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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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  • 📦 Installations
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  • 🔭 Discover the Orion APIs
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  1. Framework

Get Started

PreviousWhy Validity ML?NextContribute

Last updated 1 year ago

In this section, we will guide you to start using Orion successfully. We will help you install Cairo 1.0 and add Orion dependency in your project.

Orion supports Cairo and Scarb v2.4.0

📦 Installations

Install Cairo

Step 1: Install Cairo

There are different ways to install Cairo. Use the one that suits you best:

Step 2: Setup Language Server

Install the Cairo 1 VS Code Extension for proper syntax highlighting and code navigation. Just follow the steps indicated .

Install the Cairo package manager Scarb

Step 1: Install Scarb

Follow the installation guide on the .

Step 2: Create a new Scarb project

Follow the instructions to start a new Scarb project.

⚙️ Add orion dependency in your project

If your Scarb.toml doesn't already have a [dependencies] section, add it, then list the package name and the URL to its Git repository.

Scarb.toml
[dependencies]
orion = { git = "https://github.com/gizatechxyz/onnx-cairo" }

Now, run scarb build, and Scarb will fetch orion dependency and all its dependencies. Then it will compile your package with all of these packages included:

scarb build

You can now use the orion in your files:

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};
use orion::operators::nn::{NNTrait, I32NN};

fn relu_example() -> Tensor<i32> {
    let tensor = TensorTrait::<i32>::new(
        shape: array![2, 2].span(),
        data: array![
            IntegerTrait::new(1, false),
            IntegerTrait::new(2, false),
            IntegerTrait::new(1, true),
            IntegerTrait::new(2, true),
        ]
            .span(),
    );

    return NNTrait::relu(@tensor);
}

🔭 Discover the Orion APIs

🧱
Cairo installer.
here
Scarb's Website
here

⚙️ Operators

A set of standardized math functions that are used in the computation of neural network models.

🔢 Numbers

A full implementation of Signed Integer and Fixed Point in Cairo.