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Update README for tig-benchmarker.

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README.md
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@ -13,8 +13,7 @@ This repository contains the implementation of The Innovation Game (TIG).
## Repo Contents
* [tig-algorithms](./tig-algorithms/README.md) - A Rust crate that hosts algorithm submissions made by Innovators in TIG
* [tig-api](./tig-api/README.md) - A Rust crate for making requests to TIG's API
* [tig-benchmarker](./tig-benchmarker/README.md) - A Rust crate that implements a Benchmarker for TIG that can run in the browser
* [tig-benchmarker](./tig-benchmarker/README.md) - Python scripts for running TIG's benchmarker in master/slave configuration
* [tig-challenges](./tig-challenges/README.md) - A Rust crate that contains the implementation of TIG's challenges (computational problems adapted for proof-of-work)
* [tig-protocol](./tig-protocol/README.md) - A Rust crate that contains the implementation of TIG's core protocol logic.
* [tig-structs](./tig-structs/README.md) - A Rust crate that contains the definitions of structs used throughout TIG

233
tig-benchmarker/README.md
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@ -1,132 +1,135 @@
# tig-benchmarker
A Rust crate that implements a Benchmarker for TIG.
Python scripts that implements a master/slave Benchmarker for TIG.
## Compiling Your Benchmarker
`tig-benchmarker` can be compiled into an executable for running standalone, or in slave mode (see notes)
There are two ways to start the master benchmarker:
1. Compile into exectuable and then run the executable:
```
ALGOS_TO_COMPILE="" # See notes
# USE_CUDA="cuda" # See notes
cargo build -p tig-benchmarker --release --no-default-features --features "${ALGOS_TO_COMPILE} ${USE_CUDA}"
# edit below line for your own algorithm selection
SELECTED_ALGORITHMS='{"satisfiability":"schnoing","vehicle_routing":"clarke_wright","knapsack":"dynamic","vector_search":"basic"}'
./target/release/tig-benchmarker <address> <api_key> $SELECTED_ALGORITHMS
```
2. Compile executable in a docker, and run the docker:
```
ALGOS_TO_COMPILE="" # See notes
# USE_CUDA="cuda" # See notes
docker build -f tig-benchmarker/Dockerfile --build-arg features="${ALGOS_TO_COMPILE} ${USE_CUDA}" -t tig-benchmarker .
# edit below line for your own algorithm selection
SELECTED_ALGORITHMS='{"satisfiability":"schnoing","vehicle_routing":"clarke_wright","knapsack":"dynamic","vector_search":"optimal_ann"}'
docker run -it -v $(pwd):/app tig-benchmarker <address> <api_key> $SELECTED_ALGORITHMS
```
**Notes:**
* Setting `ALGOS_TO_COMPILE` will run the selected algorithms directly in your execution environment to filter for nonces that results in solutions. Nonces that are filtered will then be re-run in the WASM virtual machine to compute the necessary solution data for submission.
* **WARNING** before setting `ALGOS_TO_COMPILE`, be sure to thoroughly review the algorithm code for malicious routines as it will be ran directly in your execution environment (not within a sandboxed WASM virtual machine)!
* `ALGOS_TO_COMPILE` is a space separated string of algorithms with format `<challenge_name>_<algorithm_name>`. Example:
```
ALGOS_TO_COMPILE="satisfiability_schnoing vehicle_routing_clarke_wright knapsack_dynamic vector_search_optimal_ann"
```
* You can see available algorithms in the dropdowns of the [Benchmarker UI](https://play.tig.foundation/benchmarker)
* Alternatively, you can use [`script\list_algorithms.sh`](../scripts/list_algorithms.sh)
* `tig-benchmarker` starts a master node by default. The port can be set with `--port <port>` (default 5115)
* `tig-benchmarker` that are started with the option `--master <hostname>` are ran as slaves and will poll the master for jobs
* slaves will ignore any job that doesn't match their algorithm selection
* one possible setup is to run the master with `--workers 0`, and then run a separate slave for each challenge with different number of workers
* `tig-benchmarker` can be executed with `--help` to see all options including setting the number of workers, and setting the duration of a benchmark
* Uncomment `# USE_CUDA="cuda"` to compile `tig-benchmarker` to use CUDA optimisations where they are available.
* You must have a CUDA compatible GPU with CUDA toolkit installed
* You must have set `ALGOS_TO_COMPILE`
* Not all algorithms have CUDA optimisations. If they don't, it will default to using the CPU version
* CUDA optimisations may or may not be more performant
# Python Master Benchmarker
There is the option of running all your standalone benchmarkers in slave mode, and running `main.py` to act as your master. The key benefits of such a setup is:
1. Much easier to change settings for specific challenges/algorithms such as benchmark duration, and duration to wait for slaves to submit solutions
2. Much easier to integrate with dashboards and other tools
3. Much easier to modify to dump logs and other stats for refining your strategy
## Running Python Master
# Setting Up
```
cd tig-monorepo/tig-benchmarker
pip3 install -r requirements.txt
python3 main.py
# install python requirements
pip install -r requirements.txt
# compile tig-worker
cargo build -p tig-worker --release
```
## Customising Your Algorithms
# Master
Edit [tig-benchmarker/master/config.py](./master/config.py)
The master node is responsible for submitting precommits/benchmarks/proofs, generating batches for slaves to work on, and managing the overall benchmarking process.
The current implementation expects only a single instance of `master.py` per `player_id`.
## Usage
Example:
```
PLAYER_ID = "0x1234567890123456789012345678901234567890" # your player_id
API_KEY = "11111111111111111111111111111111" # your api_key
TIG_WORKER_PATH = "/<path to tig-monorepo>/target/release/tig-worker" # path to executable tig-worker
TIG_ALGORITHMS_FOLDER = "/<path to tig-monorepo>/tig-algorithms" # path to tig-algorithms folder
API_URL = "https://mainnet-api.tig.foundation"
usage: python master.py [-h] [--port PORT] [--api API] player_id api_key config_path jobs_folder
if PLAYER_ID is None or API_KEY is None or TIG_WORKER_PATH is None or TIG_ALGORITHMS_FOLDER is None:
raise Exception("Please set the PLAYER_ID, API_KEY, and TIG_WORKER_PATH, TIG_ALGORITHMS_FOLDER variables in 'tig-benchmarker/master/config.py'")
TIG Benchmarker
PORT = 5115
JOBS = dict(
# add an entry for each challenge you want to benchmark
satisfiability=dict(
# add an entry for each algorithm you want to benchmark
schnoing=dict(
benchmark_duration=10000, # amount of time to run the benchmark in milliseconds
wait_slave_duration=5000, # amount of time to wait for slaves to post solutions before submitting
num_jobs=1, # number of jobs to create. each job will sample its own difficulty
weight=1.0, # weight of jobs for this algorithm. more weight = more likely to be picked
)
),
vehicle_routing=dict(
clarke_wright=dict(
benchmark_duration=10000,
wait_slave_duration=5000,
num_jobs=1,
weight=1.0,
)
),
knapsack=dict(
dynamic=dict(
benchmark_duration=10000,
wait_slave_duration=5000,
num_jobs=1,
weight=1.0,
)
),
vector_search=dict(
optimal_ann=dict(
benchmark_duration=30000, # recommend a high duration
wait_slave_duration=30000, # recommend a high duration
num_jobs=1,
weight=1.0,
)
),
)
positional arguments:
player_id Player ID
api_key API Key
config_path Path to the configuration file
jobs_folder Folder to job jobs until their proofs are submitted
options:
-h, --help show this help message and exit
--port PORT Port to run the server on (default: 5115)
--api API API URL (default: https://mainnet-api.tig.foundation)
```
Notes:
* `weight` determine how likely a slave will benchmark that algorithm (if your slave is setup with all algorithms). If algorithm A has weight of 10, and algorithm B has weight of 1, algorithm A is 10x more likely to be picked
* `num_jobs` can usually be left at `1` unless you are running a lot of slaves and want to spread out your compute across different difficulties
* `vector_search` challenge may require longer durations due to its resource requirements
* See [tig-worker/README.md](../tig-worker/README.md) for instructions on compiling `tig-worker`
# Slave
Slave nodes poll the master for batches to work on and process them using `tig-worker`.
## Usage
```
usage: python slave.py [-h] [--workers WORKERS] [--name NAME] [--port PORT] master_ip tig_worker_path tig_algorithms_folder
TIG Slave Benchmarker
positional arguments:
master_ip IP address of the master
tig_worker_path Path to tig-worker executable
tig_algorithms_folder
Path to tig-algorithms folder. Used to save WASMs
options:
-h, --help show this help message and exit
--workers WORKERS Number of workers (default: 8)
--name NAME Name for the slave (default: randomly generated)
--port PORT Port for master (default: 5115)
```
# Benchmarking Process
1. Master submits a precommit (benchmark settings + num_nonces)
2. Once precommit is confirmed, master creates a job and generates "batches"
3. Slaves poll for batches to work on, using `tig-worker` to output the batch merkle_root and nonces for which there are solutions
4. Slaves submit batch results back to master
5. Once all batches are complete, the master calculates the top merkle root, collates solution_nonces, and submits it as a Benchmark
6. When a benchmark is confirmed, master generates priority batches corresponding to the sampled nonces
* The master stores batch merkle_roots to minimise re-computation
7. Slaves working on a priority batch generate the merkle proof for sampled nonces
8. Slaves submit priority batch results back to master
9. Once all priority batches are complete, the master calculates the merkle proofs and submits it as a proof
# Configuration
The config file (JSON format) should contain the following fields:
```
{
"max_precommits_per_block": 1,
"satisfiability": {
"algorithm": "schnoing",
"num_nonces": 100000,
"batch_size": 1024,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
"weight": 1
},
"vehicle_routing": {
"algorithm": "clarke_wright",
"num_nonces": 100000,
"batch_size": 1024,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
"weight": 1
},
"knapsack": {
"algorithm": "dynamic",
"num_nonces": 100000,
"batch_size": 1024,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
"weight": 1
},
"vector_search": {
"algorithm": "basic",
"num_nonces": 1000,
"batch_size": 128,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
"weight": 0
}
}
```
* `max_precommits_per_block`: Number of precommits submitted each block
* For each challenge type:
* `algorithm`: Algorithm to use
* `num_nonces`: Number of nonces for a precommit
* `batch_size`: Must be a power of 2
* `duration_before_batch_retry`: Time in milliseconds before a batch is requeued
* `base_fee_limit`: Maximum amount of TIG willing to pay for a precommit
* `weight`: Used in weighted sampling of a challenge for a precommit
**Important:**
Use `tig-worker compute_batch` to determine an appropiate `num_nonces` and `batch_size`.
* It is recommended that each batch takes around 5-10 seconds max.
* You should target a specific duration for your benchmark
* Example: if you got `2` slaves, and each slave processes a batch of `1000` in `5s`, setting num_nonces to 10,000 should take `10000 / (1000 x 2) x 5 = 25s`
# Finding your API Key

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tig-benchmarker/config.json
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@ -1,8 +1,8 @@
{
"max_precommits_per_block": 2,
"max_precommits_per_block": 1,
"satisfiability": {
"algorithm": "schnoing",
"num_nonces": 1000,
"num_nonces": 100000,
"batch_size": 1024,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
@ -10,7 +10,7 @@
},
"vehicle_routing": {
"algorithm": "clarke_wright",
"num_nonces": 1000,
"num_nonces": 100000,
"batch_size": 1024,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
@ -18,7 +18,7 @@
},
"knapsack": {
"algorithm": "dynamic",
"num_nonces": 1000,
"num_nonces": 100000,
"batch_size": 1024,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
@ -27,7 +27,7 @@
"vector_search": {
"algorithm": "basic",
"num_nonces": 1000,
"batch_size": 1024,
"batch_size": 128,
"duration_before_batch_retry": 30000,
"base_fee_limit": "10000000000000000",
"weight": 0

68
tig-benchmarker/master.py
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@ -1,6 +1,8 @@
import asyncio
import argparse
import os
import random
import sys
from quart import Quart, request, jsonify
from hypercorn.config import Config
from hypercorn.asyncio import serve
@ -72,33 +74,38 @@ async def main(
submissions = []
print(f"[main] {num_precommits_submitted} precommits already submitted for block '{latest_block_id}' (max: {config_manager.config.max_precommits_per_block})")
if num_precommits_submitted < config_manager.config.max_precommits_per_block:
challenge_weights = [
(
c.details.name,
getattr(config_manager.config, c.details.name).weight
challenge_weights = []
for c in query_data.challenges.values():
challenge_config = getattr(config_manager.config, c.details.name)
print(f"[main] challenge: {c.details.name}, base_fee: {c.block_data.base_fee}, config.base_fee_limit: {challenge_config.base_fee_limit}")
if c.block_data.base_fee < challenge_config.base_fee_limit:
challenge_weights.append((
c.details.name,
challenge_config.weight
))
if len(challenge_weights) == 0:
print("[main] no challenges with base_fee below limit")
else:
print(f"[main] weighted sampling challenges: {challenge_weights}")
challenge = random.choices(
[c for c, _ in challenge_weights],
weights=[w for _, w in challenge_weights]
)[0]
challenge_config = getattr(config_manager.config, challenge)
print(f"[main] selected challenge: {challenge}, algorithm: {challenge_config.algorithm}, num_nonces: {challenge_config.num_nonces}")
difficulty = difficulty_manager.sample(challenge)
num_precommits_submitted += 1
req = SubmitPrecommitRequest(
settings=BenchmarkSettings(
player_id=player_id,
algorithm_id=next(a.id for a in query_data.algorithms.values() if a.details.name == challenge_config.algorithm),
challenge_id=next(c.id for c in query_data.challenges.values() if c.details.name == challenge),
difficulty=difficulty,
block_id=query_data.block.id
),
num_nonces=challenge_config.num_nonces
)
for c in query_data.challenges.values()
]
print(f"[main] weighted sampling challenges: {challenge_weights}")
challenge = random.choices(
[c for c, _ in challenge_weights],
weights=[w for _, w in challenge_weights]
)[0]
challenge_config = getattr(config_manager.config, challenge)
print(f"[main] selected challenge: {challenge}, algorithm: {challenge_config.algorithm}, num_nonces: {challenge_config.num_nonces}")
difficulty = difficulty_manager.sample(challenge)
num_precommits_submitted += 1
req = SubmitPrecommitRequest(
settings=BenchmarkSettings(
player_id=player_id,
algorithm_id=next(a.id for a in query_data.algorithms.values() if a.details.name == challenge_config.algorithm),
challenge_id=next(c.id for c in query_data.challenges.values() if c.details.name == challenge),
difficulty=difficulty,
block_id=query_data.block.id
),
num_nonces=challenge_config.num_nonces
)
submissions.append(submissions_manager.post(req))
submissions.append(submissions_manager.post(req))
if (job := submissions_manager.find_benchmark_to_submit(query_data, job_manager)) is not None:
req = SubmitBenchmarkRequest(
@ -135,9 +142,16 @@ if __name__ == "__main__":
args = parser.parse_args()
if not os.path.exists(args.config_path):
print(f"[main] fatal error: config file not found at path: {args.config_path}")
sys.exit(1)
if not os.path.exists(args.jobs_folder):
print(f"[main] fatal error: jobs folder not found at path: {args.jobs_folder}")
sys.exit(1)
config = Config()
config.bind = [f"localhost:{args.port}"]
async def start():
await asyncio.gather(
serve(app, config),

2
tig-benchmarker/tig_benchmarker/structs.py
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@ -203,6 +203,8 @@ class ChallengeBlockData(FromDict):
base_frontier: Frontier
scaled_frontier: Frontier
scaling_factor: float
base_fee: Optional[PreciseNumber]
per_nonce_fee: Optional[PreciseNumber]
@dataclass
class Challenge(FromDict):