Try DeepSparse Now

Pick an NLP or Computer Vision use case below and benchmark an optimized model to experience best-in-class CPU performance. Take one step further and train the optimized model with your private data and deploy it to your CPU infrastructure.

With the examples below, install DeepSparse, then benchmark different models like a sparse-quantized version of Hugging Face BERT-base, YOLOv5 or ResNet-50. Try SparseML, our open-source library, to transfer learn our sparse-quantized model to your dataset with a few lines of code.

See SparseZoo for other sparse models and recipes you can benchmark and prototype from.

Natural Language Processing (NLP)

Sparse Use CaseApply Your Data
Question AnsweringTry It Now
Text Classification: Sentiment AnalysisTry It Now
Text Classification: Multi-ClassTry It Now
Text Classification: BinaryTry It Now
Token Classification: Named Entity RecognitionTry It Now

Computer Vision

*Simplified end-to-end computer vision tutorials that include installable CLIs and APIs are on our short-term roadmap. For now, use the linked GitHub tutorials linked below.

Sparse Use CaseApply Your Data
Object DetectionBenchmark and Deploy

Apply your Data
Image ClassificationBenchmark and Deploy

Apply your Data
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