Lithops with AWS Lambda as serverless compute backend.
- Install Amazon Web Services backend dependencies:
python3 -m pip install lithops[aws]
-
Login to Amazon Web Services Console (or signup if you don't have an account)
-
Navigate to IAM > Policies. Click on Create policy.
-
Select JSON tab and paste the following JSON policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": [
"s3:*",
"lambda:*",
"ec2:*",
"ecr:*",
"sts:GetCallerIdentity",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "*"
}
]
}-
Click Next: Tags and Next: Review. Fill the policy name field (you can name it
lithops-policyor simmilar) and create the policy. -
Go back to IAM and navigate to Roles tab. Click Create role.
-
Choose Lambda on the use case list and click Next: Permissions. Select the policy created before (
lithops-policy). Click Next: Tags and Next: Review. Type a role name, for examplelithops-execution-role. Click on Create Role.
- Edit your lithops config and add the following keys:
lithops:
backend: aws_lambda
aws:
region: <REGION_NAME>
access_key_id: <AWS_ACCESS_KEY_ID>
secret_access_key: <AWS_SECRET_ACCESS_KEY>
aws_lambda:
execution_role: <EXECUTION_ROLE_ARN>| Group | Key | Default | Mandatory | Additional info |
|---|---|---|---|---|
| aws | region | yes | AWS Region. For example us-east-1 |
|
| aws | access_key_id | yes | Account access key to AWS services. To find them, navigate to My Security Credentials and click Create Access Key if you don't already have one. | |
| aws | secret_access_key | yes | Account secret access key to AWS services. To find them, navigate to My Security Credentials and click Create Access Key if you don't already have one. | |
| aws | session_token | no | Session token for temporary AWS credentials | |
| aws | account_id | no | *This field will be used if present to retrieve the account ID instead of using AWS STS. The account ID is used to format full image names for container runtimes. |
| Group | Key | Default | Mandatory | Additional info |
|---|---|---|---|---|
| aws_lambda | execution_role | yes | ARN of the execution role created at step 3. You can find it in the Role page at the Roles list in the IAM section (e.g. arn:aws:iam::1234567890:role/lithops-execution-role |
|
| aws_lambda | region | no | Region where the S3 bucket is located and where Lambda functions will be invoked (e.g. us-east-1). Lithops will use the region set under the aws section if it is not set here |
|
| aws_lambda | max_workers | 1000 | no | Max number of workers per FunctionExecutor() |
| aws_lambda | worker_processes | 1 | no | Number of Lithops processes within a given worker. This can be used to parallelize function activations within a worker |
| aws_lambda | runtime | no | Docker image name | |
| aws_lambda | runtime_memory | 256 | no | Memory limit in MB. Default 256MB |
| aws_lambda | runtime_timeout | 180 | no | Runtime timeout in seconds. Default 3 minutes |
| aws_lambda | invoke_pool_threads | 64 | no | Number of concurrent threads used for invocation |
| aws_lambda | remote_invoker | False | no | Activate the remote invoker feature that uses one cloud function to spawn all the actual map() activations |
| aws_lambda | architecture | x86_64 | no | Runtime architecture. One of x86_64 or arm64 |
| aws_lambda | ephemeral_storage | 512 | no | Ephemeral storage (/tmp) size in MB (must be between 512 MB and 10240 MB) |
| aws_lambda | env_vars | {} | no | List of {name: ..., value: ...} pairs for Lambda instance environment variables |
To connect the Lithops lambda to a VPC, add the following configuration to the aws_lambda configuration section:
aws_lambda:
execution_role: <EXECUTION_ROLE_ARN>
region: <REGION_NAME>
vpc:
subnets:
- <SUBNET_ID_1>
- <SUBNET_ID_2>
...
security_groups:
- <SECURITY_GROUP_1>
- <SECURITY_GROUP_2>
...subnets: A list of VPC subnet IDs.security_groups: A list of VPC security groups IDs.
Note: To be able to create network interfaces for Lambda functions, the role created in step 3 has to have permissions to do so, for example by adding the permission EC2FullAccess.
For more information, check out AWS documentation on VPCs.
To attach EFS volumes to the Lithops lambda, add the following configuration to the aws_lambda configuration section:
aws_lambda:
execution_role: <EXECUTION_ROLE_ARN>
region: <REGION_NAME>
vpc:
...
efs:
- access_point: <EFS_ACCESS_POINT_1>
mount_path: <LAMBDA_VOLUME_MOUNT_PATH_1>
- access_point: <EFS_ACCESS_POINT_2>
mount_path: <LAMBDA_VOLUME_MOUNT_PATH_2>
...access_point: The Amazon Resource Name (ARN) of the Amazon EFS access point that provides access to the file system.mount_path: The path where the function can access the file system. It must start with/mnt.
Note: to access those volumes, the Lithops lambda has to be connected to the same VPC and subnets as the EFS volumes are mounted to.
For more information, check out AWS documentation on attaching EFS volumes to Lambda.
Once you have your compute and storage backends configured, you can run a hello world function with:
lithops hello -b aws_lambda -s aws_s3You can view the function executions logs in your local machine using the lithops client:
lithops logs poll