Use DagsHub to create reproducible versions of your data science research project, allow others to understand your project, and to contribute back to it.
DagsHub is built firmly around open, standard formats for your project. In particular:
- git
- DVC
- Standard data formats like YAML, JSON, CSV
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
This client library is meant to help you get started quickly in Python, but it's purely optional - the data formats are very simple and you can choose to work with them directly.
pip install dagshubYou can learn more by completing our short tutorial or reading the docs
from dagshub import dagshub_logger, DAGsHubLogger
# As a context manager:
with dagshub_logger() as logger:
# Metrics:
logger.log_metrics(loss=3.14, step_num=1)
# OR:
logger.log_metrics({'val_loss': 6.28}, step_num=2)
# Hyperparameters:
logger.log_hyperparams(lr=1e-4)
# OR:
logger.log_hyperparams({'optimizer': 'sgd'})
# As a normal Python object:
logger = DAGsHubLogger()
logger.log_hyperparams(num_layers=32)
logger.log_metrics(batches_per_second=100, step_num=42)
# ...
logger.save()
logger.close()The basic DagsHub logger is just plain Python, and requires no specific framework.
However, for convenience, we include some integrations with common ML frameworks, which can just work right out of the box, without having to write any logging code on your own:
- pytorch-lightning – supports version 1.4.0 or higher
- fastai v2
- keras
- More - soon to come!
Made with 🐶 by DagsHub.