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https://github.com/ccozad/ml-reference-designs

A set of reference implementations for various ML related tasks
https://github.com/ccozad/ml-reference-designs

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A set of reference implementations for various ML related tasks

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README 

        
# Introduction



This repo contains a set of reference designs for various ML topics. A few examples of the types of things you can learn here:



Direct a large language model to answer based only on context from documents

![RAG Pipeline](/images/rag-pipeline.png?raw=true "RAG Pipeline")



Intelligently select complex objects in images

![Segmented Image](/images/sam-example.jpg?raw=true "Segmented Image")



Predict future values in data that varies over time

![Time Series Prediction](/images/time-series-prediction.png?raw=true "Time Series Prediction")



# Industry domain problems



Examples in this repo cover the following industry domain problems:



 - Accounting

   - Receipt processing

 - Botany

   - Group observations into n groups based on equal variance

 - Customer Service

   - Context aware chat bots

 - Medical

   - Breast cancer diagnosis

 - Real Estate

   - Price prediction

 - Retail

   - Product image classification

 - Technology

   - Deploy machine learning models to production

   - Compose workflows involving large language models (LLMs)

   - Store and search for embedding data in vectorstores

   - Intelligently select complex objects in images

   - Expand capabilities of large language models with custom tool calling

 - Transportation

   - Seasonal airline traffic prediction

 - Zoology

   - Group observations based on data density



# Setup



## Jupyter requirement



Some of the examples in this repo are meant to be run interactively using Jupyter-Lab or Jupiter-Notebooks. See https://jupyter.org/install



Examples that only have script files will have a README file with instructions.



## Virtual environment



To avoid conflicts with your local environment, create a virtual environment and run the notebook within this environment.



### Windows

```

python -m venv .venv

.venv\Scripts\activate

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

pip install -r requirements.txt

python -m ipykernel install --user --name=virtualenv

```



Most advances in machine learning are happing on Linux targeting Nvidia GPUs with CUDA support. Some advanced models such as Llama 3 may not work well (or at all) on Windows machines.



### Working with LLMs



Workbook examples that include LLMs models are more complex than other examples and require additional setup work.



**Llama 3**

 - Download the model weights (requires an access request that is granted by Meta staff, may take 24 hours or more to be approved) https://huggingface.co/meta-llama/Meta-Llama-3-8B

 - Model weights are GBs of data, store them in a drive with sufficient space

 - Clone the model code https://github.com/meta-llama/llama3

 - Change to the directory with the model code and pip install the model and dependencies `pip install -e .`



### Selecting the new kernel



Then select the virtualenv kernel after launching Jupyter Lab with the command `jupyter lab`



### Additional resources

For additional background see https://www.linkedin.com/pulse/how-use-virtual-environment-inside-jupyter-lab-sina-khoshgoftar



# Contents



## Getting Started



Various simple examples for getting started with different frameworks



- [Terminology](/general/terminology.ipynb)

- PyTorch

  - [Hello World](/pytorch/getting-started/pytorch_hello_world.ipynb)

  - [GPU](/pytorch/getting-started/pytorch_gpu.ipynb)

  - [Tensor operations](/pytorch/getting-started/pytorch_tensor_operations.ipynb)

  - [Auto differentiation](/pytorch/getting-started/pytorch_auto_differentiation.ipynb)

  - [Getting help](/pytorch/getting-started/pytorch_getting_help.ipynb)

- TensorFlow

  - [Hello World](/tensor-flow/getting-started/tensorflow_hello_world.ipynb)

  - [GPU (Linux only)](/tensor-flow/getting-started/tensorflow_gpu.ipynb)



## Feature Engineering



Various recipes for common feature engineering tasks.



- [Pandas essentials](/general/pandas_essentials.ipynb)

- [Handle missing data](/general/handle_missing_data.ipynb)

- [Convert class labels to numbers](/general/convert_class_to_numeric.ipynb)

- [Imbalanced classification](/general/imbalanced_classification.ipynb)

- [Choose Fourier features](/scikit-learn/time-series/choosing_fourier_features.ipynb)

- [Kaggle predict home price feature prep](/pytorch/home-prices/kaggle-predict-house-price-data-prep.ipynb)



## Image Processing



Recipes for working with images



- [OpenCV essentials](/opencv/opencv_essentials.ipynb)

- [OpenCV basic image manipulation](/opencv/opencv_basic_image_manipulation.ipynb)

- [OpenCV annotate images](/opencv/opencv_annotate_images.ipynb)

- [OpenCV image enhancement](/opencv/opencv_image_enhancement.ipynb)



## Regression



Various examples that deal with predicting a value based on inputs



- [Synthetic regression with PyTorch](/pytorch/synthetic-regression/pytorch_synthetic_regression.ipynb)

- [Kaggle predict home prices with PyTorch](/pytorch/home-prices/kaggle-predict-house-prices.ipynb)



## Classification



Various examples that deal with placing inputs into one or more categories



- [Classify breast cancer diagnosis with PyTorch](/general/imbalanced_classification.ipynb)

- [Image classification with PyTorch and Fashion MNIST](/pytorch/fashion-mnist/pytorch-fashionMNIST.ipynb)



## Clustering



Various examples that deal with grouping data points by a similarity metric.

- [Cluster seed types using K-Means and scikit-learn](/scikit-learn/clustering/kmeans_clustering.ipynb)

- [Cluster penguin species using DBSCAN and scikit-learn](/scikit-learn/clustering/dbscan_clustering.ipynb)



## Time Series



Various examples that deal with time based data



- [Forecast seasonal airline traffic using scikit-learn](/scikit-learn/time-series/choosing_fourier_features.ipynb)



## Computer Vision



Various examples that deal with processing image data.



- [Image classification with PyTorch and Fashion MNIST](/pytorch/fashion-mnist/pytorch-fashionMNIST.ipynb)

- [Image segmentation using the Meta Segment Anything Model and OpenCV](/segment-anything-model/)



## Large Language Models



Examples that interact with large language models with billions of parameters that are often training across many commercial grade GPUs for many millions of hours.



### Claude 3.5

 - [Claude 3.5 Hello World](/llm/claude-3-5/hello-world/)

 - [Claude 3.5 Tool Calling](/llm/claude-3-5/tool-calling/)



### LangChain

- [LangChain Terminology](/lang-chain/terminology.md)

- [RAG based Q&A](/lang-chain/RAG/)

  - [Index web page data using BeautifulSoup, OpenAI Embeddings and Chroma](/lang-chain/RAG/index_pipeline.py)

  - [Retrieval Augmented Generation (RAG) Q&A pipeline using OpenAI Embeddings, OpenAI Chat and Chroma](/lang-chain/RAG/rag_pipeline.py)

  - [RAG Q&A pipeline using Hugging Face Embeddings, Amazon Bedrock with Meta Llama 3 foundation model and Chroma](/lang-chain/AWS-Bedrock/)



### Llama 3



- [Local Llama 3 chat demo](/llm/llama-3/hello-llama-3.ipynb)

- [Run Llama 3 on an AWS EC2 instance](/llm/llama-3/hello-world/README.md)



### Phi-3



- [Phi-3 demo](/llm/phi-3/hello-world/)



## Deployment



Various tasks that deal with using trained models



- [Kaggle predict home prices, batch evaluate blind test data](/pytorch/home-prices/kaggle-predict-house-price-evaluation.ipynb)

- [Run Llama 3 on an AWS EC2 instance](/llm/llama-3/hello-world/)

- [Serve index and RAG pipelines over HTTP using Flask](/lang-chain/RAG-serve/)

- [Run LLM pipeline Python code in a container using Docker](/lang-chain/docker-serve/)