Tag Archives: microsoft
Request for Recrawl and Indexing of Website “TrickyWorlds” on Bing
Dear Webmaster Support Team,
I hope this email finds you well. I am writing to request the recrawl and indexing of my website, TrickyWorlds, on the Bing search engine.
I have previously reached out to your support team on more than two times regarding this matter, but unfortunately, I have not received any response. It is crucial for me to have my website indexed on Bing as it plays a significant role in the online visibility and traffic generation for my business.
Below are the details of my website:
Website URL: Tricky worlds
I understand that you may be dealing with numerous requests, but I would sincerely appreciate it if you could prioritize this matter and provide me with confirmation once the recrawl and indexing process is complete.
Thank you very much for your attention to this urgent request. I look forward to a positive response and the successful indexing of TrickyWorlds on Bing.
Dear Webmaster Support Team, I hope this email finds you well. I am writing to request the recrawl and indexing of my website, TrickyWorlds, on the Bing search engine. I have previously reached out to your support team on more than two times regarding this matter, but unfortunately, I have not received any response. It is crucial for me to have my website indexed on Bing as it plays a significant role in the online visibility and traffic generation for my business. Below are the details of my website:Website URL: Tricky worlds I understand that you may be dealing with numerous requests, but I would sincerely appreciate it if you could prioritize this matter and provide me with confirmation once the recrawl and indexing process is complete. Thank you very much for your attention to this urgent request. I look forward to a positive response and the successful indexing of TrickyWorlds on Bing. Read More
DEV x AI kehittäjätapahtuma tulee taas 23.5.2024
Developer x AI 2024 webinaari kumppaneille 23.5.2024
Microsoftin suomalainen kehittämiseen ja tekoälyyn keskittyvä DEV x AI -webinaari tulee taas. Se on suunnattu kehittäjien lisäksi kaikille niille, jotka ovat tekemisissä tekoälyratkaisujen ja kehittäjätyökalujen kanssa. Mukaan on saatu huippukattaus mielenkiintoisia puhujia meiltä ja muulta. Tule mukaan ja kutsu kollegasikin!
Rekisteröidy englanninkieliseen tapahtumaan tästä.
Join us at Developer x AI, a virtual event that brings together the brightest minds in software development in Finland. This event offers a unique opportunity to gain expertise on AI technologies, specifically on GitHub Copilot and Azure AI services.
Dive into the world of GitHub Copilot, where coding meets co-creation, and explore how organizations, big and small, are integrating Azure OpenAI to revolutionize their offerings. This event is about connecting the dots between theory and practice. It’s for developers who aspire to be at the forefront of innovation, by learning from real-world applications and network with peers driven by the same passion.
If you’re looking to transform the way you think about development with AI, save the date and register now!
Agenda:
9.00 Opening Keynote
Jouni Heikniemi, Azure RD, CEO, Devisioona
9.20 GitHub Copilot – What’s new and what’s next
Jonas Helin, Cloud Solution Engineer, GitHub
9.40 Experiences from onboarding GitHub Copilot
TBA
10.00 Practical tips on using GitHub Copilot
Saija Saarenpää, Passionate Code Whisperer, Vincit
10.20 Azure AI – more than just Azure OpenAI Service
Juha Karvonen, Sr. Cloud Solution Architect, Microsoft
10.40 Case: AinoAid – hands on experiences from going to production with generative AI
Tuomas Lahtinen, Loihde and Joonas Järvinen, Loihde
11.00 Extremely Natural Language Processing – AI avatar as an LLM interface
Mikko Lehtimäki, Co-founder and Chief Data Scientist, Softlandia
11.20 Closing Remarks – Summary and thank you
Who should attend: Developers and other roles close to AI development
When: May 23, 2024, from 9:00 to 11:30
Where: Online
Language: English
Lisätietoja ja rekisteröityminen https://aka.ms/DEVxAI
Microsoft Tech Community – Latest Blogs –Read More
Getting started with Microsoft Phi-3-mini – Try running the Phi-3-mini on iPhone with ONNX Runtime
Microsoft, Google, and Apple have all released SLM (Microsoft phi3-mini, Google Gemma, and Apple OpenELM) adapted to edge devices at different times . Developers deploy SLM offline on Nvidia Jetson Orin, Raspberry Pi, and AI PC. This gives generative AI more application scenarios. We learned several ways to deploy applications from the previous article, so how do we deploy SLM applications to mobile devices?
This article is a preliminary exploration based on iPhone. We know that Microsoft phi3-mini has released three formats on Hugging Face, among which gguf and onnx are quantized models. We can deploy phi3-mini’s quantized model based on different hardware conditions. So lets get started and explore the quantitative model based on the Phi-3-mini onnx format. If you want to use the GGUF format, it is recommended to use LLM Farm app.
Generative AI with ONNX Runtime
In the era of AI , the portability of AI models is very important. ONNX Runtime can easily deploy trained models to different devices. Developers do not need to pay attention to the inference framework and use a unified API to complete model inference. In the era of generative AI, ONNX Runtime has also performed code optimization (https: //onnxruntime.ai/docs/genai/). Through the optimized ONNX Runtime, the quantized generative AI model can be inferred on different terminals. In Generative AI with ONNX Runtime, you can inferene AI model API through Python, C#, C / C++. of course,Deployment on iPhone can take advantage of C++’s Generative AI with ONNX Runtime API..
Steps
A. Preparation
macOS 14+
Xcode 15+
iOS SDK 17.x
Install Python 3.10+ (Conda is recommended)
Install the Python library – python-flatbuffers
Install CMake
B. Compiling ONNX Runtime for iOS
git clone https://github.com/microsoft/onnxruntime.git
cd onnxruntime
./build.sh –build_shared_lib –ios –skip_tests –parallel –build_dir ./build_ios –ios –apple_sysroot iphoneos –osx_arch arm64 –apple_deploy_target 17.4 –cmake_generator Xcode –config Release
Notice
Before compiling, you must ensure that Xcode is configured correctly and set it on the terminal
sudo xcode-select -switch /Applications/Xcode.app/Contents/Developer
ONNX Runtime needs to be compiled based on different platforms. For iOS, you can compile based on arm64 / x86_64
It is recommended to directly use the latest iOS SDK for compilation. Of course, you can also lower the version to be compatible with past SDKs.
C. Compiling Generative AI with ONNX Runtime for iOS
Note: Because Generative AI with ONNX Runtime is in preview, please note the changes.
git clone https://github.com/microsoft/onnxruntime-genai
cd onnxruntime-genai
git checkout yguo/ios-build-genai
mkdir ort
cd ort
mkdir include
mkdir lib
cd ../
cp ../onnxruntime/include/onnxruntime/core/session/onnxruntime_c_api.h ort/include
cp ../onnxruntime/build_ios/Release/Release-iphoneos/libonnxruntime*.dylib* ort/lib
python3 build.py –parallel –build_dir ./build_ios_simulator –ios –ios_sysroot iphoneos –osx_arch arm64 –apple_deployment_target 17.4 –cmake_generator Xcode
D. Create an App application in Xcode
I chose Objective-C as the App development method , because using Generative AI with ONNX Runtime C++ API, Objective-C is better compatible. Of course, you can also complete related calls through Swift bridging.
E. Copy the ONNX quantized INT4 model to the App application project
We need to import the INT4 quantization model in ONNX format, which needs to be downloaded first
After downloading, you need to add it to the Resources directory of the project in Xcode.
F. Add the C++ API in ViewControllers
Notice:
Add the corresponding C++ header file to the project
add onnxruntime-genai.dylib in Xcode
Directly use the code on C Samples for testing in this samples. You can also directly add moreto run(such as ChatUI)
Because you need to call C++, please change ViewController.m to ViewController.mm
NSString *llmPath = [[NSBundle mainBundle] resourcePath];
char const *modelPath = llmPath.cString;
auto model = OgaModel::Create(modelPath);
auto tokenizer = OgaTokenizer::Create(*model);
const char* prompt = “<|system|>You are a helpful AI assistant.<|end|><|user|>Can you introduce yourself?<|end|><|assistant|>”;
auto sequences = OgaSequences::Create();
tokenizer->Encode(prompt, *sequences);
auto params = OgaGeneratorParams::Create(*model);
params->SetSearchOption(“max_length”, 100);
params->SetInputSequences(*sequences);
auto output_sequences = model->Generate(*params);
const auto output_sequence_length = output_sequences->SequenceCount(0);
const auto* output_sequence_data = output_sequences->SequenceData(0);
auto out_string = tokenizer->Decode(output_sequence_data, output_sequence_length);
auto tmp = out_string;
G. Look at the running results
Sample Codes: https://github.com/Azure-Samples/Phi-3MiniSamples/tree/main/ios
Summary
This is a very preliminary running result, because I am using an iPhone 12 so the running is relatively slow, and the CPU usage reaches 130% during inference. It would be better to have Apple MLX framework to cooperate with inference under the iOS mechanism, so what I am looking forward to in this project is that Generative AI with ONNX Runtime can provide hardware acceleration for iOS. Of course you can also try a newer iPhone device to test.
This is just a preliminary exploration, but it is a good start. I look forward to the improvement of Generative AI with ONNX Runtime.
Resources
LLMFarm’s GitHub Repo https://github.com/guinmoon/LLMFarm
Phi3-mini Microsoft Blog https://aka.ms/phi3blog-april
Phi-3 technical report https://aka.ms/phi3-tech-report
Getting started with Phi3 https://aka.ms/phi3gettingstarted
Learn about ONNX Runtime https://github.com/microsoft/onnxruntime
Learn about Generative AI with ONNX Runtime https://github.com/microsoft/onnxruntime-genai
Microsoft Tech Community – Latest Blogs –Read More
Azure Fundamentals Roadmap
If you’re aspiring to become a cloud engineer and are new to the cloud world, starting with the AZ-900 Azure Fundamentals is your essential first step :rocket:. This learning path lays a solid foundation in Azure, teaching architectural components, core services, and essential features such as management tools, security, and compliance :locked:. Successfully passing the AZ-900 exam grants you the Azure Fundamentals certification, crucial for advancing in cloud technology careers :globe_with_meridians:.
Master the basics of Azure
– Fundamentals: https://learn.microsoft.com/en-us/collections/n6ga8m0jkgrwk?&sharingId=7A8BB35FBAED63E4&wt.mc_id=studentamb_374326
– Describe cloud concepts: https://learn.microsoft.com/en-us/training/paths/microsoft-azure-fundamentals-describe-cloud-concepts/?wt.mc_id=studentamb_374326
– Describe Azure architecture and services: https://learn.microsoft.com/en-us/training/paths/azure-fundamentals-describe-azure-architecture-services/?wt.mc_id=studentamb_374326
– Describe Azure management and governance: https://learn.microsoft.com/en-us/training/paths/describe-azure-management-governance/?wt.mc_id=studentamb_374326
If you’re aspiring to become a cloud engineer and are new to the cloud world, starting with the AZ-900 Azure Fundamentals is your essential first step :rocket:. This learning path lays a solid foundation in Azure, teaching architectural components, core services, and essential features such as management tools, security, and compliance :locked:. Successfully passing the AZ-900 exam grants you the Azure Fundamentals certification, crucial for advancing in cloud technology careers :globe_with_meridians:.Master the basics of Azure- Fundamentals: https://learn.microsoft.com/en-us/collections/n6ga8m0jkgrwk?&sharingId=7A8BB35FBAED63E4&wt.mc_id=studentamb_374326- Describe cloud concepts: https://learn.microsoft.com/en-us/training/paths/microsoft-azure-fundamentals-describe-cloud-concepts/?wt.mc_id=studentamb_374326- Describe Azure architecture and services: https://learn.microsoft.com/en-us/training/paths/azure-fundamentals-describe-azure-architecture-services/?wt.mc_id=studentamb_374326- Describe Azure management and governance: https://learn.microsoft.com/en-us/training/paths/describe-azure-management-governance/?wt.mc_id=studentamb_374326 Read More
Why Is My QuickBooks Payroll Updates Are Not Working Window 11?
Troubleshooting Solutions: QuickBooks Payroll Updates Are Not Working Window 11
QuickBooks Payroll is a crucial tool for managing payroll tasks efficiently. However, encountering QuickBooks Payroll Updates Are Not Working Window 11 can disrupt workflow. Here are several troubleshooting solutions to address this problem:
Check Internet Connection: Ensure that your internet connection is stable. Poor connectivity can hinder the update process. Try accessing other websites or online services to verify your internet connection’s reliability. If needed, switch to a different network or troubleshoot your current connection.
Verify System Requirements: Confirm that your system meets QuickBooks’ minimum requirements for running on Windows 11. Incompatibility issues may arise if your system does not meet the necessary specifications. Ensure your system has sufficient memory, processing power, and disk space to run QuickBooks effectively.
Restart QuickBooks and Computer: Sometimes, a simple restart can resolve software glitches. Close QuickBooks and restart your computer to refresh system processes. After rebooting, relaunch QuickBooks and attempt to update the payroll again to see if the issue persists.
Run QuickBooks as Administrator: Running QuickBooks with administrative privileges can resolve permission-related issues that may prevent updates from installing correctly. Right-click on the QuickBooks shortcut icon and select “Run as administrator” from the context menu. Then, try updating the payroll to check if this resolves the problem.
Disable Firewall and Antivirus: Security software such as firewalls and antivirus programs may block QuickBooks’ access to necessary resources for updating. Temporarily disable these security measures and attempt to update the payroll again. Remember to re-enable them once the update process is complete to maintain system security.
Check Windows Update Settings: Ensure that Windows Update settings are configured correctly to allow QuickBooks updates. Go to “Settings” > “Update & Security” > “Windows Update” and verify that automatic updates are enabled. If updates are set to manual, consider switching to automatic updates to ensure timely installation of QuickBooks updates.
Update QuickBooks Manually: If automatic updates fail, you can manually download and install the latest QuickBooks updates from the official Intuit website. Visit the QuickBooks Downloads & Updates page, select your product version, and follow the instructions to download and install the update manually. This method bypasses any potential issues with automatic updates.
Repair QuickBooks Installation: Corrupted QuickBooks installation files can cause update problems. Use the QuickBooks Installation Diagnostic Tool or QuickBooks Install Diagnostic Tool to repair the installation. These tools can identify and fix common installation issues, ensuring that QuickBooks functions properly.
Check for System Errors: Scan your system for any underlying errors or issues that may be affecting QuickBooks’ performance. Use built-in Windows utilities like System File Checker (SFC) or Deployment Image Servicing and Management (DISM) to scan and repair system files. Resolving these errors can often resolve software issues.
Contact QuickBooks Support: If none of the above solutions resolve the problem, reach out to QuickBooks customer support for further assistance. Provide detailed information about the issue, including any error messages encountered, troubleshooting steps taken, and system specifications. QuickBooks support representatives can offer specialized assistance to help resolve the issue promptly.
By following these troubleshooting solutions, you can address QuickBooks Payroll Updates Are Not Working Window 11 and ensure smooth operation of the software for managing your payroll tasks effectively.
Troubleshooting Solutions: QuickBooks Payroll Updates Are Not Working Window 11 QuickBooks Payroll is a crucial tool for managing payroll tasks efficiently. However, encountering QuickBooks Payroll Updates Are Not Working Window 11 can disrupt workflow. Here are several troubleshooting solutions to address this problem: Check Internet Connection: Ensure that your internet connection is stable. Poor connectivity can hinder the update process. Try accessing other websites or online services to verify your internet connection’s reliability. If needed, switch to a different network or troubleshoot your current connection.Verify System Requirements: Confirm that your system meets QuickBooks’ minimum requirements for running on Windows 11. Incompatibility issues may arise if your system does not meet the necessary specifications. Ensure your system has sufficient memory, processing power, and disk space to run QuickBooks effectively.Restart QuickBooks and Computer: Sometimes, a simple restart can resolve software glitches. Close QuickBooks and restart your computer to refresh system processes. After rebooting, relaunch QuickBooks and attempt to update the payroll again to see if the issue persists.Run QuickBooks as Administrator: Running QuickBooks with administrative privileges can resolve permission-related issues that may prevent updates from installing correctly. Right-click on the QuickBooks shortcut icon and select “Run as administrator” from the context menu. Then, try updating the payroll to check if this resolves the problem.Disable Firewall and Antivirus: Security software such as firewalls and antivirus programs may block QuickBooks’ access to necessary resources for updating. Temporarily disable these security measures and attempt to update the payroll again. Remember to re-enable them once the update process is complete to maintain system security.Check Windows Update Settings: Ensure that Windows Update settings are configured correctly to allow QuickBooks updates. Go to “Settings” > “Update & Security” > “Windows Update” and verify that automatic updates are enabled. If updates are set to manual, consider switching to automatic updates to ensure timely installation of QuickBooks updates.Update QuickBooks Manually: If automatic updates fail, you can manually download and install the latest QuickBooks updates from the official Intuit website. Visit the QuickBooks Downloads & Updates page, select your product version, and follow the instructions to download and install the update manually. This method bypasses any potential issues with automatic updates.Repair QuickBooks Installation: Corrupted QuickBooks installation files can cause update problems. Use the QuickBooks Installation Diagnostic Tool or QuickBooks Install Diagnostic Tool to repair the installation. These tools can identify and fix common installation issues, ensuring that QuickBooks functions properly.Check for System Errors: Scan your system for any underlying errors or issues that may be affecting QuickBooks’ performance. Use built-in Windows utilities like System File Checker (SFC) or Deployment Image Servicing and Management (DISM) to scan and repair system files. Resolving these errors can often resolve software issues.Contact QuickBooks Support: If none of the above solutions resolve the problem, reach out to QuickBooks customer support for further assistance. Provide detailed information about the issue, including any error messages encountered, troubleshooting steps taken, and system specifications. QuickBooks support representatives can offer specialized assistance to help resolve the issue promptly.By following these troubleshooting solutions, you can address QuickBooks Payroll Updates Are Not Working Window 11 and ensure smooth operation of the software for managing your payroll tasks effectively. Read More
What to Do When QuickBooks Unable to Open Company File after Windows updates?
Troubleshooting Solutions: QuickBooks Unable to Open Company File
Encountering issues when opening a company file in QuickBooks can disrupt your workflow and cause frustration. Here are several troubleshooting solutions to help resolve the QuickBooks Unable to Open Company File problem:
Verify File Location and Accessibility: Ensure that the company file is located in a directory where QuickBooks can access it. Check the file’s path and permissions to ensure it’s not stored in a restricted folder or on a network drive that’s inaccessible. Move the file to a local drive if it’s currently stored in a location with restricted access.
Check File Extension and Compatibility: Verify that the company file has the correct file extension (.QBW for QuickBooks company files). If the file extension is incorrect or the file is in an incompatible format, QuickBooks may be unable to open it. Additionally, ensure that the file was created using a compatible version of QuickBooks.
Use QuickBooks File Doctor: QuickBooks File Doctor is a diagnostic tool provided by Intuit to troubleshoot company file issues. Download and run the QuickBooks File Doctor tool, then follow the on-screen prompts to scan and repair any issues with the company file. File Doctor can often resolve common file-related errors that prevent QuickBooks from opening the company file.
Restore Backup Company File: If you have a backup of the company file, attempt to restore it to see if the issue persists. Sometimes, the current company file may be damaged or corrupted, preventing it from being opened. Restoring a backup can provide a clean version of the file that may resolve the opening issue.
Try Opening Sample Company File: QuickBooks includes sample company files that can be used for testing purposes. Try opening one of the sample company files to determine if the issue is specific to your company file or a more general problem with QuickBooks. If you can open the sample file without any issues, the problem may lie with your company file itself.
Update QuickBooks to the Latest Version: Ensure that you are using the latest version of QuickBooks and that all updates have been installed. Intuit regularly releases updates and patches to address software issues and improve compatibility. Updating QuickBooks to the latest version may resolve the problem with opening company files.
Disable Hosting Mode: If QuickBooks is set to host multi-user access, try disabling hosting mode temporarily. Hosting mode can sometimes cause conflicts when opening company files, especially if multiple users are accessing the same file simultaneously. Disable hosting mode in the QuickBooks preferences or settings and then attempt to open the company file again.
Check for Disk Space and System Resources: Insufficient disk space or system resources can prevent QuickBooks from opening company files. Check your computer’s disk space and ensure that there is enough available space to open and run QuickBooks. Additionally, close any unnecessary programs or processes running in the background to free up system resources.
Run QuickBooks as Administrator: Running QuickBooks with administrative privileges can sometimes resolve permissions issues that prevent company files from being opened. Right-click on the QuickBooks shortcut icon and select “Run as administrator” from the context menu. Then, attempt to open the company file again to see if the issue persists.
Contact QuickBooks Support: If none of the above solutions resolve the issue, consider reaching out to QuickBooks customer support for further assistance. Provide detailed information about the error message you’re encountering, any troubleshooting steps you’ve already taken, and your system specifications. QuickBooks support representatives can offer personalized assistance to help resolve the problem.
By following these troubleshooting solutions, you can address the QuickBooks Unable to Open Company File issue and regain access to your company files in QuickBooks.
Troubleshooting Solutions: QuickBooks Unable to Open Company File Encountering issues when opening a company file in QuickBooks can disrupt your workflow and cause frustration. Here are several troubleshooting solutions to help resolve the QuickBooks Unable to Open Company File problem: Verify File Location and Accessibility: Ensure that the company file is located in a directory where QuickBooks can access it. Check the file’s path and permissions to ensure it’s not stored in a restricted folder or on a network drive that’s inaccessible. Move the file to a local drive if it’s currently stored in a location with restricted access.Check File Extension and Compatibility: Verify that the company file has the correct file extension (.QBW for QuickBooks company files). If the file extension is incorrect or the file is in an incompatible format, QuickBooks may be unable to open it. Additionally, ensure that the file was created using a compatible version of QuickBooks.Use QuickBooks File Doctor: QuickBooks File Doctor is a diagnostic tool provided by Intuit to troubleshoot company file issues. Download and run the QuickBooks File Doctor tool, then follow the on-screen prompts to scan and repair any issues with the company file. File Doctor can often resolve common file-related errors that prevent QuickBooks from opening the company file.Restore Backup Company File: If you have a backup of the company file, attempt to restore it to see if the issue persists. Sometimes, the current company file may be damaged or corrupted, preventing it from being opened. Restoring a backup can provide a clean version of the file that may resolve the opening issue.Try Opening Sample Company File: QuickBooks includes sample company files that can be used for testing purposes. Try opening one of the sample company files to determine if the issue is specific to your company file or a more general problem with QuickBooks. If you can open the sample file without any issues, the problem may lie with your company file itself.Update QuickBooks to the Latest Version: Ensure that you are using the latest version of QuickBooks and that all updates have been installed. Intuit regularly releases updates and patches to address software issues and improve compatibility. Updating QuickBooks to the latest version may resolve the problem with opening company files.Disable Hosting Mode: If QuickBooks is set to host multi-user access, try disabling hosting mode temporarily. Hosting mode can sometimes cause conflicts when opening company files, especially if multiple users are accessing the same file simultaneously. Disable hosting mode in the QuickBooks preferences or settings and then attempt to open the company file again.Check for Disk Space and System Resources: Insufficient disk space or system resources can prevent QuickBooks from opening company files. Check your computer’s disk space and ensure that there is enough available space to open and run QuickBooks. Additionally, close any unnecessary programs or processes running in the background to free up system resources.Run QuickBooks as Administrator: Running QuickBooks with administrative privileges can sometimes resolve permissions issues that prevent company files from being opened. Right-click on the QuickBooks shortcut icon and select “Run as administrator” from the context menu. Then, attempt to open the company file again to see if the issue persists.Contact QuickBooks Support: If none of the above solutions resolve the issue, consider reaching out to QuickBooks customer support for further assistance. Provide detailed information about the error message you’re encountering, any troubleshooting steps you’ve already taken, and your system specifications. QuickBooks support representatives can offer personalized assistance to help resolve the problem.By following these troubleshooting solutions, you can address the QuickBooks Unable to Open Company File issue and regain access to your company files in QuickBooks. Read More
Azure Data Fundamentals Roadmap
:rocket:Kickstart your journey in data engineering with Microsoft’s DP-900 certification, an ideal gateway into the essentials of data on Azure. Whether you’re new to cloud data or looking to validate your skills, this course covers everything from relational and non-relational data concepts to transactional and analytical workloads. Plus, you might earn ACE college credit! :graduation_cap: Dive into this opportunity to prepare for advanced Azure certifications and build a robust foundation in data fundamentals. :hammer_and_wrench:️
Master the basics of Azure: https://learn.microsoft.com/en-us/collections/zopanrz572e5j?&sharingId=7A8BB35FBAED63E4&wt.mc_id=studentamb_374326
– Explore core data concepts: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-core-data-concepts/?wt.mc_id=studentamb_374326
– Explore relational data in Azure: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-relational-data/?wt.mc_id=studentamb_374326
– Explore non-relational data in Azure: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-non-relational-data/?wt.mc_id=studentamb_374326
– Explore data analytics in Azure: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-data-warehouse-analytics/?wt.mc_id=studentamb_374326
:rocket:Kickstart your journey in data engineering with Microsoft’s DP-900 certification, an ideal gateway into the essentials of data on Azure. Whether you’re new to cloud data or looking to validate your skills, this course covers everything from relational and non-relational data concepts to transactional and analytical workloads. Plus, you might earn ACE college credit! :graduation_cap: Dive into this opportunity to prepare for advanced Azure certifications and build a robust foundation in data fundamentals. :hammer_and_wrench:️Master the basics of Azure: https://learn.microsoft.com/en-us/collections/zopanrz572e5j?&sharingId=7A8BB35FBAED63E4&wt.mc_id=studentamb_374326- Explore core data concepts: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-core-data-concepts/?wt.mc_id=studentamb_374326- Explore relational data in Azure: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-relational-data/?wt.mc_id=studentamb_374326- Explore non-relational data in Azure: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-non-relational-data/?wt.mc_id=studentamb_374326- Explore data analytics in Azure: https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-data-warehouse-analytics/?wt.mc_id=studentamb_374326 Read More
Azure AI Fundamentals Roadmap
:glowing_star: If you’re keen on diving into the world of artificial intelligence and starting a career in AI, the AI-900 Azure AI Fundamentals is your perfect launchpad :rocket:. This certification journey introduces you to the exciting realms of machine learning, computer vision, natural language processing, and conversational AI :robot_face:.
Master the basics of Azure: https://learn.microsoft.com/en-us/collections/zopanqdn7w1p1?&sharingId=7A8BB35FBAED63E4&wt.mc_id=studentamb_374326
– AI Overview: https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/?wt.mc_id=studentamb_374326
– Computer Vision: https://learn.microsoft.com/en-us/training/paths/explore-computer-vision-microsoft-azure/?wt.mc_id=studentamb_374326
– Document Intelligence and Knowledge Mining: https://learn.microsoft.com/en-us/training/paths/document-intelligence-knowledge-mining/?wt.mc_id=studentamb_374326
– Natural Language Processing: https://learn.microsoft.com/en-us/training/paths/explore-natural-language-processing/?wt.mc_id=studentamb_374326
– Generative AI: https://learn.microsoft.com/en-us/training/paths/introduction-generative-ai/?wt.mc_id=studentamb_374326
:glowing_star: If you’re keen on diving into the world of artificial intelligence and starting a career in AI, the AI-900 Azure AI Fundamentals is your perfect launchpad :rocket:. This certification journey introduces you to the exciting realms of machine learning, computer vision, natural language processing, and conversational AI :robot_face:. Master the basics of Azure: https://learn.microsoft.com/en-us/collections/zopanqdn7w1p1?&sharingId=7A8BB35FBAED63E4&wt.mc_id=studentamb_374326 – AI Overview: https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/?wt.mc_id=studentamb_374326- Computer Vision: https://learn.microsoft.com/en-us/training/paths/explore-computer-vision-microsoft-azure/?wt.mc_id=studentamb_374326- Document Intelligence and Knowledge Mining: https://learn.microsoft.com/en-us/training/paths/document-intelligence-knowledge-mining/?wt.mc_id=studentamb_374326- Natural Language Processing: https://learn.microsoft.com/en-us/training/paths/explore-natural-language-processing/?wt.mc_id=studentamb_374326- Generative AI: https://learn.microsoft.com/en-us/training/paths/introduction-generative-ai/?wt.mc_id=studentamb_374326 Read More
How to copy the file path of opened folder on Windows 10?
Hello,
I have many folders opened on my computer. How do I copy all path locations of them so I can remember reopen them after I restart windows 10? After I restart windows 10, I want to open everything to be the same like before I restart.
Thanks
Hello,I have many folders opened on my computer. How do I copy all path locations of them so I can remember reopen them after I restart windows 10? After I restart windows 10, I want to open everything to be the same like before I restart. Thanks Read More
DEV x AI tulee taas 23.4.2024
Developer x AI 2024 webinaari kumppaneille 23.4.2024
Rekisteröidy englanninkieliseen tapahtumaan tästä.
Join us at Developer x AI, a virtual event that brings together the brightest minds in software development in Finland. This event offers a unique opportunity to gain expertise on AI technologies, specifically on GitHub Copilot and Azure AI services.
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Agenda:
9.00 Opening Keynote
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9.20 GitHub Copilot – What’s new and what’s next
Jonas Helin, Cloud Solution Engineer, GitHub
9.40 Experiences from onboarding GitHub Copilot
TBA
10.00 Practical tips on using GitHub Copilot
Saija Saarenpää, Passionate Code Whisperer, Vincit
10.20 Azure AI – more than just Azure OpenAI Service
Juha Karvonen, Sr. Cloud Solution Architect, Microsoft
10.40 Case: AinoAid – hands on experiences from going to production with generative AI
Tuomas Lahtinen, Loihde and Joonas Järvinen, Loihde
11.00 Extremely Natural Language Processing – AI avatar as an LLM interface
Mikko Lehtimäki, Co-founder and Chief Data Scientist, Softlandia
11.20 Closing Remarks – Summary and thank you
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Where: Online
Language: English
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Microsoft Tech Community – Latest Blogs –Read More
Why Is My QuickBooks payroll is not working after Windows updates?
Troubleshooting Solutions: QuickBooks payroll is not working
QuickBooks payroll is not working after Windows updates can be a common issue faced by users. This can happen due to compatibility issues between the QuickBooks software and the Windows updates. Here are some troubleshooting solutions to help you resolve this problem:
Update QuickBooks: The first thing you should do is check if there are any updates available for QuickBooks. Sometimes, updating the software to the latest version can resolve compatibility issues with the Windows updates.
Run QuickBooks Install Diagnostic Tool: QuickBooks Install Diagnostic Tool is a tool provided by Intuit to help fix common installation issues. You can download and run this tool to scan for any issues with the QuickBooks installation and repair them.
Check Windows Firewall settings: Sometimes, Windows Firewall settings can block QuickBooks from working properly after updates. Make sure that QuickBooks is allowed through the Windows Firewall by adding it to the list of exceptions.
Disable antivirus software: Antivirus software can also interfere with the functioning of QuickBooks after Windows updates. Temporarily disable your antivirus software and check if QuickBooks payroll starts working again.
Repair QuickBooks installation: If none of the above solutions work, you can try repairing the QuickBooks installation. Go to Control Panel > Programs and Features, select QuickBooks from the list of programs, and click on Repair. Follow the on-screen instructions to repair the installation.
Create a new Windows user account: Sometimes, user account issues can cause problems with QuickBooks payroll after Windows updates. Create a new Windows user account, log in to that account, and try running QuickBooks to see if the issue is resolved.
Clean reinstall QuickBooks: If all else fails, you can perform a clean reinstall of QuickBooks. Uninstall QuickBooks from your computer, download the latest version from the official website, and reinstall it following the installation instructions.
By following these troubleshooting solutions, you should be able to fix the issue of QuickBooks payroll is not working after Windows updates. If the problem persists, you can contact QuickBooks support for further assistance.
Troubleshooting Solutions: QuickBooks payroll is not working QuickBooks payroll is not working after Windows updates can be a common issue faced by users. This can happen due to compatibility issues between the QuickBooks software and the Windows updates. Here are some troubleshooting solutions to help you resolve this problem:Update QuickBooks: The first thing you should do is check if there are any updates available for QuickBooks. Sometimes, updating the software to the latest version can resolve compatibility issues with the Windows updates.Run QuickBooks Install Diagnostic Tool: QuickBooks Install Diagnostic Tool is a tool provided by Intuit to help fix common installation issues. You can download and run this tool to scan for any issues with the QuickBooks installation and repair them.Check Windows Firewall settings: Sometimes, Windows Firewall settings can block QuickBooks from working properly after updates. Make sure that QuickBooks is allowed through the Windows Firewall by adding it to the list of exceptions.Disable antivirus software: Antivirus software can also interfere with the functioning of QuickBooks after Windows updates. Temporarily disable your antivirus software and check if QuickBooks payroll starts working again.Repair QuickBooks installation: If none of the above solutions work, you can try repairing the QuickBooks installation. Go to Control Panel > Programs and Features, select QuickBooks from the list of programs, and click on Repair. Follow the on-screen instructions to repair the installation.Create a new Windows user account: Sometimes, user account issues can cause problems with QuickBooks payroll after Windows updates. Create a new Windows user account, log in to that account, and try running QuickBooks to see if the issue is resolved.Clean reinstall QuickBooks: If all else fails, you can perform a clean reinstall of QuickBooks. Uninstall QuickBooks from your computer, download the latest version from the official website, and reinstall it following the installation instructions.By following these troubleshooting solutions, you should be able to fix the issue of QuickBooks payroll is not working after Windows updates. If the problem persists, you can contact QuickBooks support for further assistance. Read More
Viva Engage Community search options
Viva Engage Community search shows all communities, is there any way to show only region specific communities to user.
Eg: If user is from North America region, then user should be able to search/see only North America region Communities and communities from other region should not be visible/searchable.
Viva Engage Community search shows all communities, is there any way to show only region specific communities to user.Eg: If user is from North America region, then user should be able to search/see only North America region Communities and communities from other region should not be visible/searchable. Read More
Unable to create Free form model in Syntex
Unable to create Free form model in Syntex
Hi All,
I’m trying to create these 2 models but setup got failed and showing error.
These 2 models i’m trying to create
I get this error message
I already have dataverse environment and database but still it is not allowing me to create.
Unable to create Free form model in SyntexHi All, I’m trying to create these 2 models but setup got failed and showing error. These 2 models i’m trying to create I get this error message I already have dataverse environment and database but still it is not allowing me to create. Read More
Combine 2 cells if one cell contains same text
Hi,
I want to combine the Column “Generic Name” into one cell seperated by a comma if Column “Drug Code” has the same code
As you see from the example below
Drug Code 0401000010 has 2 Generic Name
0401000010 = Amoxicillin
0401000010 = Clavulanic Acid
i want it to be combined into one cell (Coumn “Combined”)
0401000010 = Amoxicillin, Clavulanic Acid
What formula can i use to achieve this result
Thank you
DRUG CODEGeneric NameCombined0401000010AmoxicillinAmoxicillin,Clavulanic acid0401000010Clavulanic acidAmoxicillin,Clavulanic acid0401000011AmoxicillinAmoxicillin,Clavulanic acid0401000011Clavulanic acidAmoxicillin,Clavulanic acid0401000056CetirizineCetirizine, Cetirizine Hydrochloride0401000056Cetirizine HydrochlorideCetirizine, Cetirizine Hydrochloride
Hi, I want to combine the Column “Generic Name” into one cell seperated by a comma if Column “Drug Code” has the same code As you see from the example below Drug Code 0401000010 has 2 Generic Name0401000010 = Amoxicillin0401000010 = Clavulanic Acid i want it to be combined into one cell (Coumn “Combined”) 0401000010 = Amoxicillin, Clavulanic Acid What formula can i use to achieve this result Thank youDRUG CODEGeneric NameCombined0401000010AmoxicillinAmoxicillin,Clavulanic acid0401000010Clavulanic acidAmoxicillin,Clavulanic acid0401000011AmoxicillinAmoxicillin,Clavulanic acid0401000011Clavulanic acidAmoxicillin,Clavulanic acid0401000056CetirizineCetirizine, Cetirizine Hydrochloride0401000056Cetirizine HydrochlorideCetirizine, Cetirizine Hydrochloride Read More
Finetune Small Language Model (SLM) Phi-3 using Azure Machine Learning
Motivations for Small Language Models:
· Efficiency: SLMs are computationally more efficient, requiring less memory and storage, and can operate faster due to fewer parameters to process.
· Cost: Training and deploying SLMs is less expensive, making them accessible to a wider range of businesses and suitable for applications in edge computing.
· Customizability: SLMs are more adaptable to specialized applications and can be fine-tuned for specific tasks more readily than larger models· Under-Explored Potential: While large models have shown clear benefits, the potential of smaller models trained with larger datasets has been less explored. SLM aims to showcase that smaller models can achieve high performance when trained with enough data.
· Inference Efficiency: Smaller models are often more efficient during inference, which is a critical aspect when deploying models in real-world applications with resource constraints. This efficiency includes faster response times and reduces computational and energy costs.
· Accessibility for Research: By being open-source and smaller in size, SLM is more accessible to a broader range of researchers who may not have the resources to work with larger models. It provides a platform for experimentation and innovation in language model research without requiring extensive computational resources.
· Advancements in Architecture and Optimization: SLM incorporates various architectural and speed optimizations to improve computational efficiency. These enhancements allow SLM to train faster and with less memory, making it feasible to train on commonly available GPUs.
· Open-Source Contribution: The authors of SLM have made the model checkpoints and code publicly available, contributing to the open-source community and enabling further advancements and applications by others.
· End-User Applications: With its excellent performance and compact size, SLM is suitable for end-user applications, potentially even on mobile devices, providing a lightweight platform for a wide range of applications.
· Training Data and Process: SLM training process is designed to be effective and reproducible, using a mixture of natural language data and code data, aiming to make pre-training accessible and transparent.
Phi-2 (Microsoft Research)
Phi-2 is the successor of Phi-1.5, the large language model (LLM) created by Microsoft.To improve over Phi-1.5, in addition to doubling the number of parameters to 2.7 billion, Microsoft also extended the training data. Phi-2 outperforms Phi-1.5 and LLMs that are 25 times larger on several public benchmarks even though it is not aligned/fine-tuned. This is just a pre-trained model for research purposes only (non-commercial, non-revenue generating). Forget about the exorbitant fees of larger language models. Phi-2 runs efficiently on even modest hardware, democratizing access to cutting-edge AI for startups and smaller businesses. No more sky-high cloud bills, just smart, affordable solutions on your own terms. In this example, we are going to learn how to fine-tune phi-2 using QLoRA: Efficient Finetuning of Quantized LLMs with Flash Attention. QLoRA is an efficient finetuning technique that quantizes a pretrained language model to 4 bits and attaches small “Low-Rank Adapters” which are fine-tuned. This enables fine-tuning of models with up to 65 billion parameters on a single GPU; despite its efficiency, QLoRA matches the performance of full-precision fine-tuning and achieves state-of-the-art results on language tasks.
Step:1
Lets prepare the dataset. In this case we are going to download the ultrachat dataset.
from datasets import load_dataset
from random import randrange
# Load dataset from the hub
dataset = load_dataset(“HuggingFaceH4/ultrachat_200k”, split=’train_sft[:2%]’)
print(f”dataset size: {len(dataset)}”)
print(dataset[randrange(len(dataset))])
Lets take a shorter version of the dataset to create training and test example. To instruct tune our model we need to convert our structured examples into a collection of tasks described via instructions. We define a formatting_function that takes a sample and returns a string with our format instruction.
dataset = dataset.train_test_split(test_size=0.2)
train_dataset = dataset[‘train’]
train_dataset.to_json(f”data/train.jsonl”)
test_dataset = dataset[‘test’]
test_dataset.to_json(f”data/eval.jsonl”)
Lets save this training and test dataset in json format. Now let’s load the Azure ML SDK. This will help us create the necesary component.
# import required libraries
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential
from azure.ai.ml import MLClient, Input
from azure.ai.ml.dsl import pipeline
from azure.ai.ml import load_component
from azure.ai.ml import command
from azure.ai.ml.entities import Data
from azure.ai.ml import Input
from azure.ai.ml import Output
from azure.ai.ml.constants import AssetTypes
Now lets create the workspace client.
credential = DefaultAzureCredential()
workspace_ml_client = None
try:
workspace_ml_client = MLClient.from_config(credential)
except Exception as ex:
print(ex)
subscription_id= “Enter your subscription_id”
resource_group = “Enter your resource_group”
workspace= “Enter your workspace name”
workspace_ml_client = MLClient(credential, subscription_id, resource_group, workspace)
Here lets create a custom training environment.
from azure.ai.ml.entities import Environment, BuildContext
env_docker_image = Environment(
image=”mcr.microsoft.com/azureml/curated/acft-hf-nlp-gpu:latest”,
conda_file=”environment/conda.yml”,
name=”llm-training”,
description=”Environment created for llm training.”,
)
ml_client.environments.create_or_update(env_docker_image)
Let’s look at the conda.yml
name: pydata-example
channels:
– conda-forge
dependencies:
– python=3.8
– pip=21.2.4
– pip:
– bitsandbytes
– transformers
– peft
– accelerate
– einops
– datasets
Lets look at the training script. We are going to use the recently introduced method in the paper “QLoRA: Quantization-aware Low-Rank Adapter Tuning for Language Generation” by Tim Dettmers et al. QLoRA is a new technique to reduce the memory footprint of large language models during finetuning, without sacrificing performance. The TL;DR; of how QLoRA works is:
Quantize the pretrained model to 4 bits and freezing it.
Attach small, trainable adapter layers. (LoRA)
Finetune only the adapter layers, while using the frozen quantized model for context.
%%writefile src/train.py
import os
#import mlflow
import argparse
import sys
import logging
import datasets
from datasets import load_dataset
from peft import LoraConfig
import torch
import transformers
from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
from datasets import load_dataset
logger = logging.getLogger(__name__)
###################
# Hyper-parameters
###################
training_config = {
“bf16”: True,
“do_eval”: False,
“learning_rate”: 5.0e-06,
“log_level”: “info”,
“logging_steps”: 20,
“logging_strategy”: “steps”,
“lr_scheduler_type”: “cosine”,
“num_train_epochs”: 1,
“max_steps”: -1,
“output_dir”: “./checkpoint_dir”,
“overwrite_output_dir”: True,
“per_device_eval_batch_size”: 4,
“per_device_train_batch_size”: 4,
“remove_unused_columns”: True,
“save_steps”: 100,
“save_total_limit”: 1,
“seed”: 0,
“gradient_checkpointing”: True,
“gradient_checkpointing_kwargs”:{“use_reentrant”: False},
“gradient_accumulation_steps”: 1,
“warmup_ratio”: 0.2,
}
peft_config = {
“r”: 16,
“lora_alpha”: 32,
“lora_dropout”: 0.05,
“bias”: “none”,
“task_type”: “CAUSAL_LM”,
“target_modules”: “all-linear”,
“modules_to_save”: None,
}
train_conf = TrainingArguments(**training_config)
peft_conf = LoraConfig(**peft_config)
###############
# Setup logging
###############
logging.basicConfig(
format=”%(asctime)s – %(levelname)s – %(name)s – %(message)s”,
datefmt=”%Y-%m-%d %H:%M:%S”,
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = train_conf.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process a small summary
logger.warning(
f”Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}”
+ f” distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}”
)
logger.info(f”Training/evaluation parameters {train_conf}”)
logger.info(f”PEFT parameters {peft_conf}”)
################
# Modle Loading
################
checkpoint_path = “microsoft/Phi-3-mini-4k-instruct”
# checkpoint_path = “microsoft/Phi-3-mini-128k-instruct”
model_kwargs = dict(
use_cache=False,
trust_remote_code=True,
attn_implementation=”flash_attention_2″, # loading the model with flash-attenstion support
torch_dtype=torch.bfloat16,
device_map=None
)
model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
tokenizer.model_max_length = 2048
tokenizer.pad_token = tokenizer.unk_token # use unk rather than eos token to prevent endless generation
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = ‘right’
##################
# Data Processing
##################
def apply_chat_template(
example,
tokenizer,
):
messages = example[“messages”]
# Add an empty system message if there is none
if messages[0][“role”] != “system”:
messages.insert(0, {“role”: “system”, “content”: “”})
example[“text”] = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=False)
return example
def main(args):
train_dataset = load_dataset(‘json’, data_files=args.train_file, split=’train’)
test_dataset = load_dataset(‘json’, data_files=args.eval_file, split=’train’)
column_names = list(train_dataset.features)
processed_train_dataset = train_dataset.map(
apply_chat_template,
fn_kwargs={“tokenizer”: tokenizer},
num_proc=10,
remove_columns=column_names,
desc=”Applying chat template to train_sft”,
)
processed_test_dataset = test_dataset.map(
apply_chat_template,
fn_kwargs={“tokenizer”: tokenizer},
num_proc=10,
remove_columns=column_names,
desc=”Applying chat template to test_sft”,
)
###########
# Training
###########
trainer = SFTTrainer(
model=model,
args=train_conf,
peft_config=peft_conf,
train_dataset=processed_train_dataset,
eval_dataset=processed_test_dataset,
max_seq_length=2048,
dataset_text_field=”text”,
tokenizer=tokenizer,
packing=True
)
train_result = trainer.train()
metrics = train_result.metrics
trainer.log_metrics(“train”, metrics)
trainer.save_metrics(“train”, metrics)
trainer.save_state()
#############
# Evaluation
#############
tokenizer.padding_side = ‘left’
metrics = trainer.evaluate()
metrics[“eval_samples”] = len(processed_test_dataset)
trainer.log_metrics(“eval”, metrics)
trainer.save_metrics(“eval”, metrics)
# ############
# # Save model
# ############
os.makedirs(args.model_dir, exist_ok=True)
torch.save(model, os.path.join(args.model_dir, “model.pt”))
def parse_args():
# setup argparse
parser = argparse.ArgumentParser()
# add arguments
parser.add_argument(“–train-file”, type=str, help=”Input data for training”)
parser.add_argument(“–eval-file”, type=str, help=”Input data for eval”)
parser.add_argument(“–model-dir”, type=str, default=”./”, help=”output directory for model”)
parser.add_argument(“–epochs”, default=10, type=int, help=”number of epochs”)
parser.add_argument(
“–batch-size”,
default=16,
type=int,
help=”mini batch size for each gpu/process”,
)
parser.add_argument(“–learning-rate”, default=0.001, type=float, help=”learning rate”)
parser.add_argument(“–momentum”, default=0.9, type=float, help=”momentum”)
parser.add_argument(
“–print-freq”,
default=200,
type=int,
help=”frequency of printing training statistics”,
)
# parse args
args = parser.parse_args()
# return args
return args
# run script
if __name__ == “__main__”:
# parse args
args = parse_args()
# call main function
main(args)
Let’s create a training compute .
from azure.ai.ml.entities import AmlCompute
# If you have a specific compute size to work with change it here. By default we use the 1 x V100 compute from the above list
compute_cluster_size = “Standard_NC6s_v3”
# If you already have a gpu cluster, mention it here. Else will create a new one with the name ‘gpu-cluster-big’
compute_cluster = “gpu-cluster”
try:
compute = ml_client.compute.get(compute_cluster)
print(“The compute cluster already exists! Reusing it for the current run”)
except Exception as ex:
print(
f”Looks like the compute cluster doesn’t exist. Creating a new one with compute size {compute_cluster_size}!”
)
try:
print(“Attempt #1 – Trying to create a dedicated compute”)
compute = AmlCompute(
name=compute_cluster,
size=compute_cluster_size,
tier=”Dedicated”,
max_instances=1, # For multi node training set this to an integer value more than 1
)
ml_client.compute.begin_create_or_update(compute).wait()
except Exception as e:
print(“Error”)
Now lets call the compute job with the above training script in the AML compute we just created.
from azure.ai.ml import command
from azure.ai.ml import Input
from azure.ai.ml.entities import ResourceConfiguration
job = command(
inputs=dict(
train_file=Input(
type=”uri_file”,
path=”data/train.jsonl”,
),
eval_file=Input(
type=”uri_file”,
path=”data/eval.jsonl”,
),
epoch=2,
batchsize=64,
lr = 0.01,
momentum = 0.9,
prtfreq = 200,
output = “./outputs”
),
code=”./src”, # local path where the code is stored
compute = ‘gpu-a100’,
command=”accelerate launch train.py –train-file ${{inputs.train_file}} –eval-file ${{inputs.eval_file}} –epochs ${{inputs.epoch}} –batch-size ${{inputs.batchsize}} –learning-rate ${{inputs.lr}} –momentum ${{inputs.momentum}} –print-freq ${{inputs.prtfreq}} –model-dir ${{inputs.output}}”,
environment=”azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/52″,
distribution={
“type”: “PyTorch”,
“process_count_per_instance”: 1,
},
)
returned_job = workspace_ml_client.jobs.create_or_update(job)
workspace_ml_client.jobs.stream(returned_job.name)
Lets look at the pipeline output.
# check if the `trained_model` output is available
job_name = returned_job.name
print(“pipeline job outputs: “, workspace_ml_client.jobs.get(job_name).outputs)
Once the model is finetuned lets register the job in the workspace to create endpoint.
from azure.ai.ml.entities import Model
from azure.ai.ml.constants import AssetTypes
run_model = Model(
path=f”azureml://jobs/{job_name}/outputs/artifacts/paths/outputs/mlflow_model_folder”,
name=”phi-3-finetuned”,
description=”Model created from run.”,
type=AssetTypes.MLFLOW_MODEL,
)
model = workspace_ml_client.models.create_or_update(run_model)
Lets creat the endpoint.
from azure.ai.ml.entities import (
ManagedOnlineEndpoint,
IdentityConfiguration,
ManagedIdentityConfiguration,
)
# Check if the endpoint already exists in the workspace
try:
endpoint = workspace_ml_client.online_endpoints.get(endpoint_name)
print(“—Endpoint already exists—“)
except:
# Create an online endpoint if it doesn’t exist
# Define the endpoint
endpoint = ManagedOnlineEndpoint(
name=endpoint_name,
description=f”Test endpoint for {model.name}”,
identity=IdentityConfiguration(
type=”user_assigned”,
user_assigned_identities=[ManagedIdentityConfiguration(resource_id=uai_id)],
)
if uai_id != “”
else None,
)
# Trigger the endpoint creation
try:
workspace_ml_client.begin_create_or_update(endpoint).wait()
print(“n—Endpoint created successfully—n”)
except Exception as err:
raise RuntimeError(
f”Endpoint creation failed. Detailed Response:n{err}”
) from err
Once the endpoint is created we can go ahead and create the deployment.
# Initialize deployment parameters
deployment_name = “phi3-deploy”
sku_name = “Standard_NCs_v3”
REQUEST_TIMEOUT_MS = 90000
deployment_env_vars = {
“SUBSCRIPTION_ID”: subscription_id,
“RESOURCE_GROUP_NAME”: resource_group,
“UAI_CLIENT_ID”: uai_client_id,
}
For inferencing we will use a different base image.
from azure.ai.ml.entities import Model, Environment
env = Environment(
image=’mcr.microsoft.com/azureml/curated/foundation-model-inference:latest’,
inference_config={
“liveness_route”: {“port”: 5001, “path”: “/”},
“readiness_route”: {“port”: 5001, “path”: “/”},
“scoring_route”: {“port”: 5001, “path”: “/score”},
},
)
Lets deploy the model
from azure.ai.ml.entities import (
OnlineRequestSettings,
CodeConfiguration,
ManagedOnlineDeployment,
ProbeSettings,
Environment
)
deployment = ManagedOnlineDeployment(
name=deployment_name,
endpoint_name=endpoint_name,
model=model.id,
instance_type=sku_name,
instance_count=1,
#code_configuration=code_configuration,
environment = env,
environment_variables=deployment_env_vars,
request_settings=OnlineRequestSettings(request_timeout_ms=REQUEST_TIMEOUT_MS),
liveness_probe=ProbeSettings(
failure_threshold=30,
success_threshold=1,
period=100,
initial_delay=500,
),
readiness_probe=ProbeSettings(
failure_threshold=30,
success_threshold=1,
period=100,
initial_delay=500,
),
)
# Trigger the deployment creation
try:
workspace_ml_client.begin_create_or_update(deployment).wait()
print(“n—Deployment created successfully—n”)
except Exception as err:
raise RuntimeError(
f”Deployment creation failed. Detailed Response:n{err}”
) from err
If you want to delete the endpoint please see the below code.
workspace_ml_client.online_deployments.begin_delete(name = deployment_name,
endpoint_name = endpoint_name)
workspace_ml_client._online_endpoints.begin_delete(name = endpoint_name)
Hope this tutorial helps you in Finetuning and deploying Phi-3 model in Azure ML Studio.
Hope you like the blog. Please clap and follow me if you like to read more such blogs coming soon.
References:
https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
https://www.philschmid.de/sagemaker-falcon-180b-qlora
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Azure OpenAI Service is a fully managed service that allows developers to easily integrate OpenAI models into their applications. With Azure OpenAI Service, developers can quickly and easily access a wide range of AI models, including natural language processing, computer vision, and more. Azure OpenAI Service provides a simple and easy-to-use API that makes it easy to get started with AI
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The AI service provider landscape is characterized by its rapid evolution and diverse offerings. Informed decision-making requires a careful analysis of the providers’ unique strengths, their pricing models, and the congruence with an organization’s specific demands and strategic ambitions.
Let’s look at some of the common scenarios in app migrations and break down major differences
Programing SDKs
Major differences we need to make to switch our apps from OpenAI to Azure OpenAI. We are going to use Python SDK for this example.
API key – The code looks similar, but Azure OpenAI adds api_version and azure_endpoint because you’re running your own instance.
Microsoft Entra ID authentication – This is helpful in adding extra security to our client instance by adding api_version, azure_endpoint and the token_provider.
Keyword argument for model – OpenAI uses the model keyword argument to specify what model to use. Azure OpenAI has the concept of unique model deployments. When you use Azure OpenAI, model should refer to the underlying deployment name you chose when you deployed the model.
Embeddings multiple input support – OpenAI and Azure OpenAI currently support input arrays up to 2,048 input items for text-embedding-ada-002. Both require the max input token limit per API request to remain under 8,191 for this model.
Other Benefits of migrating from OpenAI to Azure OpenAI
Managed Service and Infrastructure:
Azure OpenAI is a fully managed service provided by Microsoft. You don’t need to worry about setting up and maintaining infrastructure, as Azure handles it for you. You just need to spin up your OpenAI instance and start developing.
You can also configure Azure OpenAI Service with managed identities
Security and Compliance:
Azure provides robust security features, including encryption, identity management, and compliance certifications. This acts as a more friendly reason for startups, companies and organization
If your application deals with sensitive data, Azure OpenAI ensures that your models and data are protected according to industry standards. Your companied data is retained in your own Azure OpenAI instance.
Responsible AI practices for Azure OpenAI models
Azure OpenAI supported programming languages – Azure OpenAI gives you five programing languages (C#, Go, Java, JavaScript and Python) with SDKs to help you easily interact with the models.
Scalability and High Availability:
Azure’s global infrastructure allows you to scale your AI workloads dynamically. You can handle increased demand by automatically provisioning additional resources.
Azure also provides redundancy across multiple data centers, ensuring high availability and fault tolerance.
Integration with Other Azure Services:
Azure OpenAI seamlessly integrates with other Azure services, such as Azure Machine Learning, Azure Cognitive Services, and Azure Functions.
You can also build end-to-end AI pipelines by combining different services within the Azure ecosystem.
Cost Optimization:
Azure offers flexible pricing options, including pay-as-you-go (PAYG) and Provisioned Throughput Units (PTUs). With PAYG, you can optimize costs by paying only for the resources you use, while PTUs provide throughput with minimal latency variance, making them ideal for scaling your AI solutions. Each model is priced per unit, ensuring a predictable cost structure for your AI deployments.
Additionally, Azure provides cost management tools to monitor and optimize your spending. You can event approximate the cost for your Azure resources by using the Price calculator.
Read More
Migrating from OpenAI to Azure OpenAI
How to switch between OpenAI and Azure OpenAI endpoints with Python
Work with the GPT-3.5-Turbo and GPT-4 models
Azure OpenAI Service REST API reference
Quickstart: Get started generating text using Azure OpenAI Service
Azure OpenAI supported programming languages
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I’m looking for some help for an Excel beginner. I have a spreadsheet for client management hours for my team, and currently, we’re calculating everything manually.
I’ve done some googling and found some suggested formulas for lookup tools, etc., but I’m not sure if this is the right path.
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I’ve attached an example to explain my use/needs better.
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