nemo_gym.cli.env | NeMo Gym

Module Contents

Classes

Name	Description
`PipListConfig`	-
`RunConfig`	Start NeMo Gym servers for agents, models, and resources.
`RunHelper`	-
`TestAllConfig`	Run tests for all server modules in the project.
`TestConfig`	Test a specific server module by running its pytest suite and optionally validating example data.

Functions

Name	Description
`_display_list_of_paths`	-
`_format_pct`	-
`_resolve_server_dir`	Resolve a relative server dir (e.g. `resources_servers/<name>`) to an absolute path.
`_select_shard`	Deterministically select this shard’s subset of modules.
`_test_single`	-
`_validate_data_single`	-
`dump_config`	Display the resolved Hydra configuration for debugging purposes.
`exit_cleanly_on_config_error`	Decorator: turn user-facing ConfigError into a clean message + non-zero exit.
`init_resources_server`	Initialize a new resources server with template files and directory structure.
`list_environments`	List the environments available under environments/, by short name.
`pip_list`	List packages installed in a server’s virtual environment.
`run`	Start NeMo Gym servers for agents, models, and resources.
`status`	-
`test`	-
`test_all`	-
`validate`	Validate a config without starting Ray or any server subprocess.

Data

_FORCE_KILL_REAP_TIMEOUT_SEC

_GRACEFUL_SHUTDOWN_TIMEOUT_SEC

API

class nemo_gym.cli.env.PipListConfig()

Bases: RunConfig

format

Optional[str]

outdated

bool

class nemo_gym.cli.env.RunConfig()

Bases: BaseNeMoGymCLIConfig

Start NeMo Gym servers for agents, models, and resources.

Examples:

config_paths="resources_servers/example_single_tool_call/configs/example_single_tool_call.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
gym env start "+config_paths=[${config_paths}]"

entrypoint

str

class nemo_gym.cli.env.RunHelper()

_head_server

Server

_head_server_instance

HeadServer

_head_server_thread

Thread

_processes

Dict[str, Popen]

_server_client

ServerClient

_server_instance_display_configs

List[ServerInstanceDisplayConfig]

nemo_gym.cli.env.RunHelper.check_http_server_statuses(
    successful_servers: typing.List[str]
) -> typing.List[typing.Tuple[str, nemo_gym.server_utils.ServerStatus]]

nemo_gym.cli.env.RunHelper.display_server_instance_info() -> None

nemo_gym.cli.env.RunHelper.poll() -> None

nemo_gym.cli.env.RunHelper.run_forever() -> None

nemo_gym.cli.env.RunHelper.shutdown() -> None

nemo_gym.cli.env.RunHelper.start(
    global_config_dict_parser_config: nemo_gym.global_config.GlobalConfigDictParserConfig
) -> None

nemo_gym.cli.env.RunHelper.wait_for_dry_run_spinup() -> None

nemo_gym.cli.env.RunHelper.wait_for_spinup() -> None

class nemo_gym.cli.env.TestAllConfig()

Bases: BaseNeMoGymCLIConfig

Run tests for all server modules in the project.

Examples:

gym env test

delete_venvs_after_each_test

bool

fail_on_total_and_test_mismatch

bool

num_shards

int

shard_index

int

class nemo_gym.cli.env.TestConfig()

Bases: RunConfig

Test a specific server module by running its pytest suite and optionally validating example data.

Examples:

gym env test +entrypoint=resources_servers/example_single_tool_call

_dir_path

Path

dir_path

Path

resolved_dir_path

Path

Absolute server dir resolved against the cwd, then the Gym install root.

Use this for filesystem access (reading data, running the suite); use dir_path (the relative entrypoint) for display and example commands shown to the user.

should_validate_data

bool

nemo_gym.cli.env.TestConfig.model_post_init(
    context
)

nemo_gym.cli.env._display_list_of_paths(
    paths: typing.List[pathlib.Path]
) -> str

nemo_gym.cli.env._format_pct(
    count: int,
    total: int
) -> str

nemo_gym.cli.env._resolve_server_dir(
    rel_path: pathlib.Path
) -> pathlib.Path

Resolve a relative server dir (e.g. resources_servers/<name>) to an absolute path.

Checks the current working directory first (a user’s local server), then falls back to the Gym install root (PARENT_DIR) where built-in servers live in both editable and wheel installs. This lets gym env test find and run built-in servers from any cwd, not just a repo checkout.

nemo_gym.cli.env._select_shard(
    dir_paths: typing.List[pathlib.Path],
    shard_index: int,
    num_shards: int
) -> typing.List[pathlib.Path]

Deterministically select this shard’s subset of modules.

Round-robin (stride) over a sorted list spreads heavy modules across shards more evenly than contiguous chunks, which balances wall-time when the suite is parallelized across CI runners.

nemo_gym.cli.env._test_single(
    test_config: nemo_gym.cli.env.TestConfig,
    global_config_dict: omegaconf.DictConfig
) -> subprocess.Popen

nemo_gym.cli.env._validate_data_single(
    test_config: nemo_gym.cli.env.TestConfig
) -> None

nemo_gym.cli.env.dump_config()

Display the resolved Hydra configuration for debugging purposes.

Examples:

gym env resolve "+config_paths=[<config1>,<config2>]"

nemo_gym.cli.env.exit_cleanly_on_config_error(
    fn
)

Decorator: turn user-facing ConfigError into a clean message + non-zero exit.

Config mistakes (missing/typo’d config_paths, malformed config_paths, nothing configured to run) should fail fast with an actionable message, not a Python traceback. Unexpected errors still propagate normally.

nemo_gym.cli.env.init_resources_server()

Initialize a new resources server with template files and directory structure.

Examples:

gym env init --resources-server my_server

nemo_gym.cli.env.list_environments() -> None

List the environments available under environments/, by short name.

Examples:

gym list environments
gym list environments --json

nemo_gym.cli.env.pip_list()

List packages installed in a server’s virtual environment.

nemo_gym.cli.env.run(
    global_config_dict_parser_config: typing.Optional[nemo_gym.global_config.GlobalConfigDictParserConfig] = None
)

Start NeMo Gym servers for agents, models, and resources.

This command reads configuration from YAML files specified via +config_paths and starts all configured servers. The configuration files should define server instances with their entrypoints and settings.

Examples:

# Start servers with specific configs
config_paths="resources_servers/example_single_tool_call/configs/example_single_tool_call.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml"
gym env start "+config_paths=[${config_paths}]"

nemo_gym.cli.env.status()

nemo_gym.cli.env.test()

nemo_gym.cli.env.test_all()

nemo_gym.cli.env.validate()

Validate a config without starting Ray or any server subprocess.

Runs the full config parse — config_paths resolution (missing/malformed), server cross-reference validation, mandatory ??? values, and schema — then exits 0 (valid) or, via exit_cleanly_on_config_error, 1 with a clean traceback-free message. No Ray, no servers, so it returns in well under a second instead of after Ray bootstrap.

No model config is required: a dummy policy_model is injected (the NO_MODEL parser config, as in gym list / env compose) so model interpolations (e.g. ${policy_base_url}) resolve — validation is about config well-formedness, not the model. Pass a model config / --model-type as well if you want it validated too.

Examples:

gym env validate --environment <env>
gym env validate --benchmark <benchmark>
# or by explicit config path(s):
gym env validate --config resources_servers/<env>/configs/<env>.yaml

nemo_gym.cli.env._FORCE_KILL_REAP_TIMEOUT_SEC: int = 2

nemo_gym.cli.env._GRACEFUL_SHUTDOWN_TIMEOUT_SEC: int = 1