Fix docs (#2284)

Author: andygrove

docs/generate-versions.py

@@ -25,6 +25,6 @@
 import os
 
 for version in ["0.8", "0.9"]:
-    os.system(f"git clone --depth 1 git@github.com:apache/datafusion-comet.git -b branch-{version} comet-{version}")
+    os.system(f"git clone --depth 1 https://github.com/apache/datafusion-comet.git -b branch-{version} comet-{version}")
     os.system(f"mkdir temp/user-guide/{version}")
     os.system(f"cp -rf comet-{version}/docs/source/user-guide/* temp/user-guide/{version}")

docs/source/gluten_comparison.md

@@ -56,7 +56,7 @@ wishing to implement UDFs in C++, Gluten would likely be a better choice.
 If users are just interested in speeding up their existing Spark jobs and do not need to implement UDFs in native 
 code, then we suggest benchmarking with both solutions and choosing the fastest one for your use case.
 
-![github-stars-datafusion-velox.png](../_static/images/github-stars-datafusion-velox.png)
+![github-stars-datafusion-velox.png](/_static/images/github-stars-datafusion-velox.png)
 
 ## Compatibility
 
@@ -84,7 +84,7 @@ real-world queries, especially for ETL use cases. For example, there are no comp
 manipulation, regular expressions, or other advanced expressions. We recommend running your own benchmarks based
 on your existing Spark jobs. 
 
-![tpch_allqueries_comet_gluten.png](../_static/images//benchmark-results/0.9.0/tpch_spark_comet_gluten.png)
+![tpch_allqueries_comet_gluten.png](/_static/images//benchmark-results/0.9.0/tpch_spark_comet_gluten.png)
 
 The scripts that were used to generate these results can be found [here](https://github.com/apache/datafusion-comet/tree/main/dev/benchmarks).