fix bulleted list

docs/help/embed_semantic_graph.md

@@ -9,25 +9,29 @@ PTBG is a complex tool that requires a number of preprocessing steps to use.
 
 ## PTBG Process Outline
 
- 1. Create a single directory that contains all semantic graph edges.
-   - This is produced by running `agatha.construct`.
-   - Edges are stored as small key-value json files.
-   - The directory may contain a large number of files.
- 2. Convert graph to PTBG input format.
-   - PTBG requires that we index and partition all nodes and edges.
-   - Look into `tools/convert_graph_for_pytorch_biggraph` for how to do this.
- 3. Create a PTBG config.
-   - Specify all node / edge types.
-   - Specify location of all input files.
-   - The parameters of this config must match the options used in the
-     conversion.
- 4. Launch the PTBG training cluster.
-   - Use 10-20 machines, too many will slow this process.
-   - Wait at least 5 epochs, will take days.
- 5. Index the resulting embeddings for use in Agatha.
-   - Agatha needs to know where to find each embedding, given the node name.
-   - Use `tools/py_scripts/ptbg_index_embeddings.py` to create a lookup table
-     that maps each node name to its embedding metadata.
+1. Create a single directory that contains all semantic graph edges.
+    - This is produced by running `agatha.construct`.
+    - Edges are stored as small key-value json files.
+    - The directory may contain a large number of files.
+
+2. Convert graph to PTBG input format.
+    - PTBG requires that we index and partition all nodes and edges.
+    - Look into `tools/convert_graph_for_pytorch_biggraph` for how to do this.
+
+3.  Create a PTBG config.
+    - Specify all node / edge types.
+    - Specify location of all input files.
+    - The parameters of this config must match the options used in the
+      conversion.
+
+4.  Launch the PTBG training cluster.
+    - Use 10-20 machines, too many will slow this process.
+    - Wait at least 5 epochs, will take days.
+
+5. Index the resulting embeddings for use in Agatha.
+    - Agatha needs to know where to find each embedding, given the node name.
+    - Use `tools/py_scripts/ptbg_index_embeddings.py` to create a lookup table
+      that maps each node name to its embedding metadata.
 
 ## Convert Edges to PTBG format