bad-forecast-good-decision
DeepLearningExamples
| bad-forecast-good-decision | DeepLearningExamples | |
|---|---|---|
| 1 | 7 | |
| 17 | 12,660 | |
| - | 1.3% | |
| 10.0 | 6.1 | |
| about 2 years ago | about 1 month ago | |
| TeX | Jupyter Notebook | |
| BSD 2-clause "Simplified" License | - |
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bad-forecast-good-decision
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Is it worth staying in my first iob out of college for the “experience” in my resume, even if I’m not learning any new skills?
Interacting directly with the people who have the problem you're trying to solve is a great cure for arrogance. Even if you deliver a perfect prediction, does that help anyone make a useful decision? Check out this pre-print: Bad Forecast, Good Decision which shows how there are cases where better forecasting can lead to worse outcomes.
DeepLearningExamples
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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
What are some alternatives?
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Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
alpaca_eval - An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
sktime - A unified framework for machine learning with time series
Megatron-LM - Ongoing research training transformer models at scale
darts - A python library for user-friendly forecasting and anomaly detection on time series.
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