mlrpc 0.2.4

Deploy FastAPI applications on MLFlow

mlrpc

Host REST APIs via FastAPI in Databricks Serverless Model serving via this rpc abstraction.

This tool is not a framework, in the sense that you do not modify your code! It acts as a build step/proxy layer and invokes your FastAPI code in a databricks serverless model serving environment and routes to the right endpoints properly without you having to know about custom python models.

Installation

Use the cli to install the package

pip install -U 'mlrpc[cli]'

Instructions

Keep in mind that the cli has a lot of options but all of them are also managable via the config file.

1. Make a config file

mlrpc init

2. Edit the config file

## You need to have the app section
# [app]
# name = <app name>
# uc_catalog = <catalog name>
# uc_schema = <schema name>
# endpoint_name = <endpoint name>

# Cost controls
# size = Small # Small, Medium, Large
# scale_to_zero_enabled = true # true, false

## The following lines are optional and not required

# latest_alias_name = <alternative alias> uses "current" by default

# experiment_name = <experiment name> # optional specify your experiment name

## Define where your code is
# app_root_dir = <root of your directory>

## The following lines are optional if you need secrets
# secret_scope = <secret scope in databricks>
# secret_key = <secret key>
# env_file = <location of your env file relative to this file>

## use this setting if you want to delete old versions
# only_last_n_versions=10

3. Deploy the artifacts

mlrpc deploy -p <databricks profile>

4. [OPTIONAL] verify in valid model deployment server locally

mlrpc local -p <databricks profile>

5. Deploy to model serving infra

mlrpc serve -p <databricks profile>

6. Explore the deployed endpoint via swagger proxy

mlrpc serve -p <databricks profile>