create-deployment | Create GitHub deployments from GitHub actions | Continuous Deployment library

TL;DR: You cannot compile the deployment package on your Mac or whatever pc you use. You have to do it using a specific OS/"setup", the same one that AWS Lambda uses to run your code. To do this, you have to use EC2.

I will provide here an answer on how to get Google Cloud Vision working on AWS Lambda for Python 2.7. This answer is potentially extendable for other other APIs and other programming languages on AWS Lambda.

So the my journey to a solution began with this initial posting on Github with others who have the same issue. One solution someone posted was

I had the same issue " cannot import name 'cygrpc' " while running the lambda. Solved it with pip install google-cloud-vision in the AMI amzn-ami-hvm-2017.03.1.20170812-x86_64-gp2 instance and exported the lib/python3.6/site-packages to aws lambda Thank you @tseaver

This is partially correct, unless I read it wrong, but regardless it led me on the right path. You will have to use EC2. Here are the steps I took:

1. Set up an EC2 instance by going to EC2 on Amazon. Do a quick read about AWS EC2 if you have not already. Set one up for amzn-ami-hvm-2018.03.0.20180811-x86_64-gp2 or something along those lines (i.e. the most updated one).

2. Get your EC2 .pem file. Go to your Terminal. cd into your folder where your .pem file is. ssh into your instance using

   ssh -i "your-file-name-here.pem" ec2-user@ec2-ip-address-here.compute-1.amazonaws.com

3. Create the following folders on your instance using mkdir: google-cloud-vision, protobuf, google-api-python-client, httplib2, uritemplate, google-auth-httplib2.

4. On your EC2 instance, cd into google-cloud-vision. Run the command:

   pip install google-cloud-vision -t .

Note If you get "bash: pip: command not found", then enter "sudo easy_install pip" source.

1. Repeat step 4 with the following packages, while cd'ing into the respective folder: protobuf, google-api-python-client, httplib2, uritemplate, google-auth-httplib2.

2. Copy each folder on your computer. You can do this using the scp command. Again, in your Terminal, not your EC2 instance and not the Terminal window you used to access your EC2 instance, run the command (below is an example for your "google-cloud-vision" folder, but repeat this with every folder):

   sudo scp -r -i your-pem-file-name.pem ec2-user@ec2-ip-address-here.compute-1.amazonaws.com:~/google-cloud-vision ~/Documents/your-local-directory/

3. Stop your EC2 instance from the AWS console so you don't get overcharged.

4. For your deployment package, you will need a single folder containing all your modules and your Python scripts. To begin combining all of the modules, create an empty folder titled "modules." Copy and paste all of the contents of the "google-cloud-vision" folder into the "modules" folder. Now place only the folder titled "protobuf" from the "protobuf" (sic) main folder in the "Google" folder of the "modules" folder. Also from the "protobuf" main folder, paste the Protobuf .pth file and the -info folder in the Google folder.

5. For each module after protobuf, copy and paste in the "modules" folder the folder titled with the module name, the .pth file, and the "-info" folder.

6. You now have all of your modules properly combined (almost). To finish combination, remove these two files from your "modules" folder: googleapis_common_protos-1.5.3-nspkg.pth and google_cloud_vision-0.34.0-py3.6-nspkg.pth. Copy and paste everything in the "modules" folder into your deployment package folder. Also, if you're using GCP, paste in your .json file for your credentials as well.

7. Finally, put your Python scripts in this folder, zip the contents (not the folder), upload to S3, and paste the link in your AWS Lambda function and get going!

If something here doesn't work as described, please forgive me and either message me or feel free to edit my answer. Hope this helps.

You should install the dependencies of your lambda locally before deploying the lambda via CDK. CDK does not have idea how to install the dependencies and which libraries should be installed.

In you case, you should install the dependency requests and other libraries before executing cdk deploy.

For example,

I solved this with a one-line adjustment to the original attempt. You just need to add /tmp/ to sys.path so that Python knows to search /tmp/ for the module. All you need to do is add the line sys.path.insert(1, '/tmp/').

Solution

You can use the target (t) flag of pip install to specify a target location for installation.

In use:

This codedeploy_deploy.py script is not supported anymore. The recommended way is to migrate from the CodeDeploy addon to aws-code-deploy Bitbucket Pipe. There is a deployment guide from Atlassian that will help you to get started with the pipe: https://confluence.atlassian.com/bitbucket/deploy-to-aws-with-codedeploy-976773337.html

You didn't mention which error you get when you invoke the pipeline? Are you getting this error:

"The deployment failed because no instances were found in your green fleet"

Taking this assumption, since you are using manual tagging in your CodeDeploy configuration, this is not going to work to deploy using Blue/Green with manual tags as CodeDeploy expects to see a tagSet to find the "Green" instances and there is no way to provide this information via CodePipeline.

To workaround this, please use the 'Copy AutoScaling' option for implementing Blue/Green deployments in CodeDeploy using CodePipeline. See Step 10 here [1]

Another workaround is that you can create lambda function that is invoked as an action in your CodePipeline. This lambda function can be used to trigger the CodeDeploy deployment where you specify the target-instances with the value of the green AutoScalingGroup. You will then need to make describe calls at frequent intervals to the CodeDeploy API to get the status of the deployment. Once the deployment has completed, your lambda function will need to signal back to the CodePipeline based on the status of the deployment.

Here is an example which walks through how to invoke an AWS lambda function in a pipeline in CodePipeline [2].

Ref:

[1] https://docs.aws.amazon.com/codedeploy/latest/userguide/applications-create-blue-green.html

[2] https://docs.aws.amazon.com/codepipeline/latest/userguide/actions-invoke-lambda-function.html

If you use the AWS Lambda Project(.Net Core) template, you can only write one function per project. You can see that the aws-lambda-tools-defaults.json file only contains configuration for one function.

However, if you use AWS Serverless Application(.Net Core) template, you can manage multiple Lambda functions in one project to response to different API call using API Gateway. This is achieved through CloudFormation.

Check out this AWS ReInvent video: https://www.youtube.com/watch?v=Ymn6WGCSjE4&t=24s Jump to 31:08 to see how AWS Serverless Application with multiple Lambda functions works.

-r9 is a combination of the -r and -9 switches.

The switch -9 means strongest compression, on a scale from 0 to 9.

Type zip for a list of options.

To answer your question about Cloud Shell in Azure Portal, please see this link: https://docs.microsoft.com/en-us/azure/cloud-shell/quickstart.

Regarding your comments about the quality of the content on Microsoft Learn, you can provide feedback by navigating to the bottom of the page (https://docs.microsoft.com/en-us/learn/modules/stage-deploy-app-service-deployment-slots/3-exercise-create-deployment-slots) and then clicking on reporting an issue. This will redirect you to this link.

You can provide detailed feedback there. In my experience, Microsoft folks are quite responsive to the feedback.