ModelCompressionRL
DeepLearningExamples
| ModelCompressionRL | DeepLearningExamples | |
|---|---|---|
| 2 | 7 | |
| 0 | 12,696 | |
| - | 1.6% | |
| 2.6 | 6.1 | |
| over 1 year ago | about 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | - |
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ModelCompressionRL
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Requesting help with Custom Layers (Layer Subclassing) - Model fit builds the model again! [Keras]
I saw that the number of filters depends on the height, is the height the same for all images? I guess so since you say that moving the Conv2D to another layer fixes that problem. If my guess is right, the error is that when the model is being build, height is None and you are trying to divide None by a number. To solve this problem you have to get the shape using h = tf.shape(inputs)[1] as 0 is the batch dimension. As for getting the tf.concat as a layer. You can use tf.keras.layers.concatenate, it works the same as tf.concat, but it is a layer. I am using it in a layer that performs to parallel convolutions and then concatenates both convolutions. When I print the summary I only get the name of the layer, not the tf.concat as you mention. Search for the FireLayer class in my code
- Adding new block/inputs to non-sequential network
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?
lidar-harmonization - Code release for Intensity Harmonization for Airborne LiDAR
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
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
Bangla-Spoken-Number-Recognition - recognizing spoken Bangla numbers using MFCCs and CNN.