Adlik, an LF AI & Data Foundation Incubation-Stage Project, has released version 0.3.0, called Cheetah. Adlik is a toolkit for accelerating deep learning inference, which provides an overall support for bringing trained models into production and eases the learning curves for different kinds of inference frameworks. In Adlik, Model Optimizer and Model Compiler delivers optimized and compiled models for a certain hardware environment, and Serving Engine provides deployment solutions for cloud, edge and device.
In version 0.3.0, Cheetah, you’ll find more frameworks integrated and the Adlik Optimizer succeeds in boosting inference performance of models. In a MLPerf test, a ResNet-50 model is optimized by Adlik optimizer, with model size compressed by 93%, inference latency reduced to 1.33ms. And in Adlik compiler, TVM auto scheduling, which globally and automatically searches for the optimal scheduling solution by re-designing scheduling templates, enables lower latency for ResNet-50 on x86 CPU than OpenVINO. This release enhances features, increases useability, and continues to showcase improvements across a wide range of scenarios. A few release highlights to note include the following:
- Integrate deep learning frameworks including PaddlePaddle, Caffe and MXNet
- Support compiling into TVM
- Support FP16 quantization for OpenVINO
- Support TVM auto scheduling
- Specific optimization for YOLO V4
- Pruning, distillation and quantization for ResNet-50
- Inference Engine
- Support runtime of TVM and TF-TRT
- Docker images for cloud native environments support newest version of inference components including OpenVINO (2021.1.110), TensorFlow (2.4.0), TensorRT (126.96.36.199), TFLite (2.4.0), TVM (0.7)
- Benchmark Test
- Support paddle models, such as Paddle OCR，PP-YOLO，PPresnet-50
A special thank you goes out to contributors from Paddle for their support in this release. Your contributions are greatly appreciated!
The Adlik Project invites you to adopt or upgrade to Cheetah, version 0.3.0, and welcomes feedback. To learn more about the Adlik 0.3.0 release, check out the full release notes. Want to get involved with Adlik? Be sure to join the Adlik-Announce and Adlik Technical-Discuss mailing lists to join the community and stay connected on the latest updates.
Congratulations to the Adlik team! We look forward to continued growth and success as part of the LF AI & Data Foundation. To learn about how to host an open source project with us, visit the LF AI & Data website.
Adlik Key Links
LF AI & Data Resources