| Branch | Tests |
|---|---|
| master |  |
| develop |  |
A python implementation of a class of "correct-by-construction" consensus protocols. Currently, this includes Casper the Friendly Ghost (a blockchain consensus protocol) and Casper the Friendly Binary Consensus Protocol.
Specifications for these protocols can be found here here, but the implementation and the spec may deviate from the spec, as they are still moving targets.
This pre v1.0 implementation is under active development and experimentation and might experience significant organizational and substantive changes. If you use components of this codebase, expect breaking changes to be introduced.
That said, we will try to detail any breaking changes in subsequent releases.
- Python 3
- ubuntu/debian:
sudo apt-get install python3-dev python3-venv python3-tk - OSX via Homebrew:
brew install python3
If you would like to hack on cbc-casper or run the simulations yourself, setup your dev environment with:
make install
Standard simulations are marked up for use as follows:
NOTE: after each viewgraph appears, you must manually exit the window for the simulation to continue to run!
make run-[rand | rrob | full | nofinal | binary]
rand: each round, some randomly selected validators propagate their most recent message to other randomly selected validators, who then create new messages.
rrob: each round, the creator of the last round's block sends it to the next receiver, who then creates a block.
full: each round, all validators receive all other validators previous messages, and then all create messages.
nofinal: each round, two simultaneous round-robin message propagations occur at the same time. This results in validators never being able to finalize later blocks (they may finalize initial blocks, depending on weight distribution).
binary: unlike the above message propagation schemes, this changes the protocol to cbc-casper with binary data structures! Instead of a blockchain, this protocol just comes to consensus on a single bit.
The number of validators, the number of messages that propagate per round, and the report interval can be edited in casper/settings.py.
Advanced simulations can be run with a little command line wizardy.
- First ensure that you are using the virtual environment via:
. venv/bin/activate - Simulations can then be run via
casper.py. The following are example usages:
# run a simulation with 100 validators and random message propagation
python casper.py rand --validators 100
# run a simulation without displaying the viewgraphs, but instead save them and create a GIF
python casper.py rand --hide-display --save
# run a simulation with 20 validators and 1000 rounds of round robin message propagation,
# reporting every 100 rounds
python casper.py rrob --validators 6 --rounds 300 --report-interval 100
# get help and all options for casper.py
python casper.py --help
Simulations can be created/managed by SimulationRunner. See casper.py for sample usage.
More sample simulations with data collection will be added soon.
An Experiment runs a type of simulation some number of times, collects some
specified data on on each simulation and on aggregate of the simulations and
outputs the results in .json and .csv formats for further analysis and visualizations.
Experimental output is written to out/{experiment_name-timestamp}/
The parameters of an experiment are specified via a .json file and are run
using the python script, run_experiment.py. For example:
python run_experiment.py experiments/blockchain/fast_test.json
The following are the fields that make up an experiment to be defined in a .json file:
msg_mode (string): Specifies the message generation/propagation scheme. The
available schemes are "rand", "rrob", "full", and "nofinal".
protocol (string): Specifies the protocol to test. Available protocols are
"blockchain" and "binary" (for now!).
num_simulations (number): Specifies the number of simulations to run. Each
simulation starts with a fresh setup -- messages, validators, etc.
rounds_per_sim (number): Specifies the number of rounds of the message
propagation scheme to run per simulation.
report_interval (number): Specifies at which intervals of the message
propagation to collect data. For example, if rounds_per_sim is 100 and report_interval is 20,
the experiment will collect data at round 20, 40, 60, 80, and 100.
data (list): Specifies which types of data to collect from the simulations as
strings. The available types of data are the methods of Analyzer. New types of data
can be added by simply adding a method to Analyzer and referencing the method
name in data of an experiment.
validator_info (object): Specifies the parameters for generating a validator
set for each simulation.
validator_info.gen_type (string): Specifies the type of validator generation
scheme. The available schemes are "gauss" and "weights".
validator_info.num_validators (number): [only "gauss" gen_type]
Specifies the number of validators per validator set.
validator_info.mu (number): [only "gauss" gen_type]
Specifies the mean of the gaussian distribution used to generate validator weights.
validator_info.sigma (number): [only "gauss" gen_type]
Specifies the standard deviation of the gaussian distribution used to generate validator weights.
validator_info.min_weight (number): [only "gauss" gen_type]
Specifies the absolute minimum validator weight can result from the "gauss"
gen_type
validator_info.weights (array): [only "weights" gen_type]
Specifies an explicit set of validator weights to be used in each simulation.
It is formatted as a json array with positive numbers as values.
To run all tests:
make test
To run a specific test, use (or the equivalent for whatever test you wish to run)
make test TEST=tests/test_safety_oracle.py
To run tests with visualizations:
make test-with-reports
Note: each view graph must be closed for the tests to continue running.