Configuration

Optuna Dashboard supports TOML configuration files for managing complex settings that would be cumbersome to specify via command-line arguments.

Basic Usage

$ optuna-dashboard --from-config config.toml

Configuration File Structure

Dashboard Settings

Settings that can be specified via command-line options can also be configured in TOML files. Configuration file settings are overridden by command-line arguments.

[optuna_dashboard]
storage = "sqlite:///example.db"
storage_class = "RDBStorage"
port = 8080
host = "127.0.0.1"
server = "auto"

LLM Integration

To enable LLM Integration, configure the LLM Provider.

OpenAI

Configure OpenAI or OpenAI-compatible API.

The llm.openai configuration specifies the model name and API type. When use_chat_completions_api is set to true, the OpenAI Chat Completions API will be used; setting it to false will use the Responses API.

The llm.openai.client configuration specifies the OpenAI API key and endpoint, which are passed to the constructor of the openai.OpenAI class.

Warning

Configuration files may contain sensitive information (e.g., API keys and endpoints). You can use environment variables (e.g., export OPENAI_API_KEY=your_api_key) in your shell to avoid hardcoding them.

[llm.openai]
model = "gpt-5-mini"
use_chat_completions_api = true

[llm.openai.client]
api_key = "sk-your-api-key"
base_url = "https://api.openai.example.com/v1"

Azure OpenAI

Configure Azure OpenAI API.

Just as with the OpenAI section, configure Azure OpenAI using llm.azure_openai.

[llm.azure_openai]
model = "gpt-5-mini"
use_chat_completions_api = true

[llm.azure_openai.client]
api_key = "your-azure-api-key"
azure_endpoint = "https://your-resource.openai.azure.example.com/"
api_version = "2024-02-15-preview"

Artifact Storage

You can configure an artifact storage backend to store artifacts generated during the optimization process. Refer to the optuna.artifacts for detailed configuration options.

AWS S3 (Boto3)

[artifact_store.boto3]
bucket_name = "my-optuna-artifacts"

Google Cloud Storage

[artifact_store.gcs]
bucket_name = "my-optuna-artifacts"

Local Filesystem

[artifact_store.filesystem]
base_path = "/path/to/artifacts"

Complete Example

[optuna_dashboard]
storage = "mysql://user:pass@localhost/optuna"
port = 8080
host = "127.0.0.1"

[llm.openai]
model = "gpt-5-mini"

[llm.openai.client]
api_key = "sk-your-openai-key"

[artifact_store.filesystem]
base_path = "/path/to/artifacts"

Priority Order

Configuration values are applied in the following order (higher priority overrides lower):

Command-line arguments (highest priority)
Configuration file (--from-config)
Default values (lowest priority)