archival of neurips21 work, updated documentation

README.md

@@ -2,75 +2,17 @@
 
 <http://big-ann-benchmarks.com/>
 
-## Install
-
-The only prerequisite is Python and Docker. 
-
-1. Clone the repo.
-2. Run `pip install -r requirements_py3.10.txt` (Use `requirements_py38.txt` if you have Python 3.8.)
-3. Install docker by following instructions [here](https://docs.docker.com/engine/install/ubuntu/).
-You might also want to follow the post-install steps for running docker in non-root user mode.
-3. Run `python install.py` to build all the libraries inside Docker containers.
-
-## Storing Data
-
-The framework assumes that all data is stored in `data/`.
-Please use a symlink if your datasets and indices are supposed to be stored somewhere else.
-The location of the linked folder matters a great deal for SSD-based search performance in T2. 
-A local SSD such as the one found on Azure Ls-series VMs is better than remote disks, even premium ones.
-See [T1/T2](t1_t2/README.md) for more details.
-
-## Data sets
+## Datasets
 
 See <http://big-ann-benchmarks.com/> for details on the different datasets.
 
-### Dataset Preparation
-
-Before running experiments, datasets have to be downloaded. All preparation can be carried out by calling
-
-```python
-python create_dataset.py --dataset [bigann-1B | deep-1B | text2image-1B | ssnpp-1B | msturing-1B | msspacev-1B]
-```
-
-Note that downloading the datasets can potentially take many hours.
-
-For local testing, there exist smaller random datasets `random-xs` and `random-range-xs`. 
-Furthermore, most datasets have 1M, 10M and 100M versions, run `python create_dataset -h` to get an overview.
-
-
-## Running the benchmark
-
-Run `python run.py --dataset $DS --algorithm $ALGO` where `DS` is the dataset you are running on,
-and `ALGO` is the name of the algorithm. (Use `python run.py --list-algorithms`) to get an overview.
-`python run.py -h` provides you with further options.
-
-The parameters used by the implementation to build and query the index can be found in `algos-2021.yaml`.
-
-## Running the track 1 baseline
-After running the installation, we can evaluate the baseline as follows.
-
-```bash
-
-for DS in bigann-1B  deep-1B  text2image-1B  ssnpp-1B  msturing-1B  msspacev-1B;
-do
-    python run.py --dataset $DS --algorithm faiss-t1;
-done
-```
-
-On a 28-core Xeon E5-2690 v4 that provided 100MB/s downloads, carrying out the baseline experiments took roughly 7 days.
-
-To evaluate the results, run
-```bash
-sudo chmod -R 777 results/
-python data_export.py --output res.csv
-python3.8 eval/show_operating_points.py --algorithm faiss-t1 --threshold 10000
-```
+## NeurIPS 2023 competition: Practical Vector Search
 
-## Including your algorithm and Evaluating the Results
+Please see <./neurips23/README.md> for a guide to the NeurIPS 23 competition.
 
-See [Track T1/T2](t1_t2/README.md) for more details on evaluation for Tracks T1 and T2.
+## NeurIPS 2021 competition: Billion-Scale ANN 
 
-See [Track T3](t3/README.md) for more details on evaluation for Track T3.
+Please see <./neurips21/README.md> for a guide of running billion-scale benchmarks and a summary of the results.
 
 # Credits
 

neurips21/README.md

@@ -0,0 +1,9 @@
+# NeurIPS 2021
+
+This summarizes the evaluation protocol and the results of the NeurIPS 2021 Challenge.
+
+## NeurIPS 2021: Algorithms and Result Evaluation
+
+See [Track T1/T2](t1_t2/README.md) for more details on evaluation for Tracks T1 and T2.
+
+See [Track T3](t3/README.md) for more details on evaluation for Track T3.