3 ms·
It says this, is it ok? I can check when I get home ChatGPT: Below is an AWS CloudFormation YAML template for creating an AWS Lambda function with 16GB of memo
by toxicFork 4y ago
It says this, is it ok? I can check when I get home
ChatGPT: Below is an AWS CloudFormation YAML template for creating an AWS Lambda function with 16GB of memory:
```yaml
AWSTemplateFormatVersion: '2010-09-09'
Description: 'AWS CloudFormation Lambda Function Template with 16GB Memory'
Resources:
LambdaFunctionRole:
Type: 'AWS::IAM::Role'
Properties:
AssumeRolePolicyDocument:
Version: '2012-10-17'
Statement:
- Effect: Allow
Principal:
Service:
- lambda.amazonaws.com
Action:
- sts:AssumeRole
Path: /
Policies:
- PolicyName: LambdaExecutionPolicy
PolicyDocument:
Version: '2012-10-17'
Statement:
- Effect: Allow
Action:
- logs:CreateLogGroup
- logs:CreateLogStream
- logs:PutLogEvents
Resource: 'arn:aws:logs:::*'
LambdaFunction:
Type: 'AWS::Lambda::Function'
Properties:
FunctionName: MyLambdaFunction
Runtime: python3.9
Role: !GetAtt LambdaFunctionRole.Arn
Handler: lambda_function.lambda_handler
MemorySize: 16384
Timeout: 300
Code:
ZipFile: |
import json
def lambda_handler(event, context):
return {
'statusCode': 200,
'body': json.dumps('Hello from Lambda with 16GB memory!')
}
```
This CloudFormation template creates an IAM role and a Lambda function with 16GB (16384MB) of memory. The Lambda function runtime is Python 3.9, but you can change this to another runtime if necessary. The code block contains a basic Python script for returning a simple message; you can replace this with your own script.
- maccard 4y agoYep! Except lambda limits are 10Gb [0],so this doesn't work. If you ask ChatGPT to generate a template for a lambda with 16 vCPUs, it will generate a template that allows a max of 16 concurrent invocations, which again is not what you asked it for, and unless you _know_ what reserved concurrenct executions are, you won't know that's what it's generated. [0] https://docs.aws.amazon.com/lambda/latest/operatorguide/computing-power.html#:~:text=You%20can%20configure%20the%20amount,128%20MB%20and%2010%2C240%20MB https://docs.aws.amazon.com/lambda/latest/operatorguide/comp....
- toxicFork 4y agoI told it: In here, it says that maximum memory is 10240mb, so that may not work: Memory and computing power Memory is the principal lever available to Lambda developers for controlling the performance of a function. You can configure the amount of memory allocated to a Lambda function, between 128 MB and 10,240 MB. The Lambda console defaults new functions to the smallest setting and many developers also choose 128 MB for their functions. Its response: ChatGPT: I apologize for the confusion earlier. You are correct that the maximum memory allocation for a Lambda function is 10,240 MB. Here is the revised CloudFormation YAML template with 10,240 MB memory allocation: ```yaml Resources: LambdaFunction: Type: AWS::Lambda::Function Properties: FunctionName: LambdaWith10GBMemory Handler: index.handler Runtime: nodejs14.x MemorySize: 10240 Timeout: 300 Role: !GetAtt 'LambdaRole.Arn' Code: S3Bucket: <S3_BUCKET_NAME> S3Key: <S3_KEY> LambdaRole: Type: AWS::IAM::Role Properties: RoleName: LambdaRoleWith10GBMemory AssumeRolePolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Principal: Service: - lambda.amazonaws.com Action: - sts:AssumeRole Policies: - PolicyName: LambdaBasicExecution PolicyDocument: Version: '2012-10-17' Statement: - Effect: Allow Action: - logs:CreateLogGroup - logs:CreateLogStream - logs:PutLogEvents Resource: arn:aws:logs:*:*:* ``` Replace <S3_BUCKET_NAME> and <S3_KEY> with the appropriate values for your S3 bucket and object key that contain your Lambda function code. You may also adjust Runtime, Handler, and Timeout as needed. === Then I said: What can we do to be able to use 16gb memory? Then it said: ChatGPT: To use 16 GB memory in a serverless context, you will need to consider other options as AWS Lambda currently supports up to 10,240 MB. Two alternatives are AWS Fargate or running a container with Amazon ECS. Here's an example of using AWS Fargate with a task definition for a container with 16 GB of memory: 1. Create an ECS cluster: aws ecs create-cluster --cluster-name MyFargateCluster 2. Create a task definition with 16 GB of memory (you can adjust the values as needed). Save the JSON below to a file called fargate-task-definition.json: { "family": "FargateTaskWith16GBMemory", "taskRoleArn": "<YOUR_TASK_ROLE_ARN>", "executionRoleArn": "<YOUR_EXECUTION_ROLE_ARN>", "networkMode": "awsvpc", "containerDefinitions": [ { "name": "MyContainer", "image": "<YOUR_DOCKER_IMAGE>", "cpu": 4096, "memory": 16384, "memoryReservation": 16384, "essential": true, "portMappings": [ { "containerPort": 80, "hostPort": 80, "protocol": "tcp" } ] } ], "requiresCompatibilities": [ "FARGATE" ], "cpu": "4096", "memory": "16384" } Replace <YOUR_TASK_ROLE_ARN>, <YOUR_EXECUTION_ROLE_ARN> and <YOUR_DOCKER_IMAGE> with appropriate values for your use case. 3. Register the task definition: aws ecs register-task-definition --cli-input-json file://fargate-task-definition.json 4. Launch the Fargate task: aws ecs run-task --cluster MyFargateCluster --launch-type FARGATE --task-definition FargateTaskWith16GBMemory --network-configuration "awsvpcConfiguration={subnets=[<YOUR_SUBNET_ID>],securityGroups=[<YOUR_SECURITY_GROUP_ID>]}" Replace <YOUR_SUBNET_ID> and <YOUR_SECURITY_GROUP_ID> with the appropriate values for your VPC and security group. This will give you a Fargate container running with 16 GB of memory, and you can adjust the resources allocated to the container as needed. ==== I think that's quite a nice answer.