@GIS_Rabbit
I understand you've tried both the arcpy.ia.ClassifyPixelsUsingDeepLearning function in Python and the Classify Pixels Using Deep Learning geoprocessing tool in ArcGIS Pro, and you're seeing a big difference in execution time. You also mentioned your machine has an NVIDIA Quadro T2000 (4 GB VRAM).
Both the Python API call and the ArcGIS Pro geoprocessing tool use the same underlying deep learning framework and should consume similar compute resources, so their performance should be comparable. I'm assuming that in both cases you used the same GPU or CPU, and the same batch size and tile size.
If you're using a GPU, deep learning inference can be very compute-intensive. For training and inference, the recommended GPU VRAM is 8 GB or more. For inference only (classification or detection with a pretrained model), 4 GB is the minimum, but 8 GB is still recommended for better performance and stability.
If your Python script is reading data differently (e.g., from a network location vs. a local disk), I/O speed could also be a factor. Try running both methods on the same local dataset to rule out data access bottlenecks.
Monitor your GPU usage with nvidia-smi (or in Task Manager) to see if GPU memory is the limiting factor. If it is, consider running the process on a GPU with more VRAM for large-area inference.
