@millions-hair-24956 ın kubernetes what is functıon of ıngress egress servıces ?

Sorunuzu yanıtlamak için herhangi bir veri kaynağı bulamıyorum. Kubernetes'te Ingress ve Egress hizmetlerinin işlevleri hakkında daha fazla bilgi veya belirli bir senaryo ekleyebilirsiniz. Eğer bu yardımcı olmazsa, lütfen daha fazla yardım için Livekit destek ekibiyle iletişime geçin. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 what is inggress and egress use for livekit ?

In LiveKit, Ingress is used to bring live streams from non-WebRTC sources into LiveKit rooms. It supports sources like RTMP, WHIP, and media files from HTTP servers, allowing integration with existing workflows or equipment that do not support WebRTC. Ingress can transcode the source media to ensure compatibility with LiveKit clients and publish multiple layers with simulcast (Ingress Overview). Egress, on the other hand, is used to record or livestream a room or individual tracks from a LiveKit session. It supports exporting to formats like MP4, HLS, and streaming to services like YouTube Live via RTMP. Egress can be used for recording meetings or restreaming content (Egress Overview). Would you like more detail on either of these services? Sources: - Ingress Overview - Egress Overview 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

but at kubernetes u give dıfferent infos at livekit documents : The Egress service uses redis messaging queues to load balance and communicate with your LiveKit server. The Ingress service uses Redis messaging queues to communicate with your LiveKit server. @millions-hair-24956

It could be possible that in the context of Kubernetes, both the Ingress and Egress services in LiveKit utilize Redis messaging queues for communication and load balancing with the LiveKit server. This setup helps manage the distribution of tasks and ensures efficient communication between services. For Egress, Redis is used to load balance and distribute recording or streaming tasks across multiple Egress workers, ensuring that requests are handled efficiently (Egress Service). For Ingress, Redis facilitates communication between the Ingress service and the LiveKit server, helping manage the flow of media streams into LiveKit rooms (Ingress Service). Would you like more detailed information on how these services are configured in Kubernetes? Sources: - Egress Service - Ingress Service 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

for record room what servıce ı need to use ? @millions-hair-24956

To record a room in LiveKit, you should use the Egress service. Egress allows you to record the entire room's video and/or audio using a web layout rendered by Chrome. You can export the recording to formats like MP4 or HLS, or stream it to services like YouTube Live via RTMP (Egress Overview). Would you like more details on setting up Egress for recording a room? Sources: - Egress Overview 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

in kubernetes do i need to install livekit helm chart for thıs ıngress egress servıces ? what about redis and sip server how to instal them on kubernetres ? @millions-hair-24956

To deploy LiveKit's Ingress and Egress services on Kubernetes, you should indeed use the LiveKit Helm chart. The Helm chart simplifies the deployment process by providing pre-configured templates for setting up LiveKit services, including Ingress and Egress, on Kubernetes (Deploy to Kubernetes). For Redis, which is required as a message bus for LiveKit, you can deploy it as a separate service on Kubernetes. You can use a Helm chart specifically for Redis, such as the one provided by Bitnami, to simplify the deployment process. Regarding the SIP server, it is not included in the LiveKit Helm chart and needs to be installed separately. You can deploy it on Kubernetes using a custom configuration or Docker images, depending on your specific requirements (SIP Server Deployment). Would you like more detailed instructions on deploying these components? Sources: - Deploy to Kubernetes - SIP Server Deployment 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

do livekit kubernetes must be private-cluster? @millions-hair-24956

LiveKit does not support deployment to serverless or private clusters due to the additional layers of NAT that make it unsuitable for WebRTC traffic. LiveKit requires direct access to the network with host networking, meaning the rtc.udp/tcp ports open on those nodes are directly handled by the LiveKit server (Deploy to Kubernetes). Would you like more details on the network requirements for deploying LiveKit on Kubernetes? Sources: - Deploy to Kubernetes 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

how to secure system then ? @millions-hair-24956

To secure a LiveKit deployment on Kubernetes, you should focus on several key areas: 1. SSL/TLS Certificates: Ensure that all communications are encrypted using SSL/TLS. You can use tools like Let's Encrypt to obtain certificates for your domain. This is crucial for securing WebRTC and signaling traffic (Deployment Configuration). 2. Firewall Configuration: Properly configure your firewall to allow only necessary traffic. Open only the required ports for LiveKit, such as 443 for HTTPS and TURN/TLS, and restrict access to other ports (Configuring Firewalls). 3. Secure API Keys: Use secure API keys and secrets for accessing LiveKit services. Ensure these are stored securely and not exposed in your code or configuration files (Generate Secure Keys). 4. Redis Security: If using Redis, ensure it is secured with authentication and is not exposed to the public internet. Use network policies to restrict access to Redis only from trusted sources. 5. Monitoring and Logging: Implement monitoring and logging to detect and respond to security incidents. Use tools like Prometheus for monitoring and ensure logs are securely stored and reviewed regularly. Would you like more detailed guidance on any of these security measures? Sources: - Configuring Firewalls - Generate Secure Keys - Deployment Configuration 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

so give me a prod ready setup order for best practice . i will have livekit -sip server- redis - and can i connect my agents to kubernetes with cı /cd or agents must be in kubernetes . give me best setup order . @millions-hair-24956 consider , scalabılıty , robustness , prod ready approach.

It seems likely that for a production-ready setup with LiveKit, SIP server, and Redis, you should consider the following steps: 1. Kubernetes Cluster Setup: Deploy a Kubernetes cluster on a cloud provider like AWS, GCP, or Azure. Ensure the cluster is configured for high availability and scalability. 2. Deploy Redis: Use a Helm chart to deploy Redis in your Kubernetes cluster. Ensure Redis is configured with persistence, authentication, and is not exposed to the public internet (Redis Deployment). 3. Deploy LiveKit: Use the LiveKit Helm chart to deploy LiveKit in the Kubernetes cluster. Configure SSL/TLS, firewall rules, and ensure host networking is enabled for WebRTC traffic (Deploy to Kubernetes). 4. Deploy SIP Server: Deploy your SIP server in the Kubernetes cluster. Ensure it is configured to communicate with LiveKit and is secured with proper authentication and firewall rules. 5. CI/CD for Agents: You can connect your agents to Kubernetes using a CI/CD pipeline. This allows you to deploy and update agents automatically. Agents do not necessarily need to be in Kubernetes, but deploying them there can simplify scaling and management. 6. Monitoring and Logging: Implement monitoring using Prometheus and logging solutions to track the performance and health of your services. 7. Autoscaling: Configure autoscaling for your Kubernetes deployments to handle varying loads efficiently. Would you like more detailed guidance on any specific part of this setup? Sources: - Deploy to Kubernetes - Redis Deployment 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

give me an example prod ready agent deployment confıgratıon , i thınk addıng agents to kubernetes best optıon . but ı want to chahge agent sturcture sometıme s, does it hard to change codes , or any approach , github with kubernetes deployment ? for example : Kubernetes CI/CD Pipelines with GitHub Actions is kubertes auto dispatch agent when it needs , for example 1000 calls same time , how can ı effıcently handle thıs sıtuatıon ? gıve me best prod ready ınstallıatıon guide . @millions-hair-24956

For a production-ready agent deployment on Kubernetes with CI/CD integration, you can follow these steps: 1. Kubernetes Setup: Deploy your Kubernetes cluster on a cloud provider like AWS, GCP, or Azure. Ensure it's configured for high availability and scalability. 2. CI/CD with GitHub Actions: Set up a CI/CD pipeline using GitHub Actions. This allows you to automatically build, test, and deploy your agent code to Kubernetes whenever changes are pushed to your GitHub repository. You can use Kubernetes manifests or Helm charts to manage deployments. 3. Agent Deployment: Deploy your agents as Kubernetes pods. Use a Deployment or StatefulSet to manage the pods, ensuring they are distributed across nodes for high availability. Configure autoscaling using Horizontal Pod Autoscaler to handle varying loads efficiently. 4. Code Updates: With CI/CD, updating your agent code is straightforward. Push changes to your GitHub repository, and the CI/CD pipeline will automatically build and deploy the updated code to your Kubernetes cluster. 5. Handling High Load: For scenarios like handling 1000 calls simultaneously, ensure your Kubernetes cluster is configured with sufficient resources and autoscaling is enabled. Use a load balancer to distribute traffic evenly across agent pods. 6. Monitoring and Logging: Implement monitoring with Prometheus and logging solutions to track the performance and health of your agents. Would you like more detailed guidance on setting up any specific part of this configuration? Sources: - Deploy to Kubernetes 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

lets thınk about for azure aks , so use best azure tools for best approch to handle 1000 calls same time . give me order . i need make outbound calls , for thıs chenraıo how to dipstch , autoscale , horızontal pod and not throttel agents or system best way ? @millions-hair-24956

For handling 1000 simultaneous outbound calls using Azure AKS, follow these steps to ensure scalability, efficient dispatching, and optimal resource utilization: 1. Azure AKS Setup: Deploy an Azure Kubernetes Service (AKS) cluster. Ensure it's configured for high availability with multiple nodes across different availability zones. 2. Azure Load Balancer: Use Azure's Load Balancer to distribute incoming traffic evenly across your agent pods. This ensures that no single pod is overwhelmed with requests. 3. CI/CD Pipeline: Set up a CI/CD pipeline using Azure DevOps or GitHub Actions to automate the deployment of your agent code to AKS. This allows for seamless updates and scaling. 4. Agent Deployment: Deploy your agents as Kubernetes Deployments in AKS. Use a Horizontal Pod Autoscaler (HPA) to automatically scale the number of pods based on CPU or memory usage. Configure the HPA to handle spikes in demand efficiently. 5. Azure Monitor and Log Analytics: Implement monitoring using Azure Monitor and Log Analytics to track the performance and health of your agents. Set up alerts for any anomalies or performance issues. 6. Azure Redis Cache: Use Azure Redis Cache for managing state and session data efficiently. This helps in reducing latency and improving the performance of your agents. 7. Outbound Call Handling: For making outbound calls, ensure your agents are configured to handle SIP or other telephony protocols. Use Azure Communication Services if needed for telephony integration. 8. Testing and Optimization: Conduct load testing to ensure your setup can handle 1000 simultaneous calls. Optimize your agent code and Kubernetes configurations based on test results. Would you like more detailed guidance on any specific part of this setup? Sources: - https://docs.livekit.io/agents/deployment/#Rollout - https://docs.livekit.io/home/self-hosting/ingress/#Autoscaling-with-Helm 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

is it good to deploy system at unıted states ? give me step by step approach . also need to ınsatll grafana and prometus for system watch , agent watch . ı need to know how to handle thıs sıtuatıon . @millions-hair-24956

It seems likely that deploying your system in the United States can be beneficial if your primary user base is located there, as it can reduce latency and improve performance. Here’s a step-by-step approach to deploying your system in the US, including setting up Grafana and Prometheus for monitoring: 1. Select a Cloud Provider: Choose a cloud provider with data centers in the US, such as AWS, Azure, or Google Cloud. This ensures low latency for US-based users. 2. Deploy Kubernetes Cluster: Set up a Kubernetes cluster in a US region. For example, use AWS EKS, Azure AKS, or Google GKE. Ensure the cluster is configured for high availability across multiple availability zones. 3. Install Prometheus: Deploy Prometheus in your Kubernetes cluster to collect metrics. Use Helm to simplify the installation:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
   helm install prometheus prometheus-community/prometheus

4. Install Grafana: Deploy Grafana for visualizing metrics collected by Prometheus. Again, use Helm for installation:

helm repo add grafana https://grafana.github.io/helm-charts
   helm install grafana grafana/grafana

5. Configure Monitoring: Set up Prometheus to scrape metrics from your Kubernetes nodes and applications. Configure Grafana dashboards to visualize these metrics for system and agent monitoring. 6. Deploy Your Application: Deploy your application and agents in the Kubernetes cluster. Use a Horizontal Pod Autoscaler to manage scaling based on demand. 7. Set Up Alerts: Configure alerts in Prometheus for critical metrics, such as CPU usage, memory usage, and response times. Ensure alerts are sent to your preferred notification channels. 8. Test and Optimize: Conduct load testing to ensure your system can handle the expected load. Optimize configurations based on test results. Would you like more detailed guidance on any specific part of this setup? Sources: - Deploy to Kubernetes 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 lets assume we build system on azure aks , livekitserver-redis-sipserver . what about agents ? is it sensble that usıng github actıons for connect agent s to kubernetes ? , or you say no use they all are ın same kubernetes ? i confused . https://github.com/livekit-examples/agent-deployment/tree/main/kubernetes best way to handle 1000 same outrbound tıme call , prod ready approach please .

It seems likely that using GitHub Actions for deploying agents to Azure AKS is a sensible approach. This setup allows you to automate the deployment process, ensuring that any updates to your agent code are seamlessly integrated into your Kubernetes environment. Here’s a step-by-step approach for a production-ready setup: 1. Azure AKS Setup: Deploy your Azure Kubernetes Service (AKS) cluster. Ensure it's configured for high availability and scalability across multiple availability zones. 2. Deploy Core Services: Use Helm charts to deploy LiveKit server, Redis, and SIP server within the AKS cluster. Ensure these services are configured for redundancy and high availability. 3. CI/CD with GitHub Actions: Set up a CI/CD pipeline using GitHub Actions to automate the deployment of your agent code to AKS. This allows for continuous integration and deployment, ensuring that any changes to your code are automatically tested and deployed. 4. Agent Deployment: Deploy your agents as Kubernetes Deployments within the AKS cluster. Use a Horizontal Pod Autoscaler to manage the scaling of agent pods based on CPU or memory usage. This ensures that your system can handle spikes in demand, such as 1000 simultaneous outbound calls. 5. Monitoring and Logging: Implement monitoring using Prometheus and Grafana to track the performance and health of your agents and core services. Set up alerts for any anomalies or performance issues. 6. Load Testing and Optimization: Conduct load testing to ensure your system can handle the expected load. Optimize your agent code and Kubernetes configurations based on test results. 7. Network Configuration: Ensure that your AKS cluster is configured with appropriate network policies and security groups to allow communication between services while restricting unauthorized access. Would you like more detailed guidance on any specific part of this setup? Sources: - https://docs.livekit.io/agents/deployment/#Rollout 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

how to ınstall sıp server ın kubernetes ?ıs there any helm chats or how t ınstall ıt ? @millions-hair-24956

It seems likely that installing a SIP server in Kubernetes requires a custom setup, as there is no specific Helm chart provided for the SIP server in the LiveKit documentation. Here’s a general approach to deploying a SIP server in Kubernetes: 1. Docker Image: First, ensure you have a Docker image for your SIP server. If you are using a common SIP server like Asterisk or FreeSWITCH, you can find official Docker images or create your own. 2. Kubernetes Deployment: Create a Kubernetes Deployment manifest for your SIP server. This will define the number of replicas, resource requests, and limits. 3. Service Configuration: Define a Kubernetes Service to expose your SIP server. This service will handle SIP signaling and media traffic. Ensure the correct ports (e.g., 5060 for SIP and a range for RTP) are open. 4. Persistent Storage: If your SIP server requires persistent storage (e.g., for configuration files or logs), configure a PersistentVolume and PersistentVolumeClaim. 5. Network Policies: Implement network policies to secure communication between your SIP server and other components like LiveKit and Redis. 6. Load Balancer: If needed, use a LoadBalancer service type to expose your SIP server to external clients. Here is a basic example of a Kubernetes Deployment manifest for a SIP server:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: sip-server
spec:
  replicas: 2
  selector:
    matchLabels:
      app: sip-server
  template:
    metadata:
      labels:
        app: sip-server
    spec:
      containers:
      - name: sip-server
        image: your-sip-server-image
        ports:
        - containerPort: 5060
        - containerPort: 10000-20000

Would you like more detailed guidance on any specific part of this setup? Sources: - https://docs.livekit.io/home/self-hosting/ingress/#Configuration - https://docs.livekit.io/home/self-hosting/egress/#Running-locally 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

addıng agents from github to docker and to kubernetes , is it disable lıvekıt servers job disturbiıon , you know ı need to ru 1000 agents same tıme whıch can make calls .

It seems likely that deploying agents from GitHub to Docker and then to Kubernetes should not inherently disable LiveKit's job distribution system. LiveKit is designed to handle job distribution across available workers, and deploying agents in Kubernetes can enhance scalability and management. Here's a step-by-step approach to ensure efficient deployment and scaling: 1. CI/CD Pipeline: Use GitHub Actions to automate the build and deployment of your agent Docker images to a container registry. This ensures that any updates to your agent code are automatically built and ready for deployment. 2. Kubernetes Deployment: Deploy your agents as Kubernetes Deployments. Use a Horizontal Pod Autoscaler to manage the scaling of agent pods based on CPU or memory usage. This setup allows you to handle spikes in demand, such as running 1000 agents simultaneously. 3. LiveKit Job Distribution: Ensure that your agents are configured to connect to the LiveKit server and register as workers. LiveKit will handle the distribution of jobs to these workers based on availability and load. 4. Monitoring and Optimization: Implement monitoring using Prometheus and Grafana to track the performance and health of your agents. Optimize configurations based on performance data to ensure efficient handling of 1000 simultaneous calls. 5. Testing and Validation: Conduct load testing to validate that your setup can handle the expected load. Adjust resource allocations and scaling configurations as needed. Would you like more detailed guidance on any specific part of this setup? Sources: - https://docs.livekit.io/agents/deployment/#Rollout - https://docs.livekit.io/agents/deployment/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

do i need to defıne node specs or livekit will handle it ? @millions-hair-24956

In a Kubernetes deployment, you need to define node specifications yourself. LiveKit does not automatically handle node specifications, as it relies on the underlying Kubernetes infrastructure to manage resources. Here’s what you should consider: 1. Node Specifications: Define the specifications for your Kubernetes nodes based on the expected load and resource requirements. This includes CPU, memory, and storage. Choose instance types that match your workload needs, such as compute-optimized instances for high-performance requirements. 2. Resource Requests and Limits: In your Kubernetes Deployment manifests, specify resource requests and limits for your pods. This ensures that your applications have the necessary resources to function efficiently and helps Kubernetes schedule pods appropriately. 3. Autoscaling: Use Kubernetes features like the Horizontal Pod Autoscaler to automatically scale your deployments based on resource usage. This helps manage varying loads and ensures that your system can handle spikes in demand. Would you like more detailed guidance on configuring node specifications or resource management in Kubernetes? Sources: - Deploy to Kubernetes 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

what ıs best specs for lıvekıt suggest? for every servıce and agent ? @millions-hair-24956

It seems likely that the best specifications for LiveKit services and agents depend on the specific use case and expected load. However, here are some general guidelines: 1. LiveKit Server: For a production setup, consider using compute-optimized instances. A typical setup might use instances with at least 4 vCPUs and 8 GB of RAM. This can vary based on the number of concurrent users and the complexity of the media processing. 2. Redis: Redis is generally lightweight, but for high availability and performance, consider using instances with at least 2 vCPUs and 4 GB of RAM. Ensure Redis is configured with persistence if needed. 3. SIP Server: The SIP server's requirements depend on the call volume. For moderate usage, instances with 2 vCPUs and 4 GB of RAM should suffice. Scale up based on the number of concurrent calls. 4. Agents: The specifications for agents depend on their functionality. For basic audio processing, instances with 2 vCPUs and 4 GB of RAM per agent might be sufficient. For more complex tasks, such as video processing or AI-driven tasks, you may need more powerful instances. 5. Autoscaling: Implement autoscaling to adjust resources dynamically based on load. This ensures that your system can handle spikes in demand efficiently. Would you like more detailed guidance on any specific service or agent configuration? Sources: - Deployment and Scaling 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

my agents use state machınes , with voıce pıpelıne agent wıth egress servıces . same agent mus handle max 50 calls per mınute . what ıs your suugestıon ? @millions-hair-24956

To handle a maximum of 50 calls per minute with agents using state machines and VoicePipelineAgent with Egress services, consider the following suggestions: 1. Resource Allocation: Ensure each agent has sufficient resources to handle the expected load. For handling 50 calls per minute, consider using instances with at least 4 vCPUs and 8 GB of RAM per agent. This provides enough capacity for processing audio and managing state transitions efficiently. 2. Horizontal Scaling: Deploy multiple instances of your agent to distribute the load. Use Kubernetes' Horizontal Pod Autoscaler to automatically scale the number of agent pods based on CPU or memory usage. This ensures that your system can handle fluctuations in call volume. 3. Efficient State Management: Optimize your state machine logic to minimize processing time and resource usage. Ensure that state transitions are efficient and that the agent can quickly process and respond to incoming calls. 4. Egress Configuration: Configure the Egress service to efficiently handle recording or streaming tasks. Ensure that the Egress service is scaled appropriately to match the number of concurrent calls being processed by the agents. 5. Monitoring and Optimization: Implement monitoring using tools like Prometheus and Grafana to track the performance and health of your agents. Use this data to identify bottlenecks and optimize your configurations. Would you like more detailed guidance on any specific part of this setup? Sources: - Deployment and Scaling 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

you said : 4 vCPUs and 8 GB of RAM per agent. is it for 1 agent ? cant livekit handle agent capacıty ? for example my fıst agent have %70 capacıty lıvekıt needs to dıspatch second agent not 1 agent make 50 calls same tıme ? dispatching and job dıstıruptıon how to work ? give me best approach . @millions-hair-24956

LiveKit's architecture is designed to efficiently manage agent capacity and dispatch jobs based on available resources. Here's how you can optimize agent deployment and job distribution: 1. Resource Allocation: The recommendation of 4 vCPUs and 8 GB of RAM is a general guideline for a single agent handling complex tasks. However, LiveKit can manage multiple agents, and you can adjust resources based on your specific needs and testing results. 2. Job Distribution: LiveKit automatically dispatches jobs to available agents based on their capacity. If an agent reaches a certain load threshold (e.g., 70% capacity), LiveKit will dispatch new jobs to other available agents. This ensures that no single agent is overwhelmed. 3. Load Threshold Configuration: Configure the

load_threshold

parameter in the WorkerOptions class to define when an agent should stop accepting new jobs. This helps in balancing the load across multiple agents. 4. Horizontal Scaling: Deploy multiple agent instances to handle high call volumes. Use Kubernetes' Horizontal Pod Autoscaler to automatically scale the number of agent pods based on resource usage. This ensures that your system can handle spikes in demand efficiently. 5. Monitoring and Optimization: Implement monitoring to track agent performance and adjust configurations as needed. Use tools like Prometheus and Grafana to gain insights into resource usage and optimize your deployment. Would you like more detailed guidance on configuring load thresholds or scaling strategies? Sources: - Deployment and Scaling - Dispatching Agents 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

give every ınstallment instructıons for kubernetes , livekit with egress servıce , sip server and docker . azure aks . @millions-hair-24956

To set up LiveKit with Egress services, a SIP server, and Docker on Azure AKS, follow these step-by-step instructions: 1. Azure AKS Setup: - Create an Azure Kubernetes Service (AKS) cluster using the Azure portal or Azure CLI. Ensure it's configured for high availability across multiple availability zones. 2. Install Helm: - Install Helm on your local machine to manage Kubernetes applications.

curl https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash

3. Deploy LiveKit: - Add the LiveKit Helm repository and install LiveKit using Helm.

helm repo add livekit https://helm.livekit.io
   helm install livekit-server livekit/livekit-server --namespace livekit --create-namespace

- Configure LiveKit with necessary environment variables and secrets for API keys and Redis connection. 4. Deploy Egress Service: - Use the LiveKit Helm chart to deploy the Egress service.

helm install livekit-egress livekit/egress --namespace livekit

- Ensure Egress is configured to handle recording and streaming tasks efficiently. 5. Deploy SIP Server: - Create a Docker image for your SIP server (e.g., Asterisk or FreeSWITCH) and push it to a container registry. - Create a Kubernetes Deployment and Service for the SIP server, exposing necessary ports (e.g., 5060 for SIP, 10000-20000 for RTP). 6. Deploy Redis: - Use a Helm chart to deploy Redis in your AKS cluster.

helm repo add bitnami https://charts.bitnami.com/bitnami
   helm install redis bitnami/redis --namespace livekit

7. Configure Networking: - Set up Azure Load Balancer to distribute traffic to your services. - Ensure network policies and security groups are configured to allow necessary traffic while securing your services. 8. Monitoring and Logging: - Deploy Prometheus and Grafana for monitoring.

helm install prometheus prometheus-community/prometheus --namespace monitoring
   helm install grafana grafana/grafana --namespace monitoring

- Set up dashboards to monitor the performance and health of your services. 9. CI/CD Pipeline: - Use GitHub Actions or Azure DevOps to automate the build and deployment of your Docker images to AKS. 10. Testing and Optimization: - Conduct load testing to ensure your setup can handle the expected load. - Optimize configurations based on test results and monitoring data. Would you like more detailed guidance on any specific part of this setup? Sources: - https://livekit-users.slack.com/archives/C07FVFL1H3P/p1730875867.086059 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729636735.759029 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

also add ıngress to . wheere ı can confıgure yamls ? prod ready setup please . @millions-hair-24956

To set up a production-ready environment on Azure AKS with LiveKit, including Ingress, Egress, SIP server, and Redis, follow these steps: 1. Azure AKS Setup: - Create an Azure Kubernetes Service (AKS) cluster with high availability across multiple availability zones. 2. Install Helm: - Install Helm on your local machine to manage Kubernetes applications.

curl https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash

3. Deploy Redis: - Use a Helm chart to deploy Redis in your AKS cluster.

helm repo add bitnami https://charts.bitnami.com/bitnami
   helm install redis bitnami/redis --namespace livekit

4. Deploy LiveKit Server: - Add the LiveKit Helm repository and install LiveKit using Helm.

helm repo add livekit https://helm.livekit.io
   helm install livekit-server livekit/livekit-server --namespace livekit --create-namespace

- Configure LiveKit with necessary environment variables and secrets for API keys and Redis connection. 5. Deploy Egress Service: - Use the LiveKit Helm chart to deploy the Egress service.

helm install livekit-egress livekit/egress --namespace livekit

6. Deploy Ingress Service: - Use the LiveKit Helm chart to deploy the Ingress service.

helm install livekit-ingress livekit/ingress --namespace livekit

- Configure Ingress to handle incoming media streams and ensure it is set up with the correct ports and protocols. 7. Deploy SIP Server: - Create a Docker image for your SIP server (e.g., Asterisk or FreeSWITCH) and push it to a container registry. - Create a Kubernetes Deployment and Service for the SIP server, exposing necessary ports (e.g., 5060 for SIP, 10000-20000 for RTP). 8. Configure Networking: - Set up Azure Load Balancer to distribute traffic to your services. - Ensure network policies and security groups are configured to allow necessary traffic while securing your services. 9. Monitoring and Logging: - Deploy Prometheus and Grafana for monitoring.

helm install prometheus prometheus-community/prometheus --namespace monitoring
   helm install grafana grafana/grafana --namespace monitoring

1- ımpovemented setup for livekit + egress+ıngress kubernetes helm chart setup with best config for handle 1000 calls : @millions-hair-24956

To set up a robust LiveKit environment on Kubernetes using Helm charts, capable of handling 1000 simultaneous calls, follow these steps: 1. Azure AKS Cluster Setup: - Deploy an Azure Kubernetes Service (AKS) cluster with multiple nodes across different availability zones for high availability and scalability. 2. Install Helm: - Ensure Helm is installed on your local machine to manage Kubernetes applications.

curl https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash

3. Deploy Redis: - Use a Helm chart to deploy Redis, which is essential for LiveKit's messaging and state management.

helm repo add bitnami https://charts.bitnami.com/bitnami
   helm install redis bitnami/redis --namespace livekit

4. Deploy LiveKit Server: - Add the LiveKit Helm repository and install the LiveKit server.

helm repo add livekit https://helm.livekit.io
   helm install livekit-server livekit/livekit-server --namespace livekit --create-namespace

- Configure the server with appropriate environment variables, including API keys and Redis connection details. 5. Deploy Egress Service: - Use the LiveKit Helm chart to deploy the Egress service for recording and streaming.

helm install livekit-egress livekit/egress --namespace livekit

- Ensure Egress is configured to handle high volumes of recording and streaming tasks efficiently. 6. Deploy Ingress Service: - Use the LiveKit Helm chart to deploy the Ingress service for handling incoming media streams.

helm install livekit-ingress livekit/ingress --namespace livekit

- Configure Ingress to manage media streams effectively, ensuring it is set up with the correct ports and protocols. 7. Resource Configuration: - Define resource requests and limits in your Helm values files to ensure each component has sufficient CPU and memory. For handling 1000 calls, consider using nodes with at least 8 vCPUs and 16 GB of RAM. 8. Autoscaling: - Implement Horizontal Pod Autoscalers (HPA) for LiveKit, Egress, and Ingress services to automatically scale based on CPU and memory usage. 9. Monitoring and Logging: - Deploy Prometheus and Grafana for monitoring.

helm install prometheus prometheus-community/prometheus --namespace monitoring
   helm install grafana grafana/grafana --namespace monitoring