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Author: pomonam

README.md

@@ -1,4 +1,8 @@
-# Kronfluence
+<p align="center">
+    <br>
+    <img src=".assets/kronfluence.png" width="400"/>
+    <br>
+<p>
 
 [![CI](https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/CI.yml/badge.svg)](https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/CI.yml)
 [![Lint](https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/linting.yml/badge.svg)](https://github.com/mlcommons/algorithmic-efficiency/actions/workflows/linting.yml)
@@ -37,6 +41,42 @@ pip install -e .
 
 ## Getting Started
 
+
+```diff
+  import torch
+  import torch.nn.functional as F
+  from datasets import load_dataset
++ from accelerate import Accelerator
+
++ accelerator = Accelerator()
+- device = 'cpu'
++ device = accelerator.device
+
+  model = torch.nn.Transformer().to(device)
+  optimizer = torch.optim.Adam(model.parameters())
+
+  dataset = load_dataset('my_dataset')
+  data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+  model.train()
+  for epoch in range(10):
+      for source, targets in data:
+          source = source.to(device)
+          targets = targets.to(device)
+
+          optimizer.zero_grad()
+
+          output = model(source)
+          loss = F.cross_entropy(output, targets)
+
+-         loss.backward()
++         accelerator.backward(loss)
+
+          optimizer.step()
+```
+
 (Placeholder for getting started content)
 
 ## Examples