Deep-learning-in-cloud
DataAug4Code
| Deep-learning-in-cloud | DataAug4Code | |
|---|---|---|
| 2 | 3 | |
| 731 | 50 | |
| - | - | |
| 5.2 | 6.6 | |
| 2 months ago | 2 months ago | |
| MIT License | MIT License |
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Deep-learning-in-cloud
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Noob question, is it possible to connect cheap nvidia tesla k model gpus externally?
Another option is to rent cloud time for DL tasks. A big advantage of this approach is that it can scale for training as the models and datasets get bigger. Even for inference, we are seeing models get pretty large (IIRC stable diffusion is something like 4GB for its inference model IIRC). Take a look here and see if one of the listed services can get you started.
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How to practice Deep Learning when GPUs are prohibitively overpriced?
Try cloud GPUs, they are much economical and faster. Some suggestions: https://github.com/zszazi/Deep-learning-in-cloud
DataAug4Code
What are some alternatives?
entangle - A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs.
time-series-transformers-review - A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
DataAug4NLP - Collection of papers and resources for data augmentation for NLP.
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
internet-explorer - Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset.
awesome-refreshing-llms - EMNLP'23 survey: a curation of awesome papers and resources on refreshing large language models (LLMs) without expensive retraining.