Skip to main content

Runtime package for building models on the TRAC Data & Analytics Platform

Project description

TRAC Model Runtime for Python

TRAC D.A.P. is a next-generation data and analytics platform for use in highly regulated environments

The TRAC model runtime provides all the APIs needed to write models for the TRAC platform. It includes an implementation of the runtime library that can be used as a development sandbox, so you can run and debug TRAC models right away from your favourite IDE or notebook. A number of tools are included to make it easy to plug in development data and configuration. When your models are ready they can be loaded into a real instance of TRAC for testing and eventual deployment.

Documentation for the TRAC platform is available on our website at tracdap.finos.org.

Requirements

The TRAC runtime for Python has these requirements:

  • Python: 3.7 or later
  • Pandas: 1.2 or later
  • PySpark 2.4.x or 3.x

Not every combination of versions will work, e.g. PySpark 3 requires Python 3.8.

Installing the runtime

The TRAC runtime package can be installed directly from PyPI:

pip install tracdap-runtime

The TRAC runtime depends on Pandas and PySpark, so these libraries will be pulled in as dependencies. If you want to target particular versions, install them explicitly first.

Writing a model

Once the runtime is installed you can write your first TRAC model! Start by inheriting the TracModel base class, your IDE should be able to generate stubs for you:

import tracdap.rt.api as trac

class SampleModel(trac.TracModel):

    def define_parameters(self) -> tp.Dict[str, trac.ModelParameter]:
        pass

    def define_inputs(self) -> tp.Dict[str, trac.ModelInputSchema]:
        pass

    def define_outputs(self) -> tp.Dict[str, trac.ModelOutputSchema]:
        pass

    def run_model(self, ctx: trac.TracContext):
        pass

You can fill in the three define_* methods to declare any parameters, inputs and outputs your model is going to need, then start writing your model code in run_model.

To learn about modelling with TRAC D.A.P. and what is possible, check out the modelling tutorials available in our online documentation. The tutorials are based on example models in the TRAC GitHub repository. We run these examples as part of our CI, so they will always be in sync with the corresponding version of the runtime library.

Building the runtime from source

This is not normally necessary for model development, but if you want to do it here are the commands.

cd tracdap-runtime/python

# Configure a Python environment

python -m venv ./venv
venv\Scripts\activate              # For Windows platforms
. venv/bin/activate                # For macOS or Linux
pip install -r requirements.txt

# Build the Python package files

python ./build_runtime.py --target dist

The package files will appear under build/dist

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tracdap-runtime-0.4.2.tar.gz (95.5 kB view details)

Uploaded Source

Built Distribution

tracdap_runtime-0.4.2-py3-none-any.whl (131.6 kB view details)

Uploaded Python 3

File details

Details for the file tracdap-runtime-0.4.2.tar.gz.

File metadata

  • Download URL: tracdap-runtime-0.4.2.tar.gz
  • Upload date:
  • Size: 95.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for tracdap-runtime-0.4.2.tar.gz
Algorithm Hash digest
SHA256 7d96b0c3643b334781724b3f6a39bafc4cef5d1d25fca8ca3e3ab860ab87fe19
MD5 dc55043dfcf35a68a04da0d499954de7
BLAKE2b-256 aaefefb31daa5e9ff342dfd140cfdc4501f4c27f5a990867b7f373a0d11b1538

See more details on using hashes here.

File details

Details for the file tracdap_runtime-0.4.2-py3-none-any.whl.

File metadata

File hashes

Hashes for tracdap_runtime-0.4.2-py3-none-any.whl
Algorithm Hash digest
SHA256 cc0b6ab4123f64b4c250d157c4ffe78ca55541bed647adc660aa5f137f7f9328
MD5 e215ebac80789b3e951ffb02598b6de8
BLAKE2b-256 a96e7f0df211c7a79a910a99794121e864f5a46f2d28b991b89a4881ccc92446

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page