shippedbrain

Client library to publish models on app.shippedbrain.com

Create serverless REST endpoints for machine learning models and get hosted web pages instantly.

Using the CLI:

shippedbrain upload --model_name My-Amazing-Model --run_id 6f252757005748708cd3aad75d1ff462

Using the Python API:

code

Shipped Brain

Shipped Brain is an AI platform that allows anyone to share and upload machine lerning models fast and easily.

Managed REST API endpoints

####Use anywhere When a model is published on app.shippedbrain.com it gets its unique and standardized REST API endpoint automatically that can be embedded anywhere.

To use a shipped brain model you just need to make an HTTP POST request to the model's endpoint:

app.shippedbrain.com/api/v0/<your-model-name>/<version>

• Straightfoward deployments with one line of code
• Public REST endpoints that anyone can try and verify - managed for you
• No serving code and zero configuration

Hosted Model Pages

####Interactive web pages to get started easily An interactive web page for every model - automatically created and hosted for you.

Share your models' web pages and allow anyone to experiment with your models in the browser or using the REST API.

• An interactive web page for every model - automatically created and hosted for you
• In-app experimentation using model input examples
• Auto-generated model signatures on your models' web pages

Data Science Portfolio

Build a home for your AI.

Start creating your data science portfolio. Share you Shipped Brain profile with other AI specialists, companies or include it in your resumé. Your shipped brain profiles is a great way to showcase all your models.

• Leverage Shipped Brain's profiles and infrastructure to manage your online presence as an AI specialist
• Share your profile and models on social media platforms with one click

About shippedbrain

The shippedbrain client library provides a convenient way to publish models on app.shippedbrain.com.

It integrates with the widely used mlflow library, so any mlflow model will work on shipped brain. To publish a model on app.shippedbrain.com you just need to provide a valid mlflow run_id of a logged model. You can easily log models with mlflow using mlflow.log_model.

For more information on how to log models with mlflowread the documentation

Once you've logged your model you just need to refer the mlflow run_id when uploading a model to shipped brain. ####CLI Example shippedbrain upload --run_id <some_run_id> --model_name <my-model-name>

Usage

The shippedbrain client library has a Python API and CLI flavors.

CLI

shippedbrain [OPTIONS] COMMAND [ARGS]...

####Commands

upload - Deploy a model to app.shippedbrain.com : create a REST endpoint and hosted model page

Environment variables (advised)

• SHIPPED_BRAIN_EMAIL to skip de email prompt.
• SHIPPED_BRAIN_PASSWORD to skip de passoword prompt.

upload command

Deploy a model to app.shippedbrain.com - create a REST endpoint and get a hosted model web page

#####Options:

  -r, --run_id TEXT      The run_id of logged mlflow model  [required]
  -m, --model_name TEXT  The model name to display on app.shippedbrain.com [required]
  -f, --flavor TEXT      The mlflow flow flavor of the model
  --help                 Get help on how to use the 'upload' command

#####Example: Run:

shippedbrain upload --run_id <some_run_id> --model_name <my-model-name>

Prompt:

The command above will prompt the user to input their shipped brain email and password.

email: your@email.com
password: 

If the environment variables SHIPPED_BRAIN_EMAIL or SHIPPED_BRAIN_PASSWORD are set, the respective prompt options will be skipped.

Python API

To publish a model programatically you just need call the shippedbrain.upload function.

####shippedbrain.upload #####Arguments:

• email (str) - shipped brain account email
• password (str) - shipped brain account password
• run_id (str) - run_id of logged mlflow model
• model_name (str) - model name to display on app.shippedbrain.com

Example

You can easily publish a model with one line of code:

from shippedbrain import shippedbrain
# your custom code ...
shippedbrain.upload(email="<your@email.com>", password="<your_password>", model_name="<model-name>", run_id="<model-run-id>")

###End-to-end example You can find an example under the ./examples/elastic_net directory.

• train_and_log.py: trains linear model and logs it to mlflow
• requirements.txt: python requirements to run example

Before running the ./examples/elastic_net/train_and_log.py script you should update the following variables:

• SHIPPED_BRAIN_EMAIL with your shipped brain account email
• SHIPPED_BRAIN_PASSWORD with your shipped brain account password
• MODEL_NAME with the name of your published model on shipped brain

Run the example:

• Install the requirements: pip install -r ./examples/elastic_net/
• Train and publish the model: python ./examples/elastic_net/train_and_log.py --publish

You can omit the --publish flag if you do not want your model to be published on app.shippedbrain.com

Output example:

Using the CLI

To upload the trained model to app.shippedbrain.com you just need to collect the run_id of the logged model:

• --model_name - specify the model name published on app.shippedbrain.com
• --run_id - the run_id of the logged model

Run: shippedbrain upload --model_name ElasticWine --run_id <some_run_id>

Typical workflow integration using the Python API

If SHIPPED_BRAIN_EMAIL and/or SHIPPED_BRAIN_PASSWORD are set arguments email and password can be left blank, respectively

import mlflow
from shippedbrain import shippedbrain
# other imports...

SHIPPED_BRAIN_EMAIL="your_email@mail.com"
SHIPPED_BRAIN_PASSWORD="your_shippedbrain_password"
MODEL_NAME="ElasticWine"

with mlflow.start_run() as run:

    # required to upload a valid model on shipped brain
    signature = # use mlflow.models.signature.infer_signature
    input_example = # one input example of your data Union[pandas.DataFrame | numpy.ndarray] 

    # train model
    model = # trained model

    # log model
    mlflow.sklearn.log_model(model, "model", signature=signature, input_example=input_example)
    print(f"run_id='{run.info.run_id}'")

    # publish model
    shippedbrain.upload(email=SHIPPED_BRAIN_EMAIL, password=SHIPPED_BRAIN_PASSWORD, run_id=run.info.run_id, model_name=MODEL_NAME)