pymport
MegEngine
| pymport | MegEngine | |
|---|---|---|
| 1 | 5 | |
| 76 | 4,718 | |
| - | 0.1% | |
| 8.9 | 8.9 | |
| 7 days ago | 10 days ago | |
| C++ | C++ | |
| ISC License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pymport
MegEngine
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How to speedup 31*31 conv 10 times
The Real Performance in MegEngine
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[P] Train Model 3x as large with Dynamic Tensor Rematerialization
In Deep Learning you can trade space for compute by recomputing activation in backpropagation phase, known as gradient checkpointing. Classical gradient checkpointing algorithm is great but they dont work for eager execution. Dynamic Tensor Rematerialization(DTR) is a gradient checkpointing algorithm that work with eager execution, and is implemented at Megenine, a deep learning framework. Read this blogpost to learn more!
- Training 3x larger model on the same GPU cards
What are some alternatives?
turbodbc - Turbodbc is a Python module to access relational databases via the Open Database Connectivity (ODBC) interface. The module complies with the Python Database API Specification 2.0.
DALI - A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
30-Days-Of-Python - 30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
executorch - On-device AI across mobile, embedded and edge for PyTorch
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.