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Client library for Bedrock platform

Project description

Bedrock helps data scientists own the end-to-end deployment of machine learning workflows. bdrk is the official client library for interacting with APIs on Bedrock platform.


Full documentation and tutorials on Bedrock can be found here


In order to use bdrk, you need to register an account with Basis AI. Please email to get started. Once an account is created, you will be issued a personal API token that you can use to authenticate with Bedrock.

Installing Bedrock client

You can install Bedrock client library from PyPi with the following command. We recommend running it in a virtual environment to prevent potential dependency conflicts.

pip install bdrk

Note that the client library is officially supported for python 3.7 and above.

Installing optional dependencies

The following optional dependencies can be installed to enable additional featues.

Command line support:

pip install bdrk[cli]

Model monitoring support:

pip install bdrk[model-monitoring]

Setting up your environment

Once installed, you need to add a well formed bedrock.hcl configuration file in your project's root directory. The configuration file specifies which script to run for training and deployment as well as their respective base Docker images. You can find an example directory layout here.

When using the module locally, you may need to define the following environment variables for bedrock_client and lab runs to make API calls to Bedrock. These variables will be automatically set on your workload container when running in cluster.

export BEDROCK_API_TOKEN=<your personal API token>

bedrock_client library

The bedrock_client library provides utility functions for your training runs.

Logging training metrics

You can easily export training metrics to Bedrock by adding logging code to The example below demonstrates logging charts and metrics for visualisation on Bedrock platform.

import logging

from bedrock_client.bedrock.api import BedrockApi

logger = logging.getLogger(__name__)
bedrock = BedrockApi(logger)
bedrock.log_metric("Accuracy", 0.97)
bedrock.log_chart_data([0, 1, 1], [0.1, 0.7, 0.9])

Logging feature and inference distribution

You may use the model monitoring service to save the distribution of input and model output data to a local file. The default path is /artefact/histogram.prom so as to bundle the computed distribution together with the model artefact it trained from. When trained on Bedrock, the zipped /artefact directory will be uploaded to user's blob storage bucket in the workload cluster.

import pandas as pd
from bedrock_client.bedrock.metrics.service import ModelMonitoringService
from sklearn.svm import SVC

# User code to load training data
features = pd.DataFrame({'a': [1, 2, 3], 'b': [3, 2, 1]})
model = SVC(probability=True), [False, True, False])
inference = model.predict_proba(features)[:, 0]


Logging explainability and fairness metrics

Bedrock offers facility to generate and log explainability and fairness (XAFAI) metrics. bdrk provides an easy to use API and native integration with Bedrock platform to visualize XAFAI metrics. All data is stored in your environment's blob storage to ensure nothing leaves your infrastructure. Under the hood it uses shap library to provide both global and individual explainability, and AI Fairness 360 toolkit to compare model behaviors between groups of interest for fairness assessment.

# As part of your
from bedrock_client.bedrock.analyzer.model_analyzer import ModelAnalyzer
from bedrock_client.bedrock.analyzer import ModelTypes

# Background data is used to simulate "missing" features to measure the
# impact.

# By default background data is limited to maximum of 5000 rows to
# speed up analysis
background = x_train

# Tree model: xgboost, lightgbm. Other types of model e.g Tensorflow, Pytorch... are also
# supported.
analyzer = ModelAnalyzer(

# Metrics are calculated and uploaded to blob storage

bdrk library

The bdrk library provides APIs for interacting with the Bedrock platform.

from bdrk.v1 import ApiClient, Configuration, PipelineApi
from bdrk.v1.models import (

configuration = Configuration()
configuration.api_key["X-Bedrock-Access-Token"] = "MY-TOKEN" = ""

api_client = ApiClient(configuration)
pipeline_api = PipelineApi(api_client)

pipeline = pipeline_api.get_training_pipeline_by_id(pipeline_id="MY-PIPELINE")
run_schema = TrainingPipelineRunSchema(
    resources=PipelineResourcesSchema(cpu="500m", memory="200M"),
    script_parameters={"MYPARAM": "1.23"},
run = pipeline_api.run_training_pipeline(
    pipeline_id=pipeline.public_id, training_pipeline_run_schema=run_schema

Lab run

The labrun command can be used to launch test runs of local training code on the Bedrock platform.

  # Set environment variables with credentials for this session
  $ unset HISTFILE # Don't save history for this session
  $ export BEDROCK_API_TOKEN=<your personal API token>

  $ bdrk labrun --help

  $ bdrk labrun --verbose --domain $BEDROCK_API_DOMAIN submit \
        $HOME/basis/span-example-colourtest \
        bedrock.hcl \
        canary-dev \
        -p ALPHA=0.9 \
        -p L5_RATIO=0.1 \
        -s DUMMY_SECRET_A=foo \
        -s DUMMY_SECRET_B=bar

  $ bdrk labrun logs <run_id> <step_id> <run_token>

  $ bdrk labrun artefact <run_id> <run_token>

Monitoring models in production

At serving time, users may import bdrk[model-monitoring] library to track various model performance metrics. Anomalies in these metrics can help inform users about model rot.

Logging predictions

The model monitoring service may be instantiated in to log every prediction request for offline analysis. The following example demonstrates how to enable prediction logging in a typical Flask app.

from bedrock_client.bedrock.metrics.service import ModelMonitoringService
from flask import Flask, request
from sklearn.svm import SVC

# User code to load trained model
model = SVC(probability=True)[[1, 3], [2, 2], [3, 1]], [False, True, False])

app = Flask(__name__)
monitor = ModelMonitoringService()

@app.route("/", methods=["POST"])
def predict():
    # User code to load features
    features = [2.1, 1.8]
    score = model.predict_proba([features])[:, 0].item()

    return {"True": score}

The logged predictions are persisted in low cost blob store in the workload cluster with a maximum TTL of 1 month. The blob store is partitioned by the endpoint id and the event timestamp according to the following structure: models/predictions/{endpoint_id}/2020-01-22/1415_{logger_id}-{replica_id}.txt.

  • Endpoint id is the first portion of your domain name hosted on Bedrock
  • Replica id is the name of your model server pod
  • Logger id is a Bedrock generated name that's unique to the log collector pod

These properties are injected automatically into your model server container as environment variables.

To minimize latency of request handling, all predictions are logged asynchronously in a separate thread pool. We measured the overhead along critical path to be less than 1 ms per request.

Tracking feature and inference drift

If training distribution metrics are present in /artefact directory, the model monitoring service will also track real time distribution of features and inference results. This is done using the same log_prediction call so users don't need to further instrument their serving code.

In order to export the serving distribution metrics, users may add a new /metrics endpoint to their Flask app. By default, all metrics are exported in Prometheus exposition format. The example code below shows how to extend the logging predictions example to support this use case.

@app.route("/metrics", methods=["GET"])
def get_metrics():
    """Returns real time feature values recorded by prometheus
    body, content_type = monitor.export_http(
    return Response(body, content_type=content_type)

When deployed in your workload cluster, the /metrics endpoint is automatically scraped by Prometheus every minute to store the latest metrics as timeseries data.

bdrk changelog



  • Breaking: model analyzer dependencies are now installed separately using bdrk[xafai]



  • Breaking: pipeline name and config_file_path have been remove dfrom pipeline response objects
  • Breaking: config_file_path is now nested under source field for model server deployment and pipeline run APIs
  • Breaking: model name has been removed from model collection and artefact response objects



  • Doc: Add more detailed docstrings for ModelAnalyzer class
  • Fix: Make ModelAnalyzer work offline when no Bedrock key is available



  • New: Added get_model_version_details to get model version information
  • new: Added get_model_version_download_url to get model version download url



  • Improve: Make XAI metric capturing (via SHAP) opt-in. By default only log_model_info

v0.3.2 (2020-08-25)


  • Fix: inference metric collector exporting duplicate categorical data
  • Improve: ignore unsupported value types when tracking metrics

v0.3.1 (2020-08-12)


  • New: Added ModelAnalyzer._log_model_info to capture some information of the generated model

v0.3.0 (2020-07-28)


  • New: Added ModelAnalyzer to generate and store xafai metrics


  • New: ModelMonitoringService.export_http now exports metadata about the baseline metrics (feature name, metric name, and metric type)

v0.2.2 (2020-06-05)


  • New: return prediction id from log_prediction call
  • Improve: error handling on missing baseline metrics

v0.2.1 (2020-05-06)


  • Fix: handle nans when classifying features as discrete variable
  • New: allow str type as prediction output for multi-class classification

v0.2.0 (2020-04-09)


  • Deprecated bdrk[prediction-store] component
  • Added bdrk[model-monitoring] component to capture both logging and metrics


  • Added ModelMonitoringService.export_text method for computing feature metrics on training data and exporting to a text file
  • Added ModelMonitoringService class for initialising model server metrics based on baseline metrics exported from training
  • Added ModelMonitoringService.export_http method for exposing current metrics in Prometheus registry to the scraper

v0.1.6 (2020-01-29)


  • Added bdrk[prediction-store] optional component for logging of predictions at serving time


  • Removed TrainingPipelineSchema.pipeline_source.
  • Fixed type of ModelArtefactSchema.environment_id from object to string.
  • Removed unused schemas.
  • Added entity_number to ModelArtefactSchema, TrainingPipelineRunSchema and BatchScoringPipelineRunSchema.
  • Added pipeline_name to TrainingPipelineRunSchema and BatchScoringPipelineRunSchema.

v0.1.5 (2019-11-13)


  • Added kwargs to models to allow backward compatibility.
  • Changed response schema for ModelApi.get_artefact_details from ModelArtefactDetails to ModelArtefactSchema.
  • Removed unused schemas.

v0.1.4 (2019-10-09)


  • Added get_training_pipeline_runs function to retrieve all runs from a pipeline

v0.1.3 (2019-10-01)


  • Added bdrk.v1.ModelApi with function get_artefact_details.
    from bdrk.v1 import ModelApi
    model_api = ModelApi(api_client)
    artefact = model_api.get_artefact_details(public_id=pipeline.model_id, artefact_id=run.artefact_id)
  • bdrk.v1.models.UserSchema.email_address made required.


  • Added utility functions for downloading and unzipping artefacts

    from bdrk.v1 import ApiClient, Configuration
    from bdrk.v1_util import download_and_unzip_artefact
    configuration = Configuration()
    configuration.api_key["X-Bedrock-Access-Token"] = "YOUR-TOKEN-HERE" = ""
    api_client = ApiClient(configuration)
    # There are other utility methods as well
    # `get_artefact_stream`, `download_stream`, `unzip_file_to_dir`


  • Changed command from bedrock labrun to bdrk labrun submit.
  • Added secrets using -s DUMMY_SECRET_A=foo flag.
  • Added downloading of logs and model artefacts.
    • bdrk labrun logs <run_id> <run_token>
    • bdrk labrun artefact <run_id> <run_token>

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