a unique approach where you can use the SaaS to easily get started without complicated installation and maintenance, and you can point Codefresh at your Kubernetes nodes to run the workflow on.
Feel free to Google for "Kubernetes Workflow", and discover the right platform for yourself.
Disclaimer: I work at Codefresh
I would try to use cronjobs and set the concurrency policy to forbid so it doesn't run concurrent jobs.
I have worked on IBM TWS (Workload Automation) which is a scheduler similar to cronjob where you can mention the dependencies of the jobs.
You can specify a job to run only after it's dependencies has run using follows keyword.
Currently I use PHP-FPM with NGINX for front end requests but also run some background processes through a long running PHP script using exec to run other scripts with the command line PHP. What I'm thinking though is that this would be more efficient if these were also run through PHP-FPM? Any ideas on how I would do this? Thanks.
FPM is a tool to Manage FastCGI Processes. Just shuffle the letters. While it manages long-running PHP processes, it does so only under the mental umbrella of FastCGI.
Because you're creating a background work queue, you want something designed to manage a background work queue and running processes.
Gearman is an excellent choice for the work queue half. It's platform and language agnostic, and scan scale to the heavens and back. The PECL extension works well.
For keeping those long-running processes going, take a look at Supervisor.
The two make a great duo. Check out this blog post by PHP hacker Matthew Weier O'Phinney that documents some of his exploration with Gearman and Supervisor.
Very late to this question (4 years) but the correct answer is cgi-fcgi which will let you pass commands and execute code in the already-in-memory php-fpm