Madhur Prashant madhurprash

## asyncio_bedrock_RESTapi.py
## Note: This is for titan-express, but for any model you use, you will bring your own payloads and simply run this code.

import json
import time
import asyncio
import requests as req
import botocore.session
from itertools import groupby
from operator import itemgetter
from botocore.auth import SigV4Auth

## gist:19558e9a947e15584a63e49b206bc0fd
import json
import botocore.session
import requests as req
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest

# Configuration and payload
region = "us-east-1"
model_id = 'cohere.command-text-v14'
endpoint = f"https://bedrock-runtime.{region}.amazonaws.com/model/{model_id}/invoke"

## retrieve_modelconfig_jumpstart.py
"""
Get information about models in Amazon SageMaker JumpStart.
Tested with sagemaker SDK version 2.203.0.
"""

## View the full list of models available through JumpStart here: https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html

import pandas as pd
from typing import Dict, List
from sagemaker import get_execution_role

## claude_3_metadata_tagger.txt
Human: you are a metadata tagger, see the following schema and tag the data that follows to extract the metadata fields listed in the schema:

<schema>
schema = {
                    "properties": {
                        "movie_title": { "type": "string" },
                        "critic": { "type": "string" },
                        "tone": {
                            "type": "string",
                            "enum": ["positive", "negative"]

## deploy_and_predict_hf_model_djl.ipynb

      
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                madhurprash
                / deploy_and_predict_hf_model_djl.ipynb
            
            
              Last active
              August 8, 2024 01:58
            
              
                Deploys a model from HuggingFace on Amazon SageMaker using the DJL DeepSpeed LMI (https://github.com/deepjavalibrary/djl-serving).
              
          
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## github_issues_in_csv.py
import os
import csv
import logging
import requests
from datetime import datetime
from typing import Optional, List

logging.basicConfig(format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s', level=logging.INFO)
logger = logging.getLogger(__name__)

## sagemaker_mlflow_simple_example.ipynb

      
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                madhurprash
                / sagemaker_mlflow_simple_example.ipynb
            
            
              Created
              November 13, 2024 17:37
            
              
                In this gist, we do the following: Set up `mlflow` tracking with the SageMaker tracking server, create a simple classification experiment and use MLFlow to input logs, metrics, parameters and model with simple API calls.
              
          
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## qrcode_generator.py
import qrcode
import logging
import re
import os

# Install the following package before running the script: pip install qrcode

# set a logger
logging.basicConfig(
    format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s',

## move_folders_within_s3
import os
import shutil
import concurrent.futures
import boto3
from pathlib import Path
import logging
import time

def move_folders_to_combined(bucket_name, folders_to_move=["SSE", "FPA", "Hypershield"], log_level=logging.INFO):
    """

## list_s3_uris_in_kb
import boto3
import json
from typing import Dict, List, Optional
import re

def get_detailed_s3_uris(
    knowledge_base_id: str,
    data_source_id: str
) -> Dict:
    """
	## Note: This is for titan-express, but for any model you use, you will bring your own payloads and simply run this code.

	import json
	import time
	import asyncio
	import requests as req
	import botocore.session
	from itertools import groupby
	from operator import itemgetter
	from botocore.auth import SigV4Auth
	"""
	Get information about models in Amazon SageMaker JumpStart.
	Tested with sagemaker SDK version 2.203.0.
	"""

	## View the full list of models available through JumpStart here: https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html

	import pandas as pd
	from typing import Dict, List
	from sagemaker import get_execution_role
	Human: you are a metadata tagger, see the following schema and tag the data that follows to extract the metadata fields listed in the schema:

	<schema>
	schema = {
	"properties": {
	"movie_title": { "type": "string" },
	"critic": { "type": "string" },
	"tone": {
	"type": "string",
	"enum": ["positive", "negative"]
	import os
	import csv
	import logging
	import requests
	from datetime import datetime
	from typing import Optional, List

	logging.basicConfig(format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s', level=logging.INFO)
	logger = logging.getLogger(__name__)
	import qrcode
	import logging
	import re
	import os

	# Install the following package before running the script: pip install qrcode

	# set a logger
	logging.basicConfig(
	format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
	import os
	import shutil
	import concurrent.futures
	import boto3
	from pathlib import Path
	import logging
	import time

	def move_folders_to_combined(bucket_name, folders_to_move=["SSE", "FPA", "Hypershield"], log_level=logging.INFO):
	"""
	import boto3
	import json
	from typing import Dict, List, Optional
	import re

	def get_detailed_s3_uris(
	knowledge_base_id: str,
	data_source_id: str
	) -> Dict:
	"""