conda
helm
conda | helm | |
---|---|---|
30 | 206 | |
6,092 | 26,081 | |
0.7% | 0.7% | |
9.8 | 8.9 | |
5 days ago | 4 days ago | |
Python | Go | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
conda
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How to Create Virtual Environments in Python
Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.
- Why does creating my conda environment use so much memory?
- Installing Anaconda on ChromeOS using Linux
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PSA: conda-libmamba-solver can cut two hours off of your Anaconda install, but has only 47 GitHub stars. It deserves more praise.
conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.
- Is Anaconda still open source?
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How to get the best Conda environment experience in Codespaces
The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).
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What is the status of Python 3.11?
It's worth noting that [ana]conda isn't even fully compatible yet with 3.11 (you can use it to create 3.11 environments--and you really should rather than waiting on relying on the system python--but conda itself can only run on 3.10.
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Miniconda finally released for Python 3.10
It took some time but as great Christmas present Miniconda was finally released with Python 3.10!
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TW: ZSH (and BASH?) does not show current working dir etc anymore
The September update broke it.
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Python 3.11.0 is now available
According to this this issue is high on their priority list (whatever that means).
helm
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Kubernetes CI/CD Pipelines
Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.
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deploying a minio service to kubernetes
helm
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How to take down production with a single Helm command
Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...
Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.
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Building a VoIP Network with Routr on DigitalOcean Kubernetes: Part I
Helm (Get from here https://helm.sh/)
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The 2024 Web Hosting Report
It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.
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Deploying a Web Service on a Cloud VPS Using Kubernetes MicroK8s: A Comprehensive Guide
Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!
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Deploy Kubernetes in Minutes: Effortless Infrastructure Creation and Application Deployment with Cluster.dev and Helm Charts
Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.
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Kubernets Helm Chart
We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).
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Introduction to Helm: Comparison to its less-scary cousin APT
Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.
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🎀 Five tools to make your K8s experience more enjoyable 🎀
Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.
What are some alternatives?
mamba - The Fast Cross-Platform Package Manager
crossplane - The Cloud Native Control Plane
Poetry - Python packaging and dependency management made easy
kubespray - Deploy a Production Ready Kubernetes Cluster
miniforge - A conda-forge distribution.
Packer - Packer is a tool for creating identical machine images for multiple platforms from a single source configuration.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
krew - 📦 Find and install kubectl plugins
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
skaffold - Easy and Repeatable Kubernetes Development
pip - The Python package installer
dapr-demo - Distributed application runtime demo with ASP.NET Core, Apache Kafka and Redis on Kubernetes cluster.