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@csiebler
Last active Oct 26, 2021
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Comparison of sequential vs parallel Azure Read API processing time
import requests
import io
import time
n = 50
# Enter your resource details here
url = "https://xxxxxxx.cognitiveservices.azure.com/vision/v3.2/read/analyze?language=en&pages=1&readingOrder=natural"
key = "xxxxxxx"
headers = {
"Content-Type": "application/json",
"Ocp-Apim-Subscription-Key": key
}
def start_ocr(url, headers):
json = {
"url": "https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/images/description.png"
}
start_time = time.time()
result = requests.post(url, json=json, headers=headers)
assert(result.status_code == 202)
duration = time.time() - start_time
if (duration < 0.1):
time.sleep(0.1-duration) # Ensure we stay under 10 TPS
return result.headers['Operation-Location']
def wait_for_results(url, headers):
status = "running"
while(status != "succeeded"):
start_time = time.time()
status = requests.get(url, headers=headers).json()['status']
duration = time.time() - start_time
if (duration < 0.1):
time.sleep(0.1-duration) # Ensure we stay under 10 TPS
### Run sequential test ####
start_time_seq = time.time()
for i in range(0,n):
r_url = start_ocr(url, headers)
wait_for_results(r_url, headers)
# Do something with the results :)
duration_seq = time.time() - start_time_seq
print(f"Sequential batch of {n} took {duration_seq} seconds")
#### Run parallel test ####
start_time_parallel = time.time()
urls = []
for i in range(0,n):
urls.append(start_ocr(url, headers))
assert(len(urls) == n)
for url in urls:
wait_for_results(url, headers)
# Do something with the results :)
duration_parallel = time.time() - start_time_parallel
print(f"Parallel batch of {n} took {duration_parallel} seconds ({int(100*duration_parallel/duration_seq)}%)")
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csiebler commented Oct 25, 2021

Output for westeurope:

Sequential batch of 50 took 36.734880208969116 seconds
Parallel batch of 50 took 11.86894941329956 seconds (32%)

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