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Orion
Orion
  • 👋Welcome
    • Orion
    • Why Validity ML?
  • 🧱Framework
    • Get Started
    • Contribute
    • Compatibility
    • Numbers
      • Fixed Point
        • fp.new
        • fp.new_unscaled
        • fp.from_felt
        • fp.abs
        • fp.ceil
        • fp.floor
        • fp.exp
        • fp.exp2
        • fp.log
        • fp.log2
        • fp.log10
        • fp.pow
        • fp.round
        • fp.sqrt
        • fp.sin
        • fp.atan
        • fp.sign
      • Complex Number
        • complex.acos
        • complex.acosh
        • complex.arg
        • complex.asin
        • complex.asinh
        • complex.atan
        • complex.atanh
        • complex.conjugate
        • complex.cos
        • complex.cosh
        • complex.exp
        • complex.exp2
        • complex.from_polar
        • complex.img
        • complex.ln
        • complex.log2
        • complex.log10
        • complex.mag
        • complex.new
        • complex.one
        • complex.pow
        • complex.real
        • complex.reciprocal
        • complex.sin
        • complex.sinh
        • complex.sqrt
        • complex.tan
        • complex.tanh
        • complex.to_polar
        • complex.zero
    • Operators
      • Tensor
        • tensor.new
        • tensor.at
        • tensor.min_in_tensor
        • tensor.min
        • tensor.max_in_tensor
        • tensor.max
        • tensor.stride
        • tensor.ravel_index
        • tensor.unravel_index
        • tensor.reshape
        • tensor.transpose
        • tensor.reduce_sum
        • tensor.argmax
        • tensor.argmin
        • tensor.matmul
        • tensor.exp
        • tensor.log
        • tensor.equal
        • tensor.greater
        • tensor.greater_equal
        • tensor.less
        • tensor.less_equal
        • tensor.abs
        • tensor.neg
        • tensor.ceil
        • tensor.cumsum
        • tensor.sin
        • tensor.cos
        • tensor.asin
        • tensor.flatten
        • tensor.sinh
        • tensor.asinh
        • tensor.cosh
        • tensor.acosh
        • tensor.tanh
        • tensor.atan
        • tensor.acos
        • tensor.sqrt
        • tensor.or
        • tensor.xor
        • tensor.onehot
        • tensor.slice
        • tensor.concat
        • tensor.gather
        • tensor.quantize_linear
        • tensor.dequantize_linear
        • tensor.qlinear_add
        • tensor.qlinear_mul
        • tensor.qlinear_matmul
        • tensor.qlinear_concat
        • tensor.qlinear_leakyrelu
        • tensor.qlinear_conv
        • tensor.nonzero
        • tensor.squeeze
        • tensor.unsqueeze
        • tensor.sign
        • tensor.clip
        • tensor.identity
        • tensor.and
        • tensor.where
        • tensor.bitwise_and
        • tensor.bitwise_xor
        • tensor.bitwise_or
        • tensor.resize
        • tensor.round
        • tensor.scatter
        • tensor.array_feature_extractor
        • tensor.binarizer
        • tensor.reduce_sum_square
        • tensor.reduce_l2
        • tensor.reduce_l1
        • tensor.reduce_prod
        • tensor.gather_elements
        • tensor.gather_nd
        • tensor.reduce_min
        • tensor.shrink
        • tensor.reduce_mean
        • tensor.pow
        • tensor.is_nan
        • tensor.is_inf
        • tensor.not
        • tensor.erf
        • tensor.reduce_log_sum
        • tensor.reduce_log_sum_exp
        • tensor.unique
        • tensor.compress
        • tensor.layer_normalization
        • tensor.scatter_nd
        • tensor.dynamic_quantize_linear
        • tensor.optional
        • tensor.reverse_sequence
        • tensor.split_to_sequence
        • tensor.range
        • tensor.hann_window
        • tensor.hamming_window
        • tensor.blackman_window
        • tensor.random_uniform_like
        • tensor.label_encoder
      • Neural Network
        • nn.relu
        • nn.leaky_relu
        • nn.sigmoid
        • nn.softmax
        • nn.softmax_zero
        • nn.logsoftmax
        • nn.softsign
        • nn.softplus
        • nn.linear
        • nn.hard_sigmoid
        • nn.thresholded_relu
        • nn.gemm
        • nn.grid_sample
        • nn.col2im
        • nn.conv_transpose
        • nn.conv
        • nn.conv_integer
        • nn.depth_to_space
        • nn.space_to_depth
        • nn.max_pool
        • nn.deform_conv
      • Machine Learning
        • Tree Ensemble Classifier
          • tree_ensemble_classifier.predict
        • Tree Ensemble Regressor
          • tree_ensemble_regressor.predict
        • Linear Classifier
          • linear_classifier.predict
        • Linear Regressor
          • linear_regressor.predict
        • SVM Regressor
          • svm_regressor.predict
        • SVM Classifier
          • svm_classifier.predict
        • Sequence
          • sequence.sequence_construct
          • sequence.sequence_empty
          • sequence.sequence_length
          • sequence.sequence_at
          • sequence.sequence_empty
          • sequence.sequence_erase
          • sequence.sequence_insert
          • sequence.concat_from_sequence
        • Normalizer
          • normalize.predict
  • 🏛️Hub
    • Models
    • Spaces
  • 🧑‍🎓Academy
    • Tutorials
      • MNIST Classification with Orion
      • Implement new operators in Orion
      • Verifiable Linear Regression Model
      • Verifiable Support Vector Machine
      • Verifiable Principal Components Analysis
      • Provable MLR: Forecasting AAVE's Lifetime Repayments
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  • 🤔 What is ONNX Runtime?
  • 🌱 Where to start?
  • ✨ What's new?
  • 💖 Join the community!
  • ✍️ Authors & contributors
  • License
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  1. Welcome

Orion

An Open-source ecosystem for Validity and ZK ML.

NextWhy Validity ML?

Last updated 1 year ago

Orion is an open-source, community-driven framework dedicated to Provable Machine Learning. It provides essential components and a new ONNX runtime for building verifiable Machine Learning models using .

🤔 What is ONNX Runtime?

ONNX (Open Neural Network Exchange), is an open-source standard created to represent deep learning models. The aim of its development was to enable interoperability among diverse deep learning frameworks, like TensorFlow or PyTorch. By offering a universal file format, ONNX allows models trained in one framework to be readily applied in another for inference, eliminating the need for model conversion.

Ensuring compatibility with ONNX operators facilitates integration into the ONNX ecosystem. This enables researchers and developers to pre-train models using their preferred framework, before executing verifiable inferences with Orion.

🌱 Where to start?

✨ What's new?

💖 Join the community!

✍️ Authors & contributors

License

This project is licensed under the MIT license.

For a detailed list of changes, please refer to the file.

Join the community and help build a safer and transparent AI in our !

For a full list of all authors and contributors, see .

See for more information.

👋

🧱 Framework

The building blocks for Verifiable Machine Learning models.

🏛 Hub

A curated collection of ML models and spaces built by the community using Orion framework.

🎓 Academy

Resources and tutorials for learning how to build ValidityML models using Orion.

CHANGELOG
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LICENSE
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