cuda-samples VS catboost

installing, building, and running WSL CUDA examples from https://github.com/nvidia/cuda-samples

After which nvcc should be accessible to new sessions, and you can build C++ cuda stuff like cuda-samples. Python packages like pytorch should also see CUDA and be able to use it.

I haven't found any note that these APIs do not support Windows, and it also seems that the memMapIPCDrv CUDA sample supports Windows.

I installed nvidia-cuda-toolkit and tried to build https://github.com/NVIDIA/cuda-samples but I'm getting stupid errors... it installed nvccat /usr/bin/nvcc and the samples expect /usr/local/cuda/bin/nvcc... symlinking it to that location and it dies with

Can build the cuda-samples, then you have a working nvcc.

I have installed cuda and cudnn, and was testing the installation with the cuda-samples, as the Arch Wiki suggested. But, I am not able to get samples like nbody, smokeparticles, Mandelbrot, etc. to run. Although devicequery works fine, and I get the expected output, so I think there is not a problem with my cuda installation.

Hi, I don't have much experience with Nvidia Jetsons. You can find some examples on GitHub (here https://github.com/NVIDIA/cuda-samples). You can find CUDA implementations of most functions on the internet though, you just have to look for the specific thing you are looking for. Cuda kernels are not platform specific, they should work on GPUs and embedded developer boards without problems as long as you respect the limits imposed by the "compute capability" of your device, you just have to compile your code using the right architecture flag. The biggest limit you have to deal with when developing for Jetson nano is the low amount of memory.

—> git clone https://github.com/NVIDIA/cuda-samples.git —> cd cuda-samples/Samples/1_Utilities/deviceQuery/ —> make —> ./deviceQuery (Result=pass?good) —> cd ~/ —> wget https://repo.anaconda.com/archive/Anaconda3-2021.11-Linux-x86_64.sh

is this the source code that u are talking about ? : https://github.com/NVIDIA/cuda-samples ? I dont see any CMakeLists.txt inside...

CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.

Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.

Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/

CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.

If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.

The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.