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Merge pull request #14 from NousResearch/2025-05-02-server-cli
This commit is contained in:
hjc-puro
GitHub
commit
629d8c1731
6 changed files with 373 additions and 66 deletions
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@ -20,15 +20,12 @@ import wandb
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import yaml
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from pydantic import BaseModel, Field
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from pydantic_cli import Cmd, FailedExecutionException, run_and_exit
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from rich import print as rprint
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from tenacity import retry, stop_after_attempt, wait_random_exponential
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from transformers import AutoTokenizer
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from atroposlib.envs.constants import (
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ENV_NAMESPACE,
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NAMESPACE_SEP,
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OPENAI_NAMESPACE,
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SERVER_MANAGER_NAMESPACE,
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)
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from atroposlib.envs.constants import ENV_NAMESPACE, NAMESPACE_SEP, OPENAI_NAMESPACE
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from atroposlib.envs.server_handling.openai_server import resolve_openai_configs
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from atroposlib.frontend.jsonl2html import generate_html
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from atroposlib.type_definitions import UUID
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from atroposlib.utils.cli import (
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@ -957,36 +954,150 @@ class BaseEnv(ABC):
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type: The CliServeConfig class for serving commands.
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"""
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env_config, server_configs = cls.config_init()
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# Get the default configurations defined by the specific environment class
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default_env_config, default_server_configs = cls.config_init()
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# Define namespace prefixes for CLI arguments and YAML keys
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env_full_prefix = f"{ENV_NAMESPACE}{NAMESPACE_SEP}"
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openai_full_prefix = f"{OPENAI_NAMESPACE}{NAMESPACE_SEP}"
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# Define the CLI configuration class dynamically
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class CliServeConfig(
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get_prefixed_pydantic_model(type(env_config), env_full_prefix),
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get_prefixed_pydantic_model(OpenaiConfig, openai_full_prefix),
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ServerManagerConfig,
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get_prefixed_pydantic_model(type(default_env_config), env_full_prefix),
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get_prefixed_pydantic_model(
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OpenaiConfig, openai_full_prefix
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), # Use OpenaiConfig for CLI args
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ServerManagerConfig, # ServerManager args are not namespaced by default
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Cmd,
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):
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"""
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Configuration for the serve command.
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This combines BaseEnvConfig and OpenaiConfig into a single command.
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Supports overrides via YAML config file and CLI arguments.
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Order of precedence: CLI > YAML > Class Defaults.
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"""
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config: str | None = Field(
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default=None,
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description="Path to .yaml config file. CLI args override this.",
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)
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def run(self) -> None:
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"""The logic to execute for the 'serve' command."""
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# Convert this config into the formats needed by BaseEnv
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# Set default wandb name if not provided and class has a name
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# Note: This modifies the 'self' instance based on CLI args before full parsing.
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wandb_name_attr = f"{ENV_NAMESPACE}{NAMESPACE_SEP}wandb_name"
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if getattr(self, wandb_name_attr) is None and cls.name is not None:
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if (
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getattr(self, wandb_name_attr, None) is None
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and cls.name is not None
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):
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setattr(self, wandb_name_attr, cls.name)
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model_dumped = self.model_dump(exclude_unset=True)
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server_manager_config = ServerManagerConfig(**model_dumped)
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# Create the environment instance
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# Load configuration from YAML file if specified
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if self.config is not None:
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with open(self.config, "r") as f:
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yaml_config = yaml.safe_load(f)
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print(f"Loaded config from {self.config}")
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else:
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yaml_config = {}
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# Get CLI flags passed with double dashes (e.g., --env--foo bar)
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cli_passed_flags = get_double_dash_flags()
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# --- Configuration Merging ---
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# Priority: CLI > YAML > Class Defaults
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# 1. Environment Configuration
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env_config_dict = merge_dicts(
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default_env_config.model_dump(), # Class Defaults
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yaml_config.get(ENV_NAMESPACE, {}), # YAML config
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extract_namespace(cli_passed_flags, env_full_prefix), # CLI args
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)
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# 2. OpenAI Configuration (used for potential overrides)
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oai_cli_passed_args = extract_namespace(
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cli_passed_flags, openai_full_prefix
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) # CLI args
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yaml_oai_config = yaml_config.get(OPENAI_NAMESPACE, {})
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if isinstance(default_server_configs, ServerBaseline) and (
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oai_cli_passed_args or yaml_oai_config
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):
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raise ValueError(
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"ServerBaseline is not compatible with OpenAI-namespaced CLI arguments. Please edit `config_init` directly or use OpenaiConfig." # noqa: E501
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)
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if (
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isinstance(default_server_configs, list)
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and len(default_server_configs) == 1
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):
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# can't use the same var name because it shadows the class variable and we get an error
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default_openai_config_ = default_server_configs[0]
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else:
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default_openai_config_ = default_server_configs
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if isinstance(yaml_oai_config, list) and len(yaml_oai_config) == 1:
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yaml_oai_config = yaml_oai_config[0]
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if isinstance(default_openai_config_, OpenaiConfig) and isinstance(
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yaml_oai_config, dict
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):
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openai_config_dict = merge_dicts(
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default_openai_config_.model_dump(), # Default OpenaiConfig (or from class init)
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yaml_oai_config,
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oai_cli_passed_args,
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)
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else:
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openai_config_dict = {}
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# 3. Server Manager Configuration (slurm, testing - not namespaced)
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# Extract only relevant CLI flags for ServerManager
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server_manager_cli_passed_flags = {}
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if "slurm" in cli_passed_flags:
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server_manager_cli_passed_flags["slurm"] = cli_passed_flags["slurm"]
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if "testing" in cli_passed_flags:
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server_manager_cli_passed_flags["testing"] = cli_passed_flags[
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"testing"
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]
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server_manager_yaml_dict = {}
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if "slurm" in yaml_config:
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server_manager_yaml_dict["slurm"] = yaml_config["slurm"]
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if "testing" in yaml_config:
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server_manager_yaml_dict["testing"] = yaml_config["testing"]
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server_manager_config_dict = merge_dicts(
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ServerManagerConfig().model_dump(), # Base defaults for ServerManager
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server_manager_yaml_dict, # YAML config
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server_manager_cli_passed_flags, # CLI args
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)
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# --- Instantiate Final Config Objects ---
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# Create instances from the merged dictionaries using the original default types where appropriate
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# Instantiate the final environment config using its original type
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env_config = type(default_env_config)(**env_config_dict)
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# Instantiate the final server manager config
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server_manager_config = ServerManagerConfig(
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**server_manager_config_dict
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)
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# Determine the final server_configs, handling single, multiple servers, and overrides.
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openai_configs = resolve_openai_configs(
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default_server_configs=default_server_configs,
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openai_config_dict=openai_config_dict,
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yaml_config=yaml_config,
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cli_passed_flags=cli_passed_flags,
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logger=logger,
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)
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# --- Create and Run Environment ---
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# Create the environment instance using the final, instantiated config objects
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env = cls(
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config=env_config,
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server_configs=server_configs,
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server_configs=openai_configs,
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slurm=server_manager_config.slurm,
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testing=server_manager_config.testing,
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)
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rprint(env_config)
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rprint(openai_configs)
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# Run the environment
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asyncio.run(env.env_manager())
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@ -1002,11 +1113,14 @@ class BaseEnv(ABC):
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type: The CliProcessConfig class for processing commands.
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"""
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# Define specific default configurations for the 'process' mode
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PROCESS_MODE_ENV_DEFAULT_CONFIG = BaseEnvConfig(
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group_size=8,
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total_steps=2,
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ensure_scores_are_not_same=False,
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include_messages=True,
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data_path_to_save_groups=f"data/{cls.name or 'groups'}.jsonl",
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use_wandb=True,
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)
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PROCESS_MODE_OPENAI_DEFAULT_CONFIG = OpenaiConfig(
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model_name="gpt-4.1-nano",
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@ -1018,19 +1132,24 @@ class BaseEnv(ABC):
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testing=False,
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)
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default_env_config, default_openai_config = cls.config_init()
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if isinstance(default_openai_config, list):
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default_openai_config = default_openai_config[0]
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# Get the base default configurations from the specific environment class
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(
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default_env_config,
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default_server_configs,
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) = cls.config_init()
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# Define namespace prefixes
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env_full_prefix = f"{ENV_NAMESPACE}{NAMESPACE_SEP}"
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openai_full_prefix = f"{OPENAI_NAMESPACE}{NAMESPACE_SEP}"
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# Create Pydantic model classes with the 'process' mode defaults applied.
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# These adjusted classes will be used for final instantiation.
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env_config_cls_new_defaults = adjust_model_defaults(
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type(default_env_config), PROCESS_MODE_ENV_DEFAULT_CONFIG
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)
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openai_config_cls_new_defaults = adjust_model_defaults(
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OpenaiConfig, PROCESS_MODE_OPENAI_DEFAULT_CONFIG
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OpenaiConfig,
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PROCESS_MODE_OPENAI_DEFAULT_CONFIG,
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)
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server_manager_config_cls_new_defaults = adjust_model_defaults(
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ServerManagerConfig,
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@ -1047,6 +1166,8 @@ class BaseEnv(ABC):
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):
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"""
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Configuration for the process command.
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Supports overrides via YAML config file and CLI arguments.
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Order of precedence: CLI > YAML > Process Mode Defaults > `config_init` defaults.
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"""
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config: str | None = Field(
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@ -1056,42 +1177,72 @@ class BaseEnv(ABC):
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def run(self) -> None:
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"""The logic to execute for the 'process' command."""
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# Setup environment configuration
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# Set default wandb name if not provided and class has a name
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wandb_name_attr = f"{ENV_NAMESPACE}{NAMESPACE_SEP}wandb_name"
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if getattr(self, wandb_name_attr) is None and cls.name is not None:
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if (
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getattr(self, wandb_name_attr, None) is None
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and cls.name is not None
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):
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setattr(self, wandb_name_attr, cls.name)
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# Load configuration from YAML file if specified
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if self.config is not None:
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with open(self.config, "r") as f:
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config = yaml.safe_load(f)
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yaml_config = yaml.safe_load(f)
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print(f"Loaded config from {self.config}")
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else:
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config = {}
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yaml_config = {}
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# Get CLI flags passed with double dashes
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cli_passed_flags = get_double_dash_flags()
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# cli args overrides config file which overrides class defaults which overrides process mode defaults
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env_config = env_config_cls_new_defaults(
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**merge_dicts(
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default_env_config.model_dump(),
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PROCESS_MODE_ENV_DEFAULT_CONFIG.model_dump(),
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config.get(ENV_NAMESPACE, {}),
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extract_namespace(
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cli_passed_flags, env_full_prefix
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), # only extract namespace for cli-passed args
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)
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)
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openai_config = openai_config_cls_new_defaults(
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**merge_dicts(
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default_openai_config.model_dump(),
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PROCESS_MODE_OPENAI_DEFAULT_CONFIG.model_dump(),
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config.get(OPENAI_NAMESPACE, {}),
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extract_namespace(
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cli_passed_flags, openai_full_prefix
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), # only extract namespace for cli-passed args
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)
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# --- Configuration Merging ---
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# Priority: CLI > YAML > Process Mode Defaults > `config_init` defaults
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# 1. Environment Configuration
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env_config_dict = merge_dicts(
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default_env_config.model_dump(), # Class Defaults
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PROCESS_MODE_ENV_DEFAULT_CONFIG.model_dump(), # Process Mode Defaults
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yaml_config.get(ENV_NAMESPACE, {}), # YAML config
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extract_namespace(cli_passed_flags, env_full_prefix), # CLI args
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)
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# 2. OpenAI Configuration
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oai_cli_passed_args = extract_namespace(
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cli_passed_flags, openai_full_prefix
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) # CLI args
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yaml_oai_config = yaml_config.get(OPENAI_NAMESPACE, {})
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if isinstance(default_server_configs, ServerBaseline) and (
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oai_cli_passed_args or yaml_oai_config
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):
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raise ValueError(
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"ServerBaseline is not compatible with OpenAI-namespaced CLI arguments. Please edit `config_init` directly or use OpenaiConfig." # noqa: E501
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)
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if (
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isinstance(default_server_configs, list)
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and len(default_server_configs) == 1
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):
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# can't use the same var name because it shadows the class variable and we get an error
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default_openai_config_ = default_server_configs[0]
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else:
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default_openai_config_ = default_server_configs
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if isinstance(yaml_oai_config, list) and len(yaml_oai_config) == 1:
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yaml_oai_config = yaml_oai_config[0]
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if isinstance(default_openai_config_, OpenaiConfig) and isinstance(
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yaml_oai_config, dict
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):
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openai_config_dict = merge_dicts(
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default_openai_config_.model_dump(), # Default OpenaiConfig (or from class init)
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PROCESS_MODE_OPENAI_DEFAULT_CONFIG.model_dump(), # Process Mode Defaults
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yaml_oai_config,
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oai_cli_passed_args,
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)
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else:
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openai_config_dict = {}
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# 3. Server Manager Configuration
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# Extract only relevant CLI flags
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server_manager_cli_passed_flags = {}
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if "slurm" in cli_passed_flags:
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server_manager_cli_passed_flags["slurm"] = cli_passed_flags["slurm"]
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@ -1100,36 +1251,68 @@ class BaseEnv(ABC):
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"testing"
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]
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server_manager_config = server_manager_config_cls_new_defaults(
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**merge_dicts(
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ServerManagerConfig().model_dump(),
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PROCESS_MODE_SERVER_MANAGER_DEFAULT_CONFIG.model_dump(),
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config.get(SERVER_MANAGER_NAMESPACE, {}),
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server_manager_cli_passed_flags,
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)
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server_manager_yaml_dict = {}
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if "slurm" in yaml_config:
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server_manager_yaml_dict["slurm"] = yaml_config["slurm"]
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if "testing" in yaml_config:
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server_manager_yaml_dict["testing"] = yaml_config["testing"]
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server_manager_config_dict = merge_dicts(
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ServerManagerConfig().model_dump(), # Base defaults
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PROCESS_MODE_SERVER_MANAGER_DEFAULT_CONFIG.model_dump(), # Process Mode Defaults
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server_manager_yaml_dict,
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server_manager_cli_passed_flags, # CLI args
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)
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# --- Instantiate Final Config Objects ---
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# Use the classes with adjusted defaults for instantiation
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env_config = env_config_cls_new_defaults(**env_config_dict)
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server_manager_config = server_manager_config_cls_new_defaults(
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**server_manager_config_dict
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)
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# Determine the final server_configs, handling single, multiple servers, and overrides.
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openai_configs = resolve_openai_configs(
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default_server_configs=default_server_configs,
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openai_config_dict=openai_config_dict,
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yaml_config=yaml_config,
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cli_passed_flags=cli_passed_flags,
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logger=logger,
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)
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rprint(env_config)
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rprint(openai_configs)
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# --- Create and Run Environment ---
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# Create the environment instance
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env = cls(
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config=env_config,
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server_configs=[openai_config],
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server_configs=openai_configs,
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slurm=server_manager_config.slurm,
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testing=server_manager_config.testing,
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)
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# Set the process mode parameters
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# Set specific parameters for process mode on the environment instance
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env.process_mode = True
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env.n_groups_to_process = env_config.total_steps
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env.group_size_to_process = env_config.group_size
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# Validate that an output path is set (should have a default from PROCESS_MODE_ENV_DEFAULT_CONFIG)
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if env_config.data_path_to_save_groups is None:
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# This check might be redundant if the default is always set, but good practice.
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raise ValueError(
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"data_path_to_save_groups must be set for process mode"
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)
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print(
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f"Processing {env_config.total_steps} groups of "
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f"{env_config.group_size} responses and "
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f"writing to {env_config.data_path_to_save_groups}"
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)
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# Run the environment's asynchronous process manager function
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asyncio.run(env.process_manager())
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# Actual implementation would go here
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return CliProcessConfig
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