Thursday, September 5, 2024

Tips: Azure Reserve Instance Calculation

Navigating the Azure calculator can be quite perplexing, especially when it comes to understanding the terms related to costing. Unfortunately, Microsoft’s documentation on this topic isn’t as clear as it could be, which can lead to misunderstandings and potentially costly mistakes for users.

For instance, I encountered confusion around the “Upfront Cost” and “Monthly Cost” options when selecting a 1-year reserved instance and choosing “Upfront” from the “compute payment options.” Despite the selection, the average monthly payment remains unchanged, while only the Upfront Cost and Monthly Cost at the bottom of the calculator adjust, as illustrated in Picture 1. This lack of clarity can easily lead to misinterpretations and poor decision-making, which could have serious implications for a company.

Picture 1: A screenshot from the Azure Calculator specifically on the Reservation section.


As you can see from Picture 1, I selected 1 year reserve instance. The total Upfront cost (whatever that means) is $44,985.93 and the monthly cost (consumption cost) is $4,298.24. I have been kind of confused with the separation of the 2 costs and aren't we good by just paying only monthly consumption cost?

The actual calculation for the monthly cost is per below:

Reserve Instance Cost (1 year subscription): $44,985.93/12 = $3,748.83

Consumption Cost (monthly recurring and may fluctuate): $4,298.24

Total Cost per month: $3,748.83 + $4,298.24 = $8,047.07

If anyone thought that you only have to pay for the "Monthly Cost" only will fall into the trap of surprise by the end of the month when Microsoft issues the invoice.


Tuesday, February 6, 2024

Tips: Using Vite with Vue

 Introduction

Vite is a modern front-end build tool that significantly improves the development experience for web projects. Its name, pronounced as "veet", is a French word meaning "quick" or "fast," which aptly describes its performance. Here are some key features and aspects of Vite:

  1. Fast Development Start: Vite significantly speeds up the initial loading time for development environments. Unlike traditional tools that bundle all the modules before starting the development server, Vite serves native ES modules, leveraging the browser's ability to import modules dynamically.
  2. Hot Module Replacement (HMR): Vite provides a highly efficient HMR, which means changes in the source code can be instantly reflected in the browser without needing a full reload. This feature significantly speeds up development.
  3. Built on ESBuild: Vite uses ESBuild under the hood for pre-bundling dependencies. ESBuild is known for its extremely fast JavaScript bundling, which contributes to the overall speed of Vite.
  4. Rich Features: Vite comes with out-of-the-box support for TypeScript, JSX, CSS Pre-processors, and more. It also provides a plugin system, making it highly extendable and compatible with a wide variety of existing tools and libraries.
  5. Optimized Production Builds: For production, Vite switches to a different strategy where it uses Rollup for bundling. Rollup is known for generating highly optimized and efficient bundles, which is crucial for production builds.
  6. Simple Configuration: Vite aims to provide sensible defaults while allowing extensive configuration. Its configuration file is straightforward, enhancing the developer experience.
  7. Framework Agnostic: Vite is not limited to a single framework like React or Vue. It can be used with various frameworks, offering flexibility for different projects.
  8. Community and Ecosystem: Being an open-source project, Vite has a growing community and ecosystem. There are numerous plugins and integrations available, continuously expanding its capabilities.

Vite's approach to serving code via native ES modules during development and its highly efficient build process for production make it an attractive choice for modern web development. It is particularly beneficial for large-scale projects where reducing the development server start time and improving the build performance can have a significant impact.

Underneath Vite, Node.js is used, that's why Node.js need to be installed when using Vite.

Figure 1: Vite high level architecture diagram.


Figure 2 shows the features that are Vite provide.

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Figure 2: Vite features when the dev server is started.

Vite and Vue

We can use the following command to create a new project using Vite.

npm create vite@latest Tester1 --template vanilla

Choose Vue as the target framework. Figure 3 shows the project structure in Visual Studio Code.
Figure 3: Project Structure

Shortcomings

 Vite, despite its numerous advantages and modern approach to web development, does have some limitations and potential drawbacks. Understanding these shortcomings is important for developers to make informed decisions about whether Vite is the right tool for their projects. Here are some of the notable limitations:

  1. Node.js/Vue.js Version Dependency: Vite requires a relatively recent version of Node.js. Projects using older versions of Node.js might face compatibility issues or might need to upgrade their Node.js version.
  2. Learning Curve for Configuration: While Vite aims to provide sensible defaults, advanced configuration can have a steep learning curve, especially for developers who are not familiar with modern JavaScript tooling.
  3. Plugin Ecosystem: While growing, Vite's plugin ecosystem is not as extensive as that of more established tools like Webpack. This may limit its out-of-the-box functionality for certain complex or specific use cases.
  4. Integration with Older Projects: Integrating Vite into existing, large-scale projects (especially those not already using ES modules) can be challenging and may require significant refactoring.
  5. Limited Browser Support: Because Vite relies on native ES modules, it does not support older browsers that lack this feature, such as Internet Explorer. This might be a concern for projects that require broad browser compatibility.
  6. Server-Side Rendering (SSR) Complexity: While Vite supports SSR, setting it up can be complex and might require a deeper understanding of both Vite and the underlying framework (like Vue or React).
  7. Potential Issues with Third-Party Dependencies: Some third-party libraries or modules might not be fully compatible with Vite, especially if they rely on older module systems or have specific bundler requirements.
  8. Community and Support: As a relatively new tool, Vite's community and support network, while growing rapidly, may not be as extensive or mature as those for tools like Webpack or Create React App.

It's important to note that some of these shortcomings may be addressed in future updates, and the Vite community is actively working on improving and expanding its capabilities. Developers should consider these limitations in the context of their specific project requirements and development environment.

Comparison Report: Vite vs. Webpack vs. Create React App

1. Performance

  • Vite:
    • Extremely fast startup due to native ES modules.
    • Efficient Hot Module Replacement (HMR).
    • Optimized production builds using Rollup.
  • Webpack:
    • Can be slower to start and rebuild, especially in large projects.
    • Optimized production builds but requires careful configuration.
  • CRA (uses Webpack under the hood):
    • Performance similar to Webpack.
    • Less control over build optimization compared to vanilla Webpack.

2. Ease of Use

  • Vite:
    • Simple setup with sensible defaults.
    • Minimal configuration required for most projects.
  • Webpack:
    • Complex configuration can be daunting for beginners.
    • Highly customizable, which can be both a benefit and a challenge.
  • CRA:
    • Very easy to set up and start a React project.
    • Abstracts Webpack configuration, reducing flexibility.

3. Features

  • Vite:
    • Out-of-the-box support for TypeScript, Vue, React.
    • Fast HMR and ES module serving.
  • Webpack:
    • Mature plugin ecosystem.
    • Wide range of loaders and plugins for various tasks.
  • CRA:
    • Preconfigured for React development.
    • Limited to React ecosystem.

4. Ecosystem

  • Vite:
    • Growing community and plugin ecosystem.
    • Active development and support.
  • Webpack:
    • Large, established community.
    • Extensive range of plugins and loaders.
  • CRA:
    • Strong support within the React community.
    • Limited to React-specific plugins and tools.

5. Browser Support

  • Vite:
    • Does not support older browsers (like Internet Explorer).
  • Webpack:
    • Supports older browsers with appropriate configurations and polyfills.
  • CRA:
    • Similar browser support to Webpack.

6. SSR (Server-Side Rendering) Support

  • Vite:
    • Supports SSR but requires additional setup and configuration.
  • Webpack:
    • Widely used for SSR in various frameworks.
  • CRA:
    • Does not support SSR out-of-the-box.

Conclusion

  • Vite is an excellent choice for projects that prioritize development speed and modern workflows, especially with Vue and React.
  • Webpack remains a powerful and flexible option for projects requiring extensive customization and support for a wide range of requirements.
  • Create React App is ideal for developers who want a quick and easy setup for React applications without the need to manage build configurations.

 


Tuesday, January 30, 2024

OpenAI Embeddings

Introduction

OpenAI Embeddings are vector representations of text created by OpenAI's language models. These embeddings capture the semantic meaning of the text in a high-dimensional space. This means that texts with similar meanings are close to each other in this space, and texts with different meanings are far apart.

The primary use of these embeddings is in natural language processing (NLP) tasks where understanding the context and meaning of text is crucial. Some common applications include:

  1. Semantic Text Similarity: Determining how similar two pieces of text are, which can be used in recommendation systems, search engines, or duplicate detection.
  2. Text Classification: Categorizing text into predefined classes. The embeddings can be used as input features for a classifier.
  3. Clustering: Grouping similar texts together. Since embeddings represent semantic meanings, texts on similar topics tend to cluster together.
  4. Information Retrieval: Enhancing search engines by finding documents that are semantically related to the query, not just textually similar.

To generate embeddings, you typically pass your text through the model, which then outputs a high-dimensional vector. You can then use this vector in various machine learning models or for any of the applications mentioned above. OpenAI has provided APIs for generating embeddings, making it easy for developers to integrate this technology into their applications.

Examples:

In a vector database, each text is converted into a high-dimensional vector using the embeddings, and these vectors are stored in the database. Here are some simplified examples:
  1. Text Entries:

    • Text 1: "I love reading books"
    • Text 2: "Books are my passion"
    • Text 3: "Cooking is a great hobby"
    • Text 4: "I enjoy hiking in the mountains"
    • Text 5: "Mountains are breathtaking"
  2. Corresponding OpenAI Embeddings (hypothetical and highly simplified for illustration):

    • Embedding 1 (Text 1): [0.8, 0.1, 0.1]
    • Embedding 2 (Text 2): [0.7, 0.2, 0.1]
    • Embedding 3 (Text 3): [0.1, 0.8, 0.1]
    • Embedding 4 (Text 4): [0.2, 0.1, 0.7]
    • Embedding 5 (Text 5): [0.3, 0.1, 0.6]

In a vector database, these embeddings can be indexed for various purposes such as semantic search, clustering, or finding similar texts. For instance:

  • Semantic Search: If you query the database with a vector close to [0.7, 0.2, 0.1] (representing interest in books), the database will return Text 1 and Text 2 as they have the closest vectors.
  • Clustering: The database can cluster the vectors into groups, potentially grouping Text 1 and Text 2 in one cluster (related to books), Text 3 in another (related to cooking), and Text 4 and Text 5 in a third cluster (related to outdoor activities).
  • Finding Similar Texts: If you have a new text, say "I love the mountains", converted to a vector [0.3, 0.1, 0.6], the database can quickly find Text 4 and Text 5 as the most similar texts based on the vector proximity.

In real scenarios, the vectors are high-dimensional (often more than 300 dimensions) and capture much more nuanced semantic meanings. The database operations (search, cluster, find similar) use sophisticated algorithms to handle these high-dimensional spaces efficiently.

Interepretation of the vectors:

These embeddings are high-dimensional representations, but let's break down the interpretation based on the simplified example you've provided:

  1. Dimensions Reflect Semantic Features:

    • The dimensions (each element in the vector) can be thought of as representing some abstract features of the text. In real embeddings, these features are complex and not directly interpretable by humans. However, in this simplified example, you might imagine that each dimension could loosely correspond to different topics or concepts (e.g., the first dimension might be related to literature, the second to cooking, and the third to outdoor activities).
  2. Magnitude in Each Dimension:

    • Embedding 1 (Text 1): [0.8, 0.1, 0.1]
      • This text has a high value in the first dimension and low in the others, suggesting a strong relation to the concept represented by the first dimension (e.g., literature) and weak relation to the other concepts.
    • Embedding 2 (Text 2): [0.7, 0.2, 0.1]
      • Similar to Text 1, this text is also strongly related to the first dimension but has a slightly higher relation to the second dimension compared to Text 1.
    • Embedding 3 (Text 3): [0.1, 0.8, 0.1]
      • This text is strongly related to the second dimension, suggesting a strong relation to the concept represented by that dimension (e.g., cooking).
    • Embedding 4 (Text 4): [0.2, 0.1, 0.7]
    • Embedding 5 (Text 5): [0.3, 0.1, 0.6]
      • Both texts have their highest values in the third dimension, indicating a strong relationship with the concept related to that dimension (e.g., outdoor activities), with Text 5 having a slightly stronger relation to the first dimension compared to Text 4.
  3. Distance Between Vectors:

    • The Euclidean distance or cosine similarity between vectors indicates how similar the texts are in terms of their semantic content. Texts with similar vectors are semantically similar. For instance:
      • Text 1 and Text 2 are quite close to each other, indicating that they are semantically similar.
      • Text 4 and Text 5 are also close, suggesting a similarity in their content.
  4. Application in Vector Database:

    • When you use these embeddings in a vector database (like Annoy), you typically perform operations like finding the nearest neighbors. In this context, nearest neighbors are the texts with the most similar embeddings, implying the most similar semantic content.

Coding Sample

To illustrate how you might use OpenAI embeddings with a vector database in Python, I'll provide a sample code snippet. This example uses the Annoy library for creating and using a vector database. Annoy is a C++ library with Python bindings to search for points in space that are close to a given query point. It's particularly useful for nearest neighbor searches in high-dimensional spaces.

First, install the necessary libraries by running:

pip install openai annoy

Codes:

import openai import annoy from collections import defaultdict # Initialize OpenAI with your API key openai.api_key = 'your-api-key' # Sample texts texts = [ "I love reading books", "Books are my passion", "Cooking is a great hobby", "I enjoy hiking in the mountains", "Mountains are breathtaking" ] # Get embeddings from OpenAI def get_embeddings(texts): return openai.Embedding.create(input=texts, engine="text-similarity-babbage-001")['data'] embeddings = get_embeddings(texts) # Create an Annoy index for the embeddings f = 2048 # Length of item vector that will be indexed t = annoy.AnnoyIndex(f, 'angular') for i, embedding in enumerate(embeddings): t.add_item(i, embedding['embedding']) t.build(10) # 10 trees # Save the index to disk for later use t.save('test.ann') # Load the index (can be used in another process) u = annoy.AnnoyIndex(f, 'angular') u.load('test.ann') # Find the 3 nearest neighbors to the first item nearest_neighbors = u.get_nns_by_item(0, 3) for neighbor in nearest_neighbors: print(texts[neighbor]) # If you have another text and want to find similar texts in the database new_text = "I enjoy reading about mountains" new_embedding = get_embeddings([new_text])[0]['embedding'] # Find the 3 nearest neighbors to the new embedding nearest_neighbors = u.get_nns_by_vector(new_embedding, 3) for neighbor in nearest_neighbors: print(texts[neighbor])

In the code:

  1. OpenAI Embeddings: We fetch the embeddings for our sample texts from OpenAI.
  2. Annoy Index Creation: We create an Annoy index and add our embeddings to it.
  3. Querying: We demonstrate how to query the index to find the nearest neighbors to a given point (in our case, the embeddings of a text).