Fix ann-bench Documentation

bench/ann/run.py

@@ -146,8 +146,14 @@ def main():
         conf_file = json.load(f)
 
     # Replace base, query to dataset-path
-    conf_file["dataset"]["base_file"] = os.path.join(dataset_path, "base.fbin")
-    conf_file["dataset"]["query_file"] = os.path.join(dataset_path, "query.fbin")
+    replacement_base_filepath = \
+        os.path.normpath(conf_file["dataset"]["base_file"]).split(os.path.sep)[-1]
+    conf_file["dataset"]["base_file"] = \
+        os.path.join(dataset_path, replacement_base_filepath)
+    replacement_query_filepath = \
+        os.path.normpath(conf_file["dataset"]["query_file"]).split(os.path.sep)[-1]
+    conf_file["dataset"]["query_file"] = \
+        os.path.join(dataset_path, replacement_query_filepath)
     # Ensure base and query files exist for dataset
     if not os.path.exists(conf_file["dataset"]["base_file"]):
         raise FileNotFoundError(conf_file["dataset"]["base_file"])

docs/source/raft_ann_benchmarks.md

@@ -6,13 +6,13 @@ This project provides a benchmark program for various ANN search implementations
 
 The easiest way to install these benchmarks is through conda. We suggest using mamba as it generally leads to a faster install time::
 ```bash
+git clone https://github.com/rapidsai/raft.git && cd raft
+export RAFT_HOME=$(pwd)
+
 mamba env create --name raft_ann_benchmarks -f conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
 conda activate raft_ann_benchmarks
 
 mamba install -c rapidsai -c conda-forge -c nvidia libraft libraft-ann-bench cudatoolkit=11.8*
-
-git clone https://github.com/rapidsai/raft.git && cd raft
-export RAFT_HOME=$(pwd)
 ```
 The channel `rapidsai` can easily be substituted `rapidsai-nightly` if nightly benchmarks are desired.
 
@@ -35,50 +35,50 @@ expected to be defined to run these scripts; this variable holds the directory w
 ### End-to-end example: Million-scale
 ```bash
 export RAFT_HOME=$(pwd)
-# All scripts are present in directory raft/scripts/ann-benchmarks
+# All scripts are present in directory raft/bench/ann
 
 # (1) prepare dataset
-python scripts/ann-benchmarks/get_dataset.py --dataset glove-100-angular --normalize
+python bench/ann/get_dataset.py --dataset glove-100-angular --normalize
 
 # (2) build and search index
-python scripts/ann-benchmarks/run.py --configuration bench/ann/conf/glove-100-inner.json
+python bench/ann/run.py --dataset glove-100-inner
 
 # (3) evaluate results
-python scripts/ann-benchmarks/data_export.py --output out.csv --dataset glove-100-inner result/glove-100-inner/
+python bench/ann/data_export.py --output out.csv --dataset glove-100-inner result/glove-100-inner/
 
 # (4) plot results
-python scripts/ann-benchmarks/plot.py --result-csv out.csv
+python bench/ann/plot.py --result-csv out.csv
 ```
 
 ### End-to-end example: Billion-scale
-`scripts/get_dataset.py` cannot be used to download the [billion-scale datasets](ann_benchmarks_dataset.md#billion-scale) 
+`bench/ann/get_dataset.py` cannot be used to download the [billion-scale datasets](ann_benchmarks_dataset.md#billion-scale) 
 because they are so large. You should instead use our billion-scale datasets guide to download and prepare them.
-All other python scripts mentioned below work as intended once the
+All other python  mentioned below work as intended once the
 billion-scale dataset has been downloaded.
 To download Billion-scale datasets, visit [big-ann-benchmarks](http://big-ann-benchmarks.com/neurips21.html)
 
 ```bash
 export RAFT_HOME=$(pwd)
-# All scripts are present in directory raft/scripts/ann-benchmarks
+# All scripts are present in directory raft/bench/ann
 
 mkdir -p data/deep-1B
 # (1) prepare dataset
 # download manually "Ground Truth" file of "Yandex DEEP"
 # suppose the file name is deep_new_groundtruth.public.10K.bin
-python scripts/ann-benchmarks/split_groundtruth.py --groundtruth data/deep-1B/deep_new_groundtruth.public.10K.bin
+python bench/ann/split_groundtruth.py --groundtruth data/deep-1B/deep_new_groundtruth.public.10K.bin
 # two files 'groundtruth.neighbors.ibin' and 'groundtruth.distances.fbin' should be produced
 
 # (2) build and search index
-python scripts/ann-benchmarks/run.py --configuration bench/ann/conf/deep-1B.json
+python bench/ann/run.py --dataset deep-1B
 
 # (3) evaluate results
-python scripts/ann-benchmarks/data_export.py --output out.csv --dataset deep-1B result/deep-1B/
+python bench/ann/data_export.py --output out.csv --dataset deep-1B result/deep-1B/
 
 # (4) plot results
-python scripts/ann-benchmarks/plot.py --result-csv out.csv
+python bench/ann/plot.py --result-csv out.csv
 ```
 
-The usage of `scripts/ann-benchmarks/split-groundtruth.py` is:
+The usage of `bench/ann/split-groundtruth.py` is:
 ```bash
 usage: split_groundtruth.py [-h] --groundtruth GROUNDTRUTH
 
@@ -89,7 +89,7 @@ options:
 ```
 
 ##### Step 1: Prepare Dataset<a id='prep-dataset'></a>
-The script `scripts/ann-benchmarks/get_dataset.py` will download and unpack the dataset in directory
+The script `bench/ann/get_dataset.py` 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).
 
@@ -107,9 +107,10 @@ options:
 When option `normalize` is provided to the script, any dataset that has cosine distances
 will be normalized to inner product. So, for example, the dataset `glove-100-angular` 
 will be written at location `${RAFT_HOME}/bench/ann/data/glove-100-inner/`.
+```
 
 #### Step 2: Build and Search Index
-The script `scripts/ann-benchmarks/run.py` will build and search indices for a given dataset and its
+The script `bench/ann/run.py` will build and search indices for a given dataset and its
 specified configuration.
 To confirgure which algorithms are available, we use `algos.yaml`.
 To configure building/searching indices for a dataset, look at [index configuration](#json-index-config).
@@ -123,7 +124,7 @@ raft_ivf_pq:
 available in `raft/cpp/build/`.
 `disabled` : denotes whether an algorithm should be excluded from benchmark runs.
 
-The usage of the script `scripts/run.py` is:
+The usage of the script `bench/ann/run.py` is:
 ```bash
 usage: run.py [-h] [--configuration CONFIGURATION] [--dataset DATASET] [--build] [--search] [--algorithms ALGORITHMS] [--indices INDICES] [-f]
 
@@ -161,7 +162,7 @@ it is assumed both are `True`.
 is available in `algos.yaml` and not disabled, as well as having an associated executable.
 
 #### Step 3: Evaluating Results
-The script `scripts/ann-benchmarks/data_export.py` will evaluate results for a dataset whose index has been built
+The script `bench/ann/data_export.py` will evaluate results for a dataset whose index has been built
 and search with at least one algorithm. For every result file that is supplied to the script, the output
 will be combined and written to a CSV file.
 
@@ -181,7 +182,7 @@ options:
 `result-filepaths` : whitespace delimited list of result files/directories that can be captured via pattern match. For more [information and examples](ann_benchmarks_low_level.html#result-filepath-example)
 
 #### Step 4: Plot Results
-The script `scripts/ann-benchmarks/plot.py` will plot all results evaluated to a CSV file for a given dataset.
+The script `bench/ann/plot.py` will plot all results evaluated to a CSV file for a given dataset.
 
 The usage of this script is:
 ```bash