fastdeploy 3.0.0rc1

Deploy DL/ ML inference pipelines with minimal extra code.

fastDeploy

• Deploy any python inference pipeline with minimal extra code
• Auto batching of concurrent inputs is enabled out of the box
• Promethues metrics (open metrics) are exposed for monitoring
• Auto generates clean kubernetes friendly dockerfiles and apis
• chained inference pipelines are supported out of the box
• optimized REST, websocket and rpc apis are exposed for inference

Installation:

pip install --upgrade fastdeploy

Usage:

# Invoke fastdeploy 
fastdeploy --help
# or
python -m fastdeploy --help

# Start prediction "loop" for recipe "echo_json"
fastdeploy --loop --recipe recipes/echo_json

# Start rest apis for recipe "echo_json"
fastdeploy --rest --recipe recipes/echo_json

# Writes the dockerfile for recipe "echo_json"
# and builds the docker image if docker is installed
fastdeploy --build --recipe recipes/echo_json

# Run docker image
docker run -it -p8080:8080 fastdeploy_echo_json

• Writing your model/pipeline's recipe

Where to use fastDeploy?

• to deploy any non ultra light weight models i.e: most DL models, >50ms inference time per example
• if you are going to have individual inputs (example, user's search input which needs to be vectorized or image to be classified)
• in the case of individual inputs, requests coming in at close intervals will be batched together and sent to the model as a batch
• perfect for creating internal micro services separating your model, pre and post processing from business logic
• since prediction loop and inference endpoints are separated and are connected via sqlite backed queue, can be scaled independently

Where not to use fastDeploy?

• non cpu/gpu heavy models that are better of running parallely rather than in batch
• if your predictor calls some external API or db etc
• io heavy non batching use cases (eg: query ES or db for each input)
• for these cases better to directly do from rest api code so that high concurrency can be achieved