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 utilizes one of two machine learning algorithms:
- K-nearest neighbours
- Multi-layer Perceptron
These can be chosen with the --classifier-type flag in classifier.py.
See ./classifier.py --help for more 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 ./classifier.py:
./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.
Copyright 2021 Sigma Prime and blockprint contributors
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.