sourangshupal

import json
import boto3

ENDPOINT = "huggingface-pytorch-tgi-inference-"

sagemaker_runtime = boto3.client("sagemaker-runtime", region_name='us-east-1')

def lambda_handler(event, context):
    query_params = event['queryStringParameters']
    query = query_params['query']

sourangshupal

import json
import boto3
import botocore.config
from datetime import datetime


### AWS BEDROCK CALL ###

# {
#  "modelId": "meta.llama4-scout-17b-instruct-v1:0",

sourangshupal

File Link :- https://colab.research.google.com/drive/1fx_JOzNB95mf91BMVQgQdSuShHD6L4Bz?usp=sharing

sourangshupal

content_writer:
  role: >
    Educational Content Writer
  goal: >
    Create engaging, informative content that thoroughly explains the assigned topic
    and provides valuable insights to the reader
  backstory: >
    You are a talented educational writer with expertise in creating clear, engaging
    content. You have a gift for explaining complex concepts in accessible language
    and organizing information in a way that helps readers build their understanding.

sourangshupal

# You can use mlflow context manager to log any param or metric values. One example is shown below 

with mlflow.start_run():
    mlflow.log_param("param_name", param_value)
    mlflow.log_metric("metric_name", metric_value)

sourangshupal

#Check the GPU
!nvidia-smi
#Check the packages installed
!pip freeze
#tensorflow-gpu package is by default installed in Colab

sourangshupal

import warnings

with warnings.catch_warnings():
    warnings.filterwarnings('ignore', category=FutureWarning)
    import h5py

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'

import tensorflow as tf

sourangshupal

# It is highly recommended to use Pandas for such data processing problems
import pandas as pd
import numpy as np

dataset = {'feature1': np.random.rand(5000),
                 'feature2': np.random.rand(5000),
                 'feature3': np.random.rand(5000)
                 }