Update README.md.

PiperOrigin-RevId: 390606818

wikigraphs/README.md

@@ -11,12 +11,12 @@ conditioned on graph and generate graphs given text.
 
 [Jax](https://github.com/google/jax#installation),
 [Haiku](https://github.com/deepmind/dm-haiku#installation),
-[Optax](https://github.com/deepmind/dm-haiku#installation), and
+[Optax](https://optax.readthedocs.io/en/latest/#installation), and
 [Jraph](https://github.com/deepmind/jraph) are needed for this package. It has
 been developed and tested on python 3 with the following packages:
 
 *   Jax==0.2.13
-*   Haiku==0.0.5
+*   Haiku==0.0.5.dev
 *   Optax==0.0.6
 *   Jraph==0.0.1.dev
 
@@ -167,7 +167,56 @@ it elsewhere.
 
 ## Run baseline models
 
-Note: our code supports training with multiple GPUs.
+To quickly test-run a small model with 1 GPU:
+
+```base
+python main.py --model_type=graph2text \
+  --dataset=freebase2wikitext \
+  --checkpoint_dir=/tmp/graph2text \
+  --job_mode=train \
+  --train_batch_size=2 \
+  --gnn_num_layers=1 \
+  --num_gpus=1
+```
+
+To run the default baseline unconditional TransformerXL on Wikigraphs with 8
+GPUs:
+
+```base
+python main.py --model_type=text \
+  --dataset=freebase2wikitext \
+  --checkpoint_dir=/tmp/text \
+  --job_mode=train \
+  --train_batch_size=64 \
+  --gnn_num_layers=1 \
+  --num_gpus=8
+```
+
+To run the default baseline BoW-based TransformerXL on Wikigraphs with 8
+GPUs:
+
+```base
+python main.py --model_type=bow2text \
+  --dataset=freebase2wikitext \
+  --checkpoint_dir=/tmp/bow2text \
+  --job_mode=train \
+  --train_batch_size=64 \
+  --gnn_num_layers=1 \
+  --num_gpus=8
+```
+
+To run the default baseline Nodes-only GNN-based TransformerXL on Wikigraphs
+with 8 GPUs:
+
+```base
+python main.py --model_type=bow2text \
+  --dataset=freebase2wikitext \
+  --checkpoint_dir=/tmp/bow2text \
+  --job_mode=train \
+  --train_batch_size=64 \
+  --gnn_num_layers=0 \
+  --num_gpus=8
+```
 
 To run the default baseline GNN-based TransformerXL on Wikigraphs with 8
 GPUs:
@@ -182,22 +231,11 @@ python main.py --model_type=graph2text \
   --num_gpus=8
 ```
 
-We ran our experiments in the paper using 8 Nvidia V100 GPUs. To allow for
-batch parallization for the GNN-based (graph2text) model, we pad graphs to
-the largest graph in the batch. The full run takes almost 4 days. BoW- and
-nodes-based models can be trained within 14 hours because there is no
-additional padding.
-
-Or to quickly test-run a small model:
-
-```base
-python main.py --model_type=graph2text \
-  --dataset=freebase2wikitext \
-  --checkpoint_dir=/tmp/graph2text \
-  --job_mode=train \
-  --train_batch_size=2 \
-  --gnn_num_layers=1
-```
+We ran our experiments in the paper using 8 Nvidia V100 GPUs. Reduce the batch
+size if the model does not fit into memory. To allow for batch parallization for
+the GNN-based (graph2text) model, we pad graphs to the largest graph in the
+batch. The full run takes almost 4 days. BoW- and nodes-based models can be
+trained within 14 hours because there is no additional padding.
 
 To evaluate the model on the validation set (this only uses 1 GPU):
 

wikigraphs/setup.py

@@ -33,7 +33,7 @@ from setuptools import setup
 
 setup(
     name='wikigraphs',
-    version='0.0.2',
+    version='0.1.0',
     description='A Wikipedia - knowledge graph paired dataset.',
     url='https://github.com/deepmind/deepmind-research/tree/master/wikigraphs',
     author='DeepMind',