Python Launcher

Neural Coder can be used as a Python Launcher. Users can run the Python model code as it is with automatic enabling of Deep Learning optimizations by using Neural Coder’s inline Python Launcher design.

Quick-Start

Example: Let’s say you are running an NLP model using run_glue.py from HuggingFace transformers examples, you generally run with a Python command line like this:

python run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result

With Neural Coder’s Launcher, users can easily enjoy Deep Learning optimizations (e.g. default - INT8 dynamic quantization by Intel® Neural Compressor for PyTorch models) by simply adding an inline prefix

-m neural_coder

to the Python command line, and everything else remains the same:

python -m neural_coder run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result

This will run run_glue.py with the Deep Learning optimization automatically enabled, while everything else (e.g. your input arguments for the code itself) remains the same as the original code. You can also check out the optimized code run_glue_optimized.py auto-generated by the Launcher under the same folder if you want to learn the code enabling.

Note: Any modification on the optimized code run_glue_optimized.py will be overwritten every time you run Neural Coder Launcher on run_glue.py, so please make any modification on the original code run_glue.py instead of the optimized one. The optimized code is only saved for your reference.

Launcher Arguments (Optional)

Users can specify which Deep Learning optimization they want to conduct using --opt argument. The list of supported Deep Learning optimization features can be found here.

Note that if specifically optimizing with INT8 quantization by Intel® Neural Compressor, to choose a quantization approach (strategy), --approach argument can be specified with either static, static_ipex or dynamic. For example, to run INT8 static quantization by Intel® Neural Compressor:

python -m neural_coder --approach static run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result