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The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution. Among many uses, the toolkit supports techniques used to reduce latency and inference costs for cloud and edge devices (e.g. mobile, IoT). Deploy models to edge devices with restrictions on processing, memory, power consumption, network usage, and model storage space. Enable execution on and optimize for existing hardware or new special purpose accelerators. Choose the model and optimization tool depending on your task. In many cases, pre-optimized models can improve the efficiency of your application. Try the post-training tools to optimize an already-trained TensorFlow model. Use training-time optimization tools and learn about the techniques.
Features
- Improve performance with off-the-shelf models
- Use the TensorFlow Model Optimization Toolkit
- Optimize further
- Use training-time optimization tools and learn about the techniques
- Pre-optimized models can improve the efficiency of your application
- Optimize machine learning models
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