Category: Torch

AGENTIC AI IN THE ENTERPRISE: STRATEGY, ARCHITECTURE, AND IMPLEMENTATION – PART 3

Posted on by SatyakiDe in agents, ai, anthropic, api, audio, Azure, bharatgpt, BOT, call, circuitbreaker, clob, cloud, Computer-Vision, computing, CPU, Crossplatform, Data Science, deepseek, design, exposure, Fabric, faiss, features, function, gpt3, GPU, grok, gui, Haystack, HuggingFace, ibm, IoT, json, Keras, LangChain, Langflow, Linear-Regression, Listagg, llm, Logistic-regression, loop, machine-learning, mcpprotocol, Microsoft, mobile, Model, mulesoft, natural-language, neural prophet, ngrok, objects, Open-CV, openai, oracle-cloud, Performance, pl sql, Polars, prophet-api, React, Real-time, sarvam, Silicon, StabilityAI, StableDefussion, Technology, Tensorflow, Torch, video, voice, watson

This is a continuation of my previous post, which can be found here.

Let us recap the key takaways from our previous post –

Enterprise AI, utilizing the Model Context Protocol (MCP), leverages an open standard that enables AI systems to securely and consistently access enterprise data and tools. MCP replaces brittle “N×M” integrations between models and systems with a standardized client–server pattern: an MCP host (e.g., IDE or chatbot) runs an MCP client that communicates with lightweight MCP servers, which wrap external systems via JSON-RPC. Servers expose three assets—Resources (data), Tools (actions), and Prompts (templates)—behind permissions, access control, and auditability. This design enables real-time context, reduces hallucinations, supports model- and cloud-agnostic interoperability, and accelerates “build once, integrate everywhere” deployment. A typical flow (e.g., retrieving a customer’s latest order) encompasses intent parsing, authorized tool invocation, query translation/execution, and the return of a normalized JSON result to the model for natural-language delivery. Performance introduces modest overhead (RPC hops, JSON (de)serialization, network transit) and scale considerations (request volume, significant results, context-window pressure). Mitigations include in-memory/semantic caching, optimized SQL with indexing, pagination, and filtering, connection pooling, and horizontal scaling with load balancing. In practice, small latency costs are often outweighed by the benefits of higher accuracy, stronger governance, and a decoupled, scalable architecture.

How does MCP compare with other AI integration approaches?

Compared to other approaches, the Model Context Protocol (MCP) offers a uniquely standardized and secure framework for AI-tool integration, shifting from brittle, custom-coded connections to a universal plug-and-play model. It is not a replacement for underlying systems, such as APIs or databases, but instead acts as an intelligent, secure abstraction layer designed explicitly for AI agents.

MCP vs. Custom API integrations:

This approach was the traditional method for AI integration before standards like MCP emerged.

  • Custom API integrations (traditional): Each AI application requires a custom-built connector for every external system it needs to access, leading to an N x M integration problem (the number of connectors grows exponentially with the number of models and systems). This approach is resource-intensive, challenging to maintain, and prone to breaking when underlying APIs change.
  • MCP: The standardized protocol eliminates the N x M problem by creating a universal interface. Tool creators build a single MCP server for their system, and any MCP-compatible AI agent can instantly access it. This process decouples the AI model from the underlying implementation details, drastically reducing integration and maintenance costs.

For more detailed information, please refer to the following link.

MCP vs. Retrieval-Augmented Generation (RAG):

RAG is a technique that retrieves static documents to augment an LLM’s knowledge, while MCP focuses on live interactions. They are complementary, not competing. 

  • RAG:
    • Focus: Retrieving and summarizing static, unstructured data, such as documents, manuals, or knowledge bases.
    • Best for: Providing background knowledge and general information, as in a policy lookup tool or customer service bot.
    • Data type: Unstructured, static knowledge.
  • MCP:
    • Focus: Accessing and acting on real-time, structured, and dynamic data from databases, APIs, and business systems.
    • Best for: Agentic use cases involving real-world actions, like pulling live sales reports from a CRM or creating a ticket in a project management tool.
    • Data type: Structured, real-time, and dynamic data.

MCP vs. LLM plugins and extensions:

Before MCP, platforms like OpenAI offered proprietary plugin systems to extend LLM capabilities.

  • LLM plugins:
    • Proprietary: Tied to a specific AI vendor (e.g., OpenAI).
    • Limited: Rely on the vendor’s API function-calling mechanism, which focuses on call formatting but not standardized execution.
    • Centralized: Managed by the AI vendor, creating a risk of vendor lock-in.
  • MCP:
    • Open standard: Based on a public, interoperable protocol (JSON-RPC 2.0), making it model-agnostic and usable across different platforms.
    • Infrastructure layer: Provides a standardized infrastructure for agents to discover and use any compliant tool, regardless of the underlying LLM.
    • Decentralized: Promotes a flexible ecosystem and reduces the risk of vendor lock-in. 

How enterprise AI with MCP has opened up a specific Architecture pattern for Azure, AWS & GCP?

Microsoft Azure: 

The “agent factory” pattern: Azure focuses on providing managed services for building and orchestrating AI agents, tightly integrated with its enterprise security and governance features. The MCP architecture is a core component of the Azure AI Foundry, serving as a secure, managed “agent factory.” 

Azure architecture pattern with MCP:

  • AI orchestration layer: The Azure AI Agent Service, within Azure AI Foundry, acts as the central host and orchestrator. It provides the control plane for creating, deploying, and managing multiple specialized agents, and it natively supports the MCP standard.
  • AI model layer: Agents in the Foundry can be powered by various models, including those from Azure OpenAI Service, commercial models from partners, or open-source models.
  • MCP server and tool layer: MCP servers are deployed using serverless functions, such as Azure Functions or Azure Logic Apps, to wrap existing enterprise systems. These servers expose tools for interacting with enterprise data sources like SharePoint, Azure AI Search, and Azure Blob Storage.
  • Data and security layer: Data is secured using Microsoft Entra ID (formerly Azure AD) for authentication and access control, with robust security policies enforced via Azure API Management. Access to data sources, such as databases and storage, is managed securely through private networks and Managed Identity. 

Amazon Web Services (AWS): 

The “composable serverless agent” pattern: AWS emphasizes a modular, composable, and serverless approach, leveraging its extensive portfolio of services to build sophisticated, flexible, and scalable AI solutions. The MCP architecture here aligns with the principle of creating lightweight, event-driven services that AI agents can orchestrate. 

AWS architecture pattern with MCP:

  • The AI orchestration layer, which includes Amazon Bedrock Agents or custom agent frameworks deployed via AWS Fargate or Lambda, acts as the MCP hosts. Bedrock Agents provide built-in orchestration, while custom agents offer greater flexibility and customization options.
  • AI model layer: The models are sourced from Amazon Bedrock, which provides a wide selection of foundation models.
  • MCP server and tool layer: MCP servers are deployed as serverless AWS Lambda functions. AWS offers pre-built MCP servers for many of its services, including the AWS Serverless MCP Server for managing serverless applications and the AWS Lambda Tool MCP Server for invoking existing Lambda functions as tools.
  • Data and security layer: Access is tightly controlled using AWS Identity and Access Management (IAM) roles and policies, with fine-grained permissions for each MCP server. Private data sources like databases (Amazon DynamoDB) and storage (Amazon S3) are accessed securely within a Virtual Private Cloud (VPC). 

Google Cloud Platform (GCP): 

The “unified workbench” pattern: GCP focuses on providing a unified, open, and data-centric platform for AI development. The MCP architecture on GCP integrates natively with the Vertex AI platform, treating MCP servers as first-class tools that can be dynamically discovered and used within a single workbench. 

GCP architecture pattern with MCP:

  • AI orchestration layer: The Vertex AI Agent Builder serves as the central environment for building and managing conversational AI and other agents. It orchestrates workflows and manages tool invocation for agents.
  • AI model layer: Agents use foundation models available through the Vertex AI Model Garden or the Gemini API.
  • MCP server and tool layer: MCP servers are deployed as containerized microservices on Cloud Run or managed by services like App Engine. These servers contain tools that interact with GCP services, such as BigQuery, Cloud Storage, and Cloud SQL. GCP offers pre-built MCP server implementations, such as the GCP MCP Toolbox, for integration with its databases.
  • Data and security layer: Vertex AI Vector Search and other data sources are encapsulated within the MCP server tools to provide contextual information. Access to these services is managed by Identity and Access Management (IAM) and secured through virtual private clouds. The MCP server can leverage Vertex AI Context Caching for improved performance.

Note that all the native technology is referred to in each respective cloud. Hence, some of the better technologies can be used in place of the tool mentioned here. This is more of a concept-level comparison rather than industry-wise implementation approaches.

We’ll go ahead and conclude this post here & continue discussing on a further deep dive in the next post.

Till then, Happy Avenging! 🙂

Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. There is always room for improvement in this kind of model & the solution associated with it. I’ve shown the basic ways to achieve the same for educational purposes only.

AGENTIC AI IN THE ENTERPRISE: STRATEGY, ARCHITECTURE, AND IMPLEMENTATION – PART 2

Posted on by SatyakiDe in agents, ai, anthropic, api, Azure, bharatgpt, BOT, call, clob, cloud, Computer-Vision, computing, CPU, Crossplatform, Data Science, deepseek, design, exposure, Fabric, faiss, function, gpt3, GPU, gui, integration, IoT, json, LangChain, Langflow, Linear-Regression, llm, Logistic-regression, machine-learning, Microsoft, mobile, Model, mulesoft, natural-language, neural prophet, ngrok, objects, Open-CV, openai, oracle-cloud, Performance, pl sql, pyttsx3, React, read, Real-time, Silicon, sql, StabilityAI, Technology, Tensorflow, Torch, vector, video, voice, vpdb, watson

This is a continuation of my previous post, which can be found here.

Let us recap the key takaways from our previous post –

Agentic AI refers to autonomous systems that pursue goals with minimal supervision by planning, reasoning about next steps, utilizing tools, and maintaining context across sessions. Core capabilities include goal-directed autonomy, interaction with tools and environments (e.g., APIs, databases, devices), multi-step planning and reasoning under uncertainty, persistence, and choiceful decision-making.

Architecturally, three modules coordinate intelligent behavior: Sensing (perception pipelines that acquire multimodal data, extract salient patterns, and recognize entities/events); Observation/Deliberation (objective setting, strategy formation, and option evaluation relative to resources and constraints); and Action (execution via software interfaces, communications, or physical actuation to deliver outcomes). These functions are enabled by machine learning, deep learning, computer vision, natural language processing, planning/decision-making, uncertainty reasoning, and simulation/modeling.

At enterprise scale, open standards align autonomy with governance: the Model Context Protocol (MCP) grants an agent secure, principled access to enterprise tools and data (vertical integration), while Agent-to-Agent (A2A) enables specialized agents to coordinate, delegate, and exchange information (horizontal collaboration). Together, MCP and A2A help organizations transition from isolated pilots to scalable programs, delivering end-to-end automation, faster integration, enhanced security and auditability, vendor-neutral interoperability, and adaptive problem-solving that responds to real-time context.

Great! Let’s dive into this topic now.

Enterprise AI with MCP refers to the application of the Model Context Protocol (MCP), an open standard, to enable AI systems to securely and consistently access external enterprise data and applications. 

The problem MCP solves in enterprise AI:

Before MCP, enterprise AI integration was characterized by a “many-to-many” or “N x M” problem. Companies had to build custom, fragile, and costly integrations between each AI model and every proprietary data source, which was not scalable. These limitations left AI agents with limited, outdated, or siloed information, restricting their potential impact. 
MCP addresses this by offering a standardized architecture for AI and data systems to communicate with each other.

How does MCP work?

The MCP framework uses a client-server architecture to enable communication between AI models and external tools and data sources. 

  • MCP Host: The AI-powered application or environment, such as an AI-enhanced IDE or a generative AI chatbot like Anthropic’s Claude or OpenAI’s ChatGPT, where the user interacts.
  • MCP Client: A component within the host application that manages the connection to MCP servers.
  • MCP Server: A lightweight service that wraps around an external system (e.g., a CRM, database, or API) and exposes its capabilities to the AI client in a standardized format, typically using JSON-RPC 2.0. 

An MCP server provides AI clients with three key resources: 

  • Resources: Structured or unstructured data that an AI can access, such as files, documents, or database records.
  • Tools: The functionality to perform specific actions within an external system, like running a database query or sending an email.
  • Prompts: Pre-defined text templates or workflows to help guide the AI’s actions. 

Benefits of MCP for enterprise AI:

  • Standardized integration: Developers can build integrations against a single, open standard, which dramatically reduces the complexity and time required to deploy and scale AI initiatives.
  • Enhanced security and governance: MCP incorporates native support for security and compliance measures. It provides permission models, access control, and auditing capabilities to ensure AI systems only access data and tools within specified boundaries.
  • Real-time contextual awareness: By connecting AI agents to live enterprise data sources, MCP ensures they have access to the most current and relevant information, which reduces hallucinations and improves the accuracy of AI outputs.
  • Greater interoperability: MCP is model-agnostic & can be used with a variety of AI models (e.g., Anthropic’s Claude or OpenAI’s models) and across different cloud environments. This approach helps enterprises avoid vendor lock-in.
  • Accelerated development: The “build once, integrate everywhere” approach enables internal teams to focus on innovation instead of writing custom connectors for every system.

Flow of activities:

Let us understand one sample case & the flow of activities.

A customer support agent uses an AI assistant to get information about a customer’s recent orders. The AI assistant utilizes an MCP-compliant client to communicate with an MCP server, which is connected to the company’s PostgreSQL database.

The interaction flow:

1. User request: The support agent asks the AI assistant, “What was the most recent order placed by Priyanka Chopra Jonas?”

2. AI model processes intent: The AI assistant, running on an MCP host, analyzes the natural language query. It recognizes that to answer this question, it needs to perform a database query. It then identifies the appropriate tool from the MCP server’s capabilities. 

3. Client initiates tool call: The AI assistant’s MCP client sends a JSON-RPC request to the MCP server connected to the PostgreSQL database. The request specifies the tool to be used, such as get_customer_orders, and includes the necessary parameters: 

{
  "jsonrpc": "2.0",
  "method": "db_tools.get_customer_orders",
  "params": {
    "customer_name": "Priyanka Chopra Jonas",
    "sort_by": "order_date",
    "sort_order": "desc",
    "limit": 1
  },
  "id": "12345"
}

4. Server handles the request: The MCP server receives the request and performs several key functions: 

  • Authentication and authorization: The server verifies that the AI client and the user have permission to query the database.
  • Query translation: The server translates the standardized MCP request into a specific SQL query for the PostgreSQL database.
  • Query execution: The server executes the SQL query against the database.
SELECT order_id, order_date, total_amount
FROM orders
WHERE customer_name = 'Priyanka Chopra Jonas'
ORDER BY order_date DESC
LIMIT 1;

5. Database returns data: The PostgreSQL database executes the query and returns the requested data to the MCP server. 

6. Server formats the response: The MCP server receives the raw database output and formats it into a standardized JSON response that the MCP client can understand.

{
  "jsonrpc": "2.0",
  "result": {
    "data": [
      {
        "order_id": "98765",
        "order_date": "2025-08-25",
        "total_amount": 11025.50
      }
    ]
  },
  "id": "12345"
}

7. Client returns data to the model: The MCP client receives the JSON response and passes it back to the AI assistant’s language model. 

8. AI model generates final response: The language model incorporates this real-time data into its response and presents it to the user in a natural, conversational format. 

“Priyanka Chopra Jonas’s most recent order was placed on August 25, 2025, with an order ID of 98765, for a total of $11025.50.”

What are the performance implications of using MCP for database access?

Using the Model Context Protocol (MCP) for database access introduces a layer of abstraction that affects performance in several ways. While it adds some latency and processing overhead, strategic implementation can mitigate these effects. For AI applications, the benefits often outweigh the costs, particularly in terms of improved accuracy, security, and scalability.

Sources of performance implications::

Added latency and processing overhead:

The MCP architecture introduces extra communication steps between the AI agent and the database, each adding a small amount of latency. 

  • RPC overhead: The JSON-RPC call from the AI’s client to the MCP server adds a small processing and network delay. This is an out-of-process request, as opposed to a simple local function call.
  • JSON serialization: Request and response data must be serialized and deserialized into JSON format, which requires processing time.
  • Network transit: For remote MCP servers, the data must travel over the network, adding latency. However, for a local or on-premise setup, this is minimal. The physical location of the MCP server relative to the AI model and the database is a significant factor.

Scalability and resource consumption:

The performance impact scales with the complexity and volume of the AI agent’s interactions. 

  • High request volume: A single AI agent working on a complex task might issue dozens of parallel database queries. In high-traffic scenarios, managing numerous simultaneous connections can strain system resources and require robust infrastructure.
  • Excessive data retrieval: A significant performance risk is an AI agent retrieving a massive dataset in a single query. This process can consume a large number of tokens, fill the AI’s context window, and cause bottlenecks at the database and client levels.
  • Context window usage: Tool definitions and the results of tool calls consume space in the AI’s context window. If a large number of tools are in use, this can limit the AI’s “working memory,” resulting in slower and less effective reasoning. 

Optimizations for high performance::

Caching:

Caching is a crucial strategy for mitigating the performance overhead of MCP. 

  • In-memory caching: The MCP server can cache results from frequent or expensive database queries in memory (e.g., using Redis or Memcached). This approach enables repeat requests to be served almost instantly without requiring a database hit.
  • Semantic caching: Advanced techniques can cache the results of previous queries and serve them for semantically similar future requests, reducing token consumption and improving speed for conversational applications. 

Efficient queries and resource management:

Designing the MCP server and its database interactions for efficiency is critical. 

  • Optimized SQL: The MCP server should generate optimized SQL queries. Database indexes should be utilized effectively to expedite lookups and minimize load.
  • Pagination and filtering: To prevent a single query from overwhelming the system, the MCP server should implement pagination. The AI agent can be prompted to use filtering parameters to retrieve only the necessary data.
  • Connection pooling: This technique reuses existing database connections instead of opening a new one for each request, thereby reducing latency and database load. 

Load balancing and scaling:

For large-scale enterprise deployments, scaling is essential for maintaining performance. 

  • Multiple servers: The workload can be distributed across various MCP servers. One server could handle read requests, and another could handle writes.
  • Load balancing: A reverse proxy or other load-balancing solution can distribute incoming traffic across MCP server instances. Autoscaling can dynamically add or remove servers in response to demand.

The performance trade-off in perspective:

For AI-driven tasks, a slight increase in latency for database access is often a worthwhile trade-off for significant gains. 

  • Improved accuracy: Accessing real-time, high-quality data through MCP leads to more accurate and relevant AI responses, reducing “hallucinations”.
  • Scalable ecosystem: The standardization of MCP reduces development overhead and allows for a more modular, scalable ecosystem, which saves significant engineering resources compared to building custom integrations.
  • Decoupled architecture: The MCP server decouples the AI model from the database, allowing each to be optimized and scaled independently. 

We’ll go ahead and conclude this post here & continue discussing on a further deep dive in the next post.

Till then, Happy Avenging! 🙂

Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. There is always room for improvement in this kind of model & the solution associated with it. I’ve shown the basic ways to achieve the same for educational purposes only.

Creating a local LLM Cluster Server using Apple Silicon GPU

Posted on by SatyakiDe in ai, api, Apple, call, cloud, code, combining, computing, CPU, Crossplatform, Data Science, design, exo, features, GPU, gui, HuggingFace, integration, IoT, json, llm, machine-learning, Model, numpy, objects, Open-CV, openai, Pandas, Python, React, Real-time, Technology, Tensorflow, Torch

Today, we’re going to discuss creating a local LLM server and then utilizing it to execute various popular LLM models. We will club the local Apple GPUs together via a new framework that binds all the available Apple Silicon devices into one big LLM server. This enables people to run many large models, which was otherwise not possible due to the lack of GPUs.

This is certainly a new way; One can create virtual computation layers by adding nodes to the resource pool, increasing the computation capacity.

Why not witness a small demo to energize ourselves –

Let us understand the scenario. I’ve one Mac Book Pro M4 & 2 Mac Mini Pro M4 (Base models). So, I want to add them & expose them as a cluster as follows –

As you can see, I’ve connected my MacBook Pro with both the Mac Mini using high-speed thunderbolt cables for better data transmissions. And, I’ll be using an open-source framework called “Exo” to create it.

Also, you can see that my total computing capacity is 53.11 TFlops, which is slightly more than the last category.

What is Exo?

“Exo” is an open-source framework that helps you merge all your available devices into a large cluster of available resources. This extracts all the computing juice needed to handle complex tasks, including the big LLMs, which require very expensive GPU-based servers.

For more information on “Exo”, please refer to the following link.

In our previous diagram, we can see that the framework also offers endpoints.

  • One option is a local ChatGPT interface, where any question you ask will receive a response from models by combining all available computing power.
  • The other endpoint offers users a choice of any standard LLM API endpoint, which helps them integrate it into their solutions.

Let us see, how the devices are connected together –

How to establish the Cluster?

To proceed with this, you need to have at least Python 3.12, Anaconda or Miniconda & Xcode installed in all of your machines. Also, you need to install some Apple-specific MLX packages or libraries to get the best performance.

Depending on your choice, you need to use the following link to download Anaconda or Miniconda.

You can download the following link to download the Python 3.12. However, I’ve used Python 3.13 on some machines & some machines, I’ve used Python 3.12. And it worked without any problem.

Sometimes, after installing Anaconda or Miniconda, the environment may not implicitly be activated after successful installation. In that case, you may need to use the following commands in the terminal -> source ~/.bash_profile

To verify, whether the conda has been successfully installed & activated, you need to type the following command –

(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % conda --version
conda 24.11.3
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas %

Once you verify it. Now, we need to install the following supplemental packages in all the machines as –

satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
satyaki_de@Satyakis-MacBook-Pro-Max Pandas % conda install anaconda::m4
Channels:
 - defaults
 - anaconda
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /opt/anaconda3

  added / updated specs:
    - anaconda::m4


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    m4-1.4.18                  |       h1230e6a_1         202 KB  anaconda
    ------------------------------------------------------------
                                           Total:         202 KB

The following NEW packages will be INSTALLED:

  m4                 anaconda/osx-arm64::m4-1.4.18-h1230e6a_1 


Proceed ([y]/n)? y


Downloading and Extracting Packages:
                                                                                                                                                                                                                      
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

Also, you can use this package to install in your machines –

(base) satyakidemini2@Satyakis-Mac-mini-2 exo % 
(base) satyakidemini2@Satyakis-Mac-mini-2 exo % pip install mlx
Collecting mlx
  Downloading mlx-0.23.2-cp312-cp312-macosx_14_0_arm64.whl.metadata (5.3 kB)
Downloading mlx-0.23.2-cp312-cp312-macosx_14_0_arm64.whl (27.6 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 27.6/27.6 MB 8.8 MB/s eta 0:00:00
Installing collected packages: mlx
Successfully installed mlx-0.23.2
(base) satyakidemini2@Satyakis-Mac-mini-2 exo % 
(base) satyakidemini2@Satyakis-Mac-mini-2 exo %

Main Setup:

Till now, we’ve installed all the important packages. Now, we need to setup the final “eco” framework in all the machines like our previous steps.

Now, we’ll first clone the “eco” framework by the following commands –

(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % git clone https://github.com/exo-explore/exo.git
Cloning into 'exo'...
remote: Enumerating objects: 9736, done.
remote: Counting objects: 100% (411/411), done.
remote: Compressing objects: 100% (148/148), done.
remote: Total 9736 (delta 333), reused 263 (delta 263), pack-reused 9325 (from 3)
Receiving objects: 100% (9736/9736), 12.18 MiB | 8.41 MiB/s, done.
Resolving deltas: 100% (5917/5917), done.
Updating files: 100% (178/178), done.
Filtering content: 100% (9/9), 3.16 MiB | 2.45 MiB/s, done.
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas %

And, the content of the “Exo” folder should look like this –

total 28672
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 docs
-rwx------  1 satyaki_de  staff     1337 Mar  9 17:06 configure_mlx.sh
-rwx------  1 satyaki_de  staff    11107 Mar  9 17:06 README.md
-rwx------  1 satyaki_de  staff    35150 Mar  9 17:06 LICENSE
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 examples
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 exo
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 extra
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 scripts
-rwx------  1 satyaki_de  staff      390 Mar  9 17:06 install.sh
-rwx------  1 satyaki_de  staff      792 Mar  9 17:06 format.py
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 test
-rwx------  1 satyaki_de  staff     2476 Mar  9 17:06 setup.py
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:10 build
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:17 exo.egg-info

Similar commands need to fire to other devices. Here, I’m showing one Mac-Mini examples –

(base) satyakidemini2@Satyakis-Mac-mini-2 Pandas % 
(base) satyakidemini2@Satyakis-Mac-mini-2 Pandas % git clone https://github.com/exo-explore/exo.git
Cloning into 'exo'...
remote: Enumerating objects: 9736, done.
remote: Counting objects: 100% (424/424), done.
remote: Compressing objects: 100% (146/146), done.
remote: Total 9736 (delta 345), reused 278 (delta 278), pack-reused 9312 (from 4)
Receiving objects: 100% (9736/9736), 12.18 MiB | 6.37 MiB/s, done.
Resolving deltas: 100% (5920/5920), done.
(base) satyakidemini2@Satyakis-Mac-mini-2 Pandas %

After that, I’ll execute the following sets of commands to install the framework –

(base) satyaki_de@Satyakis-MacBook-Pro-Max Pandas % cd exo
(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % conda create --name exo1 python=3.13
WARNING: A conda environment already exists at '/opt/anaconda3/envs/exo1'

Remove existing environment?
This will remove ALL directories contained within this specified prefix directory, including any other conda environments.

 (y/[n])? y

Channels:
 - defaults
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /opt/anaconda3/envs/exo1

  added / updated specs:
    - python=3.13


The following NEW packages will be INSTALLED:

  bzip2              pkgs/main/osx-arm64::bzip2-1.0.8-h80987f9_6 
  ca-certificates    pkgs/main/osx-arm64::ca-certificates-2025.2.25-hca03da5_0 
  expat              pkgs/main/osx-arm64::expat-2.6.4-h313beb8_0 
  libcxx             pkgs/main/osx-arm64::libcxx-14.0.6-h848a8c0_0 
  libffi             pkgs/main/osx-arm64::libffi-3.4.4-hca03da5_1 
  libmpdec           pkgs/main/osx-arm64::libmpdec-4.0.0-h80987f9_0 
  ncurses            pkgs/main/osx-arm64::ncurses-6.4-h313beb8_0 
  openssl            pkgs/main/osx-arm64::openssl-3.0.16-h02f6b3c_0 
  pip                pkgs/main/osx-arm64::pip-25.0-py313hca03da5_0 
  python             pkgs/main/osx-arm64::python-3.13.2-h4862095_100_cp313 
  python_abi         pkgs/main/osx-arm64::python_abi-3.13-0_cp313 
  readline           pkgs/main/osx-arm64::readline-8.2-h1a28f6b_0 
  setuptools         pkgs/main/osx-arm64::setuptools-75.8.0-py313hca03da5_0 
  sqlite             pkgs/main/osx-arm64::sqlite-3.45.3-h80987f9_0 
  tk                 pkgs/main/osx-arm64::tk-8.6.14-h6ba3021_0 
  tzdata             pkgs/main/noarch::tzdata-2025a-h04d1e81_0 
  wheel              pkgs/main/osx-arm64::wheel-0.45.1-py313hca03da5_0 
  xz                 pkgs/main/osx-arm64::xz-5.6.4-h80987f9_1 
  zlib               pkgs/main/osx-arm64::zlib-1.2.13-h18a0788_1 


Proceed ([y]/n)? y


Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate exo1
#
# To deactivate an active environment, use
#
#     $ conda deactivate

(base) satyaki_de@Satyakis-MacBook-Pro-Max exo % conda activate exo1
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % ls -lrt
total 24576
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 docs
-rwx------  1 satyaki_de  staff     1337 Mar  9 17:06 configure_mlx.sh
-rwx------  1 satyaki_de  staff    11107 Mar  9 17:06 README.md
-rwx------  1 satyaki_de  staff    35150 Mar  9 17:06 LICENSE
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 examples
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 exo
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 extra
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 scripts
-rwx------  1 satyaki_de  staff      390 Mar  9 17:06 install.sh
-rwx------  1 satyaki_de  staff      792 Mar  9 17:06 format.py
drwx------  1 satyaki_de  staff  1048576 Mar  9 17:06 test
-rwx------  1 satyaki_de  staff     2476 Mar  9 17:06 setup.py
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % 
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % pip install .
Processing /Volumes/WD_BLACK/PythonCourse/Pandas/exo
  Preparing metadata (setup.py) ... done
Collecting tinygrad@ git+https://github.com/tinygrad/tinygrad.git@ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8 (from exo==0.0.1)
  Cloning https://github.com/tinygrad/tinygrad.git (to revision ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8) to /private/var/folders/26/dj11b57559b8r8rl6ztdpc840000gn/T/pip-install-q18fzk3r/tinygrad_7917114c483a4d9c83c795b69dbeb5c7
  Running command git clone --filter=blob:none --quiet https://github.com/tinygrad/tinygrad.git /private/var/folders/26/dj11b57559b8r8rl6ztdpc840000gn/T/pip-install-q18fzk3r/tinygrad_7917114c483a4d9c83c795b69dbeb5c7
  Running command git rev-parse -q --verify 'sha^ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8'
  Running command git fetch -q https://github.com/tinygrad/tinygrad.git ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8
  Running command git checkout -q ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8
  Resolved https://github.com/tinygrad/tinygrad.git to commit ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8
  Preparing metadata (setup.py) ... done
Collecting aiohttp==3.10.11 (from exo==0.0.1)
.
.
(Installed many more dependant packages)
.
.
Downloading propcache-0.3.0-cp313-cp313-macosx_11_0_arm64.whl (44 kB)
Building wheels for collected packages: exo, nuitka, numpy, uuid, tinygrad
  Building wheel for exo (setup.py) ... done
  Created wheel for exo: filename=exo-0.0.1-py3-none-any.whl size=901357 sha256=5665297f8ea09d06670c9dea91e40270acc4a3cf99a560bf8d268abb236050f7
  Stored in directory: /private/var/folders/26/dj118r8rl6ztdpc840000gn/T/pip-ephem-wheel-cache-0k8zloo3/wheels/b6/91/fb/c1c7d8ca90cf16b9cd8203c11bb512614bee7f6d34
  Building wheel for nuitka (pyproject.toml) ... done
  Created wheel for nuitka: filename=nuitka-2.5.1-cp313-cp313-macosx_11_0_arm64.whl size=3432720 sha256=ae5a280a1684fde98c334516ee8a99f9f0acb6fc2f625643b7f9c5c0887c2998
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/f6/c9/53/9e37c6fb34c27e892e8357aaead46da610f82117ab2825
  Building wheel for numpy (pyproject.toml) ... done
  Created wheel for numpy: filename=numpy-2.0.0-cp313-cp313-macosx_15_0_arm64.whl size=4920701 sha256=f030b0aa51ec6628f708fab0af14ff765a46d210df89aa66dd8d9482e59b5
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/e0/d3/66/30d07c18e56ac85e8d3ceaf22f093a09bae124a472b85d1
  Building wheel for uuid (setup.py) ... done
  Created wheel for uuid: filename=uuid-1.30-py3-none-any.whl size=6504 sha256=885103a90d1dc92d9a75707fc353f4154597d232f2599a636de1bc6d1c83d
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/cc/9d/72/13ff6a181eacfdbd6d761a4ee7c5c9f92034a9dc8a1b3c
  Building wheel for tinygrad (setup.py) ... done
  Created wheel for tinygrad: filename=tinygrad-0.10.0-py3-none-any.whl size=1333964 sha256=1f08c5ce55aa3c87668675beb80810d609955a81b99d416459d2489b36a
  Stored in directory: /Users/satyaki_de/Library/Caches/pip/wheels/c7/bd/02/bd91c1303002619dad23f70f4c1f1c15d0c24c60b043e
Successfully built exo nuitka numpy uuid tinygrad
Installing collected packages: uuid, sentencepiece, nvidia-ml-py, zstandard, uvloop, urllib3, typing-extensions, tqdm, tinygrad, scapy, safetensors, regex, pyyaml, pygments, psutil, protobuf, propcache, prometheus-client, pillow, packaging, ordered-set, numpy, multidict, mlx, mdurl, MarkupSafe, idna, grpcio, fsspec, frozenlist, filelock, charset-normalizer, certifi, attrs, annotated-types, aiohappyeyeballs, aiofiles, yarl, requests, pydantic-core, opencv-python, nuitka, markdown-it-py, Jinja2, grpcio-tools, aiosignal, rich, pydantic, huggingface-hub, aiohttp, tokenizers, aiohttp_cors, transformers, mlx-lm, exo
Successfully installed Jinja2-3.1.4 MarkupSafe-3.0.2 aiofiles-24.1.0 aiohappyeyeballs-2.5.0 aiohttp-3.10.11 aiohttp_cors-0.7.0 aiosignal-1.3.2 annotated-types-0.7.0 attrs-25.1.0 certifi-2025.1.31 charset-normalizer-3.4.1 exo-0.0.1 filelock-3.17.0 frozenlist-1.5.0 fsspec-2025.3.0 grpcio-1.67.0 grpcio-tools-1.67.0 huggingface-hub-0.29.2 idna-3.10 markdown-it-py-3.0.0 mdurl-0.1.2 mlx-0.22.0 mlx-lm-0.21.1 multidict-6.1.0 nuitka-2.5.1 numpy-2.0.0 nvidia-ml-py-12.560.30 opencv-python-4.10.0.84 ordered-set-4.1.0 packaging-24.2 pillow-10.4.0 prometheus-client-0.20.0 propcache-0.3.0 protobuf-5.28.1 psutil-6.0.0 pydantic-2.9.2 pydantic-core-2.23.4 pygments-2.19.1 pyyaml-6.0.2 regex-2024.11.6 requests-2.32.3 rich-13.7.1 safetensors-0.5.3 scapy-2.6.1 sentencepiece-0.2.0 tinygrad-0.10.0 tokenizers-0.20.3 tqdm-4.66.4 transformers-4.46.3 typing-extensions-4.12.2 urllib3-2.3.0 uuid-1.30 uvloop-0.21.0 yarl-1.18.3 zstandard-0.23.0
(exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo %

And, you need to perform the same process in other available devices as well.

Now, we’re ready to proceed with the final command –

(.venv) (exo1) satyaki_de@Satyakis-MacBook-Pro-Max exo % exo
/opt/anaconda3/envs/exo1/lib/python3.13/site-packages/google/protobuf/runtime_version.py:112: UserWarning: Protobuf gencode version 5.27.2 is older than the runtime version 5.28.1 at node_service.proto. Please avoid checked-in Protobuf gencode that can be obsolete.
  warnings.warn(
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
Selected inference engine: None

  _____  _____  
 / _ \ \/ / _ \ 
|  __/>  < (_) |
 \___/_/\_\___/ 
    
Detected system: Apple Silicon Mac
Inference engine name after selection: mlx
Using inference engine: MLXDynamicShardInferenceEngine with shard downloader: SingletonShardDownloader
[60771, 54631, 54661]
Chat interface started:
 - http://127.0.0.1:52415
 - http://XXX.XXX.XX.XX:52415
 - http://XXX.XXX.XXX.XX:52415
 - http://XXX.XXX.XXX.XXX:52415
ChatGPT API endpoint served at:
 - http://127.0.0.1:52415/v1/chat/completions
 - http://XXX.XXX.X.XX:52415/v1/chat/completions
 - http://XXX.XXX.XXX.XX:52415/v1/chat/completions
 - http://XXX.XXX.XXX.XXX:52415/v1/chat/completions
has_read=True, has_write=True
╭────────────────────────────────────────────────────────────────────────────────────────────── Exo Cluster (2 nodes) ───────────────────────────────────────────────────────────────────────────────────────────────╮
Received exit signal SIGTERM...
Thank you for using exo.

  _____  _____  
 / _ \ \/ / _ \ 
|  __/>  < (_) |
 \___/_/\_\___/

Note that I’ve masked the IP addresses for security reasons.

Run Time:

At the beginning, if we trigger the main MacBook Pro Max, the “Exo” screen should looks like this –

And if you open the URL, you will see the following ChatGPT-like interface –

Connecting without the Thunderbolt bridge with the relevant port or a hub may cause performance degradation. Hence, how you connect will play a major role in the success of this intention. However, this is certainly a great idea to proceed with.

So, we’ve done it.

We’ll cover the detailed performance testing, Optimized configurations & many other useful details in our next post.

Till then, Happy Avenging! 🙂

Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. There is always room for improvement in this kind of model & the solution associated with it. I’ve shown the basic ways to achieve the same for educational purposes only.

Monitoring & evaluating the leading LLMs (both the established & new) by Python-based evaluator

Posted on by SatyakiDe in ai, anthropic, api, Azure, bharatgpt, cloud, code, CPU, Crossplatform, Data Science, deepseek, GPU, HuggingFace, json, llm, natural-language, numpy, objects, openai, Pandas, Python, Real-time, Silicon, snippet, Technology, Torch

As we’re leaping more & more into the field of Generative AI, one of the frequent questions or challenges people are getting more & more is the performance & other evaluation factors. These factors will eventually bring the fruit of this technology; otherwise, you will end up in technical debt.

This post will discuss the key snippets of the monitoring app based on the Python-based AI app. But before that, let us first view the demo.

Isn’t it exciting?

Let us deep dive into it. But, here is the flow this solution will follow.

So, the current application will invoke the industry bigshots and some relatively unknown or new LLMs.

In this case, we’ll evaluate Anthropic, Open AI, DeepSeek, and Bharat GPT’s various models. However, Bharat GPT is open source, so we’ll use the Huggingface library and execute it locally against my MacBook Pro M4 Max.

The following are the KPIs we’re going to evaluate:

Package Installation:

Here are the lists of dependant python packages that is require to run this application –

pip install certifi==2024.8.30
pip install anthropic==0.42.0
pip install huggingface-hub==0.27.0
pip install nltk==3.9.1
pip install numpy==2.2.1
pip install moviepy==2.1.1
pip install numpy==2.1.3
pip install openai==1.59.3
pip install pandas==2.2.3
pip install pillow==11.1.0
pip install pip==24.3.1
pip install psutil==6.1.1
pip install requests==2.32.3
pip install rouge_score==0.1.2
pip install scikit-learn==1.6.0
pip install setuptools==70.2.0
pip install tokenizers==0.21.0
pip install torch==2.6.0.dev20250104
pip install torchaudio==2.6.0.dev20250104
pip install torchvision==0.22.0.dev20250104
pip install tqdm==4.67.1
pip install transformers==4.47.1

CODE:

clsComprehensiveLLMEvaluator.py (This is the main Python class that will apply all the logic to collect stats involving important KPIs. Note that we’re only going to discuss a few important functions here.)

    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
    def get_claude_response(self, prompt: str) -> str:
        response = self.anthropic_client.messages.create(
            model=anthropic_model,
            max_tokens=maxToken,
            messages=[{"role": "user", "content": prompt}]
        )
        return response.content[0].text
  1. The Retry Mechanism
    • The @retry line means this function will automatically try again if it fails.
    • It will stop retrying after 3 attempts (stop_after_attempt(3)).
    • It will wait longer between retries, starting at 4 seconds and increasing up to 10 seconds (wait_exponential(multiplier=1, min=4, max=10)).
  2. The Function Purpose
    • The function takes a message, called prompt, as input (a string of text).
    • It uses a service (likely an AI system like Claude) to generate a response to this prompt.
  3. Sending the Message
    • Inside the function, the code self.anthropic_client.messages.create is the part that actually sends the prompt to the AI.
    • It specifies:Which AI model to use (e.g., anthropic_model).
    • The maximum length of the response (controlled by maxToken).
    • The input message for the AI has a “role” (user), as well as the content of the prompt.
  4. Getting the Response
    • Once the AI generates a response, it’s saved as response.
    • The code retrieves the first part of the response (response.content[0].text) and sends it back to whoever called the function.

Similarly, it will work for Open AI as well. 

    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
    def get_deepseek_response(self, prompt: str) -> tuple:
        deepseek_api_key = self.deepseek_api_key

        headers = {
            "Authorization": f"Bearer {deepseek_api_key}",
            "Content-Type": "application/json"
            }
        
        payload = {
            "model": deepseek_model,  
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": maxToken
            }
        
        response = requests.post(DEEPSEEK_API_URL, headers=headers, json=payload)

        if response.status_code == 200:
            res = response.json()["choices"][0]["message"]["content"]
        else:
            res = "API request failed with status code " + str(response.status_code) + ":" + str(response.text)

        return res
  1. Retry Mechanism:
    • The @retry line ensures the function will try again if it fails.
    • It will stop retrying after 3 attempts (stop_after_attempt(3)).
    • It waits between retries, starting at 4 seconds and increasing up to 10 seconds (wait_exponential(multiplier=1, min=4, max=10)).
  1. What the Function Does:
    • The function takes one input, prompt, which is the message or question you want to send to the AI.
    • It returns the AI’s response or an error message.
  1. Preparing to Communicate with the API:
    • API Key: It gets the API key for the DeepSeek service from self.deepseek_api_key.
    • Headers: These tell the API that the request will use the API key (for security) and that the data format is JSON (structured text).
    • Payload: This is the information sent to the AI. It includes:
      • Model: Specifies which version of the AI to use (deepseek_model).
      • Messages: The input message with the role “user” and your prompt.
      • Max Tokens: Defines the maximum size of the AI’s response (maxToken).
  1. Sending the Request:
    • It uses the requests.post() method to send the payload and headers to the DeepSeek API using the URL DEEPSEEK_API_URL.
  1. Processing the Response:
    • If the API responds successfully (status_code == 200):
      • It extracts the AI’s reply from the response data.
      • Specifically, it gets the first choice’s message content: response.json()["choices"][0]["message"]["content"].
    • If there’s an error:
      • It constructs an error message with the status code and detailed error text from the API.
  1. Returning the Result:
    • The function outputs either the AI’s response or the error message.
    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
    def get_bharatgpt_response(self, prompt: str) -> tuple:
        try:
            messages = [[{"role": "user", "content": prompt}]]
            
            response = pipe(messages, max_new_tokens=maxToken,)

            # Extract 'content' field safely
            res = next((entry.get("content", "")
                        for entry in response[0][0].get("generated_text", [])
                        if isinstance(entry, dict) and entry.get("role") == "assistant"
                        ),
                        None,
                        )
            
            return res
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return ""
  1. Retry Mechanism:The @retry ensures the function will try again if it fails.
    • It will stop retrying after 3 attempts (stop_after_attempt(3)).
    • The waiting time between retries starts at 4 seconds and increases exponentially up to 10 seconds (wait_exponential(multiplier=1, min=4, max=10)).
  2. What the Function Does:The function takes one input, prompt, which is the message or question you want to send to BharatGPT.
    • It returns the AI’s response or an empty string if something goes wrong.
  3. Sending the Prompt:Messages Structure: The function wraps the user’s prompt in a format that the BharatGPT AI understands:
    • messages = [[{"role": "user", "content": prompt}]]
    • This tells the AI that the prompt is coming from the “user.”
  4. Pipe Function: It uses a pipe() method to send the messages to the AI system.
    • max_new_tokens=maxToken: Limits how long the AI’s response can be.
  5. Extracting the Response:The response from the AI is in a structured format. The code looks for the first piece of text where:
    • The role is “assistant” (meaning it’s the AI’s reply).
    • The text is in the “content” field.
    • The next() function safely extracts this “content” field or returns None if it can’t find it.
  6. Error Handling:If something goes wrong (e.g., the AI doesn’t respond or there’s a technical issue), the code:
    • Captures the error message in e.
    • Prints the error message: print('Error: ', x).
    • Returns an empty string ("") instead of crashing.
  7. Returning the Result:If everything works, the function gives you the AI’s response as plain text.
    • If there’s an error, it gives you an empty string, indicating no response was received.

    def get_model_response(self, model_name: str, prompt: str) -> ModelResponse:
        """Get response from specified model with metrics"""
        start_time = time.time()
        start_memory = psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024

        try:
            if model_name == "claude-3":
                response_content = self.get_claude_response(prompt)
            elif model_name == "gpt4":
                response_content = self.get_gpt4_response(prompt)
            elif model_name == "deepseek-chat":
                response_content = self.get_deepseek_response(prompt)
            elif model_name == "bharat-gpt":
                response_content = self.get_bharatgpt_response(prompt)

            # Model-specific API calls 
            token_count = len(self.bert_tokenizer.encode(response_content))
            
            end_memory = psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024
            memory_usage = end_memory - start_memory
            
            return ModelResponse(
                content=response_content,
                response_time=time.time() - start_time,
                token_count=token_count,
                memory_usage=memory_usage
            )
        except Exception as e:
            logging.error(f"Error getting response from {model_name}: {str(e)}")
            return ModelResponse(
                content="",
                response_time=0,
                token_count=0,
                memory_usage=0,
                error=str(e)
            )

Start Tracking Time and Memory:

    • The function starts a timer (start_time) to measure how long it takes to get a response.
    • It also checks how much memory is being used at the beginning (start_memory).

    Choose the AI Model:

    • Based on the model_name provided, the function selects the appropriate method to get a response:
      • "claude-3" → Calls get_claude_response(prompt).
      • "gpt4" → Calls get_gpt4_response(prompt).
      • "deepseek-chat" → Calls get_deepseek_response(prompt).
      • "bharat-gpt" → Calls get_bharatgpt_response(prompt).

    Process the Response:

    • Once the response is received, the function calculates:
      • Token Count: The number of tokens (small chunks of text) in the response using a tokenizer.
      • Memory Usage: The difference between memory usage after the response (end_memory) and before it (start_memory).

    Return the Results:

    • The function bundles all the information into a ModelResponse object:
      • The AI’s reply (content).
      • How long the response took (response_time).
      • The number of tokens in the reply (token_count).
      • How much memory was used (memory_usage).

    Handle Errors:

    • If something goes wrong (e.g., the AI doesn’t respond), the function:
      • Logs the error message.
      • Returns an empty response with default values and the error message.
        def evaluate_text_quality(self, generated: str, reference: str) -> Dict[str, float]:
        """Evaluate text quality metrics"""
        # BERTScore
        gen_embedding = self.sentence_model.encode([generated])
        ref_embedding = self.sentence_model.encode([reference])
        bert_score = cosine_similarity(gen_embedding, ref_embedding)[0][0]

        # BLEU Score
        generated_tokens = word_tokenize(generated.lower())
        reference_tokens = word_tokenize(reference.lower())
        bleu = sentence_bleu([reference_tokens], generated_tokens)

        # METEOR Score
        meteor = meteor_score([reference_tokens], generated_tokens)

        return {
            'bert_score': bert_score,
            'bleu_score': bleu,
            'meteor_score': meteor
        }

    Inputs:

    • generated: The text produced by the AI.
    • reference: The correct or expected version of the text.

    Calculating BERTScore:

    • Converts the generated and reference texts into numerical embeddings (mathematical representations) using a pre-trained model (self.sentence_model.encode).
    • Measures the similarity between the two embeddings using cosine similarity. This gives the bert_score, which ranges from -1 (completely different) to 1 (very similar).

    Calculating BLEU Score:

    • Breaks the generated and reference texts into individual words (tokens) using word_tokenize.
    • Converts both texts to lowercase for consistent comparison.
    • Calculates the BLEU Score (sentence_bleu), which checks how many words or phrases in the generated text overlap with the reference. BLEU values range from 0 (no match) to 1 (perfect match).

    Calculating METEOR Score:

    • Also uses the tokenized versions of generated and reference texts.
    • Calculates the METEOR Score (meteor_score), which considers exact matches, synonyms, and word order. Scores range from 0 (no match) to 1 (perfect match).

    Returning the Results:

    • Combines the three scores into a dictionary with the keys 'bert_score', 'bleu_score', and 'meteor_score'.

    Similarly, other functions are developed.

        def run_comprehensive_evaluation(self, evaluation_data: List[Dict]) -> pd.DataFrame:
        """Run comprehensive evaluation on all metrics"""
        results = []
        
        for item in evaluation_data:
            prompt = item['prompt']
            reference = item['reference']
            task_criteria = item.get('task_criteria', {})
            
            for model_name in self.model_configs.keys():
                # Get multiple responses to evaluate reliability
                responses = [
                    self.get_model_response(model_name, prompt)
                    for _ in range(3)  # Get 3 responses for reliability testing
                ]
                
                # Use the best response for other evaluations
                best_response = max(responses, key=lambda x: len(x.content) if not x.error else 0)
                
                if best_response.error:
                    logging.error(f"Error in model {model_name}: {best_response.error}")
                    continue
                
                # Gather all metrics
                metrics = {
                    'model': model_name,
                    'prompt': prompt,
                    'response': best_response.content,
                    **self.evaluate_text_quality(best_response.content, reference),
                    **self.evaluate_factual_accuracy(best_response.content, reference),
                    **self.evaluate_task_performance(best_response.content, task_criteria),
                    **self.evaluate_technical_performance(best_response),
                    **self.evaluate_reliability(responses),
                    **self.evaluate_safety(best_response.content)
                }
                
                # Add business impact metrics using task performance
                metrics.update(self.evaluate_business_impact(
                    best_response,
                    metrics['task_completion']
                ))
                
                results.append(metrics)
        
        return pd.DataFrame(results)
    • Input:
      • evaluation_data: A list of test cases, where each case is a dictionary containing:
        • prompt: The question or input to the AI model.
        • reference: The ideal or expected answer.
        • task_criteria (optional): Additional rules or requirements for the task.
    • Initialize Results:
      • An empty list results is created to store the evaluation metrics for each model and test case.
    • Iterate Through Test Cases:
      • For each item in the evaluation_data:
        • Extract the prompt, reference, and task_criteria.
    • Evaluate Each Model:
      • Loop through all available AI models (self.model_configs.keys()).
      • Generate three responses for each model to test reliability.
    • Select the Best Response:
      • Out of the three responses, pick the one with the most content (best_response), ignoring responses with errors.
    • Handle Errors:
      • If a response has an error, log the issue and skip further evaluation for that model.
    • Evaluate Metrics:
      • Using the best_response, calculate a variety of metrics, including:
        • Text Quality: How similar the response is to the reference.
        • Factual Accuracy: Whether the response is factually correct.
        • Task Performance: How well it meets task-specific criteria.
        • Technical Performance: Evaluate time, memory, or other system-related metrics.
        • Reliability: Check consistency across multiple responses.
        • Safety: Ensure the response is safe and appropriate.
    • Evaluate Business Impact:
      • Add metrics for business impact (e.g., how well the task was completed, using task_completion as a key factor).
    • Store Results:
      • Add the calculated metrics for this model and prompt to the results list.
    • Return Results as a DataFrame:
      • Convert the results list into a structured table (a pandas DataFrame) for easy analysis and visualization.

    Great! So, now, we’ve explained the code.

    Observation from the result:

    Let us understand the final outcome of this run & what we can conclude from that.

    1. BERT Score (Semantic Understanding):
      • GPT4 leads slightly at 0.8322 (83.22%)
      • Bharat-GPT close second at 0.8118 (81.18%)
      • Claude-3 at 0.8019 (80.19%)
      • DeepSeek-Chat at 0.7819 (78.19%) Think of this like a “comprehension score” – how well the models understand the context. All models show strong understanding, with only a 5% difference between best and worst.
    2. BLEU Score (Word-for-Word Accuracy):
      • Bharat-GPT leads at 0.0567 (5.67%)
      • Claude-3 at 0.0344 (3.44%)
      • GPT4 at 0.0306 (3.06%)
      • DeepSeek-Chat lowest at 0.0189 (1.89%) These low scores suggest models use different wording than references, which isn’t necessarily bad.
    3. METEOR Score (Meaning Preservation):
      • Bharat-GPT leads at 0.4684 (46.84%)
      • Claude-3 close second at 0.4507 (45.07%)
      • GPT4 at 0.2960 (29.60%)
      • DeepSeek-Chat at 0.2652 (26.52%) This shows how well models maintain meaning while using different words.
    4. Response Time (Speed):
      • Claude-3 fastest: 4.40 seconds
      • Bharat-GPT: 6.35 seconds
      • GPT4: 6.43 seconds
      • DeepSeek-Chat slowest: 8.52 seconds
    5. Safety and Reliability:
      • Error Rate: Perfect 0.0 for all models
      • Toxicity: All very safe (below 0.15%) 
        • Claude-3 safest at 0.0007GPT4 at 0.0008Bharat-GPT at 0.0012
        • DeepSeek-Chat at 0.0014
    6. Cost Efficiency:
      • Claude-3 most economical: $0.0019 per response
      • Bharat-GPT close: $0.0021
      • GPT4: $0.0038
      • DeepSeek-Chat highest: $0.0050

    Key Takeaways by Model:

    1. Claude-3: ✓ Fastest responses ✓ Most cost-effective ✓ Excellent meaning preservation ✓ Lowest toxicity
    2. Bharat-GPT: ✓ Best BLEU and METEOR scores ✓ Strong semantic understanding ✓ Cost-effective ✗ Moderate response time
    3. GPT4: ✓ Best semantic understanding ✓ Good safety metrics ✗ Higher cost ✗ Moderate response time
    4. DeepSeek-Chat: ✗ Generally lower performance ✗ Slowest responses ✗ Highest cost ✗ Slightly higher toxicity

    Reliability of These Statistics:

    Strong Points:

    • Comprehensive metric coverage
    • Consistent patterns across evaluations
    • Zero error rates show reliability
    • Clear differentiation between models

    Limitations:

    • BLEU scores are quite low across all models
    • Doesn’t measure creative or innovative responses
    • May not reflect specific use case performance
    • Single snapshot rather than long-term performance

    Final Observation:

    1. Best Overall Value: Claude-3
      • Fast, cost-effective, safe, good performance
    2. Best for Accuracy: Bharat-GPT
      • Highest meaning preservation and precision
    3. Best for Understanding: GPT4
      • Strongest semantic comprehension
    4. Consider Your Priorities: 
      • Speed → Choose Claude-3
      • Cost → Choose Claude-3 or Bharat-GPT
      • Accuracy → Choose Bharat-GPT
      • Understanding → Choose GPT4

    These statistics provide reliable comparative data but should be part of a broader decision-making process that includes your specific needs, budget, and use cases.

    For the Bharat GPT model, we’ve tested this locally on my MacBook Pro 4 Max. And, the configuration is as follows –

    I’ve tried the API version locally, & it provided a similar performance against the stats that we received by running locally. Unfortunately, they haven’t made the API version public yet.

    So, apart from the Anthropic & Open AI, I’ll watch this new LLM (Bharat GPT) for overall stats in the coming days.

    So, we’ve done it.

    You can find the detailed code at the GitHub link.

    I’ll bring some more exciting topics in the coming days from the Python verse.

    Till then, Happy Avenging! 🙂

    Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. There is always room for improvement in this kind of model & the solution associated with it. I’ve shown the basic ways to achieve the same for educational purposes only.

    Enabling & Exploring Stable Defussion – Part 3

    Posted on by SatyakiDe in ai, Apple, cloud, code, Computer-Vision, CPU, Crossplatform, Data Science, design, function, GPU, json, machine-learning, Model, numpy, Open-CV, Pandas, Performance, pytesseract, Python, Real-time, Silicon, StableDefussion, Technology, Torch

    Before we dive into the details of this post, let us provide the previous two links that precede it.

    Enabling & Exploring Stable Defussion – Part 1

    Enabling & Exploring Stable Defussion – Part 2

    For, reference, we’ll share the demo before deep dive into the actual follow-up analysis in the below section –

    Now, let us continue our discussions from where we left.

    CODE:

    clsText2Image.py (This class converts input prompts to an intermediate image)

    class clsText2Image:
    def __init__(self, pipe, output_path, filename):

        self.pipe = pipe
        
        # More aggressive attention slicing
        self.pipe.enable_attention_slicing(slice_size=1)

        self.output_path = f"{output_path}{filename}"
        
        # Warm up the pipeline
        self._warmup()
    
    def _warmup(self):
        """Warm up the pipeline to optimize memory allocation"""
        with torch.no_grad():
            _ = self.pipe("warmup", num_inference_steps=1, height=512, width=512)
        torch.mps.empty_cache()
        gc.collect()
    
    def generate(self, prompt, num_inference_steps=12, guidance_scale=3.0):
        try:
            torch.mps.empty_cache()
            gc.collect()
            
            with torch.autocast(device_type="mps"):
                with torch.no_grad():
                    image = self.pipe(
                        prompt,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        height=1024,
                        width=1024,
                    ).images[0]
            
            image.save(self.output_path)
            return 0
        except Exception as e:
            print(f'Error: {str(e)}')
            return 1
        finally:
            torch.mps.empty_cache()
            gc.collect()

    def genImage(self, prompt):
        try:

            # Initialize generator
            x = self.generate(prompt)

            if x == 0:
                print('Successfully processed first pass!')
            else:
                print('Failed complete first pass!')
                raise 

            return 0

        except Exception as e:
            print(f"\nAn unexpected error occurred: {str(e)}")

            return 1

    1. CLASS INSTANTIATE(pipe, output_path, filename)

    This is the initialization method for the clsText2Image class:

    • Takes a pre-configured pipe (text-to-image pipeline), an output_path, and a filename.
    • Enables more aggressive memory optimization by setting “attention slicing.”
    • Prepares the full file path for saving generated images.
    • Calls a _warmup method to pre-load the pipeline and optimize memory allocation.

    2. _warmup

    This private method warms up the pipeline:

    • Sends a dummy “warmup” request with basic parameters to allocate memory efficiently.
    • Clears any cached memory (torch.mps.empty_cache()) and performs garbage collection (gc.collect()).
    • Ensures smoother operation for future image generation tasks.

    3. generate(prompt, num_inference_steps=12, guidance_scale=3.0)

    This method generates an image from a text prompt:

    • Clears memory cache and performs garbage collection before starting.
    • Uses the text-to-image pipeline (pipe) to generate an image:
      • Takes the prompt, number of inference steps, and guidance scale as input.
      • Outputs an image at 1024×1024 resolution.
    • Saves the generated image to the specified output path.
    • Returns 0 on success or 1 on failure.
    • Ensures cleanup by clearing memory and collecting garbage, even in case of errors.

    4. genImage(prompt)

    This method simplifies image generation:

    • Calls the generate method with the given prompt.
    • Prints a success message if the image is generated (0 return value).
    • On failure, logs the error and raises an exception.
    • Returns 0 on success or 1 on failure.

    clsImage2Video.py (This class converts an image to video as part of the second pass)

    class clsImage2Video:
    def __init__(self, pipeline):
        
        # Optimize model loading
        torch.mps.empty_cache()
        self.pipeline = pipeline

    def generate_frames(self, pipeline, init_image, prompt, duration_seconds=10):
        try:
            torch.mps.empty_cache()
            gc.collect()

            base_frames = []
            img = Image.open(init_image).convert("RGB").resize((1024, 1024))
            
            for _ in range(10):
                result = pipeline(
                    prompt=prompt,
                    image=img,
                    strength=0.45,
                    guidance_scale=7.5,
                    num_inference_steps=25
                ).images[0]

                base_frames.append(np.array(result))
                img = result
                torch.mps.empty_cache()

            frames = []
            for i in range(len(base_frames)-1):
                frame1, frame2 = base_frames[i], base_frames[i+1]
                for t in np.linspace(0, 1, int(duration_seconds*24/10)):
                    frame = (1-t)*frame1 + t*frame2
                    frames.append(frame.astype(np.uint8))
            
            return frames
        except Exception as e:
            frames = []
            print(f'Error: {str(e)}')

            return frames
        finally:
            torch.mps.empty_cache()
            gc.collect()

    # Main method
    def genVideo(self, prompt, inputImage, targetVideo, fps):
        try:
            print("Starting animation generation...")
            
            init_image_path = inputImage
            output_path = targetVideo
            fps = fps
            
            frames = self.generate_frames(
                pipeline=self.pipeline,
                init_image=init_image_path,
                prompt=prompt,
                duration_seconds=20
            )
            
            imageio.mimsave(output_path, frames, fps=30)

            print("Animation completed successfully!")

            return 0
        except Exception as e:
            x = str(e)
            print('Error: ', x)

            return 1

    1. CLASS INSTANTIATE (pipeline)

    This initializes the clsImage2Video class:

    • Clears the GPU cache to optimize memory before loading.
    • Sets up the pipeline for generating frames, which uses an image-to-video transformation model.

    2. generate_frames(pipeline, init_image, prompt, duration_seconds=10)

    This function generates frames for a video:

    • Starts by clearing GPU memory and running garbage collection.
    • Loads the init_image, resizes it to 1024×1024 pixels, and converts it to RGB format.
    • Iteratively applies the pipeline to transform the image:
      • Uses the prompt and specified parameters like strength, guidance_scale, and num_inference_steps.
      • Stores the resulting frames in a list.
    • Interpolates between consecutive frames to create smooth transitions:
      • Uses linear blending for smooth animation across a specified duration and frame rate (24 fps for 10 segments).
    • Returns the final list of generated frames or an empty list if an error occurs.
    • Always clears memory after execution.

    3. genVideo(prompt, inputImage, targetVideo, fps)

    This is the main function for creating a video from an image and text prompt:

    • Logs the start of the animation generation process.
    • Calls generate_frames() with the given pipeline, inputImage, and prompt to create frames.
    • Saves the generated frames as a video using the imageio library, setting the specified frame rate (fps).
    • Logs a success message and returns 0 if the process is successful.
    • On error, logs the issue and returns 1.

    Now, let us understand the performance. But, before that let us explore the device on which we’ve performed these stress test that involves GPU & CPUs as well.

    And, here is the performance stats –

    From the above snapshot, we can clearly communicate that the GPU is 100% utilized. However, the CPU has shown a significant % of availability.

    As you can see, the first pass converts the input prompt to intermediate images within 1 min 30 sec. However, the second pass constitutes multiple hops (11 hops) on an avg 22 seconds. Overall, the application will finish in 5 minutes 36 seconds for a 10-second video clip.

    So, we’ve done it.

    You can find the detailed code at the GitHub link.

    I’ll bring some more exciting topics in the coming days from the Python verse.

    Till then, Happy Avenging! 🙂

    Note: All the data & scenarios posted here are representational data & scenarios & available over the internet & for educational purposes only. There is always room for improvement in this kind of model & the solution associated with it. I’ve shown the basic ways to achieve the same for educational purposes only.