Website clean

.github/workflows/benchmarks.yml

@@ -1,61 +1,61 @@
-# Contributed by @GuilhemN in https://github.com/erikbern/ann-benchmarks/pull/233
-name: Billion-Scale ANN Benchmarks
-
-on: [push, pull_request]
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        include:
-          - algorithm: faiss-ivf
-            library: faissconda
-            dataset: random-xs
-          - algorithm: faiss-t1
-            dataset: random-xs
-            library: faissconda
-          - algorithm: faiss-t1
-            dataset: random-range-xs
-            library: faissconda
-          - algorithm: diskann-t2
-            dataset: random-xs
-            library: diskann
-          - algorithm: diskann-t2
-            dataset: random-range-xs
-            library: diskann
-          - algorithm: httpann_example
-            dataset: random-xs
-            library: httpann_example
-          - algorithm: httpann_example
-            dataset: random-range-xs
-            library: httpann_example
-      fail-fast: false
-
-    steps:
-    - uses: actions/checkout@v2 # Pull the repository
-
-    - name: Set up Python 3.6
-      uses: actions/setup-python@v2
-      with:
-        python-version: 3.6
-
-    - name: Install dependencies
-      run: |
-        pip install -r requirements.txt
-        python install.py
-      env:
-        LIBRARY: ${{ matrix.library }}
-        DATASET: ${{ matrix.dataset }}
-
-    - name: Run the benchmark
-      run: |
-        python create_dataset.py --dataset $DATASET
-        python run.py --algorithm $ALGORITHM --max-n-algorithms 2 --dataset $DATASET --timeout 600
-        sudo chmod -R 777 results/
-        python plot.py --dataset $DATASET --output plot.png
-        python data_export.py --output test.csv
-
-      env:
-        ALGORITHM: ${{ matrix.algorithm}}
-        DATASET: ${{ matrix.dataset }} 
+# Contributed by @GuilhemN in https://github.com/erikbern/ann-benchmarks/pull/233
+name: Billion-Scale ANN Benchmarks
+
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - algorithm: faiss-ivf
+            library: faissconda
+            dataset: random-xs
+          - algorithm: faiss-t1
+            dataset: random-xs
+            library: faissconda
+          - algorithm: faiss-t1
+            dataset: random-range-xs
+            library: faissconda
+          - algorithm: diskann-t2
+            dataset: random-xs
+            library: diskann
+          - algorithm: diskann-t2
+            dataset: random-range-xs
+            library: diskann
+          - algorithm: httpann_example
+            dataset: random-xs
+            library: httpann_example
+          - algorithm: httpann_example
+            dataset: random-range-xs
+            library: httpann_example
+      fail-fast: false
+
+    steps:
+    - uses: actions/checkout@v2 # Pull the repository
+
+    - name: Set up Python 3.6
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.6
+
+    - name: Install dependencies
+      run: |
+        pip install -r requirements.txt
+        python install.py
+      env:
+        LIBRARY: ${{ matrix.library }}
+        DATASET: ${{ matrix.dataset }}
+
+    - name: Run the benchmark
+      run: |
+        python create_dataset.py --dataset $DATASET
+        python run.py --algorithm $ALGORITHM --max-n-algorithms 2 --dataset $DATASET --timeout 600
+        sudo chmod -R 777 results/
+        python plot.py --dataset $DATASET --output plot.png
+        python data_export.py --output test.csv
+
+      env:
+        ALGORITHM: ${{ matrix.algorithm}}
+        DATASET: ${{ matrix.dataset }} 

README.md

@@ -1,77 +1,77 @@
-# Billion-Scale ANN
-
-<http://big-ann-benchmarks.com/>
-
-## Install
-
-The only prerequisite is Python (tested with 3.6) and Docker. Works with newer versions of Python as well but probably requires an updated `requirements.txt` on the host. (Suggestion: copy `requirements.txt` to `requirements${PYTHON_VERSION}.txt` and remove all fixed versions. `requirements.txt` has to be kept for the docker containers.)
-
-1. Clone the repo.
-2. Run `pip install -r requirements.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
-
-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.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 $DS --algorithm faiss-t1;
-done
-```
-
-On a 28-core Xeon E5-2690 v4 that provided 100MB/s downloads, carrying out the baseline experiments took roughly 7 days.
-
-To evaluate the results, run
-```bash
-sudo chmod -R 777 results/
-python data_export.py --output res.csv
-python3.8 eval/show_operating_points.py --algorithm faiss-t1 --threshold 10000
-```
-
-## Including your algorithm and Evaluating the Results
-
-See [Track T1/T2](t1_t2/README.md) for more details on evaluation for Tracks T1 and T2.
-
-See [Track T3](t3/README.md) for more details on evaluation for Track T3.
-
-# Credits
-
-This project is a version of [ann-benchmarks](https://github.com/erikbern/ann-benchmarks) by [Erik Bernhardsson](https://erikbern.com/) and contributors targetting billion-scale datasets.
+# Billion-Scale ANN
+
+<http://big-ann-benchmarks.com/>
+
+## Install
+
+The only prerequisite is Python (tested with 3.6) and Docker. Works with newer versions of Python as well but probably requires an updated `requirements.txt` on the host. (Suggestion: copy `requirements.txt` to `requirements${PYTHON_VERSION}.txt` and remove all fixed versions. `requirements.txt` has to be kept for the docker containers.)
+
+1. Clone the repo.
+2. Run `pip install -r requirements.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
+
+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.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
+```
+
+## Including your algorithm and Evaluating the Results
+
+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.
+
+# Credits
+
+This project is a version of [ann-benchmarks](https://github.com/erikbern/ann-benchmarks) by [Erik Bernhardsson](https://erikbern.com/) and contributors targetting billion-scale datasets.