Build Your Own VS Code-Connected AI Coding Companion: A Step-by-Step Guide

By
Ashish Abraham
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Table of Contents

Example H2
Example H3
Example H4
Example H5
Example H6

Introduction

This new era of AI has swiftly transformed the global landscape in a matter of months. One of the areas which has undergone a dramatic shift is the software engineering workspace. Programming has evolved into a less strenuous and demanding endeavour for software engineers. The first innovation, in my personal experience, was with the GitHub Copilot. I was very excited about it and I applied to the first queue of waiting lists. As expected, it was too good and changed the way I code. It really made development faster and helped in writing clean code. Unfortunately, as many people did, I lost access as they moved to subscription plans. 

With the advent of LLMs which are built exclusively for coding, there is a lot of excitement. Models like CodeLlama and StarCoder can produce exceptionally good code in seconds. In this tutorial, I will show you how to harness StarCoder, fine-tune the model, and deploy it as your coding assistant on Visual Studio Code.

StarCoder

StarCoder is an open-source code large language model specially intended for code completion and related tasks. It was first released by the BigCode open-source community in 2023. Boasting a 1 trillion token training on the dataset The Stack, StarCoder surpasses all open code LLMs that accommodate various programming languages and equals or exceeds the performance of the OpenAI code-cushman-001 model. It is one of the closest competitors to CodeLlama.

Prerequisites

This tutorial will use the model and functions from the Hugging Face ecosystem. The model will be fetched from Hugging Face models and will be fine-tuned using the huggingface trainer. Make sure git and docker are installed in your system.

I am using the fine-tuning scripts from the github repo by Hugging Face. Clone the repo.


!git clone https://github.com/pacman100/DHS-LLM-Workshop.git

Install the required libraries.


!pip install packaging
!pip uninstall -y ninja && pip install ninja
!ninja --version
!echo $?

%cd DHS-LLM-Workshop
!git pull
!pip install -r requirements.txt

Login using your Hugging Face credentials.


from huggingface_hub import notebook_login


notebook_login()

Fine-Tuning StarCoder

Let us fine-tune the StarCoder 1B parameter version.  For fine-tuning the model on a code corpus, we will use the hf-stack-peft dataset from Hugging Face datasets. This particular dataset is composed of 158 rows of code content, encompassing a variety of programming languages.

I am using the A100 115 GB instance from E2E TIR AI Platform. Run the fine-tuning script by specifying the model and dataset as follows. Feel free to tweak the parameters as necessary.


%cd personal_copilot/training/
!python train.py \
    --model_path "bigcode/starcoderbase-1b" \
    --dataset_name "smangrul/hf-stack-peft" \
    --subset "data" \
    --data_column "content" \
    --split "train" \
    --seq_length 2048 \
    --max_steps 2000 \
    --batch_size 4 \
    --gradient_accumulation_steps 4 \
    --learning_rate 5e-4 \
    --lr_scheduler_type "cosine" \
    --weight_decay 0.01 \
    --num_warmup_steps 30 \
    --eval_freq 100 \
    --save_freq 100 \
    --log_freq 25 \
    --num_workers 4 \
    --bf16 \
    --no_fp16 \
    --output_dir "starcoder1B-personal-copilot" \
    --fim_rate 0.5 \
    --fim_spm_rate 0.5 \
    --use_peft_lora \
    --lora_r 32 \
    --lora_alpha 64 \
    --lora_dropout 0.0 \
    --lora_target_modules "c_proj,c_attn,q_attn,c_fc,c_proj" \
    --use_4bit_qunatization \
    --use_nested_quant \
    --bnb_4bit_compute_dtype "bfloat16" \
    --push_to_hub

A model repository with the fine-tuned model will be created in your Hugging Face profile. You will be using the inference endpoints from this repository.   

‍

Serving the Fine-Tuned LLM

To use the fine-tuned model in real time, we need to deploy it to a server. In this section, you’ll find a detailed guide on how to accomplish it using E2E Cloud. Numerous methods, including the Triton server, PyTorch server, and others, can be utilized for deployment and inference on E2E. We will do it by building a custom docker container.

Build a Custom Docker Container

A Docker Container is a software unit that encapsulates code and its dependencies, ensuring the application operates seamlessly and dependably across different computing environments. To create the inference API, we must create an API handler. API handler will vary depending upon the LLM and configurations you choose to use. 

First, create a new directory for your model with the following files.

Model       

├── model_server.py

├── requirements.txt

└── Dockerfile

‍

In this API handler, the model inference is wrapped using the Kserve model server. The contents of the files are as shown.


from kserve import Model, ModelServer
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
from typing import List, Dict
from huggingface_hub._login import _login


_login(token='YOUR_TOKEN', add_to_git_credential=False)


class StarCoder(Model):
    def __init__(self, name: str):
       super().__init__(name)
       self.name = name
       self.ready = False
       self.tokenizer = None
       #
       self.model_id = 'username/starcoder1B-personal-copilot'
       self.load()


    def load(self):
        # this step fetches the model from huggingface directly. the downloads may take longer and be slow depending on upstream link. We recommend using TIR Models
        # instead
        self.model = AutoModelForCausalLM.from_pretrained(self.model_id,
                                                          trust_remote_code=True,
                                                          device_map='auto')


        self.tokenizer = AutoTokenizer.from_pretrained('bigcode/starcoderbase-1b')
        self.pipeline  = transformers.pipeline(
            "text-generation",
            model=self.model,
            torch_dtype=torch.float16,
            tokenizer=self.tokenizer,
            device_map="auto",
        )
        self.ready = True


    def predict(self, payload: Dict, headers: Dict[str, str] = None) -> Dict:
        inputs = payload["instances"]
        source_text = inputs[0]["text"]
        # Encode the source text
        inputs = self.tokenizer.encode(source_text, return_tensors="pt").to(self.device)
        # Generate sequences
        sequences = self.model.generate(inputs,
                                        do_sample=True,
                                        top_k=10,
                                        num_return_sequences=1,
                                        eos_token_id=self.tokenizer.eos_token_id,
                                        max_length=200)
        results = []
        for seq in sequences:
            results.append(self.tokenizer.decode(seq))
        return {"predictions": results}


if __name__ == "__main__":
    model = StarCoder("starcoderbase-1b")
    ModelServer().start([model])

The tokenizer should be the base model and the model would be the fine-tuned version. Load the models and define the pipeline. Define the inference function predict, which takes input and generates code sequences. 

Define the dockerfile.

Dockerfile


# Use an official Python runtime as a parent image
FROM python:3.10-slim-buster


# Set the working directory in the container to /app
WORKDIR /app


# Add the current directory contents into the container at /app
ADD . /app


# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt


# Make port 80 available to the world outside this container
EXPOSE 80


# Run model_server.py when the container launches
CMD ["python", "model_server.py"]

Define the requirements file.

requirements.txt


kserve
ray[serve]
transformers
torch
huggingface_hub
accelerate
peft
bitsandbytes

Run this command to build the docker image.


docker build -t Model .‍

After building the docker image, make sure it is working using the run command.


docker run -p 4000:80 Model

After testing, push it to the docker hub. starcoder1b will be the repo name in the docker hub and the tagname will be the name of the version you are pushing. It should be different if you are pushing new content into this repo later. 


docker login
docker tag starcoder1b:tagname username/starcoder1b:tagname 
docker push username/starcoder1b:tagname

Creating Inference Endpoint

Before moving to endpoints, create an authorization token in the API Tokens section. To create a model endpoint for our object detection model, go to the Model Endpoints and click on Create Endpoint button. Select the GPU configuration required for your model and create an inference endpoint. 

From the frameworks, choose custom container. 

In Container Details, enter the image as <your-docker-handle-here>/starcoder1b and select other parameters as necessary. In Environment details, enter these key-values: HUGGING_FACE_HUB_TOKEN: get the token from HuggingFace website.               TRANSFORMERS_CACHE: /mnt/models. 

Proceed without selecting any model in the models section as the model has to be fetched from the Hugging Face repo and not any EOS bucket. Click Finish and create the inference endpoint.

If everything goes fine, you will see the logs somewhat similar to this.

‍

Connecting to VSCode

You are almost there! Now connect your StarCoder to VSCode. We will be using the llm-vscode extension by Hugging Face. Download the extension in your VSCode text editor. By default, the extension will be using another LLM from a Hugging Face endpoint. 

Login to Hugging Face

You can supply your HF token using these steps:

  1. Cmd/Ctrl+Shift+P to open the VSCode command palette
  2. Type: Llm: Login
  3. Enter the token 

Configure the Endpoints

Go to the Settings page by pressing cmd+ on Mac or Ctrl+ on Windows. In the settings, you should find an option to set your own HTTP endpoint for code generation requests. Change the following configurations in settings.json.


"llm.modelIdOrEndpoint": "YOUR_E2E_INFERENCE_ENDPOINT"
"llm.tokenizer": {
    "repository": "bigcode/starcoderbase-1b"
  }
"llm.attributionEndpoint": "YOUR_E2E_INFERENCE_ENDPOINT"

Save your settings and then open a file and start coding. The extension will fetch results from StarCoder in the inference endpoint and auto-complete your code like the GitHub CoPilot.

‍

Wrapping Up

You have now learnt how to create and use your own AI code copilot. We encourage you to try larger models like CodeLlama. E2E inference endpoints have their own pre-built containers for CodeLlama and other frameworks with ready API handlers. You may have to scale your infrastructure based on the models you use. 

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October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

‍

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website. 

‍

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

‍

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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