Kye
2 years ago
parent
e7cd99a3d4
commit
9d18f0968e
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version: '3.8'
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services:
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swarms:
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build: .
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ports:
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- "${EVAL_PORT}:${EVAL_PORT}"
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environment:
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EVAL_PORT: 8000
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MODEL_NAME: gpt-4
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CELERY_BROKER_URL: redis://redis:6379
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SERVER: http://localhost:${EVAL_PORT}
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USE_GPU: False
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PLAYGROUND_DIR: playground
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LOG_LEVEL: INFO
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BOT_NAME: Orca
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# You will need to set these environment variables to your actual keys in production
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OPENAI_API_KEY: your_openai_api_key
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WINEDB_HOST: your_winedb_host
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WINEDB_PASSWORD: your_winedb_password
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BING_SEARCH_URL: your_bing_search_url
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BING_SUBSCRIPTION_KEY: your_bing_subscription_key
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SERPAPI_API_KEY: your_serpapi_api_key
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depends_on:
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- redis
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volumes:
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- .:/usr/src/app
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redis:
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image: redis:alpine
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ports:
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- 6379:6379
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@ -0,0 +1,42 @@
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: swarms-deployment
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spec:
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replicas: 3
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selector:
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matchLabels:
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app: swarms
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template:
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metadata:
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labels:
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app: swarms
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spec:
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containers:
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- name: swarms
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image: your_dockerhub_username/swarms:latest
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ports:
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- containerPort: 8000
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env:
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- name: EVAL_PORT
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value: "8000"
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- name: MODEL_NAME
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value: "gpt-4"
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- name: CELERY_BROKER_URL
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value: "redis://redis:6379"
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- name: SERVER
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value: "http://localhost:8000"
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- name: USE_GPU
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value: "False"
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- name: PLAYGROUND_DIR
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value: "playground"
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- name: LOG_LEVEL
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value: "INFO"
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- name: BOT_NAME
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value: "Orca"
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- name: OPENAI_API_KEY
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valueFrom:
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secretKeyRef:
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name: openai-secret
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key: OPENAI_API_KEY
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# Other environment variables
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@ -0,0 +1,12 @@
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apiVersion: v1
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kind: Service
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metadata:
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name: swarms-service
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spec:
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selector:
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app: swarms
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ports:
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- protocol: TCP
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port: 80
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targetPort: 8000
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type: LoadBalancer
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@ -0,0 +1,208 @@
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To create a Terraform configuration for deploying the Swarm application on an AWS EC2 instance with a T4 GPU, you would typically need the following resources:
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1. **AWS Provider:** This is needed to configure the AWS resources.
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2. **AWS Key Pair:** This is required for SSH access to the EC2 instances.
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3. **Security Group:** This defines the firewall rules for your instances.
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4. **EC2 Instance:** This is where you deploy your application. Be sure to choose an instance type that supports T4 GPUs (like `g4dn.xlarge` for example).
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5. **IAM Role and Policy:** These are optional but recommended for managing permissions.
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The Terraform configuration file(s) should be written in HashiCorp Configuration Language (HCL). The conventional file extension is `.tf`.
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Here's an example of what the Terraform configuration might look like:
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```hcl
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provider "aws" {
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region = "us-west-2"
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}
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resource "aws_key_pair" "deployer" {
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key_name = "deployer-key"
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public_key = file("~/.ssh/id_rsa.pub")
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}
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resource "aws_security_group" "swarm-sg" {
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name = "swarm-sg"
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description = "Security group for Swarm app"
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ingress {
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from_port = 22
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to_port = 22
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protocol = "tcp"
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cidr_blocks = ["0.0.0.0/0"]
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}
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ingress {
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from_port = 8000
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to_port = 8000
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protocol = "tcp"
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cidr_blocks = ["0.0.0.0/0"]
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}
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egress {
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from_port = 0
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to_port = 0
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protocol = "-1"
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cidr_blocks = ["0.0.0.0/0"]
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}
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}
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resource "aws_instance" "swarm" {
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ami = "ami-0c94855ba95c574c8" # Update this with the correct AMI ID
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instance_type = "g4dn.xlarge"
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key_name = aws_key_pair.deployer.key_name
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vpc_security_group_ids = [aws_security_group.swarm-sg.id]
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tags = {
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Name = "SwarmInstance"
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}
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user_data = <<-EOF
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#!/bin/bash
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sudo apt-get update
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sudo apt-get install -y docker.io
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sudo docker pull your_docker_image_name
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sudo docker run -d -p 8000:8000 your_docker_image_name
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EOF
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}
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```
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Please replace the `"ami-0c94855ba95c574c8"` with the correct AMI ID for your desired operating system and `"your_docker_image_name"` with the name of your Docker image.
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This is a simple configuration and may not cover all your requirements. You might need to modify this to fit your needs, such as adding persistent storage (EBS volumes), load balancers, auto scaling groups, etc.
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Remember to install Terraform and initialize it in your working directory using `terraform init` before running `terraform apply` to create the resources. Also, ensure your AWS credentials are correctly set up in your environment.
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Incorporating persistent storage, load balancers, and auto scaling will make our Terraform configuration significantly more complex. Below is a skeleton of what the configuration might look like:
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```hcl
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provider "aws" {
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region = "us-west-2"
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}
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data "aws_ami" "ubuntu" {
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most_recent = true
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filter {
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name = "name"
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values = ["ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-*"]
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}
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filter {
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name = "virtualization-type"
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values = ["hvm"]
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}
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owners = ["099720109477"]
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}
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resource "aws_key_pair" "deployer" {
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key_name = "deployer-key"
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public_key = file("~/.ssh/id_rsa.pub")
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}
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resource "aws_security_group" "swarm-sg" {
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name = "swarm-sg"
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description = "Security group for Swarm app"
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ingress {
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from_port = 22
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to_port = 22
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protocol = "tcp"
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cidr_blocks = ["0.0.0.0/0"]
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}
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ingress {
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from_port = 8000
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to_port = 8000
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protocol = "tcp"
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cidr_blocks = ["0.0.0.0/0"]
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}
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egress {
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from_port = 0
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to_port = 0
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protocol = "-1"
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cidr_blocks = ["0.0.0.0/0"]
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}
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}
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resource "aws_launch_configuration" "swarm" {
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name = "swarm-configuration"
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image_id = data.aws_ami.ubuntu.id
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instance_type = "g4dn.xlarge"
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key_name = aws_key_pair.deployer.key_name
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security_groups = [aws_security_group.swarm-sg.id]
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user_data = <<-EOF
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#!/bin/bash
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sudo apt-get update
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sudo apt-get install -y docker.io
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sudo docker pull your_docker_image_name
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sudo docker run -d -p 8000:8000 your_docker_image_name
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EOF
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root_block_device {
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volume_type = "gp2"
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volume_size = 30 # size in GBs
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}
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lifecycle {
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create_before_destroy = true
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}
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}
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resource "aws_autoscaling_group" "swarm" {
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name_prefix = "swarm-asg"
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max_size = 5
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min_size = 1
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desired_capacity = 1
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launch_configuration = aws_launch_configuration.swarm.id
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lifecycle {
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create_before_destroy = true
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}
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}
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resource "aws_elb" "swarm" {
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name = "swarm-elb"
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subnets = ["subnet-id1", "subnet-id2"]
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listener {
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instance_port = 8000
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instance_protocol = "http"
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lb_port = 80
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lb_protocol = "http"
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}
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health_check {
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healthy_threshold = 2
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unhealthy_threshold = 2
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timeout = 3
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target = "HTTP:8000/"
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interval = 30
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}
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instances = [aws_instance.swarm.id]
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cross_zone_load_balancing = true
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idle_timeout = 400
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connection_draining = true
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connection_draining_timeout = 400
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}
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```
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In this example, the `aws_launch_configuration` sets up the details
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for launching new instances, including attaching an EBS volume for persistent storage. The `aws_autoscaling_group` uses this configuration to scale instances up and down as required.
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The `aws_elb` resource creates a load balancer that distributes incoming traffic across all the instances in the autoscaling group. The `health_check` block inside `aws_elb` is used to check the health of the instances. If an instance fails the health check, it is replaced by the autoscaling group.
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Please replace `"subnet-id1"` and `"subnet-id2"` with your actual subnet IDs and `"your_docker_image_name"` with the name of your Docker image.
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Again, note that this is a simplified example and may need to be adjusted to suit your particular use case. For instance, this configuration assumes that you are using a single security group for all instances, which might not be the best setup for a real-world scenario.
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Before running this Terraform configuration, make sure to initialize Terraform in your working directory using `terraform init`, and ensure that your AWS credentials are correctly set up in your environment.
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@ -0,0 +1,115 @@
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provider "aws" {
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region = "us-west-2"
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}
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data "aws_ami" "ubuntu" {
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most_recent = true
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filter {
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name = "name"
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values = ["ubuntu/images/hvm-ssd/ubuntu-focal-20.04-amd64-server-*"]
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}
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filter {
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name = "virtualization-type"
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values = ["hvm"]
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}
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owners = ["099720109477"]
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}
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resource "aws_key_pair" "deployer" {
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key_name = "deployer-key"
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public_key = file("~/.ssh/id_rsa.pub")
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}
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resource "aws_security_group" "swarm-sg" {
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name = "swarm-sg"
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description = "Security group for Swarm app"
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ingress {
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from_port = 22
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to_port = 22
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protocol = "tcp"
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cidr_blocks = ["0.0.0.0/0"]
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}
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ingress {
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from_port = 8000
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to_port = 8000
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protocol = "tcp"
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cidr_blocks = ["0.0.0.0/0"]
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}
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egress {
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from_port = 0
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to_port = 0
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protocol = "-1"
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cidr_blocks = ["0.0.0.0/0"]
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}
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}
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resource "aws_launch_configuration" "swarm" {
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name = "swarm-configuration"
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image_id = data.aws_ami.ubuntu.id
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instance_type = "g4dn.xlarge"
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key_name = aws_key_pair.deployer.key_name
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security_groups = [aws_security_group.swarm-sg.id]
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user_data = <<-EOF
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#!/bin/bash
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sudo apt-get update
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sudo apt-get install -y docker.io
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sudo docker pull your_docker_image_name
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sudo docker run -d -p 8000:8000 your_docker_image_name
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EOF
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root_block_device {
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volume_type = "gp2"
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volume_size = 30 # size in GBs
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}
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lifecycle {
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create_before_destroy = true
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}
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}
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resource "aws_autoscaling_group" "swarm" {
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name_prefix = "swarm-asg"
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max_size = 5
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min_size = 1
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desired_capacity = 1
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launch_configuration = aws_launch_configuration.swarm.id
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lifecycle {
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create_before_destroy = true
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}
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}
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resource "aws_elb" "swarm" {
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name = "swarm-elb"
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subnets = ["subnet-id1", "subnet-id2"]
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listener {
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instance_port = 8000
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instance_protocol = "http"
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lb_port = 80
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lb_protocol = "http"
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}
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health_check {
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healthy_threshold = 2
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unhealthy_threshold = 2
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timeout = 3
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target = "HTTP:8000/"
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interval = 30
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}
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instances = [aws_instance.swarm.id]
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cross_zone_load_balancing = true
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idle_timeout = 400
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connection_draining = true
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connection_draining_timeout = 400
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}
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