Based on our record, Google Kubernetes Engine should be more popular than Consul. It has been mentiond 49 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Is the address at which the gRPC endpoint is served. In this case, we’re using Consul DNS to expose the service’s address. If we look at the Recommendation Service’s Nomad jobspec, you’ll see that the name of the gRPC service is recommendationservice. So when we query it in Consul, it should be accessible at this address recommendationservice.service.consul. We can test this by logging into the HashiQube image. Do... - Source: dev.to / about 2 years ago
By default, the service is registered to Consul. Although we don’t explicitly say so, it’s the equivalent of adding a provider = "consul" attribute to the service stanza. You can register your services to either Nomad or Consul. - Source: dev.to / over 2 years ago
Before you start, just a friendly reminder that HashiQube by default runs Nomad, Vault, and Consul on Docker. In addition, we’ll be deploying 21 job specs to Nomad. This means that we’ll need a decent amount of CPU and RAM, so Please make sure that you have enough resources allocated in your Docker desktop. For reference, I’m running an M1 Macbook Pro with 8 cores and 32 GB RAM. My Docker Desktop Resource... - Source: dev.to / over 2 years ago
IP Addresses are hard to remember, so let's allow everything to interact based on hostname and domain name (I use PiHole and consul.io for this as it gives me ad blocking and service discovery). Source: about 3 years ago
We'll begin by going the Consul.io website and downloading it. Consul will act as our Service Registry. Just for the purposes of this tutorial, we'll be running Consul in developer mode. After downloading Consul, you can add it to you system PATH, or run it from wherever directory you want it. - Source: dev.to / over 3 years ago
Integration with Google Kubernetes Engine (GKE), which supports up to 65,000 nodes per cluster, facilitating robust AI infrastructure. - Source: dev.to / about 2 months ago
In my previous post, we explored how LangChain simplifies the development of AI-powered applications. We saw how its modularity, flexibility, and extensibility make it a powerful tool for working with large language models (LLMs) like Gemini. Now, let's take it a step further and see how we can deploy and scale our LangChain applications using the robust infrastructure of Google Kubernetes Engine (GKE) and the... - Source: dev.to / 4 months ago
Kubernetes cluster: You need a running Kubernetes cluster that supports persistent volumes. You can use a local cluster, like kind or Minikube, or a cloud-based solution, like GKE%20orEKS or EKS. The cluster should expose ports 80 (HTTP) and 443 (HTTPS) for external access. Persistent storage should be configured to retain Keycloak data (e.g., user credentials, sessions) across restarts. - Source: dev.to / 5 months ago
In a later post, I will take a look at how you can use LangChain to connect to a local Gemma instance, all running in a Google Kubernetes Engine (GKE) cluster. - Source: dev.to / 8 months ago
Google Kubernetes Engine (GKE) is another managed Kubernetes service that lets you spin up new cloud clusters on demand. It's specifically designed to help you run Kubernetes workloads without specialist Kubernetes expertise, and it includes a range of optional features that provide more automation for admin tasks. These include powerful capabilities around governance, compliance, security, and configuration... - Source: dev.to / 11 months ago
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