a644350589
Tacotron2+Waveglow/PyT * AMP support * Data preprocessing for Tacotron 2 training * Fixed dropouts on LSTMCells SSD/PyT * script and notebook for inference * AMP support * README update * updates to examples/* BERT/PyT * initial release GNMT/PyT * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP * Added inference throughput and latency results on NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line NCF/PyT * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs Transformer/PyT * new README * jit support added UNet Medical/TF * inference example scripts added * inference benchmark measuring latency added * TRT/TF-TRT support added * README updated GNMT/TF * Performance improvements Small updates (mostly README) for other models. |
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| .. | ||
| dllogger | ||
| dummy_run.py | ||
| README.md | ||
| setup.py | ||
Tools for logging DL training
DLLogger is a tool to generate logs during Deep Learning training.
Installation
git clone https://gitlab-master.nvidia.com/dl/JoC/DLLogger.git
pip install DLLogger/.
Usage
You can use DLLogger with the simplest LOGGER.log() API:
from logger.logger import LOGGER
from logger import tags
LOGGER.model = 'ResNet'
LOGGER.log(key=tags.INPUT_BATCH_SIZE, value=128)
For the more advanced usage, please refer to the dummy_run.py example.
Tags
All available tags are listed in the logger/tags.py file.