DeepPurpose
DeepLearningExamples
| DeepPurpose | DeepLearningExamples | |
|---|---|---|
| 1 | 7 | |
| 919 | 12,660 | |
| - | 1.3% | |
| 4.6 | 6.1 | |
| 7 months ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | - |
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.
DeepPurpose
DeepLearningExamples
-
A small example from Tacotron2 trained on Brandon "Atrioc" Ewing
GitHub Used: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2
- Retraining Single Shot MultiBox Detector model on a custom data set?
-
Nvidia Scientists Take Top Spots in 2021 Brain Tumor Segmentation Challenge
Disclosure: I used to work on Google Cloud.
I dunno, their A100 results took about 20-30 minutes on 8 x A100s [1]. 8xA100s is like $24/hr on GCP at on-demand rates.
The efficiency was okay but not linear, so if you were more cost constrained you might go with 1xA100 for $3/hr and have ~2.5hr training times.
Getting that performance out of a GPU is more challenging than getting access to the GPUs. All the major cloud providers offer them.
(Nit: GCP deployed the 40 GiB cards rather than the later 80 GiB parts, but let's ignore that).
but it often doesn't matter
[1] https://github.com/NVIDIA/DeepLearningExamples/tree/master/P...
-
Tacotron2 CPU Inferencing
Entrypoint.py file in tacotron2 folder: source code
-
Skyrim Voice Synthesis Mega Tutorial
For those asking about differences to xVASynth, the models trained with xVASynth are the FastPitch models (https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch). As a quick explainer:
-
Modders develop AI based app for creating new voice lines using neural speech synthesis.
There's another separate tool set from Nvidia that's on GitHub that the creator used to train the models. I'm not going to pretend like I understand it, but you can find it here.
-
[R] Data Movement Is All You Need: A Case Study on Optimizing Transformers
The Nvidia's implementation of BERT has a long way to go (I don't know about the implementations of input independent gradient computations in their backprop). But, there are scaled benchmarks on DGX A100's -https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT
What are some alternatives?
awesome-drug-pair-scoring - Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
lidar-harmonization - Code release for Intensity Harmonization for Airborne LiDAR
equidock_public - EquiDock: geometric deep learning for fast rigid 3D protein-protein docking
alpaca_eval - An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
Megatron-LM - Ongoing research training transformer models at scale
ontogpt - LLM-based ontological extraction tools, including SPIRES
llm-search - Querying local documents, powered by LLM
deep_navigation - Deep Learning based wall/corridor following P3AT robot (ROS, Tensorflow 2.0)
notebooks - Notebooks illustrating the use of Norse, a library for deep-learning with spiking neural networks.
AutoCog - Automaton & Cognition
pix2seq - Pix2Seq codebase: multi-tasks with generative modeling (autoregressive and diffusion)
libffm - A Library for Field-aware Factorization Machines