Comments (5)
Hi @NripeshN,
It is quite interesting. I see it is young framework and already collected 28k stars in GitHub. Really great work!
However, is there any objective reason about why research scientist and engineers will select MLX framework for training new models instead of PyTorch? We observe PyTorch to keep a trend despite some new frameworks like JAX, MindSpore made attempts to accelerate training, etc. but anyway scientists select PyTorch. That seems a fact. So our current efforts are absolutely concentrated around PyTorch models support.
In the meantime, we are always open and welcome to our external contributors who want to add new frontend. We can provide more details about how to do this and support this contribution.
Best regards,
Roman
from openvino.
is there any objective reason about why research scientist and engineers will select MLX framework for training new models instead of PyTorch?
Hi @rkazants
I think this comment ml-explore/mlx#12 (comment) explains this in detail. I'd love to contribute and try add this new frontend. Could you please provide me more details to do so?
from openvino.
Hi @rkazants I think this comment ml-explore/mlx#12 (comment) explains this in detail. I'd love to contribute and try add this new frontend. Could you please provide me more details to do so?
@NripeshN, cool! Give me some time and I will be back with instructions.
Best regards,
Roman
from openvino.
@NripeshN, thank you for your interest!
Could you elaborate a bit on proposed workflow and technical side of the proposal? I suppose that we can catch the compute graph from MLX at the same point when mx.eval
or mx.compile
is called. So it wouldn't suppose that we need to trace something at the level of OpenVINO frontend, and graph representation is provided by MLX itself instead. Right? Could you point to some description that gives the understanding how this graph looks like? Does it have C++ or Python API to access the nodes?
from openvino.
@NripeshN any updates on the question above?
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Related Issues (20)
- [Bug]: Ubuntu 22.04 and 24.04 python3-openvino packages and thereby APT installation broken HOT 1
- [Good First Issue][TF FE]: Support SparseTensorDenseAdd operation for TensorFlow HOT 2
- Add signature or change default extension of weights files HOT 2
- [Bug]: convert onnx model into openvino(failed at SplitToSequence, ConcatFromSequence)
- [Bug]: flux model can not convert HOT 7
- [Bug]: FakeQuant failed during import_model(exported quantized_compiled_model) when F.interpolate or nn.Upsample exists HOT 2
- [Bug]: -- No conversion rule found for operations: aten::istft, aten::stft -- Conversion is failed for: prim::Constant HOT 5
- [Feature Request]: Consistent support for large (>1024) number of cores (i.e. dynamically sized cpu_set) HOT 5
- [Feature Request]: Xe Kernel Driver Support HOT 3
- [Bug]: OpenVINO does not support the following ONNX operations: SparseConvolution, ScatterDense HOT 3
- [Bug]: openvino.tools.ovc.error.Error: Unknown model type: <class 'onnx.onnx_ml_pb2.ModelProto'> HOT 6
- [Build]: Ubuntu 22.04 OV Linux build fails HOT 2
- [Good First Issue]: Extend Python API with `BitwiseLeftShift-15` and `BitwiseRightShift-15` HOT 2
- [Bug]: predictor compile on gpu failed HOT 3
- Unable to get working OpenVINO GPU accelleration on Ubuntu 24.04, i5-1240p HOT 10
- [Performance]: inference takes too long on simple tasks HOT 1
- [Build]: No NPU is found HOT 6
- [Bug]: Benchmark cannot measure real performance because benchmark input is not randomized correctly HOT 1
- [Bug]: OpenVINO disables GPU programming via Level Zero API. HOT 1
- openvino gave RuntimeError at Notebook:Convert Detectron2 Models to OpenVINO with Integrated GPU HOT 9
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