Small tweaks (#152)

* Updates in Colab (small tweaks) * More tweaks * Oops, removing second Colab button. * Following the instructions for once!
jax-ml · Jan 17, 2025 · b33a446 · b33a446
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diff --git a/docs/source/JAX_porting_PyTorch_model.ipynb b/docs/source/JAX_porting_PyTorch_model.ipynb
diff --git a/docs/source/JAX_porting_PyTorch_model.md b/docs/source/JAX_porting_PyTorch_model.md
@@ -16,8 +16,14 @@ kernelspec:
 
 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax-ai-stack/blob/main/docs/source/JAX_porting_PyTorch_model.ipynb)
 
+**Note: On Colab we recommend running this on a T4 GPU instance.  On Kaggle we recommend a T4x2 or P100 instance.**
+
 In this tutorial we will learn how to port a PyTorch model to JAX and [Flax](https://flax.readthedocs.io/en/latest/nnx_basics.html). Flax provides an API very similar to the PyTorch `torch.nn` module and porting PyTorch models is rather straightforward. To install Flax, we can simply execute the following command: `pip install -U flax treescope`.
 
+```{code-cell} ipython3
+!pip install -Uq flax treescope
+```
+
 Say we have a trained PyTorch computer-vision model to classify images that we would like to port to JAX. We will use [`TorchVision`](https://pytorch.org/vision/stable/index.html) to provide a [MaxVit](https://pytorch.org/vision/stable/models/maxvit.html) model trained on ImageNet (MaxViT: Multi-Axis Vision Transformer, https://arxiv.org/abs/2204.01697).
 
 First, we set up the model using TorchVision and explore briefly the model's architecture and the blocks we need to port. Next, we define equivalent blocks and the whole model using Flax. After that, we port the weights. Finally, we run some tests to ensure the correctness of the ported model.
@@ -44,11 +50,10 @@ torch_model = maxvit_t(weights=MaxVit_T_Weights.IMAGENET1K_V1)
 ```
 
 We can use `flax.nnx.display` to display the model's architecture:
-```python
-nnx.display(torch_model)
-```
 
-+++
+```{code-cell} ipython3
+# nnx.display(torch_model)
+```
 
 We can see that there are four MaxViT blocks in the model and each block contains:
 - MaxViT layers: two layers for blocks 0, 1, 3 and five layers for the block 4
@@ -81,9 +86,18 @@ print(output.shape)  # (2, 1000)
 
 We can download an image of a Pembroke Corgy dog from [TorchVision's gallery](https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true) together with [ImageNet classes dictionary](https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json):
 
-```bash
-wget "https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true" -O dog1.jpg
-wget "https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json" -O imagenet_class_index.json
+```{code-cell} ipython3
+%%bash
+if [ -f "dog1.jpg" ]; then
+  echo "dog1.jpg already exists."
+else
+  wget -nv "https://github.com/pytorch/vision/blob/main/gallery/assets/dog1.jpg?raw=true" -O dog1.jpg
+fi
+if [ -f "imagenet_class_index.json" ]; then
+  echo "imagenet_class_index.json already exists."
+else
+  wget -nv "https://raw.githubusercontent.com/pytorch/vision/refs/heads/main/gallery/assets/imagenet_class_index.json" -O imagenet_class_index.json
+fi
 ```
 
 ```{code-cell} ipython3
@@ -1615,5 +1629,5 @@ cosine_dist
 
 ## Further reading
 
-- [Flax documentation: Core Exampels](https://flax.readthedocs.io/en/latest/examples/core_examples.html)
+- [Flax documentation: Core Examples](https://flax.readthedocs.io/en/latest/examples/core_examples.html)
 - [JAX AI Stack tutorials](https://jax-ai-stack.readthedocs.io/en/latest/tutorials.html)