Pytorch VS scikit-learn

PyTorch

> their own custom stack to interact with GPUs

lol completely made up.

are you conflating CUDA the platform with the C/C++ like language that people write into files that end with .cu? because while some people are indeed not writing .cu files, absolutely no one is skipping the rest of the "stack".

source: i work at one of these "mega corps". hell if you don't believe me go look at how many CUDA kernels pytorch has https://github.com/pytorch/pytorch/tree/main/aten/src/ATen/n....

> Everybody thinks it’s CUDA that makes Nvidia the dominant player.

it 100% does

PyTorch - An open source machine learning framework. PyTorch Tutorials - Tutorials and documentation.

In this guide, we provided a comprehensive, step-by-step explanation of how to implement a simple GPT (Generative Pre-trained Transformer) model using PyTorch. We walked through the process of creating a custom dataset, building the GPT model, training it, and generating text. This hands-on implementation demonstrates the fundamental concepts behind the GPT architecture and serves as a foundation for more complex applications. By following this guide, you now have a basic understanding of how to create, train, and utilize a simple GPT model. This knowledge equips you to experiment with different configurations, larger datasets, and additional techniques to enhance the model's performance and capabilities. The principles and techniques covered here will help you apply transformer models to various NLP tasks, unlocking the potential of deep learning in natural language understanding and generation. The methodologies presented align with the advancements in transformer models introduced by Vaswani et al. (2017), emphasizing the power of self-attention mechanisms in processing sequences of data more effectively than traditional approaches (Vaswani et al., 2017). This understanding opens pathways to explore and innovate in the field of natural language processing using cutting-edge deep learning techniques (Kingma & Ba, 2015).

PyTorch is a powerful and flexible deep learning framework that offers a rich set of features for building and training neural networks.

Oddly enough, sometimes, the best way to learn is by putting forth incorrect opinions or questions. Recently, while wrestling with AI project complexities, I pondered aloud whether all Docker images with AI models would inevitably be bulky due to PyTorch dependencies. To my surprise, this sparked many helpful responses, offering insights into optimizing image sizes. Being willing to be wrong opens up avenues for rapid learning.

Tinygrad targets consumer hardware (to be precise, only Radeon 7900XTX and nothing else[1]), while ROCm does not actually provide good support for such hardware. For example, last release of hipBLASLt-6.1.1 library has deep integration with PyTorch[1], while working only on AMD Instinct hardware. And even for the professional hardware out there, the support period is ridiculous: AMD Instinct MI100 (2020) is not supported. Only 4 years and tens of thousands of dollars worth of hardware is going to the trash, yay!

And to be more precise, they still use some core libraries from ROCm stack[3], they just don't use all these fancy multi-gigabyte[4] hardware-limited rocBLAS/hipBLASlt/rocWMMA/rocRAND/etc. libraries.

[1] https://tinygrad.org/#tinybox

[2] https://github.com/pytorch/pytorch/issues/119081

[3] https://github.com/tinygrad/tinygrad/blob/v0.9.0/tinygrad/ru...

[4] https://repo.radeon.com/rocm/yum/6.1.1/main/

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.

Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!

I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.

As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

[1] https://en.wikipedia.org/wiki/Platt_scaling

[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

Scikit-learn Documentation

Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.