williamFalcon/test-tube
Python library to easily log experiments and parallelize hyperparameter search for neural networks
When training deep learning models, it's crucial to find the best settings (hyperparameters) for optimal performance. This tool helps machine learning engineers and researchers manage and accelerate this process by logging experiment details and efficiently testing many hyperparameter combinations in parallel. It takes your model code and a list of hyperparameter values, then automatically runs and tracks experiments, providing insights into which settings work best.
735 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher who needs to systematically explore and track many hyperparameter configurations for your deep learning models, especially across multiple GPUs or CPU cores, or on an HPC cluster.
Not ideal if you are looking for a simple model training script or if you are not working with deep learning models and hyperparameter optimization.
Stars
735
Forks
72
Language
JavaScript
License
MIT
Category
Last pushed
Jul 22, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/williamFalcon/test-tube"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepspeedai/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference...
helmholtz-analytics/heat
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
hpcaitech/ColossalAI
Making large AI models cheaper, faster and more accessible
horovod/horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
bsc-wdc/dislib
The Distributed Computing library for python implemented using PyCOMPSs programming model for HPC.