In this article, I will show you how to use Azure Cognitive Services for text summarization.
Hope this was useful. Cheers!
In this article, I will show you how to use an Azure AI Service available within the Azure AI Foundry project. We'll use the language translator as an example.
import os, requests, uuid, json
resource_key = 'resource_key_here'
region = 'region_here'
endpoint = 'https://api.cognitive.microsofttranslator.com/'
# If you encounter any issues with the base_url or path, make sure
# that you are using the latest endpoint: https://docs.microsoft.com/azure/cognitive-services/translator/reference/v3-0-translate
path = '/translate?api-version=3.0'
params = '&to=ml'
constructed_url = endpoint + path + params
headers = {
'Ocp-Apim-Subscription-Key': resource_key,
'Ocp-Apim-Subscription-Region': region,
'Content-type': 'application/json',
'X-ClientTraceId': str(uuid.uuid4())
}
# You can pass more than one object in body.
body = [{
'text' : 'where are you right now?'
}]
request = requests.post(constructed_url, headers=headers, json=body)
response = request.json()
print(json.dumps(response, sort_keys=True, indent=4, separators=(',', ': ')))
Sample output:
What you see is a unicode string and once it is converted you will see the corresponding Malayalam text.
curl -X POST "https://api.cognitive.microsofttranslator.com/translate?api-version=3.0&to=ml" \
> -H "Ocp-Apim-Subscription-Key: your_key_here" \
> -H "Ocp-Apim-Subscription-Region: your_region_here" \
> -H "Content-Type: application/json" \
> -d "[{'Text':'where are you now?'}]" -v
Sample output:
Hope this was useful. Cheers!
You can use the following PowerCLI commandlets to get the uptime and boot time of ESXi hosts and VMs.
> Get-Cluster | Get-VMHost | Select Name, @{Name="Boot time";Expression={$_.ExtensionData.Runtime.BootTime}}
Name Boot time
---- ---------
esxi1.wxxxxx.com 2/27/2025 10:13:45 AM
esxi2.wxxxxx.com 6/18/2025 2:39:38 PM
> Get-Cluster | Get-VMHost | Select Name, @{Name="Uptime (Days)";Expression={(Get-Date) - $_.ExtensionData.Runtime.BootTime | Select-Object -ExpandProperty Days}}
Name Uptime (Days)
---- -------------
esxi1.wxxxxx.com 116
esxi2.wxxxxx.com 5
> Get-Cluster | Get-VM | Select Name, @{Name="Boot time";Expression={$_.ExtensionData.Runtime.BootTime}}
Name Boot time
---- ---------
test-vineeth
rhel84-vineeth
VMware vCenter Server 2/25/2025 11:28:13 AM
vCLS-50a1b507-5a1d-5e00-8410-7599fc72e5b5 6/18/2025 2:51:59 PM> Get-Cluster | Get-VM | Select Name, @{Name="Uptime (Days)";Expression={(Get-Date) - $_.ExtensionData.Runtime.BootTime | Select-Object -ExpandProperty Days}}
Name Uptime (Days)
---- -------------
test-vineeth
rhel84-vineeth
VMware vCenter Server 118
vCLS-50a1b507-5a1d-5e00-8410-7599fc72e5b5 5
> Get-Cluster | select Name, @{N="VcsHealthStatus";E={$PSItem.ExtensionData.SummaryEx.VcsHealthStatus}} Name VcsHealthStatus ---- --------------- Cluster-01 healthy Cluster-02 healthy
To verify the most common selected status attributes of the cluster:
Get-Cluster | Get-VMHost | Select Name,@{N='HAState';E={$_.ExtensionData.Runtime.DasHostState.State}},ConnectionState,PowerState,@{N='OverallStatus';E={$_.ExtensionData.OverallStatus}},@{N='ConfigStatus';E={$_.ExtensionData.ConfigStatus}},@{N='InMaintenanceMode';E={$_.ExtensionData.Runtime.InMaintenanceMode}},@{N='RebootRequired';E={$_.ExtensionData.Summary.RebootRequired}},@{N='BootTime';E={$_.ExtensionData.Runtime.BootTime}} | ft
> Get-Cluster | Get-VMHost | Select Name,@{N='HAState';E={$_.ExtensionData.Runtime.DasHostState.State}},ConnectionState,PowerState,@{N='OverallStatus';E={$_.ExtensionData.OverallStatus}},@{N='ConfigStatus';E={$_.ExtensionData.ConfigStatus}},@{N='InMaintenanceMode';E={$_.ExtensionData.Runtime.InMaintenanceMode}},@{N='RebootRequired';E={$_.ExtensionData.Summary.RebootRequired}},@{N='BootTime';E={$_.ExtensionData.Runtime.BootTime}} | ft Name HAState ConnectionState PowerState OverallStatus ConfigStatus InMaintenanceMode RebootRequired BootTime ---- ------- --------------- ---------- ------------- ------------ ----------------- -------------- -------- 10.90.1.4 connectedToMaster Connected PoweredOn yellow yellow False False 8/27/2024 7:31:10 AM 10.90.1.5 master Connected PoweredOn yellow yellow False False 9/6/2024 9:08:09 PM
Few years ago, I had the opportunity to explore the intricacies of storage performance benchmarking using tools like FIO, DISKSPD, and Iometer. Those studies provided valuable insights into the performance characteristics of various storage solutions, shaping my understanding and approach to storage performance analysis. As I prepare for an upcoming project in this domain, I find it essential to revisit my previous work, reflect on the lessons learned, and share my experiences. This blog post aims to provide a comprehensive overview of my benchmarking journey and the evolving landscape of storage performance studies.
The field of storage technology has seen significant advancements in recent years. The rise of NVMe and storage-class memory technologies has also redefined high-end storage performance, offering unprecedented speed and efficiency. These advancements highlight the dynamic nature of storage performance benchmarking and underscore the importance of staying updated with the latest tools and methodologies.
Benchmarking storage performance is not without its challenges. One of the primary difficulties is ensuring a consistent and controlled testing environment, as variations in hardware, software, and network conditions can significantly impact results. Another challenge is the selection of appropriate benchmarks that accurately reflect real-world workloads, which requires a deep understanding of the specific use cases and performance metrics. Additionally, interpreting the results can be complex, as it involves analyzing multiple metrics such as IOPS, throughput, and latency, and understanding their interplay. These challenges necessitate meticulous planning and a thorough understanding of both the benchmarking tools and the storage systems being tested.
Following are some of the articles on storage benchmarking that I’ve published in the past:
While there are numerous storage benchmarking tools available, such as VMFleet and HCIBench, I wanted to highlight a custom framework I developed a few years ago. Here are some reasons why we created this custom tool:
You can access the scripts and readme on my GitHub repository:
https://github.com/vineethac/vsan_cluster_storage_benchmarking_with_diskspd
Here is an overview.
Note: These scripts were created about five years ago, and I haven’t had the opportunity to refactor them according to current best practices and new PowerShell scripting standards. I plan to enhance them in the coming months!
This overview should provide you with a clear understanding of the overall process and workflow involved in the storage benchmarking process. I hope it was useful. Cheers!