Feat: ai frameworks

.vitepress/sidebar.ts

@@ -579,6 +579,10 @@ export function getSidebar() {
               },
             ],
           },
+          {
+            text: '🤖 AI Framework Support',
+            link: '/protocol/ai',
+          },
         ],
       },
     ],

src/protocol/ai.md

@@ -0,0 +1,179 @@
+---
+title: AI Framework Support
+description:
+  Comprehensive guide to AI framework compatibility and support on the iExec
+  platform
+---
+
+# 🤖 AI Framework Support
+
+The iExec Platform provides comprehensive support for popular AI and machine
+learning frameworks. Deploy confidential AI with ease. iExec supports popular
+AI/ML frameworks, running in secure Trusted Execution Environments (TEEs) with
+optimized configurations.
+
+## 🚀 Quick Start
+
+**Want to get started immediately?**
+
+- 📚
+  **[AI Frameworks Hello World](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world)** -
+  Ready-to-use Docker examples for TensorFlow, PyTorch, and more
+- 🛠️ **[Build & Deploy](/guides/build-iapp/build-&-deploy)** - General iApp
+  development guide (not AI-specific)
+- 🔬
+  **[TDX App Guide](/guides/build-iapp/advanced/create-your-first-tdx-app)** -
+  Build TDX applications (works well for AI workloads)
+
+## 🛡️ Why iExec for AI?
+
+### Confidential Computing
+
+Trusted Execution Environments (TEEs) protect your AI models and data
+end-to-end:
+
+- **Data Privacy**: TEEs isolate AI computations in secure enclaves
+- **Secure Training & Inference**: Unauthorized entities can never access your
+  models and data
+- **Hardware-Level Security**: Intel SGX and TDX provide enterprise-grade
+  protection
+
+### AI Monetization
+
+Monetize your AI assets easily and securely:
+
+- **Datasets**: Encrypt and sell access to your training data
+- **Models**: Deploy and monetize your trained AI models
+- **Agents**: Create and sell AI agents and applications
+- **Ownership Preserved**: Your digital assets always remain yours
+
+### Decentralized Infrastructure
+
+Scale AI applications without centralized cloud dependencies:
+
+- **On-Demand Compute**: Access powerful resources when you need them
+- **Fair Pricing**: Blockchain verifies execution costs transparently
+- **Global Network**: Deploy across a worldwide network of secure workers
+
+## 🤖 AI Framework Support
+
+### Overview
+
+| Framework        | TDX Support     | SGX Support     | Best For                      |
+| ---------------- | --------------- | --------------- | ----------------------------- |
+| **TensorFlow**   | ✅ Yes (3.01GB) | ❌ No           | Deep learning, production ML  |
+| **PyTorch**      | ✅ Yes (6.44GB) | ❌ No           | Research, computer vision     |
+| **Scikit-learn** | ✅ Yes (1.18GB) | ✅ Yes (1.01GB) | Traditional ML, data analysis |
+| **OpenVINO**     | ✅ Yes (1.82GB) | ❌ No           | Computer vision, inference    |
+| **NumPy**        | ✅ Yes (1.25GB) | ✅ Yes (1.08GB) | Scientific computing          |
+| **Matplotlib**   | ✅ Yes (1.25GB) | ✅ Yes (1.08GB) | Data visualization            |
+
+### Framework Details
+
+| Framework        | Version     | Description                                   | TDX Support | SGX Support         | Use Cases                              | Resources                                                                                                                                                                                                                           |
+| ---------------- | ----------- | --------------------------------------------- | ----------- | ------------------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **TensorFlow**   | 2.19.0      | Google's ML framework for production AI       | ✅ 3.01GB   | ❌ Too large        | Deep learning, CV, NLP                 | [Docs](https://www.tensorflow.org/) • [Quickstart](https://www.tensorflow.org/tutorials/quickstart/beginner) • [Docker](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world/tree/main/tensorflow)                 |
+| **PyTorch**      | 2.7.0+cu126 | Facebook's research-focused DL framework      | ✅ 6.44GB   | ❌ Too large        | Research, DL, CV, NLP                  | [Docs](https://pytorch.org/docs/) • [Quickstart](https://docs.pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html) • [Docker](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world/tree/main/pytorch)   |
+| **Scikit-learn** | 1.6.1       | Comprehensive ML library for Python           | ✅ 1.18GB   | ✅ 1.01GB           | Classification, regression, clustering | [Docs](https://scikit-learn.org/stable/) • [Examples](https://scikit-learn.org/stable/auto_examples/index.html) • [Docker](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world/tree/main/scikit)                  |
+| **OpenVINO**     | 2024.6.0    | Intel's high-performance AI inference toolkit | ✅ 1.82GB   | ❌ Execution issues | Computer vision, inference             | [Docs](https://docs.openvino.ai/) • [Tutorial](https://docs.openvino.ai/2023.3/notebooks/004-hello-detection-with-output.html) • [Docker](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world/tree/main/openvino) |
+| **NumPy**        | 2.0.2       | Fundamental package for scientific computing  | ✅ 1.25GB   | ✅ 1.08GB           | Scientific computing, data analysis    | [Docs](https://numpy.org/doc/) • [User Guide](https://numpy.org/doc/stable/user/index.html) • [Docker](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world/tree/main/numpy)                                       |
+| **Matplotlib**   | 3.9.4       | Comprehensive library for data visualization  | ✅ 1.25GB   | ✅ 1.08GB           | Data visualization, plotting           | [Docs](https://matplotlib.org/) • [Gallery](https://matplotlib.org/stable/gallery/index.html) • [Docker](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world/tree/main/matplotlib)                                |
+
+## 🐳 Getting Started with Docker Examples
+
+### What's Included
+
+Our
+[AI Frameworks Hello World repository](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world)
+includes ready-to-use examples:
+
+```
+ai-frameworks-hello-world/
+├── tensorflow/     # TensorFlow 2.19.0 example
+├── pytorch/        # PyTorch 2.7.0+cu126 example
+├── scikit/         # Scikit-learn 1.6.1 example
+├── openvino/       # OpenVINO 2024.6.0 example
+├── numpy/          # NumPy 2.0.2 example
+└── matplotlib/     # Matplotlib 3.9.4 example
+```
+
+### Quick Start Commands
+
+```bash
+# Clone the repository
+git clone https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world.git
+cd ai-frameworks-hello-world
+
+# Try TensorFlow example
+cd tensorflow
+docker build -t hello-tensorflow .
+docker run --rm hello-tensorflow
+
+# Try PyTorch example
+cd ../pytorch
+docker build -t hello-pytorch .
+docker run --rm hello-pytorch
+```
+
+### Features
+
+- **✅ Isolated Testing**: Each framework runs in its own container
+- **✅ Reproducible**: Consistent environment across systems
+- **✅ TDX Ready**: All containers tested for Intel TDX compatibility
+- **✅ Easy Deployment**: Simple build and run commands
+
+## 📊 Technology Comparison
+
+### TDX vs SGX for AI
+
+| Feature               | Intel TDX                  | Intel SGX                          |
+| --------------------- | -------------------------- | ---------------------------------- |
+| **Memory Limit**      | Multi-GB+                  | ~1.95GB                            |
+| **Framework Support** | All major frameworks       | Limited (Scikit-learn, NumPy)      |
+| **Code Changes**      | Minimal ("lift and shift") | Significant modifications required |
+| **Production Ready**  | ✅ Yes                     | ⚠️ Limited                         |
+| **AI Workloads**      | ✅ Excellent               | ❌ Restricted                      |
+
+### Recommendations
+
+#### For Production AI Applications
+
+- **Use TDX** for TensorFlow, PyTorch, and OpenVINO
+- **Use SGX** for lightweight ML with Scikit-learn and NumPy
+
+#### For Development and Testing
+
+- **Start with SGX** for simple ML tasks
+- **Migrate to TDX** for complex AI workloads
+
+#### Important Considerations
+
+- **SGX Limitations**: Expect potential library incompatibilities and code
+  modifications
+- **TDX Advantages**: Minimal code changes required ("lift and shift" approach)
+
+## 📚 Next Steps
+
+### Learn TEE Technologies
+
+- **[Intel SGX Technology](/protocol/tee/intel-sgx)** - SGX limitations and
+  capabilities
+- **[Intel TDX Technology](/protocol/tee/intel-tdx)** - TDX advantages for AI
+- **[SGX vs TDX Comparison](/protocol/tee/sgx-vs-tdx)** - Detailed comparison
+
+### Build AI Applications
+
+- **[Build & Test](/guides/build-iapp/build-&-test)** - Build and test your AI
+  application
+- **[Deploy & Run](/guides/build-iapp/deploy-&-run)** - Deploy and run your AI
+  application
+- **[Build Intel TDX App](/guides/build-iapp/advanced/build-your-first-tdx-iapp)** -
+  TDX applications for AI workloads
+- **[Inputs and Outputs](/guides/build-iapp/inputs-and-outputs)** - Handle data
+  flow in TEE environment
+
+### Explore Examples
+
+- **[AI Frameworks Hello World](https://github.com/iExecBlockchainComputing/ai-frameworks-hello-world)** -
+  Ready-to-use Docker examples
+- **[iExec Discord](https://discord.com/invite/pbt9m98wnU)** - Community support