sparktorch
fastT5
sparktorch | fastT5 | |
---|---|---|
1 | 5 | |
334 | 540 | |
- | - | |
2.5 | 0.0 | |
12 months ago | about 1 year ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
sparktorch
-
Spark2 + pytorch on GPU
Was reading the documentation of sparktorch (https://github.com/dmmiller612/sparktorch) which says you need spark >= 2.4.4. But to the best of my knowledge spark2 doesn't have gpu compute capabilities. Does that mean it can only use cpu compute? Am I missing something?
fastT5
-
Speeding up T5
I've tried https://github.com/Ki6an/fastT5 but it works with CPU only.
-
Convert Pegasus model to ONNX
I am working on a project where I fine-tuned a Pegasus model on the Reddit dataset. Now, I need to convert the fine-tuned model to ONNX for the deployment stage. I have followed this guide from Huggingface to convert to the ONNX model for unsupported architects. I got it done but the ONNX model can't generate text. Turned out that Pegasus is an encoder-decoder model and most guides are for either encoder-model (e.g. BERT) or decoder-model (e.g. GPT2). I found the only example of converting an encoder-decoder model to ONNX from here https://github.com/Ki6an/fastT5.
-
[P] What we learned by making T5-large 2X faster than Pytorch (and any autoregressive transformer)
Microsoft Onnx Runtime T5 export tool / FastT5: to support caching, it exports 2 times the decoder part, one with cache, and one without (for the first generated token). So the memory footprint is doubled, which makes the solution difficult to use for these large transformer models.
-
Conceptually, what are the "Past key values" in the T5 Decoder?
Here is the fastT5 model code for reference code:https://github.com/Ki6an/fastT5/blob/master/fastT5/onnx_models.py
-
[P] boost T5 models speed up to 5x & reduce the model size by 3x using fastT5.
for more information on the project refer to the repository here.
What are some alternatives?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
torch2trt - An easy to use PyTorch to TensorRT converter
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
json-translate - Translate json files with DeepL or AWS
openfl - An open framework for Federated Learning.
frame-semantic-transformer - Frame Semantic Parser based on T5 and FrameNet
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
FasterTransformer - Transformer related optimization, including BERT, GPT