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DeepLearningExamples Alternatives
Similar projects and alternatives to DeepLearningExamples
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alpaca_eval
An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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notebooks
Notebooks illustrating the use of Norse, a library for deep-learning with spiking neural networks. (by norse)
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pix2seq
Pix2Seq codebase: multi-tasks with generative modeling (autoregressive and diffusion) (by google-research)
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finite-element-networks
Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better DeepLearningExamples alternative or higher similarity.
DeepLearningExamples reviews and mentions
Posts with mentions or reviews of DeepLearningExamples.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-19.
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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?
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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...
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Tacotron2 CPU Inferencing
Entrypoint.py file in tacotron2 folder: source code
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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:
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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.
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[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
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A note from our sponsor - SaaSHub
www.saashub.com | 25 Apr 2024
Stats
Basic DeepLearningExamples repo stats
7
12,607
6.1
21 days ago
The primary programming language of DeepLearningExamples is Jupyter Notebook.
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