[DGL-Go][Doc] Update DGL-Go version to 0.0.2 and misc fix from bug bash (#4236)

* Update

* Update

* Update

* Update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-53-142.us-west-2.compute.internal>
Co-authored-by: Xin Yao <xiny@nvidia.com>

dglgo/README.md

@@ -61,7 +61,7 @@ Let's use one of the most classical setups -- training a GraphSAGE model for nod
 classification on the Cora citation graph dataset as an
 example.
 
-### Step one: `dgl configure`
+### Step 1: `dgl configure`
 
 First step, use `dgl configure` to generate a YAML configuration file.
 
@@ -85,7 +85,7 @@ At this point you can also change options to explore optimization potentials.
 The snippet below shows the configuration file generated by the command above.
 
 ```yaml
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cpu
@@ -181,7 +181,7 @@ That's all! Basically you only need two commands to train a graph neural network
 
 ### Step 3: `dgl export` for more advanced customization
 
-That's not everything yet. You may want to open the hood and and invoke deeper
+That's not everything yet. You may want to open the hood and invoke deeper
 customization. DGL-Go can export a **self-contained, reproducible** Python
 script for you to do anything you like.
 

dglgo/dglgo/utils/enter_config.py

@@ -23,7 +23,7 @@ class PipelineConfig(DGLBaseModel):
     loss: str = "CrossEntropyLoss"
 
 class UserConfig(DGLBaseModel):
-    version: Optional[str] = "0.0.1"
+    version: Optional[str] = "0.0.2"
     pipeline_name: PipelineFactory.get_pipeline_enum()
     pipeline_mode: str
     device: str = "cpu"

dglgo/recipes/graphpred_hiv_gin.yaml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: graphpred
 pipeline_mode: train
 device: cuda:0                # Torch device name, e.q. cpu or cuda or cuda:0

dglgo/recipes/graphpred_hiv_pna.yaml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: graphpred
 pipeline_mode: train
 device: cuda:0                # Torch device name, e.q. cpu or cuda or cuda:0

dglgo/recipes/graphpred_pcba_gin.yaml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: graphpred
 pipeline_mode: train
 device: cuda:0                # Torch device name, e.q. cpu or cuda or cuda:0

dglgo/recipes/linkpred_citation2_sage.yaml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: linkpred
 pipeline_mode: train
 device: cpu

dglgo/recipes/linkpred_collab_sage.yaml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: linkpred
 pipeline_mode: train
 device: cpu

dglgo/recipes/linkpred_cora_sage.yaml

-version: 0.0.1
+version: 0.0.2
 pipeline_name: linkpred
 pipeline_mode: train
 device: cuda

dglgo/recipes/nodepred-ns_arxiv_gcn.yaml

 # Accuracy across 5 runs: 0.593288 ± 0.006103
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred-ns
 pipeline_mode: train
 device: 'cuda:0'

dglgo/recipes/nodepred-ns_product_sage.yaml

 # Accuracy across 1 runs: 0.796911
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred-ns
 pipeline_mode: train
 device: cuda

dglgo/recipes/nodepred_citeseer_gat.yaml

 # Accuracy across 10 runs: 0.7097 ± 0.006914
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_citeseer_gcn.yaml

 # Accuracy across 10 runs: 0.6852 ± 0.008875
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_citeseer_sage.yaml

 # Accuracy across 10 runs: 0.6994 ± 0.004005
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_cora_gat.yaml

 # Accuracy across 10 runs: 0.8208 ± 0.00663
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_cora_gcn.yaml

 # Accuracy across 10 runs: 0.802 ± 0.005329
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_cora_sage.yaml

 # Accuracy across 10 runs: 0.8163 ± 0.006856
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_pubmed_gat.yaml

 # Accuracy across 10 runs: 0.7788 ± 0.002227
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_pubmed_gcn.yaml

 # Accuracy across 10 runs: 0.7826 ± 0.004317
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/recipes/nodepred_pubmed_sage.yaml

 # Accuracy across 10 runs: 0.7819 ± 0.003176
-version: 0.0.1
+version: 0.0.2
 pipeline_name: nodepred
 pipeline_mode: train
 device: cuda:0

dglgo/setup.py

@@ -4,7 +4,7 @@ from setuptools import find_packages
 from distutils.core import setup
 
 setup(name='dglgo',
-      version='0.0.1',
+      version='0.0.2',
       description='DGL',
       author='DGL Team',
       author_email='wmjlyjemaine@gmail.com',