DeepLearningExamples VS deep_navigation

GitHub Used: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2

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...

Entrypoint.py file in tacotron2 folder: source code

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:

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.

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

Implement some basic stuff: Do filtering and/or clustering on pointcloud data using PCL. Try room segmentation on a 2D gridmap using OpenCV (hint). Global planner with A* (if you don't want to implement the algorithm check my repo). Local planner using pure pursuit (or some weird stuff like this). Don't forget to check other ROS projects on GitHub and try to read the code a bit.