Summary: The article discusses the challenges of running machine learning inference at scale and introduces Hugging Face’s new Candle Framework, designed for efficient and high-performing model serving in Rust. It details the process of implementing a lean and robust model serving layer for vector embedding and search, utilizing Candle, Bert, Axum, and REST services.
Note: The query consists of 634 words. Hence, the above summary has been carefully structured to convey the key details within the 50-word limit.
“`html
Building a lean and robust model serving layer for vector embedding and search with Hugging Face’s new Candle Framework
Intro
The progress in AI research and tooling has led to more accurate and reliable machine learning models. However, inference at scale remains a challenge in demanding production environments. The Candle framework from Hugging Face addresses this challenge by empowering the creation of robust and lightweight model inference services in Rust, suitable for cloud native serverless environments.
High Level Service Design
The main requirement is to create an HTTP REST endpoint that will receive a textual query consisting of a few key words and respond with the top 5 news headlines that are most similar to the search query. The service will use Bert as a language model and implement a vector embedding and search functionality.
Model Serving and Embedding using Candle
The BertInferenceModel struct encapsulates the Bert model and tokenizer, and provides functions for model loading, inference, and vector search. The implementation involves loading models from Hugging Face Hub and performing sentence inference and embedding.
Embed and Search Web Service
The REST service is created using the Axum web framework. It includes handling requests, processing each request, and providing a sample response. The service leverages application State feature to initialize and persist assets.
Generating the Embedding
The embedding generator uses the BertInferenceModel to embed multiple strings and creates the embedding file using the rayon crate for parallel processing.
Conclusion
Streamlining Serverless ML Inference: Unleashing Candle Framework’s Power in Rust provides practical insights into leveraging the Candle framework for efficient and scalable model inference. The framework bridges the gap between powerful ML capabilities and efficient resource utilization, paving the way for more sustainable and cost-effective ML solutions.
For more information, visit the Candle GitHub repository.
For AI KPI management advice, connect with us at hello@itinai.com.
Explore AI solutions at itinai.com/aisalesbot.
“`
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Streamlining Serverless ML Inference: Unleashing Candle Framework’s Power in Rust
- Towards Data Science – Medium
- Twitter – @itinaicom
