Blockprint is a tool for measuring client diversity on the Ethereum beacon chain.
It's the backend behind these tweets:
- 12 Jan 2022: https://twitter.com/sproulM_/status/1481109509544513539
- 21 Oct 2021: https://twitter.com/sproulM_/status/1451065804183662592
- 22 Sep 2021: https://twitter.com/sproulM_/status/1440512518242197516
As of Feb 11 2022 Blockprint is hosted on a server managed by Sigma Prime.
For API documentation please see docs/api.md.
Blockprint needs to run alongside a Lighthouse node v2.1.2 or newer.
It uses the /lighthouse/analysis/block_rewards endpoint.
All Python commands should be run from a virtualenv with the dependencies from requirements.txt
installed.
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt
Blockprint's classifier is a k-nearest neighbours classifier in knn_classifier.py.
See ./knn_classifier.py --help for command line options including cross
validation (CV) and manual classification.
The classifier is trained from a directory of reward batches. You can fetch batches with the
load_blocks.py script by providing a start slot, end slot and output directory:
./load_blocks.py 2048001 2048032 testdata
The directory testdata now contains 1 or more files of the form slot_X_to_Y.json downloaded
from Lighthouse.
To train the classifier on this data, use the prepare_training_data.py script:
./prepare_training_data.py testdata testdata_proc
This will read files from testdata and write the graffiti-classified training data to
testdata_proc, which is structured as directories of single block reward files for each
client.
$ tree testdata_proc
testdata_proc
├── Lighthouse
│ ├── 0x03ae60212c73bc2d09dd3a7269f042782ab0c7a64e8202c316cbcaf62f42b942.json
│ └── 0x5e0872a64ea6165e87bc7e698795cb3928484e01ffdb49ebaa5b95e20bdb392c.json
├── Nimbus
│ └── 0x0a90585b2a2572305db37ef332cb3cbb768eba08ad1396f82b795876359fc8fb.json
├── Prysm
│ └── 0x0a16c9a66800bd65d997db19669439281764d541ca89c15a4a10fc1782d94b1c.json
└── Teku
├── 0x09d60a130334aa3b9b669bf588396a007e9192de002ce66f55e5a28309b9d0d3.json
├── 0x421a91ebdb650671e552ce3491928d8f78e04c7c9cb75e885df90e1593ca54d6.json
└── 0x7fedb0da9699c93ce66966555c6719e1159ae7b3220c7053a08c8f50e2f3f56f.json
You can then use this directory as the datadir argument to ./knn_classifier.py:
./knn_classifier.py testdata_proc --classify testdata
If you then want to use the classifier to build an sqlite database:
./build_db.py --db-path block_db.sqlite --classify-dir testdata --data-dir testdata_proc
gunicorn api_server:app --timeout 1800
It will take a few minutes to start-up while it loads all of the training data into memory.