More updates

docs/source/raft_ann_benchmarks.md

@@ -96,7 +96,7 @@ We provide a collection of lightweight Python scripts to run the benchmarks. The
 4. Plot Results
 
 ### Step 1: Prepare Dataset
-The script `raft-ann-bench.get_dataset` will download and unpack the dataset in directory
+The script `raft_ann_bench.get_dataset` will download and unpack the dataset in directory
 that the user provides. As of now, only million-scale datasets are supported by this
 script. For more information on [datasets and formats](ann_benchmarks_dataset.md).
 
@@ -117,10 +117,10 @@ will be normalized to inner product. So, for example, the dataset `glove-100-ang
 will be written at location `datasets/glove-100-inner/`.
 
 ### Step 2: Build and Search Index
-The script `raft-ann-bench.run` will build and search indices for a given dataset and its
+The script `raft_ann_bench.run` will build and search indices for a given dataset and its
 specified configuration.
 
-The usage of the script `raft-ann-bench.run` is:
+The usage of the script `raft_ann_bench.run` is:
 ```bash
 usage: __main__.py [-h] [--subset-size SUBSET_SIZE] [-k COUNT] [-bs BATCH_SIZE] [--dataset-configuration DATASET_CONFIGURATION] [--configuration CONFIGURATION] [--dataset DATASET]
                    [--dataset-path DATASET_PATH] [--build] [--search] [--algorithms ALGORITHMS] [--groups GROUPS] [--algo-groups ALGO_GROUPS] [-f] [-m SEARCH_MODE]
@@ -186,8 +186,8 @@ it is assumed both are `True`.
 is available in `algos.yaml` and not disabled, as well as having an associated executable.
 
 ### Step 3: Data Export
-The script `raft-ann-bench.data_export` will convert the intermediate JSON outputs produced by `raft-ann-bench.run` to more
-easily readable CSV files, which are needed to build charts made by `raft-ann-bench.plot`.
+The script `raft_ann_bench.data_export` will convert the intermediate JSON outputs produced by `raft_ann_bench.run` to more
+easily readable CSV files, which are needed to build charts made by `raft_ann_bench.plot`.
 
 ```bash
 usage: data_export.py [-h] [--dataset DATASET] [--dataset-path DATASET_PATH]
@@ -206,7 +206,7 @@ and index search statistics CSV file in `<dataset-path/<dataset>/result/search/<
 
 
 ### Step 4: Plot Results
-The script `raft-ann-bench.plot` will plot results for all algorithms found in index search statistics
+The script `raft_ann_bench.plot` will plot results for all algorithms found in index search statistics
 CSV files `<dataset-path/<dataset>/result/search/*.csv`.
 
 The usage of this script is:
@@ -277,7 +277,7 @@ python -m raft_ann_bench.data_export --dataset deep-image-96-inner
 python -m raft_ann_bench.plot --dataset deep-image-96-inner
 ```
 
-Configuration files already exist for the following list of the million-scale datasets. Please refer to [ann-benchmarks datasets](https://github.com/erikbern/ann-benchmarks/#data-sets) for more information, including actual train and sizes. These all work out-of-the-box with the `--dataset` argument. Other million-scale datasets from `ann-benchmarks.com` will work, but will require a json configuration file to be created in `$CONDA_PREFIX/lib/python3.xx/site-packages/raft-ann-bench/run/conf`, or you can specify the `--configuration` option to use a specific file.
+Configuration files already exist for the following list of the million-scale datasets. Please refer to [ann-benchmarks datasets](https://github.com/erikbern/ann-benchmarks/#data-sets) for more information, including actual train and sizes. These all work out-of-the-box with the `--dataset` argument. Other million-scale datasets from `ann-benchmarks.com` will work, but will require a json configuration file to be created in `$CONDA_PREFIX/lib/python3.xx/site-packages/raft_ann_bench/run/conf`, or you can specify the `--configuration` option to use a specific file.
 
 | Dataset Name | Train Rows | Columns | Test Rows      | Distance   | 
 |-----|------------|----|----------------|------------|
@@ -293,7 +293,7 @@ All of the datasets above contain ground test datasets with 100 neighbors. Thus
 
 ### End to end: large-scale benchmarks (>10M vectors)
 
-`raft-ann-bench.get_dataset` cannot be used to download the [billion-scale datasets](ann_benchmarks_dataset.md#billion-scale)
+`raft_ann_bench.get_dataset` cannot be used to download the [billion-scale datasets](ann_benchmarks_dataset.md#billion-scale)
 due to their size. You should instead use our billion-scale datasets guide to download and prepare them.
 All other python commands mentioned below work as intended once the
 billion-scale dataset has been downloaded.

python/raft-ann-bench/src/raft_ann_bench/run/conf/algos/hnswlib.yaml

@@ -1,6 +1,6 @@
 name: hnswlib
 constraints:
-  search: raft-ann-bench.constraints.hnswlib_search_constraints
+  search: raft_ann_bench.constraints.hnswlib_search_constraints
 groups:
   base:
     build:

python/raft-ann-bench/src/raft_ann_bench/run/conf/algos/raft_cagra.yaml

@@ -1,7 +1,7 @@
 name: raft_cagra
 constraints:
-  build: raft-ann-bench.constraints.raft_cagra_build_constraints
-  search: raft-ann-bench.constraints.raft_cagra_search_constraints
+  build: raft_ann_bench.constraints.raft_cagra_build_constraints
+  search: raft_ann_bench.constraints.raft_cagra_search_constraints
 groups:
   base:
     build:

python/raft-ann-bench/src/raft_ann_bench/run/conf/algos/raft_cagra_hnswlib.yaml

@@ -1,6 +1,6 @@
 name: raft_cagra_hnswlib
 constraints:
-  search: raft-ann-bench.constraints.hnswlib_search_constraints
+  search: raft_ann_bench.constraints.hnswlib_search_constraints
 groups:
   base:
     build:

python/raft-ann-bench/src/raft_ann_bench/run/conf/algos/raft_ivf_pq.yaml

@@ -1,7 +1,7 @@
 name: raft_ivf_pq
 constraints:
-  build: raft-ann-bench.constraints.raft_ivf_pq_build_constraints
-  search: raft-ann-bench.constraints.raft_ivf_pq_search_constraints
+  build: raft_ann_bench.constraints.raft_ivf_pq_build_constraints
+  search: raft_ann_bench.constraints.raft_ivf_pq_search_constraints
 groups:
   base:
     build: