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-0.5.0rc4.tar.gz (112.2 kB view details)

Uploaded Source

Built Distribution

tracdap_runtime-0.5.0rc4-py3-none-any.whl (156.2 kB view details)

Uploaded Python 3

File details

Details for the file tracdap-runtime-0.5.0rc4.tar.gz.

File metadata

  • Download URL: tracdap-runtime-0.5.0rc4.tar.gz
  • Upload date:
  • Size: 112.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.8

File hashes

Hashes for tracdap-runtime-0.5.0rc4.tar.gz
Algorithm Hash digest
SHA256 8506e1dcf03388483ba7d76cc4bc7f69ce4cb17cf82f86c6413eb7cd827f45cb
MD5 411df6754c256d7c5bd04fdfecada2bc
BLAKE2b-256 465796abf6a4f4f676ef572c23c44629e0748f7614f847b7508e1a5b976e0eaa

See more details on using hashes here.

File details

Details for the file tracdap_runtime-0.5.0rc4-py3-none-any.whl.

File metadata

File hashes

Hashes for tracdap_runtime-0.5.0rc4-py3-none-any.whl
Algorithm Hash digest
SHA256 de429c2f8a9500fe8d70436047ac565d003d4b47b82c8bab4f639e537e4d4239
MD5 7b61fc6346ece7b6863734bb4c1e282a
BLAKE2b-256 19a458af0437491713e99e25e2dc78a91ee6febbc3c2746ada2584273426607d

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