Question: I try to run my lambda function with localstack. I installed awscli-local and localstack

> 
> pip3 install awscli-local
> 
> 
> pip3 install --user localstack --ignore-installed six
> 
> 
> 

And then I started localstack 

> 
> LAMDBA\_EXECUTOR=docker localstack start --docker
> 
> 
> 

When I now want to create my lambda function

> 
> aws lambda create-function --function-name Test --zip-file
>  fileb://myLambda.zip --handler index.handler --runtime
>  'nodejs6.10' --endpoint <http://localhost:4574> --role admin
> 
> 
> 

I get this error

> 
> An error occurred (ResourceConflictException) when calling the
>  CreateFunction operation: Function already exist: Test
> 
> 
> 

Listing the functions returns nothing

> 
> aws lambda list-functions --endpoint <http://localhost:4574>
> 
> 
> 

Does someone know why localstack thinks that the function is already there?

Answer: 
You can invoke lambdas directly in localstack from the [Commandeer App](https://getcommandeer.com). It installs localstack under the hood with docker.

[![enter image description here](https://i.stack.imgur.com/kL7K2.png)](https://i.stack.imgur.com/kL7K2.png)

There is a button on the lambda detail that allows you to specify the payload and then view the cloudwatch logs.

[![enter image description here](https://i.stack.imgur.com/hucJn.png)](https://i.stack.imgur.com/hucJn.png)
I'm also seeing this issue. Though it does not happen each time I try to create a lambda in localstack. What I have noticed is that lambda create seems to take a rather long time and cause a lot of CPU consumption on my mac while it is creating the lambda. My initial guess is that because of the time being take to create the ambda, something is timing out during the lambda creation and it's as if the creation is retried internally and it finds the lambda exits. If I query for the lambda after receiving this error message with `awslocal`, I see it exists.

I am running this on a MBP with 32Gb of memory and upped the allocation of resources to the Docker engine to 16Gb and 8 processors in hopes of solving this with additional resources, but that has not seemed to help. Suggestions welcome.