Skip to content

About

No description, website, or topics provided.

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

2 forks
Report repository
BranchesTags

Latest commit

 

History

4 Commits

Folders and files

NameName
Last commit message
Last commit date
README.md
README.md
 
 

Repository files navigation

Data Science Cohort

Welcome to the Data Science cohort! This repository contains resources, code, and materials for a smooth learning and onboarding process.

🎯 Overview

This cohort is designed to help you understand and implement Machine Learning and Artificial Intellignece Models from scratch. We'll cover everything from the fundamentals of building and training your own models. You will each work on a use-case that you will do end-to-end with your teammates.

Table of Contents

What is Data Science?

^ back to top ^

Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts.

Link Preview
What is Data Science @ O'reilly Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”
What is Data Science @ Quora Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways.
The sexiest job of 21st century Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs.
Wikipedia Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
How to Become a Data Scientist Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations.
a very short history of #datascience The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms.
Software Development Resources for Data Scientists Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools.
Data Scientist Roadmap Data science is an excellent career choice in today’s data-driven world where approx 328.77 million terabytes of data are generated daily. And this number is only increasing day by day, which in turn increases the demand for skilled data scientists who can utilize this data to drive business growth.
Navigating Your Path to Becoming a Data Scientist _Data science is one of the most in-demand careers today. With businesses increasingly relying on data to make decisions, the need for skilled data scientists has grown rapidly. Whether it’s tech companies, healthcare organizations, or even government institutions, data scientists play a crucial role in turning raw data into valuable insights. But how do you become a data scientist, especially if you’re just starting out? _

Where do I Start?

^ back to top ^

While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is Python, closely followed by R. Python is a general-purpose scripting language that sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.

Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as pip install), the package manager that comes bundled with Python, and Anaconda (invoked as conda install), a powerful package manager that can install packages for Python, R, and can download executables like Git.

Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: Scikit-Learn is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With Pandas, one can collect and analyze their data into a convenient table format. Numpy provides very fast tooling for mathematical operations, with a focus on vectors and matrices. Seaborn, itself based on the Matplotlib package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.

When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the Free courses we've listed below!

Real World

^ back to top ^

Data science is a powerful tool that is utilized in various fields to solve real-world problems by extracting insights and patterns from complex data.

Disaster

^ back to top ^

Training Resources

^ back to top ^

How do you learn data science? By doing data science, of course! Okay, okay - that might not be particularly helpful when you're first starting out. In this section, we've listed some learning resources, in rough order from least to greatest commitment - Tutorials, Massively Open Online Courses (MOOCs), Intensive Programs, and Colleges.

Tutorials

^ back to top ^

The Data Science Toolbox

^ back to top ^

This section is a collection of packages, tools, algorithms, and other useful items in the data science world.

Algorithms

^ back to top ^

These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.

Three kinds of Machine Learning Systems

  • Based on training with human supervision
  • Based on learning incrementally on fly
  • Based on data points comparison and pattern detection

Comparison

  • datacompy - DataComPy is a package to compare two Pandas DataFrames.

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

Reinforcement Learning

Data Mining Algorithms

Deep Learning architectures

General Machine Learning Packages

^ back to top ^

Deep Learning Packages

PyTorch Ecosystem

TensorFlow Ecosystem

Keras Ecosystem

Visualization Tools

^ back to top ^

Miscellaneous Tools

^ back to top ^

Link Description
The Data Science Lifecycle Process The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo
Data Science Lifecycle Template Repo Template repository for data science lifecycle project
RexMex A general purpose recommender metrics library for fair evaluation.
ChemicalX A PyTorch based deep learning library for drug pair scoring.
PyTorch Geometric Temporal Representation learning on dynamic graphs.
Little Ball of Fur A graph sampling library for NetworkX with a Scikit-Learn like API.
Karate Club An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
ML Workspace All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code)
Neptune.ai Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
steppy Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
steppy-toolkit Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
Datalab from Google easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.
Hortonworks Sandbox is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.
R is a free software environment for statistical computing and graphics.
Tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
RStudio IDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux.
Python - Pandas - Anaconda Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing
Pandas GUI Pandas GUI
Scikit-Learn Machine Learning in Python
NumPy NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays.
Vaex Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds.
SciPy SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization.
Data Science Toolbox Coursera Course
Data Science Toolbox Blog
Wolfram Data Science Platform Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language.
Datadog Solutions, code, and devops for high-scale data science.
Variance Build powerful data visualizations for the web without writing JavaScript
Kite Development Kit The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.
Domino Data Labs Run, scale, share, and deploy your models — without any infrastructure or setup.
Apache Flink A platform for efficient, distributed, general-purpose data processing.
Apache Hama Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce.
Weka Weka is a collection of machine learning algorithms for data mining tasks.
Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab)
Apache Spark Lightning-fast cluster computing
Hydrosphere Mist a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services.
Data Mechanics A data science and engineering platform making Apache Spark more developer-friendly and cost-effective.
Caffe Deep Learning Framework
Torch A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT
Nervana's python based Deep Learning Framework Intel® Nervana™ reference deep learning framework committed to best performance on all hardware.
Skale High performance distributed data processing in NodeJS
Aerosolve A machine learning package built for humans.
Intel framework Intel® Deep Learning Framework
Datawrapper An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com
Tensor Flow TensorFlow is an Open Source Software Library for Machine Intelligence
Natural Language Toolkit An introductory yet powerful toolkit for natural language processing and classification
Annotation Lab Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents.
nlp-toolkit for node.js This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step.
Julia high-level, high-performance dynamic programming language for technical computing
IJulia a Julia-language backend combined with the Jupyter interactive environment
Apache Zeppelin Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more
Featuretools An open source framework for automated feature engineering written in python
Optimus Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend.
Albumentations А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
DVC An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files.
Lambdo is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation.
Feast A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
Polyaxon A platform for reproducible and scalable machine learning and deep learning.
UBIAI Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling
Trains Auto-Magical Experiment Manager, Version Control & DevOps for AI
Hopsworks Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale.
MindsDB MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
Lightwood A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code.
AWS Data Wrangler An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc).
Amazon Rekognition AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications.
Amazon Textract Automatically extract printed text, handwriting, and data from any document.
Amazon Lookout for Vision Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control.
Amazon CodeGuru Automate code reviews and optimize application performance with ML-powered recommendations.
CML An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests.
Dask An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data)
Statsmodels A Python-based inferential statistics, hypothesis testing and regression framework
Gensim An open-source library for topic modeling of natural language text
spaCy A performant natural language processing toolkit
Grid Studio Grid studio is a web-based spreadsheet application with full integration of the Python programming language.
Python Data Science Handbook Python Data Science Handbook: full text in Jupyter Notebooks
Shapley A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
DAGsHub A platform built on open source tools for data, model and pipeline management.
Deepnote A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud.
Valohai An MLOps platform that handles machine orchestration, automatic reproducibility and deployment.
PyMC3 A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning)
PyStan Python interface to Stan (Bayesian inference and modeling)
hmmlearn Unsupervised learning and inference of Hidden Markov Models
Chaos Genius ML powered analytics engine for outlier/anomaly detection and root cause analysis
Nimblebox A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser.
Towhee A Python library that helps you encode your unstructured data into embeddings.
LineaPy Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines.
envd 🏕️ machine learning development environment for data science and AI/ML engineering teams
Explore Data Science Libraries A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources
MLEM 🐶 Version and deploy your ML models following GitOps principles
MLflow MLOps framework for managing ML models across their full lifecycle
cleanlab Python library for data-centric AI and automatically detecting various issues in ML datasets
AutoGluon AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data
Arize AI Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift.
Aureo.io Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data.
ERD Lab Free cloud based entity relationship diagram (ERD) tool made for developers.
Arize-Phoenix MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models.
Comet An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production.
Opik Evaluate, test, and ship LLM applications across your dev and production lifecycles.
Synthical AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place
teeplot Workflow tool to automatically organize data visualization output
Streamlit App framework for Machine Learning and Data Science projects
Gradio Create customizable UI components around machine learning models
Weights & Biases Experiment tracking, dataset versioning, and model management
DVC Open-source version control system for machine learning projects
Optuna Automatic hyperparameter optimization software framework
Ray Tune Scalable hyperparameter tuning library
Apache Airflow Platform to programmatically author, schedule, and monitor workflows
Prefect Workflow management system for modern data stacks
Kedro Open-source Python framework for creating reproducible, maintainable data science code
Hamilton Lightweight library to author and manage reliable data transformations
SHAP Game theoretic approach to explain the output of any machine learning model
LIME Explaining the predictions of any machine learning classifier
flyte Workflow automation platform for machine learning
dbt Data build tool
SHAP Game theoretic approach to explain the output of any machine learning model
LIME Explaining the predictions of any machine learning classifier
zasper Supercharged IDE for Data Science 
skrub A Python library to ease preprocessing and feature engineering for tabular machine learning 

Datasets

^ back to top ^

Data Science Project Ideas

# Industry Project Idea Problem Statement Data Sources
1 Healthcare Early Detection of Diabetes Predict diabetes onset using health metrics Pima Diabetes
2 E-commerce/Retail Personalized Product Recommendation Recommend relevant products based on user behavior Amazon SNAP
3 Finance Fake News Detection in Financial News Classify financial news articles as real or fake Fake News
5 Social Media Toxic Comment Classification Detect and classify abusive comments for moderation Jigsaw Dataset
8 Entertainment Movie Success Predictor Predict box office revenue or IMDb rating from metadata TMDB Dataset
9 Cybersecurity Anomaly Detection in Network Traffic Detect malicious behavior or unusual activity in network traffic UNSW-NB15
10 Real Estate House Price Prediction Estimate property prices using features like area, location, and number of rooms Kaggle House Prices
11 Finance Credit Card Fraud Detection Detect fraudulent credit card transactions in real-time Kaggle Fraud Dataset
12 Finance Loan Default Prediction Predict whether a borrower will default on a loan Lending Club Dataset
13 Finance Stock Price Movement Prediction Predict stock movement (up/down) based on historical data and news sentiment Yahoo Finance, FinBERT
15 Finance Bank Customer Churn Prediction Predict which customers are likely to leave the bank soon Bank Churn Dataset
16 Healthcare Disease Risk Prediction with Genomic Data Predict risk of diseases like heart disease or cancer based on genetic and lifestyle factors UK Biobank, Kaggle
17 Healthcare Medical Imaging Classification Detect diseases in medical images like X-rays Chest X-ray Dataset
18 Healthcare Symptom-Based Disease Recommender Input symptoms and predict possible diseases or recommend specialists Healthline/WebMD
19 Healthcare AI-powered Drug Recommendation for Doctors Recommend medications for diagnosed conditions MIMIC-III Database
20 Healthcare Healthcare Insurance Claim Prediction Predict the cost of future claims based on demographics and history Medical Cost Personal Dataset
21 Recommendation Systems Movie + TV Show Hybrid Recommendation Engine Recommend a mix of trending and niche content based on user preferences MovieLens, IMDb
22 Recommendation Systems Music Playlist Generator with Mood Detection Create personalized playlists based on mood and listening habits Spotify API, Million Song Dataset
23 Recommendation Systems Book Recommender with NLP Recommend books based on description similarity and user ratings Goodreads Dataset
25 Recommendation Systems News Article Recommender with Freshness Score Recommend current news that balances user interest and article recency MIND Dataset, Google News RSS

Credits: https://github.com/academic/awesome-datascience?tab=readme-ov-file#datasets

💻 Development Environment Setup

System Requirements

  • Python 3.11 or higher
  • 16GB RAM minimum (32GB recommended)
  • NVIDIA GPU with 8GB+ VRAM (recommended for local model training)
  • 50GB+ free disk space

Step-by-Step Setup Guide

  1. Install Python 3.11+

    # Windows (using winget)
winget install Python.Python.3.11

# macOS (using Homebrew)
brew install python@3.11

# Linux (Ubuntu/Debian)
sudo apt update
sudo apt install python3.11 python3.11-venv

  2. Create and Activate Virtual Environment

    # Create virtual environment
python -m venv llm-env

# Activate on Windows
.\llm-env\Scripts\activate

# Activate on macOS/Linux
source llm-env/bin/activate

  3. Install Required Packages

    # Install basic requirements
pip install --upgrade pip
pip install torch torchvision torchaudio
pip install transformers datasets accelerate
pip install sentencepiece protobuf
pip install bitsandbytes  # for 4-bit quantization
pip install scipy numpy pandas

  4. Install CUDA (for NVIDIA GPUs)

    • Download and install CUDA Toolkit from NVIDIA website
    • Install cuDNN for better performance
    • Verify installation: 
      nvidia-smi
python -c "import torch; print(torch.cuda.is_available())"

💡 Best Practices & Tips

Memory-Efficient Model Loading

# Best Practice: Use quantization and model offloading
def load_model_efficiently(model_name, device="cuda"):
    try:
        # 4-bit quantization
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            load_in_4bit=True,
            device_map="auto",
            torch_dtype=torch.float16,
            low_cpu_mem_usage=True
        )
        
        # Enable model offloading
        if device == "cuda":
            model.enable_model_cpu_offload()
            
        return model
    except Exception as e:
        logger.error(f"Failed to load model: {e}")
        raise

Error Handling and Logging

# Best Practice: Implement comprehensive error handling
import logging
from functools import wraps
import time

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('llm_operations.log'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

# Retry decorator for API calls
def retry_on_failure(max_retries=3, delay=1):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_retries - 1:
                        logger.error(f"Failed after {max_retries} attempts: {e}")
                        raise
                    logger.warning(f"Attempt {attempt + 1} failed: {e}")
                    time.sleep(delay * (attempt + 1))
            return None
        return wrapper
    return decorator

Tips and Tricks

  1. Caching Responses

    from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_model_response(prompt):
    # Your model inference here
    pass

  2. Batch Processing

    def process_batch(prompts, batch_size=32):
    results = []
    for i in range(0, len(prompts), batch_size):
        batch = prompts[i:i + batch_size]
        # Process batch
        results.extend(process_single_batch(batch))
    return results

  3. Memory Management

    import gc

def clear_memory():
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

Best Practices Checklist

  1. API Management

    • ✅ Use environment variables for API keys
    • ✅ Implement rate limiting
    • ✅ Use connection pooling
    • ✅ Implement retry mechanisms

  2. Model Loading

    • ✅ Use quantization (4-bit or 8-bit)
    • ✅ Enable model offloading
    • ✅ Use mixed precision training
    • ✅ Implement gradient checkpointing

  3. Error Handling

    • ✅ Implement comprehensive logging
    • ✅ Use try-except blocks
    • ✅ Add retry mechanisms
    • ✅ Implement fallback options

  4. Performance Optimization

    • ✅ Use batch processing
    • ✅ Implement caching
    • ✅ Optimize memory usage
    • ✅ Use async operations where appropriate

  5. Security

    • ✅ Never hardcode API keys
    • ✅ Implement input validation
    • ✅ Use secure connections
    • ✅ Implement proper error messages

Common Pitfalls to Avoid

  1. Memory Issues

    • ❌ Loading full precision models without quantization
    • ❌ Not clearing GPU memory between operations
    • ❌ Keeping unnecessary model copies in memory

  2. API Usage

    • ❌ Not implementing rate limiting
    • ❌ Not handling API timeouts
    • ❌ Not implementing retry mechanisms

  3. Error Handling

    • ❌ Catching all exceptions without specific handling
    • ❌ Not logging errors properly
    • ❌ Not implementing fallback options

  4. Performance

    • ❌ Not using batch processing
    • ❌ Not implementing caching
    • ❌ Not optimizing memory usage

☁️ Cloud Development with Google Cloud Platform

Getting Started with GCP (Optional)

Note: You can also use any other cloud provider of your choice. Here is a list of cloud providers:

https://github.com/cloudcommunity/Cloud-Free-Tier-Comparison

  1. Sign Up and Free Credits

    • Create a Google Cloud account
    • Get $300 free credits (valid for 90 days)
    • Enable billing (required even for free tier)

  2. Install Google Cloud CLI

    # Windows (using winget)
winget install Google.CloudSDK

# macOS (using Homebrew)
brew install google-cloud-sdk

# Linux
echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list
sudo apt-get install apt-transport-https ca-certificates gnupg
sudo apt-get update && sudo apt-get install google-cloud-sdk

  3. Initialize and Configure GCP

    # Login to GCP
gcloud auth login

# Set your project
gcloud config set project YOUR_PROJECT_ID

# Enable required APIs
gcloud services enable compute.googleapis.com
gcloud services enable aiplatform.googleapis.com

Setting Up Cloud Environment

  1. Create a Virtual Machine

    # Create a VM with GPU
gcloud compute instances create llm-dev \
  --machine-type=n1-standard-4 \
  --zone=us-central1-a \
  --accelerator="type=nvidia-tesla-t4,count=1" \
  --maintenance-policy=TERMINATE \
  --image-family=debian-11-gpu \
  --image-project=debian-cloud \
  --boot-disk-size=100GB

  2. Connect to VM

    # SSH into the VM
gcloud compute ssh llm-dev --zone=us-central1-a

Cloud Development Setup

  1. Install Dependencies on VM

    # Update system
sudo apt-get update
sudo apt-get upgrade -y

# Install Python and dependencies
sudo apt-get install python3.11 python3.11-venv
python3.11 -m venv llm-env
source llm-env/bin/activate

# Install CUDA and cuDNN
sudo apt-get install nvidia-cuda-toolkit

  2. Configure Python Environment

    # Install required packages
pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers datasets accelerate
pip install google-cloud-aiplatform

Using Google Cloud AI Platform

  1. Initialize Vertex AI

    from google.cloud import aiplatform

def setup_vertex_ai():
    # Initialize Vertex AI
    aiplatform.init(
        project='your-project-id',
        location='us-central1',
        experiment='llm-experiment'
    )
    
    # Create or get endpoint
    endpoint = aiplatform.Endpoint(
        endpoint_name='projects/your-project-id/locations/us-central1/endpoints/your-endpoint-id'
    )
    return endpoint

  2. Deploy Model to Vertex AI

    def deploy_model_to_vertex(model_path):
    # Create model
    model = aiplatform.Model.upload(
        display_name='llm-model',
        artifact_uri=model_path,
        serving_container_image_uri='us-docker.pkg.dev/cloud-aiplatform/prediction/pytorch-gpu.1-10:latest'
    )
    
    # Deploy model
    endpoint = model.deploy(
        machine_type='n1-standard-4',
        accelerator_type='NVIDIA_TESLA_T4',
        accelerator_count=1
    )
    return endpoint

Cost Optimization Tips

  1. Use Preemptible VMs

    # Create preemptible VM (up to 80% cheaper)
gcloud compute instances create llm-dev \
  --preemptible \
  --machine-type=n1-standard-4 \
  --zone=us-central1-a

  2. Auto-shutdown Script

    # Create shutdown script
echo '#!/bin/bash
sudo shutdown -h now' > shutdown.sh
chmod +x shutdown.sh

# Add to VM creation
gcloud compute instances create llm-dev \
  --metadata-from-file=shutdown-script=shutdown.sh

  3. Cost Monitoring

    # Set budget alerts
gcloud billing budgets create \
  --billing-account=YOUR_BILLING_ACCOUNT \
  --display-name="LLM Development Budget" \
  --budget-amount=100USD \
  --threshold-rule=percent=0.5 \
  --threshold-rule=percent=0.9

Best Practices for Cloud Development

  1. Resource Management

    • Use appropriate machine types
    • Implement auto-shutdown
    • Monitor resource usage
    • Clean up unused resources

  2. Data Management

    • Use Cloud Storage for datasets
    • Implement proper backup strategies
    • Use version control for code
    • Store models in Cloud Storage

  3. Security

    • Use service accounts
    • Implement IAM roles
    • Secure API keys
    • Enable audit logging

  4. Performance

    • Use GPU instances when needed
    • Implement caching
    • Use batch processing
    • Optimize model size

Cost Comparison (Approximate)

Resource Local Cost GCP Cost (Free Tier) GCP Cost (Paid)
GPU (T4) Hardware $300 credits $0.35/hour
Storage Free 5GB free $0.02/GB/month
Network Free 1GB/day free $0.12/GB

Note: Prices may vary by region and time. Check Google Cloud pricing calculator for current rates.

🙏 Acknowledgments

We would like to express our sincere gratitude to the Open Source Community.

About

No description, website, or topics provided.

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

2 forks
Report repository

Releases

Packages

Contributors