update readme instructions

Author: rohitc5

README.md

@@ -60,10 +60,243 @@ hints to the student to arrive at correct answer, enhancing student engagement a
 
   ![](./assets/Tech-Stack.png)
 
-   List Down all technologies used to Build the prototype **Clearly mentioning Intel® AI Analytics Toolkits, it's libraries and the SYCL/DCP++ Libraries used**
-   
 ## Step-by-Step Code Execution Instructions:
-  Stay Tuned!
+
+a) Easy Option to Start Demo
+
+- Clone the Repository
+```console
+ $ git clone https://github.com/rohitc5/intel-oneAPI/tree/main
+ $ cd Intel-oneAPI
+
+```
+- Start the LEAP RESTFul Service to consume both components (Ask Question/Doubt and Interactive Conversational AI Examiner) over API
+
+```console
+  $ cd api
+  
+  # build the docker file
+  $ docker build -t leap-api:v1 .  
+
+  # get the docker image ID
+  $ docker images
+
+  # run the docker container
+  $ docker run -it -p 8500:8500 --name=leap-api [IMAGE_ID]
+
+  $ cd ../
+
+```
+
+- Start the demo webapp build using streamlit
+
+```console
+  $ cd webapp
+  
+  # build the docker file
+  $ docker build -t leap-demo:v1 .
+
+  # get the docker image ID
+  $ docker images
+
+  # run the docker container
+  $ docker run -it -p 8502:8502 --name=leap-demo [IMAGE_ID]
+
+```
+
+b) Step-by-Step Option
+
+- Clone the Repository
+
+```console
+ $ git clone https://github.com/rohitc5/intel-oneAPI/tree/main
+ $ cd Intel-oneAPI
+
+```
+
+- Train/Fine-tune the Extractive QA Multilingual Model (Part of our Ask Question/Doubt Component).
+Please note that, by default we use this (https://huggingface.co/ai4bharat/indic-bert) as a Backbone (BERT topology)
+and finetune it on SQuAD v1 dataset. Moreover, IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. So finetuning, on SQuAD v1 (English) dataset automatically results in cross-lingual
+transfer on other 11 indian languages.
+
+Here is the detailed architecture of `Ask Question/Doubt` component:
+
+![](./assets/Ask-Doubt.png)
+
+```console
+  $ cd nlp/question_answering
+
+  # install dependencies
+  $ pip install -r requirements.txt
+  
+  # modify the fine-tuning params mentioned in finetune_qa.sh
+  $ vi finetune_qa.sh
+
+  ''''
+  export MODEL_NAME_OR_PATH=ai4bharat/indic-bert
+  export BACKBONE_NAME=indic-mALBERT-base
+  export DATASET_NAME=squad # squad, squad_v2 (pass --version_2_with_negative)
+  export TASK_NAME=qa
+
+  # hyperparameters
+  export SEED=42
+  export BATCH_SIZE=32
+  export MAX_SEQ_LENGTH=512
+  export NUM_TRAIN_EPOCHS=5
+  ...
+  
+  ''''
+
+  # start the training after modifying params
+  $ bash finetune_qa.sh
+```
+
+- Optimize using IPEX, Intel® Neural Compressor and run the bennchmark for comparison with Pytorch(Base)-FP32
+
+```console
+  # modify the params in pot_benchmark_qa.sh
+  $ vi pot_benchmark_qa.sh
+
+  ''''
+  export MODEL_NAME_OR_PATH=artifacts/qa/squad/indic-mALBERT
+  export BACKBONE_NAME=indic-mALBERT
+  export DATASET_NAME=squad # squad, squad_v2 (pass --version_2_with_negative)
+  export TASK_NAME=qa
+  export USE_OPTIMUM=True  # whether to use hugging face wrapper optimum around intel neural compressor
+
+  # other parameters
+  export BATCH_SIZE=8
+  export MAX_SEQ_LENGTH=256
+  export DOC_STRIDE=128
+  export KEEP_ACCENTS=False
+  export DO_LOWER_CASE=True
+  export MAX_EVAL_SAMPLES=200
+
+  export TUNE=True  # whether to tune or not
+  export PTQ_METHOD="static_int8" # "dynamic_int8", "static_int8", "static_smooth_int8"
+  export BACKEND="default" # default, ipex
+  export ITERS=100
+  ...
+
+  ''''
+  
+  $ bash pot_benchmark_qa.sh
+
+  Please note that, above shell script can perform optimization using IPEX to get Pytorch-(IPEX)-FP32 model
+  or It can perform optimization/quantization using Intel® Neural Compressor to get Static-QAT-INT8, 
+  Static-Smooth-QAT-INT8 models. Moreover, you can choose the backend as `default` or `ipex` for INT8 models.
+
+```
+
+- Run quick inference to test the model output
+
+```console
+  $ python run_qa_inference.py --model_name_or_path=[FP32 or INT8 finetuned model]  --model_type=["vanilla_fp32" or "quantized_int8"] --do_lower_case  --keep_accents --ipex_enable
+
+```
+
+- Train/Infer/Benchmark TFIDF Embedding model for Scikit-Learn (Base) vs Intel® Extension for Scikit-Learn
+
+```console
+  $ cd nlp/feature_extractor
+
+  # train (.fit_transform func), infer (.transform func) and perform benchmark
+  $ python run_benchmark_tfidf.py --course_dir=../../dataset/courses --is_preprocess 
+  
+  # now rerun but turn on  Intel® Extension for Scikit-Learn
+  $ python run_benchmark_tfidf.py --course_dir=../../dataset/courses --is_preprocess  --intel_scikit_learn_enabled
+```
+
+- Setup LEAP API
+
+```console
+  $ cd api
+  
+  # install dependencies
+  $ pip install -r requirements.txt
+
+  $ cd src/
+
+  # create a local vector store of course content for faster retrieval during inference
+  # Here we get semantic or syntactic (TFIDF) embedding of each content from course and index it.
+  $ python core/create_vector_index.py --course_dir=../../dataset/courses --emb_model_type=[semantic or syntactic] \
+      --model_name_or_path=[Hugging face model name for semantic] --keep_accents
+  
+  # update config.py
+  $ cd ../ 
+  $ vi config.py 
+  
+  ''''
+    ASK_DOUBT_CONFIG = {
+      # hugging face BERT topology model name
+      "emb_model_name_or_path": "ai4bharat/indic-bert", 
+      "emb_model_type": "semantic",  #options: syntactic, semantic
+      
+      # finetuned Extractive QA model path previously done
+      "qa_model_name_or_path": "vanichandna/indic-bert-finetuned-squad", 
+      "qa_model_type": "vanilla_fp32",  #options: vanilla_fp32, quantized_int8
+      
+      # faiss index file path created previously
+      "faiss_vector_index_path": "artifacts/index/faiss_emb_index"
+    }
+    ...
+
+  ''''
+
+```
+
+- For our Interactive Conversational AI Examiner component, as of now we are not doing any training as its based on 
+recent Generative AI LLM (Large Language model) (open access models like LLaMA, Falcon etc.). You can update the API configuration by specifying hf_model_name (LLM name available in huggingface Hub). Please checkout https://huggingface.co/models
+
+Here for performance gain, we can use INT8 quantized model optimized using Intel®  Neural Compressor (Few options are like https://huggingface.co/decapoda-research/llama-7b-hf-int8 etc.)  
+
+Please Note that for fun 😄, we also provide usage of Azure OpenAI Cognitive Service to use models like GPT3 paid subscription API. You just need to provide `azure_deployment_name` below configuration and `<your_key>`
+
+```console
+
+  AI_EXAMINER_CONFIG = {
+      "llm_name": "azure_gpt3",
+      "azure_deployment_name": "text-davinci-003-prod",
+      "hf_model_name": "TheBloke/falcon-7b-instruct-GPTQ", # mosaicml/mpt-7b-instruct
+      "device": 0, # cuda:0
+      "llm_kwargs":{
+          "do_sample": True,
+          "temperature": 0.5, 
+          "max_new_tokens": 300,
+          "top_p": 1.0,
+          "top_k": 0,
+          "repetition_penalty": 1.1,
+          "num_return_sequences": 1,
+          "stop_sequence": "<|endoftext|>"
+    }
+```
+
+- Start the API server
+
+```console
+  $ cd api/src/
+  
+  # start the gunicorn server
+  $ bash start.sh
+```
+
+- Start the Streamlit web UI demo
+
+```console
+  $ cd webapp
+
+  # install dependencies
+  $ pip install -r requirements.txt
+
+  $ streamlit run app.py
+
+```
+
+- Go to http://localhost:8502
+
+# Benchmark Results with Intel® oneAPI AI Analytics Toolkit
+
+- We have already added several benchmark results to compare how beneficial Intel® oneAPI AI Analytics Toolkit is compared to baseline. Please go to`benchmark` folder to view the results.
 
 # What I learned ![image](https://user-images.githubusercontent.com/72274851/218499685-e8d445fc-e35e-4ab5-abc1-c32462592603.png)
 

api/src/config.py

@@ -6,7 +6,7 @@
 ASK_DOUBT_CONFIG = {
   "emb_model_name_or_path": "ai4bharat/indic-bert",
   "emb_model_type": "semantic",  #options: syntactic, semantic
-  "qa_model_name_or_path": "artifacts/model/indic-mALBERT-uncased",
+  "qa_model_name_or_path": "vanichandna/indic-bert-finetuned-squad",
   "qa_model_type": "vanilla_fp32",  #options: vanilla_fp32, quantized_int8
 
   "intel_scikit_learn_enabled": True,