cilium-cli VS zenml

resource "tls_private_key" "this" { algorithm = "ECDSA" ecdsa_curve = "P384" } resource "hcloud_ssh_key" "this" { name = var.stack_name public_key = tls_private_key.this.public_key_openssh } resource "hcloud_server" "this" { name = var.stack_name server_type = "cax11" image = "ubuntu-22.04" location = "nbg1" ssh_keys = [ hcloud_ssh_key.this.id, ] public_net { ipv4 = hcloud_primary_ip.this["ipv4"].id ipv6 = hcloud_primary_ip.this["ipv6"].id } user_data = <<-EOF #cloud-config users: - name: ${var.username} groups: users, admin, adm sudo: ALL=(ALL) NOPASSWD:ALL shell: /bin/bash ssh_authorized_keys: - ${tls_private_key.this.public_key_openssh} packages: - certbot package_update: true package_upgrade: true runcmd: - sed -i -e '/^\(#\|\)PermitRootLogin/s/^.*$/PermitRootLogin no/' /etc/ssh/sshd_config - sed -i -e '/^\(#\|\)PasswordAuthentication/s/^.*$/PasswordAuthentication no/' /etc/ssh/sshd_config - sed -i '$a AllowUsers ${var.username}' /etc/ssh/sshd_config - | curl https://get.k3s.io | \ INSTALL_K3S_VERSION="v1.29.3+k3s1" \ INSTALL_K3S_EXEC="--disable traefik --kube-apiserver-arg=service-account-jwks-uri=https://${cloudflare_record.this.name}/openid/v1/jwks --kube-apiserver-arg=service-account-issuer=https://${cloudflare_record.this.name} --disable-network-policy --flannel-backend none --write-kubeconfig /home/${var.username}/.kube/config --secrets-encryption" \ sh - - chown -R ${var.username}:${var.username} /home/${var.username}/.kube/ - | CILIUM_CLI_VERSION=v0.16.4 CLI_ARCH=arm64 curl -L --fail --remote-name-all https://github.com/cilium/cilium-cli/releases/download/$CILIUM_CLI_VERSION/cilium-linux-$CLI_ARCH.tar.gz{,.sha256sum} sha256sum --check cilium-linux-$CLI_ARCH.tar.gz.sha256sum sudo tar xzvfC cilium-linux-$CLI_ARCH.tar.gz /usr/local/bin - kubectl completion bash | tee /etc/bash_completion.d/kubectl - k3s completion bash | tee /etc/bash_completion.d/k3s - | cat << 'EOF2' >> /home/${var.username}/.bashrc alias k=kubectl complete -F __start_kubectl k EOF2 - reboot EOF }

network plugin to be used, based on the documentation. (Project Calico ,Flannel, Cilium )

Isovalent makes an enterprise version of Cilium, an open source tool that uses eBPF to provide security and observability for cloud native environments. Liz gave a great talk at KubeCon Los Angeles about eBPF that I highly recommend. My reaction to her talk was that I wished I had Cilium years ago to troubleshoot some difficult incidents. When I first heard about eBPF I had thought of it more from the observability standpoint, but Cilium also provides a CNI plugin, transparent encryption, logs for security audits, and much more.

Pixie is one of a handful of observability tools that offer eBPF or kernel-level observability. Other well-known tools are Cilium and CVF.

A lot of projects are currently heavily focused on K8S (like Cilium - ebpf service mesh).

Using a container network interface (Cilium) and service mesh (Istio) on top of your K8s infrastructure to more easily manage your distributed applications.

Isovalent | Multiple roles | Mountain View (US), Zürich (CH), or Remote

We're the company behind the open source Cilium project (https://cilium.io) (11K stars on GitHub) providing eBPF-based networking, observability, and security for container workloads and clusters.

We have an amazing and in-demand product using revolutionary technology and are looking for top talent to help us build and explore all of its possibilities.

We're remote-first, mainly in the EU and US timezones.

If you're interested please apply through our careers site https://isovalent.com/careers and mention Hacker News in your application.

Keywords for searchers: open source, Go/Golang, eBPF, C, C++, Kubernetes, networking, OpenShift, Linux kernel, performance, CI, SRE, technical writing, marketing, community advocate

network plugin to be used, based on the documentation. (Project Calico ,Flannel, Cilium )

Currently, you can see the integrations we support here and it includes a lot of tools in your list. I also feel I agree with your categorization (it is exactly the categorization we use in our docs pretty much). Perhaps one thing missing might be feature stores but that is a minor thing in the bigger picture.

GitHub: https://github.com/zenml-io/zenml

Hey everyone! At ZenML, we released today an integration that allows users to train and deploy models from pipelines in a simple way. I wanted to ask the community here whether the example we showcased makes sense in a real-world setting:

As of early March 2022 this is the new CI pipeline that we use here at ZenML and the feedback from my colleagues -- fellow engineers -- has been very positive overall. I am sure there will be tweaks, changes and refactorings in the future, but for now, this feels Zen.

ZenML is hiring for a Design Engineer.

ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.

We’re looking for a Design Engineer with a multi-disciplinary skill-set who can take over the look and feel of the ZenML experience. ZenML is a tool designed for developers and we want to delight them from the moment they land on our web page, to after they start using it on their machines. We would like a consistent design experience across our many touchpoints (including the [landing page](https://zenml.io), the [docs](https://docs.zenml.io), the [blog](https://blog.zenml.io), the [podcast](https://podcast.zenml.io), our social media, the product itself which is a [python package](https://github.com/zenml-io/zenml) etc).

A lot of this job is about communicating complex ideas in a beautiful way. You could be a developer or a non-coding designer, full time or part-time, employee or freelance. We are not so picky about the exact nature of this role. If you feel like you are a visually creative designer, and are willing to get stuck in the details of technical topics like MLOps, we can’t wait to work with you!

Apply here: https://zenml.notion.site/Design-Engineer-m-f-1d1a219f18a341...

The best place to see MLflow Tracking and ZenML being used together in a simple use case is our example that showcases the integration. It builds on the quickstart example, but shows how you can add in MLflow to handle the tracking. In order to enable MLflow to track artifacts inside a particular step, all you need is to decorate the step with @enable_mlflow and then to specify what you want logged within the step. Here you can see how this is employed in a model training step that uses the autolog feature I mentioned above: