Presenting Digest AI: An Open-Source Resource to Help Developers Comprehend Machine Learning Models.
AMD has created an open-source tool to assist developers in exploring and comprehending machine learning models in partnership with the Linux Foundation and the ONNX Community. Digest is a potent model analysis tool that can assist you in drawing insightful conclusions from your machine learning models. Digest facilitates the organisation, reporting, and optimization of your models, increasing the efficacy and efficiency of your workflow.
The Benefits of Digest AI
- Model Analysis: The application carries out model analysis, including sophisticated insights like operation histograms and model resemblance graphs in addition to specifics like parameters and FLOPs.
- Platform Compatibility: It is compatible with Linux and Windows, providing versatility for a range of development environments.
- Reporting: By producing reports that provide an overview of the study, including all nodes, parameters, FLOPs, attributes, and model inputs and outputs, Digest AI allows you to store your model analysis. You can also save histograms of operation counts and pictures of the heatmap, which displays how similar the model is to other publicly available models.
- It allows you to display a global overview and analyze numerous models at once due to its support for multi-model analysis.
- Integration with Hugging Face: A beta module of the tool allows users to download and examine ONNX models straight from the Hugging Face portal.
- Dynamic Input Handling: You can define static dimensions for more precise analysis by using Digest AI to handle models with dynamic input shapes.
- API Support: An API for ingesting, altering, and evaluating machine learning models is included.
Beginning to Use Digest AI
Digest AI is compatible with Linux and Windows and allows you to extract insights from your models with a single click. Digest AI can be installed from the source code on the GitHub repository at github.com/onnx/digest to get started. Run digest from your command line to start the application after the installation is finished. Instructions for creating an executable, which may start the application, are also available in the GitHub repository.
Digest AI is an effective and intuitive tool that may greatly improve your machine learning research. Digest AI assists you in analyzing your models by offering comprehensive model analysis, support for multiple models, and connection with the Hugging Face hub.
What is Digest AI?
Essentially serving as a model analysis platform for improved model comprehension and optimization, Digest AI is an open-source tool created by AMD that assists developers in analyzing and comprehending machine learning models. It enables them to organize their models, extract insightful information, generate reports, and optimize their workflow by giving them comprehensive details about model parameters, operations, and performance metrics.
Key points about Digest AI
Function
Mostly used to examine machine learning models, offering information about their composition, functionality, and effectiveness.
Open-source
Developers get free access to this tool, which is a component of the ONNX community.
Features
- Details of the model study, including model similarity graphs, operation histograms, parameters, and FLOPs.
- Reporting features that provide a visual summary of model analysis.
- Using multi-model analysis to compare several models at once.
- Hugging Face integration for ONNX model access and analysis.
Benefit to the developer
Through the identification of possible bottlenecks and areas for optimization, Digest AI can assist developers in improving their model development process.
Use cases for Digest AI
- Model debugging is the process of analyzing a model’s internal operations to find possible problems with its architecture or training procedure.
- Model optimization is the process of directing optimization efforts by identifying the components of a model that most influence its performance.
- Model comparison is the process of assessing various models for a job by examining their performance indicators and structural differences.
- Model exploitability is the ability to analyze a model’s decision-making process in order to learn more about how it makes decisions.
