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PMAT 0.24.0 (#5)
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kongzii committed May 7, 2024
1 parent 70e1e3e commit 266b2e4
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4 changes: 2 additions & 2 deletions mech_tool_deployment/agents/mech_agent/deploy.py
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from prediction_market_agent_tooling.benchmark.utils import OutcomePrediction
from prediction_market_agent_tooling.deploy.agent import DeployableAgent
from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
from prediction_market_agent_tooling.markets.agent_market import AgentMarket

from mech_tool_deployment.utils import MechResponse, MechTool, mech_request_local


class DeployableMechAgentBase(DeployableAgent):
class DeployableMechAgentBase(DeployableTraderAgent):
bet_on_n_markets_per_run: int = 5

def load(self) -> None:
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2 changes: 1 addition & 1 deletion mech_tool_deployment/run_agent.py
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"""
Entrypoint for running the agent in GKE.
If the agent adheres to PMAT standard (subclasses DeployableAgent),
If the agent adheres to PMAT standard (subclasses DeployableTraderAgent),
simply add the agent to the `RunnableAgent` enum and then `RUNNABLE_AGENTS` dict.
Can also be executed locally, simply by running `python mech_tool_deployment/run_agent.py <agent> <market_type>`.
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103 changes: 52 additions & 51 deletions poetry.lock

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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -12,7 +12,7 @@ mypy = "^1.8.0"
black = "^23.12.1"
autoflake = "^2.2.1"
isort = "^5.13.2"
prediction-market-agent-tooling = { version = "^0.23.0", extras = ["google"] }
prediction-market-agent-tooling = { version = "^0.24.0", extras = ["google"] }
mech = { git = "https://github.com/evangriffiths/mech.git", branch = "evan/0179545-dev-tomte-dep" }

[build-system]
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