This project aims to provide an example on how to use MLFLOW within rasa in order to track config.yaml updates impact on the bot performance.
It serves to automatically track multiple experiements of rasa train for a specific config file.
First you need to create and setup a virtual environment to install dependencies needed to run the code by running within your venv the following commands in the project root directory.
python3.8 -m venv .mlflow
source .mlflow/bin/activate
pip install --upgrade pip
pip install mlflow
Second, you need to update the files of the sub-project you want to exeucte by adding you own test_data.yml and training_data.yml and config.yml files.
You can generate a test/train split in rasa using the command rasa data split nlu from you rasa project root directory. Than copy/paste them in files.
The code is organized as a MLFLOW Project. It contains a complete workflow that spans a search space in search for best parameters and leverages multithreading for faster execution.
You can use this project in different ways:
- By running from root directory with default params read
files/:mlflow run .
- By running from root directory and passing custom files:
mlflow run . -P config="path/to/config" -P validation_data="path/to/val/data" -P training_data="path/to/train/data"
If you don't want to clone this project, you canrun this command :
- without default params:
mlflow run git@github.com:YahyaGrb/mlflow_rasa_track.git - with your own params/files:
mlflow run git@github.com:YahyaGrb/mlflow_rasa_track.git -P param1=val1 -P param2=val2
From a jupyter notebook:track
import mlflow
project_uri = "https://github.com/YahyaGrb/mlops_rasa/mlflow_rasa_hp"
params = your_params # (as needed by the project)
mlflow.run(project_uri, parameter=params)
You can track the results in Mlflow running mlflow ui and opening http://127.0.0.1:5000 from you browser.