Practical-Deep-Learning-at-Scale-with-MLFlow

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Practical Deep Learning at Scale with MLFlow, published by Packt

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- first_dl.py
- README.md
- requirements-gpu-additional.txt
- requirements.txt
- first_dl_with_mlflow.py
- README.md
- requirements.txt
- - .env
  - docker-compose.yml
  - start_mlflow.sh
  - stop_mlflow.sh
- dl_model-non-auto-tracking.ipynb
- dl_model_tracking.ipynb
- README.md
- requirements.txt
- - fine_tuning.py
  - fine_tuning_databricks.py
- - download_data.py
  - fine_tuning_model.py
  - register_model.py
- conda.yaml
- main.py
- MLproject
- README.md
- requirements.txt
- - download_data.py
  - fine_tuning_model.py
  - register_model.py
- cluster_spec.json
- conda.yaml
- main.py
- MLproject
- README.md
- requirements.txt
- - download_data.py
  - hpo_finetuning_model.py
  - hpo_finetuning_model_optuna.py
- conda.yaml
- main.py
- MLproject
- README.md
- requirements.txt
- - basic_custom_dl_model.py
  - multistep_inference_model.py
- - download_data.py
  - fine_tuning_model.py
  - inference_pipeline_model.py
  - register_model.py
- conda.yaml
- main.py
- MLproject
- README.md
- - batch_inference.py
- - download_data.py
  - fine_tuning_model.py
  - inference_pipeline_model.py
  - register_model.py
- - query_ray_serve_endpoint.py
- - deploy_to_sagemaker.py
  - query_sagemaker_endpoint.py
- cluster_spec.json
- conda-batch-inference.yaml
- conda.yaml
- Dockerfile
- main.py
- MLproject
- README.md
- - distilbert_viz.html
  - gradient_explain.ipynb
  - shap_explain.ipynb
- conda.yaml
- README.md
- - shap_mlflow_log_artifact.py
  - shap_mlflow_pyfunc_explainer.py
  - shap_mlflow_pyspark_explainer.py
- - nlp_sentiment_classifier_explainer.py
- conda-pyspark-explain.yaml
- conda.yaml
- README.md
LICENSE
README.md

# Installation Guide

1. Get the code

git clone https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow

Downloads the entire project code from GitHub to your computer.

cd Practical-Deep-Learning-at-Scale-with-MLFlow

Moves into the project folder you just downloaded.

2. Docker

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Docker Desktop Needed to build and run containers. Install it and keep it running in the background.

docker compose -f chapter03/mlflow_docker_setup/docker-compose.yml up -d --build

Runs the command against the services defined in the compose file.

✅ Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r chapter01/requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

✅ If it runs without errors and prints output in the terminal, it worked.