Convert a Transformer model trained with OpenNMT-py, OpenNMT-tf, or Fairseq:
#CEMU 1.7.0 INTEL GPU INSTALL#
Install the Python package: pip install -upgrade pipĢ. The steps below assume a Linux OS and a Python installation (3.6 or above).ġ. See the Decoding documentation for examples. biasing translations towards a given prefix (see section 4.2 in Arivazhagan et al.replacing unknown target tokens by source tokens with the highest attention.approximating the generation using a pre-compiled vocabulary map.returning multiple translation hypotheses.returning alternatives at a specific location in the target.random sampling from the output distribution.The translation API supports several decoding options: Some of these features are difficult to achieve with standard deep learning frameworks and are the motivation for this project. The project has few dependencies and exposes translation APIs in Python and C++ to cover most integration needs.Īdvanced decoding features allow autocompleting a partial translation and returning alternatives at a specific location in the translation.
#CEMU 1.7.0 INTEL GPU FULL#
A full featured Docker image supporting GPU and CPU requires less than 500MB (with CUDA 10.0). Quantization can make the models 4 times smaller on disk with minimal accuracy loss. The memory usage changes dynamically depending on the request size while still meeting performance requirements thanks to caching allocators on both CPU and GPU. Translations can be run efficiently in parallel and asynchronously using multiple GPUs or CPU cores. AVX, AVX2) that are automatically selected at runtime based on the CPU information. Intel MKL and oneDNN) and instruction set architectures (e.g. One binary can include multiple backends (e.g.
#CEMU 1.7.0 INTEL GPU CODE#
Automatic CPU detection and code dispatch.The project supports x86-64 and ARM64 processors and integrates multiple backends that are optimized for these platforms: Intel MKL, oneDNN, OpenBLAS, Ruy, and Apple Accelerate. The model serialization and computation support weights with reduced precision: 16-bit floating points (FP16), 16-bit integers (INT16), and 8-bit integers (INT8). The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: padding removal, batch reordering, in-place operations, caching mechanism, etc. Fast and efficient execution on CPU and GPU.The project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration. It currently supports Transformer models trained with: It aims to provide comprehensive inference features and be the most efficient and cost-effective solution to deploy standard neural machine translation systems on CPU and GPU. CTranslate2 is a fast and full-featured inference engine for Transformer models.