t2 ec2 instance spec and price table

 

a table with the specs and hourly prices for T2 instances as of March 2023 in the US East (N. Virginia) region:

Instance Type	vCPUs	Memory (GiB)	Network Performance	Baseline Performance	CPU Credits/hr	Hourly Price
t2.nano	1	0.5	Low to Moderate	1 vCPU for a 1h 12m burst	$0.0036	$0.0058
t2.micro	1	1.0	Low to Moderate	1 vCPU for a 2h 24m burst	$0.0072	$0.0116
t2.small	1	2.0	Low to Moderate	1 vCPU for a 4h 48m burst	$0.0144	$0.0230
t2.medium	2	4.0	Low to Moderate	2 vCPUs for a 4h 48m burst	$0.0287	$0.0464
t2.large	2	8.0	Low to Moderate	2 vCPUs for a 7h 12m burst	$0.0575	$0.0928
t2.xlarge	4	16.0	Moderate	4 vCPUs for a 5h 24m burst	$0.1150	$0.1856
t2.2xlarge	8	32.0	Moderate	8 vCPUs for a 4h 4.8m burst	$0.2300	$0.3712

Note that the prices and specifications may vary depending on the region and other factors.

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run shell script in parallel and kill all shell when it stop

 refer to shell script

.

#!/bin/bash

# Run each script in the background

./loop_invocations.sh &

./loop_invocations.sh &

./loop_invocations.sh &

./loop_invocations.sh &

./loop_ping.sh &

# Define a function to kill all child processes

function kill_children {

echo "Stopping all child processes..."

pkill -P $$

exit 0

}

# Trap the SIGINT signal and call the function to kill child processes

trap kill_children SIGINT

# Wait for all background jobs to complete

wait

..

In this example, the kill_children function uses pkill to find and kill all child processes of the current process. The $$ variable represents the PID of the current process. The trap command sets up a handler for the SIGINT signal that calls the kill_children function. When the script is stopped with CTRL+C, the kill_children function is called, which kills all child processes before exiting the script.




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Folder existing check in s3 bucket, python example code

Check folder exist in s3 bucket

refer to code:

.

import boto3

import botocore.exceptions

s3 = boto3.client('s3')

bucket_name = 'your-bucket-name'

folder_path = 'f1/f2/f3/f4/'

try:

response = s3.list_objects_v2(Bucket=bucket_name, Prefix=folder_path)

if 'Contents' in response or 'CommonPrefixes' in response:

print(f"Folder {folder_path} exists in bucket {bucket_name}")

else:

print(f"Folder {folder_path} does not exist in bucket {bucket_name}")

except botocore.exceptions.ClientError as e:

if e.response['Error']['Code'] == "NoSuchBucket":

print(f"Bucket {bucket_name} does not exist")

else:

raise

..

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Useful Docker Commands: List, Stop, Remove, Logs, Build, Push, Pull, and Delete Images

 

refer to cmd

.

# List all running containers

docker ps

# List all containers, including stopped ones

docker ps -a

# Stop a running container

docker stop <container-id>

# Remove a stopped container

docker rm <container-id>

# View logs from a container

docker logs <container-id>

# Execute a command in a running container

docker exec <container-id> <command>

# Build an image from a Dockerfile

docker build -t <image-name> <path-to-dockerfile>

# Push an image to a Docker registry

docker push <registry>/<image-name>

# Pull an image from a Docker registry

docker pull <registry>/<image-name>

# Remove all Docker images using prune option

docker image prune -a -f

..

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Uploading Multiple Files to a Flask API using cURL example code

refer to code:

curl cmd.

.

curl -X POST -H "Content-Type: multipart/form-data" -H "Content-Type: application/json" -F 'data=@data.json;type=application/json' -H "Content-Type: image/jpeg" -F 'image=@image.jpg;type=image/jpeg' http://your-api-url.com/your-endpoint

..

flask code

.

from flask import Flask, request

import json

app = Flask(__name__)

@app.route('/your-endpoint', methods=['POST'])

def handle_upload():

if 'data' not in request.files:

return 'No data file uploaded', 400

if 'image' not in request.files:

return 'No image file uploaded', 400

data_file = request.files['data']

image_file = request.files['image']

if data_file.content_type != 'application/json':

return 'Invalid data file format', 400

if not is_valid_image_type(image_file.content_type):

return 'Invalid image file format', 400

# read the JSON data from the file

data = json.load(data_file)

# read the image data from the file

image_data = image_file.read()

# do something with the file data

return 'File upload successful'

def is_valid_image_type(content_type):

valid_image_types = ['image/jpeg', 'image/png', 'image/gif']

return content_type in valid_image_types

..

In this updated cURL command, the content type headers for the request and each file are specified using multiple -H flags. The Content-Type header for the request is set to multipart/form-data, which is the required encoding for file uploads. The content type for the JSON file is set to application/json, and the content type for the image file is set to image/jpeg. Note that you should adjust the content types as needed for your specific file formats.

With these changes, you should be able to upload a JSON file and an image file to your Flask API using cURL. The uploaded files will be checked for errors in the API code and then processed as needed.

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save tokeniser from AutoTokenizer, AutoProcessor

 refer to code:

.

from transformers import AutoTokenizer, AutoProcessor

model_name_or_path = 'nielsr/layoutlmv3-finetuned-funsd'

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

processor = AutoProcessor.from_pretrained(model_name_or_path)

# Save tokenizer

tokenizer.save_pretrained('tokenizer_dir')

# Save processor

processor.save_pretrained('processor_dir')

..

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split filename and dir using os.path.split

 refer to code

.

import os

path = '/Users/source_code/final_data/test/X51006647933.jpg'

# Split the path into directory name and file name

dirname, filename = os.path.split(path)

# Print the file name

print(filename) # Output: X51006647933.jpg

..

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