To get started with mlbuild on Windows, visit the official releases page here:
https://github.com/Sidgithub18/mlbuild/releases
This page contains all the available versions of mlbuild. Look for the latest release marked as stable. You will find executable files and related resources there.
Once you have downloaded the setup file or executable from the release page, follow these steps:
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Locate the file you downloaded. Most likely, it will be in your "Downloads" folder unless you chose a different location.
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Run the installer or executable.
- If it is an installer (.exe), double-click the file and follow the prompts.
- If it is a standalone executable, double-click it to start the application.
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Allow the program to make changes. Windows may ask if you want to allow this app to make changes to your device. Confirm by clicking "Yes."
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Complete installation if prompted. Follow on-screen instructions to finish.
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Launch mlbuild. After installation, you can find mlbuild in your Start Menu or by searching for "mlbuild."
mlbuild helps teams make sure their machine learning models run fast enough before they are used in real environments. It works by checking models during continuous integration (CI) processes. If a model is slow, mlbuild stops it from being deployed.
Key facts about mlbuild:
- Focuses on performance checks for ML models.
- Fits into existing CI/CD pipelines.
- Supports popular frameworks like PyTorch and ONNX.
- Works on Apple Silicon and Windows.
- Command-line interface for easy automation.
- Validates multiple model formats and runtime environments.
Before installing mlbuild, check your system meets these requirements:
- Operating System: Windows 10 (64-bit) or newer.
- Processor: Intel or AMD processor, or Apple Silicon with a Windows-compatible emulator.
- Memory: At least 8GB RAM recommended.
- Storage: 500MB free space for installation.
- Additional Software: Python 3.8 or higher if you plan to use Python features.
- Network: Internet connection required for downloading and updates.
mlbuild measures the speed of machine learning model predictions during testing. It relies on metrics called Service Level Agreements (SLAs), which are rules about how fast a model must respond.
If a model takes too long, mlbuild stops it from moving forward in your pipeline. This avoids slow or underperforming models reaching the users or applications.
It supports:
- Checking models built with PyTorch or ONNX.
- Running benchmarks on different hardware.
- Automatic blocking in CI before deployment.
- Reporting detailed performance results.
You don't need to write code to use mlbuild on your Windows machine. Once installed, you interact through a simple command prompt window. Here’s how to get started immediately:
- Open the Command Prompt (search "cmd" in Windows Start menu).
- Type
mlbuild --helpand press Enter to see available commands. - Use basic commands to load and test models (the user guide can help with specific commands).
If you do not know your model file path, ask your team or the ML engineer who built it.
- Open Command Prompt.
- Enter:
mlbuild run --model path\to\your\model.onnx
Replacepath\to\your\model.onnxwith your actual model file location.
mlbuild will execute tests and return a report on model speed.
- After testing, mlbuild saves a report you can view as a text file.
- Open the report with Notepad or any text editor.
- The report tells you if the model met the speed limits set in the SLA.
- Check the GitHub repository for detailed documentation and release notes.
- Visit https://github.com/Sidgithub18/mlbuild/releases often for updates.
- Contact your ML or dev team if you need help with model file paths or specific testing.
New versions of mlbuild include improvements and bug fixes. To update:
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Visit the release page:
https://github.com/Sidgithub18/mlbuild/releases -
Download the latest setup or executable file.
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Run the installer or replace your old executable with the new one.
mlbuild supports many machine learning model formats and tools used in production:
- ONNX models (.onnx files).
- PyTorch models (.pt files).
- CoreML for Apple devices.
- ONNX Runtime for fast inference.
- Continuous integration tools like Jenkins or GitHub Actions.
You do not need to install these separately to use mlbuild basic features, but some advanced testing requires Python packages.
- If mlbuild does not start, make sure you have Windows 10 or newer.
- Check if you have permissions to install new software.
- If your model is not found, verify the file path in the command.
- Restart your computer after installation if mlbuild is not recognized.
SLA means Service Level Agreement. It tells mlbuild the maximum time allowed for a model to respond.
If your model is slower than the SLA, mlbuild will block it in automation.
You can adjust SLA settings using command line options or config files that control the maximum allowed latency.
