Comments (1)
To resolve the issue where inference-gpu defaults to CPU execution on systems with CUDA 12, due to compatibility with onnxruntime, consider extending the platform.py script to detect the CUDA version. This can be achieved by invoking system commands to extract the CUDA version information and including this data within the retrieve_platform_specifics function's return dictionary. Once CUDA version detection is implemented, use this information in benchmark_adapter.py before running benchmarks or initializing models that require GPU support. Check the detected CUDA version and, if CUDA 12 is identified, ensure the appropriate version of onnxruntime-gpu that supports CUDA 12 is used.
from inference.
Related Issues (20)
- Keypoints kind
- Store prediction type in sv.Detections, remove from blocks outputs
- Remove parametrization of fields names
- Store image metadata in sv.Detections.data
- Remove PARENT_ID from top-level outputs and keep only in sv.Detections.data
- `workflows` Clip block - do not recompute text embeddings over and over
- Better structure for LMM `workflows` blocks
- `workflows` GPT-4 block to be able to accept multiple images
- Change the way on how workflows runs code remotely
- Discussion: How to structure LLM, LMM blocks regarding structured usage of outputs in other blocks
- OCR models in `inference` and OCR models blocks in `workflows`
- `DocTR` model output missing important information that model produces
- More metadata in core `workflows` blocks
- Make the descriptions of all models parameters easier to understand (especially for people without in-depth CV context)
- Improve `workflows` docs to explain on how init parameters are passed to blocks
- Should we have multiple types of models [in `workflows`]?
- Get rid of `asyncio` in `workflows` and in some parts of `inference`
- How to handle cropping in `workflows`?
- Tests assets - static files or pulled from Roboflow hosting?
- Shall blocks types be shadowed by plugins?
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from inference.