📝 Polish readme & naming conventions

.github/workflows/build.yml

@@ -17,8 +17,8 @@ jobs:
         include:
           - os: ubuntu-latest
             python: "3.12"
-          - os: macOS-latest
-            python: "3.12"
+          # - os: macOS-latest
+          #   python: "3.12"
     timeout-minutes: 15
 
     steps:
@@ -39,6 +39,6 @@ jobs:
           import lamindb as ln
           ln.Project(name='Arrayloader benchmarks v2').save()
           "
-      - run: python scripts/run_data_loading_benchmark_on_tahoe100m.py MappedCollection
-      - run: python scripts/run_data_loading_benchmark_on_tahoe100m.py scDataset
-      - run: python scripts/run_data_loading_benchmark_on_tahoe100m.py annbatch
+      - run: python scripts/run_loading_benchmark_on_collection.py MappedCollection
+      - run: python scripts/run_loading_benchmark_on_collection.py scDataset
+      - run: python scripts/run_loading_benchmark_on_collection.py annbatch

README.md

@@ -1,31 +1,43 @@
-# `arrayloader-benchmarks`: Data loader benchmarks for scRNA-seq counts et al.
+# Data loader benchmarks for scRNA-seq counts et al.
 
 _A collaboration between scverse, Lamin, and anyone interested in contributing!_
 
 This repository contains benchmarking scripts & utilities for scRNA-seq data loaders and allows to collaboratively contribute new benchmarking results.
 
-A user can choose between different benchmarking dataset collections:
+## Quickstart
 
-https://lamin.ai/laminlabs/arrayloader-benchmarks/collections
+Setup:
 
-<img width="500" height="481" alt="image" src="https://github.com/user-attachments/assets/b539b13a-9b50-4f66-9b51-16d32fd8566b" />
+```bash
+git clone https://github.com/laminlabs/arrayloader-benchmarks
+cd arrayloader-benchmarks
+uv pip install -e ".[scdataset,annbatch]"  # provide tools you'd like to install
+lamin connect laminlabs/arrayloader-benchmarks  # to contribute results to the hosted lamindb instance, call `lamin init` to create a new lamindb instance
+```
 
 Typical calls of the main benchmarking script are:
 
+```bash
+cd scripts
+python run_loading_benchmark_on_collection.py annbatch   # run annbatch on collection Tahoe100M_tiny, n_datasets = 1
+python run_loading_benchmark_on_collection.py MappedCollection   # run MappedCollection
+python run_loading_benchmark_on_collection.py scDataset   # run scDataset
+python run_loading_benchmark_on_collection.py annbatch --n_datasets -1  # run against all datasets in the collection
+python run_loading_benchmark_on_collection.py annbatch --collection Tahoe100M --n_datasets -1  # run against the full 100M cells
+python run_loading_benchmark_on_collection.py annbatch --collection Tahoe100M --n_datasets 1  # run against the the first dataset, 2M cells
+python run_loading_benchmark_on_collection.py annbatch --collection Tahoe100M --n_datasets 5  # run against the the first dataset, 10M cells
 ```
-python scripts/run_data_loading_benchmark_on_tahoe100m.py annbatch   # run with collection Tahoe100M_tiny, n_datasets = 1
-python scripts/run_data_loading_benchmark_on_tahoe100m.py MappedCollection   # run MappedCollection
-python scripts/run_data_loading_benchmark_on_tahoe100m.py scDataset   # run scDataset
-python scripts/run_data_loading_benchmark_on_tahoe100m.py annbatch --n_datasets -1  # run against all datasets in the collection
-python scripts/run_data_loading_benchmark_on_tahoe100m.py annbatch --collection Tahoe100M --n_datasets -1  # run against the full 100M cells
-python scripts/run_data_loading_benchmark_on_tahoe100m.py annbatch --collection Tahoe100M --n_datasets 1  # run against the the first dataset, 2M cells
-python scripts/run_data_loading_benchmark_on_tahoe100m.py annbatch --collection Tahoe100M --n_datasets 5  # run against the the first dataset, 10M cells
-```
 
-Parameters and results for each run are automatically tracked in a parquet file. Source code and datasets are tracked via data lineage.
+You can choose between different benchmarking [dataset collections](https://lamin.ai/laminlabs/arrayloader-benchmarks/collections).
+
+<img width="1000" height="600" alt="image" src="https://github.com/user-attachments/assets/b539b13a-9b50-4f66-9b51-16d32fd8566b" />
+<br>
+<br>
 
-<img width="1298" height="904" alt="image" src="https://github.com/user-attachments/assets/60c3262f-1bdc-44a4-a488-4784918a6905" />
+When running the script, [parameters and results](https://lamin.ai/laminlabs/arrayloader-benchmarks/artifact/0EiozNVjberZTFHa) are automatically tracked in a parquet file, along with source code, run environment, and input and output datasets.
 
-Results can be downloaded and reproduced from here: https://lamin.ai/laminlabs/arrayloader-benchmarks/artifact/0EiozNVjberZTFHa
+<img width="1000" height="904" alt="image" src="https://github.com/user-attachments/assets/60c3262f-1bdc-44a4-a488-4784918a6905" />
+<br>
+<br>
 
 Note: A previous version of this repo contained the benchmarking scripts accompanying the 2024 blog post: [lamin.ai/blog/arrayloader-benchmarks](https://lamin.ai/blog/arrayloader-benchmarks).