## 3 Results
### Framework Compatibility
#### 3.1.2 AMD Card Compatibility  
The AMD graphics card is fully compatible with the Pytorch, Tensorflow, JAX, and ONNX frameworks. In these frameworks, no functional weaknesses were found due to the use of the RX 7900 XT graphics card, so the maximum number of points was awarded for these criteria. Running libraries looks identical to Nvidia cards. Software using these libraries can be transferred without modifying the code, thanks to the use of an identical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AMD** & **Intel** & **Nvidia** \\ \hline Pytorch & 2 & 1 & 2 \\ \hline Tensorflow & 2 & 1 & 2 \\ \hline JAX & 2 & 1 & 2 \\ \hline SciKit & 0 & 1 & 2 \\ \hline ONNX & 2 & 1 & 2 \\ \hline OpenCV & 2 & 1 & 2 \\ \hline Numba & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of compatibility with frameworks.  
initialization method. AMD graphics cards in the Pytorch, Tensorflow, and JAX frameworks are detected as CUDA devices, while in the ONNX library, the names of the runtime devices are required. OpenCV does not have dedicated support for AMD graphics cards, so the only way to run calculations on the GPU is to compile them with the OpenCL library. This solution is characterized by the low speed of calculations compared to other solutions (based on tests carried out, the results of which are presented in later sections), but it provides the ability to perform calculations using hardware acceleration and no shortcomings in functionality were found. This criterion evaluates the level of compatibility, which is why the card obtained the maximum score despite the low processing speed. SciKit was not originally designed to use graphics cards for computational acceleration [69] and never supported AMD graphics cards or the OpenCL framework. RX 7900 XT was found to be completely incompatible with the SciKit library, resulting in a score of zero points for this criterion. The Numba framework used to support AMD graphics cards, but based on the official Numba repository [70], it is no longer developed. Instead, it uses an outdated version of the ROCm library, which is not compatible with the GPU used. Support for AMD cards was also provided by Intel one API [71], but there is no support for RX 7000 series graphics cards. AMD released a git repository that provides the ability to run Numba on AMD MI series professional cards [72], which, despite the lack of official consumer card support, works fine with the Radeon RX 7900 XT card. In the case of this library, it is also possible to run code prepared for Nvidia cards, thanks to the function that allows you to mimic a CUDA device. The library documentation mentions significant limitations such as a lack of support for half-precision operations (fp16), no ability to debug code executed on the card, no simulator similar to the one from CUDA, and no support for atomic operations using tuples and arrays. Despite the lack of official support and functionality, it is possible to run the code in the NUMBA framework on an AMD graphics card, which is why the card received one point for this criterion.  
A comparison of the results presented in Table 1 for AMD and Nvidia cards indicates that the use of an AMD graphics card is associated with compatibility limitations compared to a competing product. These limitations apply to two frameworks, SciKit and Numba, which can make it difficult to work with this system. In other criteria, the AMD card provides compatibility at a level similar to the Nvidia product.