MegEngine
ivy
MegEngine | ivy | |
---|---|---|
5 | 17 | |
4,718 | 14,015 | |
0.1% | 0.1% | |
8.9 | 10.0 | |
8 days ago | 5 days ago | |
C++ | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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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
ivy
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Keras 3.0
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
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Show HN: Carton – Run any ML model from any programming language
is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?
- Ivy: All in one machine learning framework
- Ivy ML Transpiler and Framework
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[D] Keras 3.0 Announcement: Keras for TensorFlow, JAX, and PyTorch
https://unify.ai/ They are trying to do what Ivy is doing already.
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Ask for help: what is the best way to have code both support torch and numpy?
Check Ivy.
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CoreML Stable Diffusion
ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring...
There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477
It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai
I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.
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DL Frameworks in a nutshell
Won't it all come together with https://lets-unify.ai/ ?
- Unified Machine Learning
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[Discussion] Opinions on unify AI
What do you think about unify AI https://lets-unify.ai.
What are some alternatives?
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