Comments (2)
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Not sure if there is an option to disable that check, if not we will have to compile more kernels automatically based on handle ids I guess.
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Related Issues (20)
- Conflicts bewteen Contributing.md and scripts
- [LibTorch] tensor slice error for aggregated tensors
- ONNX import issue: `index out of bounds` in `pad_config()` due to `node.input` only having len of `2` HOT 5
- CLI output doesn't have enough precision for learning rate
- ONNX import issue: `Ident cannot be a number; use Literal instead` in `TensorType::new()` HOT 6
- FaceONNX models import issues tracker
- Wgpu backend of image-classification-web example cannot work HOT 13
- Fail to build examples/mnist-inference-web HOT 1
- Unable to train models with generic inner modules HOT 3
- Tensor::Interpolate function like torch.nn.functional.interpolate HOT 4
- Burn fusion optimisation HOT 1
- basic-workflow training.md has mistyped code HOT 1
- ONNX import issue: `ceil_mode is not supported` in `avg_pool1d_config()` HOT 3
- Img links of burn/examples /onnx-inference are missing. HOT 1
- Rewrite pooling operations from CPA to CubeCL in Burn-JIT
- Rewrititng select from CPA to CubeCL in Burn-JIT
- Rewrititng gather from CPA to CubeCL in Burn-JIT
- Rewrititng scatter from CPA to CubeCL in Burn-JIT
- ONNX import issue: `Pad: only float values are currently supported for constant value, submit an issue on github` in `pad_config()`
- xtask default args are not used HOT 6
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from burn.