init release

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "cargo" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.github/workflows/rust.yml

@@ -0,0 +1,22 @@
+name: Rust
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+env:
+  CARGO_TERM_COLOR: always
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Build
+      run: cargo build --verbose
+    - name: Run tests
+      run: cargo test --verbose

Cargo.toml

@@ -0,0 +1,25 @@
+[package]
+name = "tinygrad"
+version = "0.1.0"
+edition = "2021"
+description = "You like pytorch? You like micrograd? You love tinygrad! ❤️"
+license = "MIT"
+keywords = ["pytorch", "machine-learning ", "deep-learning", "tinygrad"]
+categories = ["Science"]
+repository = "https://github.com/wiseaidev/tinygrad"
+documentation = "https://docs.rs/tinygrad"
+authors = ["Mahmoud Harmouch <[email protected]>"]
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+ndarray = "0.15.6"
+
+[lib]
+crate-type = ["cdylib"]
+
+[profile.release]
+codegen-units = 1
+opt-level = "z"
+lto = "thin"
+strip = "symbols"

README.md

@@ -1,2 +1,116 @@
-# tinygrad
-You like pytorch? You like micrograd? You love tinygrad! ❤️ 
+# ✨️ tinygrad
+
+[![Crates.io](https://img.shields.io/crates/v/tinygrad.svg)](https://crates.io/crates/tinygrad)
+[![docs](https://docs.rs/tinygrad/badge.svg)](https://docs.rs/tinygrad/)
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
+
+A Rust crate for building and training neural networks. `tinygrad` provides a simple interface for defining tensors, performing forward and backward passes, and implementing basic operations such as dot products and summation.
+
+## 🚀 Quick Start
+
+Get started with the `tinygrad` library by following these simple steps:
+
+1. Install the `tinygrad` crate by adding the following line to your `Cargo.toml` file:
+
+```toml
+[dependencies]
+tinygrad = "0.1.0"
+```
+
+1. Use the `Tensor` and `ForwardBackward` traits to create and work with tensors:
+
+```rust
+use ndarray::{array, Array1};
+use tinygrad::{Tensor, Context, TensorTrait};
+
+// Create a tensor
+let value = array![1.0, 2.0, 3.0];
+let tensor = Tensor::new(value);
+
+// Perform forward and backward passes
+let mut ctx = Context::new();
+let result = tensor.forward(&mut ctx, vec![tensor.get_value()]);
+tensor.backward(&mut ctx, array![1.0, 1.0, 1.0].view());
+```
+
+3. Implement custom operations by defining structs that implement the `ForwardBackward` trait:
+
+```rust
+use ndarray::ArrayView1;
+use tinygrad::{ForwardBackward, Context, TensorTrait};
+
+// Example operation: Dot product
+struct Dot;
+
+impl ForwardBackward for Dot {
+    fn forward(&self, _ctx: &mut Context, inputs: Vec<ArrayView1<f64>>) -> f64 {
+        let input = &inputs[0];
+        let weight = &inputs[1];
+        input.dot(weight)
+    }
+
+    fn backward(&self, ctx: &mut Context, grad_output: ArrayView1<f64>) {
+        // Implement backward pass
+        // ...
+    }
+}
+```
+
+# 🔧 Usage Example
+
+```rust
+use ndarray::{array, Array1};
+use tinygrad::{Tensor, Context, TensorTrait};
+
+fn main() {
+    let input = array![1.0, 2.0, 3.0];
+    let weight = array![4.0, 5.0, 6.0];
+
+    let input_tensor = Box::new(Tensor::new(input));
+    let weight_tensor = Box::new(Tensor::new(weight));
+
+    let dot_fn = Dot;
+    let mut ctx = Context::new();
+
+    let inputs = vec![
+        input_tensor.get_value(),
+        weight_tensor.get_value(),
+    ];
+    let output = dot_fn.forward(&mut ctx, inputs);
+
+    println!("Dot product: {:?}", output);
+
+    let grad_output = array![1.0, 1.0, 1.0];
+    dot_fn.backward(&mut ctx, grad_output.view());
+
+    let grad_input = &input_tensor.grad.clone();
+    let grad_weight = &weight_tensor.grad.clone();
+
+    println!("Gradient for input: {:?}", grad_input);
+    println!("Gradient for weight: {:?}", grad_weight);
+}
+```
+
+# 🧪 Testing
+
+Run tests for the `tinygrad` crate using:
+
+```bash
+cargo test
+```
+
+## 🌐 GitHub Repository
+
+You can access the source code for the `tinygrad` crate on [GitHub](https://github.com/wiseaidev/tinygrad).
+
+## 🤝 Contributing
+
+Contributions and feedback are welcome! If you'd like to contribute, report an issue, or suggest an enhancement, please engage with the project on [GitHub](https://github.com/wiseaidev/tinygrad). Your contributions help improve this crate for the community.
+
+# 📘 Documentation
+
+Full documentation for `tinygrad` is available on [docs.rs](https://docs.rs/tinygrad/).
+
+# 📄 License
+
+This project is licensed under the [MIT License](LICENSE).