Merge pull request #113 from NillionNetwork/fix/aivm

fix: AIVM Docs

docs/aivm-supported-models.md

@@ -0,0 +1,37 @@
+# Supported Models
+
+AIVM currently supports the following pre-trained models for specific learning tasks. You can either utilize these existing models or use the [training scripts](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples) to produce your own custom-trained versions:
+
+## BertTiny
+
+- [SMS Spam Classification](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/1-getting-started.ipynb)
+  - Binary classification for detecting spam messages
+  - Input: Text string
+  - Output: Binary classification (spam/not spam)
+- [Movie Rating Sentiment Analysis](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/3a-upload-your-bert-tiny-model.ipynb)
+  - [Training Script](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/2a-fine-tuning-bert-tiny.ipynb)
+  - Sentiment analysis for movie reviews
+  - Input: Text string
+  - Output: Sentiment score (-1 to 1) for positive and negative
+- [Tweet Sentiment Analysis](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/3c-upload-your-bert-tiny-for-tweet-sentiment.ipynb)
+  - [Training Script](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/2c-fine-tuning-bert-tiny-tweet-dataset.ipynb)
+  - Sentiment analysis for tweets in
+  - Input: Text string
+  - Output: Sentiment score (-1 to 1) for positive, neutral and negative
+
+## LeNet5
+
+- [Handwritten Digit Recognition (MNIST)](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/1-getting-started.ipynb)
+  - Classification of handwritten digits
+  - Input: 28x28 grayscale image
+  - Output: Digit classification (0-9)
+- [Cats vs Dogs Classification](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/3b-upload-your-lenet5-model.ipynb)
+  - [Training Script](https://github.com/NillionNetwork/nillion-aivm/blob/main/examples/2b-fine-tuning-lenet5.ipynb)
+  - Binary image classification
+  - Input: 28x28 grayscale image
+  - Output: Binary classification (cat/dog)
+
+## Project Ideas
+
+- [Nillion AIVM Discord bot for message sentiment analysis](https://github.com/NillionNetwork/aivm-discord-bot)
+- [Nillion AIVM Slack not for hotdog image detection](https://github.com/crypblizz8/hotdog_or_not_aivm)

docs/aivm.md

@@ -8,30 +8,24 @@
 
 ## Supported Models
 
-AIVM currently supports the following pre-trained models for specific learning tasks. You can either utilize these existing models or deploy your own custom-trained versions:
-
-### BertTiny
-- SMS Spam Classification
-  - Binary classification for detecting spam messages
-  - Input: Text string
-  - Output: Binary classification (spam/not spam)
-- Movie Rating Sentiment Analysis
-  - Sentiment analysis for movie reviews
-  - Input: Text string
-  - Output: Sentiment score (-1 to 1)
-
-### LeNet5
-- Handwritten Digit Recognition (MNIST)
-  - Classification of handwritten digits
-  - Input: 28x28 grayscale image
-  - Output: Digit classification (0-9)
-- Cats vs Dogs Classification
-  - Binary image classification
-  - Input: 28x28 grayscale image
-  - Output: Binary classification (cat/dog)
+AIVM currently supports the following pre-trained models for specific learning tasks. You can either utilize these existing models or use the [training scripts](https://github.com/NillionNetwork/nillion-aivm/examples) to produce your own custom-trained versions:
+
+- BertTiny
+  - SMS Spam Classification
+  - Movie Rating Sentiment Analysis
+  - Tweet Sentiment Analysis
+- LeNet5
+  - Handwritten Digit Recognition (MNIST)
+  - Cats vs Dogs Classification
+
+We also have real-life project examples using these models including a Discord Bot for sentiment analysis and hotdog image detection.
+
+More information on the supported models and project ideas can be found [here](./aivm-supported-models.md)
 
 ## Installation
 
+### Recommended Installation
+
 Installing Nillion AIVM is straightforward:
 
 1. Create a virtual environment:
@@ -60,9 +54,9 @@ Installing Nillion AIVM is straightforward:
    pip install "nillion-aivm[examples]"
    ```
 
-This command installs all necessary dependencies for performing secure inference on AIVM.
+This command installs all necessary dependencies for performing secure inference on AIVM. You can now check the  [examples to test Nillion AIVM](https://github.com/NillionNetwork/nillion-aivm)
 
-### Starting the Development Network
+#### Starting the Development Network
 
 Launch the AIVM development network with:
 
@@ -78,6 +72,16 @@ This command starts a persistent process that manages the secure computation inf
 If you get stuck on this with VSCode, ensure the correct venv is selected. If it asks to pip install your packages / no pip is found, you can use `pip install ipykernel -U --force-reinstall` to install it.
 :::
 
+### Using Docker to deploy `aivm-devnet`
+
+You can choose to use Docker for deploying `aivm-devnet`. For that, you **must have a working Docker installation**. The only required command is:
+
+```shell
+docker run -it -p 50050:50050 nillion/aivm-devnet
+```
+
+This commands forwards port 50050 from the Docker to your local machine, which is the port used to connect with the cluster.
+
 ## Performing Secure Inference
 
 ### Basic Usage
@@ -92,20 +96,37 @@ available_models = aic.get_supported_models()
 print(available_models)
 ```
 
-2. Prepare your input data. Here's an example using PyTorch to generate a random input:
+2. Prepare your input data. Here's an example using PyTorch to load an MNIST dataset input:
 
 ```python
+# Load the libraries
 import torch
-
-# Create a sample input (e.g., for LeNet5 MNIST)
-random_input = torch.randn((1, 1, 28, 28))  # Batch size 1, 1 channel, 28x28 pixels
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+
+# Define the required transformations for MNIST dataset
+trans = transforms.Compose(
+    [
+        transforms.ToTensor(),                # Transform to tensor
+        transforms.Resize((28, 28)),          # Resize to (28 x 28)
+        transforms.Normalize((0.5,), (1.0,)), # Normalize the image
+    ]
+)
+# Load the dataset
+dataset = dset.MNIST(
+    root="/tmp/mnist", train=True, transform=trans, download=True
+) 
+
+# Get entry #20 of the dataset
+inputs, _ =  dataset[20]
+inputs = inputs.reshape(1, 1, 28, 28)
 ```
 
 3. Encrypt your input using the appropriate Cryptensor:
 
 ```python
 # Encrypt the input
-encrypted_input = aic.LeNet5Cryptensor(random_input)
+encrypted_input = aic.LeNet5Cryptensor(inputs)
 ```
 
 4. Perform secure inference:

sidebars.js

@@ -358,6 +358,7 @@ const sidebars = {
       },
       items: [
         'aivm-reference',
+        'aivm-supported-models',
       ],
     },
     'limitations',