Only one instance on GCP deployment and small tweaks #18
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WalkthroughThe recent updates introduce a more flexible deployment strategy for agents on Google Cloud Platform (GCP), enhancing the deployment script to support dynamic agent selection, scheduling, and branch configuration. Additionally, the prediction market agent tooling has been improved with new agent behaviors, including a random market selection and a deliberate error-raising agent. Furthermore, deployment utility enhancements include timeout settings and automatic retry on failure, improving reliability and control over deployment processes. Changes
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Review Status
Actionable comments generated: 3
Configuration used: CodeRabbit UI
Files selected for processing (3)
- examples/cloud_deployment/gcp/deploy.py (2 hunks)
- prediction_market_agent_tooling/deploy/agent_example.py (1 hunks)
- prediction_market_agent_tooling/deploy/gcp/utils.py (2 hunks)
Additional comments: 4
prediction_market_agent_tooling/deploy/agent_example.py (2)
- 9-9: The change to
DeployableCoinFlipAgentsimplifies the market selection process by always returning a random sample of markets. This approach assumes there is at least one market available. Ensure that the list of markets passed topick_marketsis never empty to avoid potential runtime errors.- 15-17: The introduction of
DeployableAlwaysRaiseAgentwith a method that unconditionally raises aRuntimeErroris a clear way to test error handling mechanisms. However, ensure that this behavior aligns with the testing strategy and that there are mechanisms in place to catch and properly handle this exception during deployment tests.examples/cloud_deployment/gcp/deploy.py (1)
- 36-37: The use of
typer.run(main)to execute the script provides a simple and effective CLI interface. Ensure that the documentation is updated to reflect the new parameters and usage instructions for deploying agents.prediction_market_agent_tooling/deploy/gcp/utils.py (1)
- 18-19: Adding
timeoutandretry_on_failureparameters to thegcloud_deploy_cmdfunction improves the configurability of deployment operations. Ensure that the default values chosen for these parameters are sensible for the majority of deployment scenarios. Additionally, document these parameters to inform users about how to use them effectively.
| def main( | ||
| agent_name: str, cron_schedule: str = "0 */2 * * *", branch: str = "main" | ||
| ) -> None: | ||
| agent: DeployableAgent = { | ||
| "coin_flip": DeployableCoinFlipAgent, | ||
| "always_raise": DeployableAlwaysRaiseAgent, | ||
| }[agent_name]() |
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The dynamic selection of agents based on the agent_name parameter is a significant improvement for deployment flexibility. However, consider validating the agent_name input against the available agent classes to prevent runtime errors from invalid inputs.
+ if agent_name not in ["coin_flip", "always_raise"]:
+ raise ValueError(f"Invalid agent_name: {agent_name}. Available options: 'coin_flip', 'always_raise'")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def main( | |
| agent_name: str, cron_schedule: str = "0 */2 * * *", branch: str = "main" | |
| ) -> None: | |
| agent: DeployableAgent = { | |
| "coin_flip": DeployableCoinFlipAgent, | |
| "always_raise": DeployableAlwaysRaiseAgent, | |
| }[agent_name]() | |
| def main( | |
| agent_name: str, cron_schedule: str = "0 */2 * * *", branch: str = "main" | |
| ) -> None: | |
| if agent_name not in ["coin_flip", "always_raise"]: | |
| raise ValueError(f"Invalid agent_name: {agent_name}. Available options: 'coin_flip', 'always_raise'") | |
| agent: DeployableAgent = { | |
| "coin_flip": DeployableCoinFlipAgent, | |
| "always_raise": DeployableAlwaysRaiseAgent, | |
| }[agent_name]() |
| f"--timeout {timeout}s " | ||
| # Explicitly set no concurrency, min instances to 0 (agent is executed only once in a while) and max instances to 1 (parallel agents aren't allowed). | ||
| "--concurrency 1 " | ||
| "--min-instances 0 " | ||
| "--max-instances 1 " | ||
| ) | ||
| if retry_on_failure: | ||
| cmd += "--retry " |
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The implementation of the timeout and retry_on_failure parameters in the deployment command is correct. However, consider adding error handling or logging to provide feedback when a deployment fails despite retries. This can help in diagnosing deployment issues more efficiently.
+ if retry_on_failure:
+ cmd += "--retry "
+ print("Note: Deployment will retry on failure.")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| f"--timeout {timeout}s " | |
| # Explicitly set no concurrency, min instances to 0 (agent is executed only once in a while) and max instances to 1 (parallel agents aren't allowed). | |
| "--concurrency 1 " | |
| "--min-instances 0 " | |
| "--max-instances 1 " | |
| ) | |
| if retry_on_failure: | |
| cmd += "--retry " | |
| f"--timeout {timeout}s " | |
| # Explicitly set no concurrency, min instances to 0 (agent is executed only once in a while) and max instances to 1 (parallel agents aren't allowed). | |
| "--concurrency 1 " | |
| "--min-instances 0 " | |
| "--max-instances 1 " | |
| ) | |
| if retry_on_failure: | |
| cmd += "--retry " | |
| print("Note: Deployment will retry on failure.") |
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Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (1)
- examples/cloud_deployment/gcp/deploy.py (2 hunks)
Files skipped from review as they are similar to previous changes (1)
- examples/cloud_deployment/gcp/deploy.py
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Review Status
Actionable comments generated: 0
Configuration used: CodeRabbit UI
Files selected for processing (2)
- examples/cloud_deployment/gcp/deploy.py (2 hunks)
- prediction_market_agent_tooling/markets/data_models.py (1 hunks)
Files skipped from review as they are similar to previous changes (1)
- examples/cloud_deployment/gcp/deploy.py
Additional comments: 1
prediction_market_agent_tooling/markets/data_models.py (1)
- 159-159: The change to make
lastBetTimeoptional in theManifoldMarketclass by usingt.Optional[datetime]is appropriate for scenarios where the last bet time might not be available. This enhances the flexibility of the data model to represent various states of a market.Ensure that all parts of the codebase that interact with
ManifoldMarketinstances are updated to handleNonevalues forlastBetTimegracefully. This might involve adding checks or default values wherelastBetTimeis used to prevent any runtime errors or unexpected behavior.
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Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files selected for processing (2)
- examples/cloud_deployment/gcp/deploy.py (2 hunks)
- prediction_market_agent_tooling/deploy/agent.py (2 hunks)
Files skipped from review as they are similar to previous changes (1)
- examples/cloud_deployment/gcp/deploy.py
Additional comments: 3
prediction_market_agent_tooling/deploy/agent.py (3)
- 74-74: The addition of the
gcp_fnameparameter with a default value ofNoneto thedeploy_gcpmethod is a good practice for enhancing flexibility. This allows for dynamic naming of GCP functions based on the deployment context, which aligns with the PR's objectives of improving the deployment process's configurability and reliability.- 89-89: The logic to assign
gcp_fnameusing a conditional expression is correctly implemented. It ensures that ifgcp_fnameis provided, it will be used; otherwise, it falls back to generating a name using theget_gcloud_fnamemethod. This approach maintains backward compatibility while introducing new functionality, which is a positive aspect of these changes.- 71-77: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [1-104]
Overall, the
DeployableAgentclass and the changes made in thedeploy_gcpmethod align well with the PR's objectives of enhancing the deployment process to GCP. The code is well-structured, follows Python best practices, and introduces meaningful improvements to the deployment process's flexibility and reliability. It's recommended to continue monitoring the deployment process for any edge cases or errors that may arise from the new changes and to consider adding unit tests for the new functionality to ensure its correctness and stability.
| return "Success" | ||
| """ | ||
|
|
||
| gcp_fname = self.get_gcloud_fname(market_type) | ||
| gcp_fname = gcp_fname or self.get_gcloud_fname(market_type) | ||
|
|
||
| with tempfile.NamedTemporaryFile(mode="w", suffix=".py") as f: | ||
| f.write(entrypoint_template) |
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📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [74-104]
The deploy_gcp method demonstrates a comprehensive approach to deploying agents to GCP, including dynamic entrypoint generation, deployment parameter handling, and error checking. However, it's important to ensure that the deploy_to_gcp and related functions are robustly implemented to handle the dynamic nature of the entrypoint generation and deployment parameters. Additionally, consider adding more detailed error messages to help diagnose deployment failures more effectively.
Consider enhancing error messages in the deployment process to provide more context on failures, which could aid in troubleshooting.
| ) | ||
| if retry_on_failure: | ||
| cmd += "--retry " |
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I'm not sure what happened before with OpenAI-powered agents, this retry is disabled by default. I tried to deploy agents that always fail with changes from this PR and without, and both are executed just once per 10 min (as scheduled).
I think this PR introduces changes that are worth merging, but it doesn't solve the retries problem. I'll deploy the openai-powered agents tomorrow and monitor it...
kongzii
changed the title
Summary by CodeRabbit
DeployableAlwaysRaiseAgentclass that raises aRuntimeErrorwith a specific message for binary market scenarios.DeployableCoinFlipAgentto consistently return a random sample of markets.timeoutandretry_on_failureparameters for more reliable deployments.