tango
thinc
| tango | thinc | |
|---|---|---|
| 5 | 4 | |
| 510 | 2,794 | |
| 0.8% | 0.5% | |
| 6.5 | 7.6 | |
| 6 days ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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tango
- AI2 Tango
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AllenNLP will be unmaintained in December
Maybe we need to re-work the docs if the DAG aspects stick out to you so much. The main functionality is the cache. If you have a complex experiment, you can still write the code as if all the steps were fast, and let them be slow only the first time you run it. The DAG stuff is also nice, but less important.
That said, you could execute sklearn. If that's what your experiment needs, it's the right thing to do. This is why it gives us the flexibility to also support Jax: https://github.com/allenai/tango/pull/313
The DL-specific stuff is in the components we supply. Like the trainer, dataset handling stuff, file formats, and increasingly, https://github.com/allenai/catwalk.
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AI2 Introduces Tango, A Python Library For Choreographing Machine Learning Research Experiments By Executing A Series Of Steps
Tango ensures you never operate on outdated data by taking care of your intermediate and final outcomes and finding them again when needed.
thinc
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Agree, though I wouldn’t call PyTorch a drop-in for NumPy either. CuPy is the drop-in. Excepting some corner cases, you can use the same code for both. Thinc’s ops work with both NumPy and CuPy:
https://github.com/explosion/thinc/blob/master/thinc/backend...
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Tinygrad: A simple and powerful neural network framework
I love those tiny DNN frameworks, some examples that I studied in the past (I still use PyTorch for work related projects) :
thinc.by the creators of spaCy https://github.com/explosion/thinc
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good examples of functional-like python code that one can study?
thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable
- thinc - A refreshing functional take on deep learning, compatible with your favorite libraries
What are some alternatives?
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
quantulum3 - Library for unit extraction - fork of quantulum for python3
allennlp - An open-source NLP research library, built on PyTorch.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
catwalk - This project studies the performance and robustness of language models and task-adaptation methods.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
primeqa - The prime repository for state-of-the-art Multilingual Question Answering research and development.
extending-jax - Extending JAX with custom C++ and CUDA code
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
dm-haiku - JAX-based neural network library
ai-tools - Simple command-line AI chat assistant built using the OpenAI API
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.