Amazon Web Services deployments

Examples of AWS deployments can be found in github.com/developmentseed/titiler/tree/main/deployment/aws. Those examples use AWS Cloud Development Kit to define stacks using python code.

Configuration/Settings

Deployment settings are managed via pydantic.BaseSettings and stored in config.py. Pydantic BaseSettings can receive input to overwrite the default value from a .env file or from environment variables.

Variables in .env or in environment variable need to be prefixed with TITILER_STACK_:

TITILER_STACK_NAME="my-tiler"
TITILER_STACK_STAGE="dev"

TITILER_STACK_BUCKETS='["my-bucket*", "*"]'

TITILER_STACK_MEMORY=3008

# Uncomment to allow lambda to access content on requester-payer buckets
# TITILER_STACK_ENV='{"AWS_REQUEST_PAYER":"requester"}'

# Uncomment if you only on the /cog endpoint
# TITILER_STACK_ENV='{"TITILER_API_DISABLE_STAC": "TRUE", "TITILER_API_DISABLE_MOSAIC": "TRUE"}'

Default values from config.py:

name: str = "titiler"
stage: str = "production"

owner: Optional[str]
client: Optional[str]

# Default options are optimized for CloudOptimized GeoTIFF
# For more information on GDAL env see: https://gdal.org/user/configoptions.html
env: Dict = {
    "CPL_VSIL_CURL_ALLOWED_EXTENSIONS": ".tif,.TIF,.tiff",
    "GDAL_CACHEMAX": "200" # 200 mb
    "GDAL_DISABLE_READDIR_ON_OPEN": "EMPTY_DIR",
    "GDAL_HTTP_MERGE_CONSECUTIVE_RANGES": "YES",
    "GDAL_HTTP_MULTIPLEX": "YES",
    "GDAL_HTTP_VERSION": "2",
    "PYTHONWARNINGS": "ignore",
    "VSI_CACHE": "TRUE",
    "VSI_CACHE_SIZE": "5000000" # 5 MB (per file-handle)
}

# add S3 bucket where TiTiler could do HEAD and GET Requests
buckets: List = []

###########################################################################
# AWS ECS
# The following settings only apply to AWS ECS deployment
min_ecs_instances: int = 5
max_ecs_instances: int = 50

# CPU value      |   Memory value
# 256 (.25 vCPU) | 0.5 GB, 1 GB, 2 GB
# 512 (.5 vCPU)  | 1 GB, 2 GB, 3 GB, 4 GB
# 1024 (1 vCPU)  | 2 GB, 3 GB, 4 GB, 5 GB, 6 GB, 7 GB, 8 GB
# 2048 (2 vCPU)  | Between 4 GB and 16 GB in 1-GB increments
# 4096 (4 vCPU)  | Between 8 GB and 30 GB in 1-GB increments
task_cpu: int = 256
task_memory: int = 512

# GUNICORN configuration
# Ref: https://github.com/developmentseed/titiler/issues/119

# WORKERS_PER_CORE
# This image will check how many CPU cores are available in the current server running your container.
# It will set the number of workers to the number of CPU cores multiplied by this value.
workers_per_core: int = 1

# MAX_WORKERS
# You can use it to let the image compute the number of workers automatically but making sure it's limited to a maximum.
# should depends on `task_cpu`
max_workers: int = 1

# WEB_CONCURRENCY
# Override the automatic definition of number of workers.
# Set to the number of CPU cores in the current server multiplied by the environment variable WORKERS_PER_CORE.
# So, in a server with 2 cores, by default it will be set to 2.
web_concurrency: Optional[int]

image_version: str = "latest"

###########################################################################
# AWS LAMBDA
# The following settings only apply to AWS Lambda deployment
timeout: int = 10
memory: int = 1536
# more about lambda config: https://www.sentiatechblog.com/aws-re-invent-2020-day-3-optimizing-lambda-cost-with-multi-threading

# The maximum of concurrent executions you want to reserve for the function.
# Default: - No specific limit - account limit.
max_concurrent: Optional[int]