&`iMf ddlZddlZddlZddlmZddlmZmZddlm Z m Z m Z m Z m Z mZmZmZddlZddlmZddlmZddlmZddlmZddlmZddlmZdd lm Z m!Z!dd l"m#Z#dd l$m%Z&dd l'm(Z(ej)e*Z+e(d Gdde,Z%e%j-e!d<e(Gdde,Z.de.fdZ/ dde e.de e.de0de e.fdZ1dS)N)deepcopy)asdict is_dataclass)AnyCallableDictListOptionalSetTupleUnion)#DEFAULT_RUNTIME_ENV_TIMEOUT_SECONDS)get_uri)get_image_uri_plugin_cls)RuntimeEnvPluginSchemaManager)OPTION_TO_NO_PATH_VALIDATION_FNOPTION_TO_VALIDATION_FN) from_dict)RuntimeEnvConfig) PublicAPIstable) stabilityc eZdZUdZhdZeeed<edgdZ e ed<eddfde d e d e eeffd Zed ee dfd dfdZedZd efdZedefdZd e fdZxZS)ra.Used to specify configuration options for a runtime environment. The config is not included when calculating the runtime_env hash, which means that two runtime_envs with the same options but different configs are considered the same for caching purposes. Args: setup_timeout_seconds: The timeout of runtime environment creation, timeout is in seconds. The value `-1` means disable timeout logic, except `-1`, `setup_timeout_seconds` cannot be less than or equal to 0. The default value of `setup_timeout_seconds` is 600 seconds. eager_install: Indicates whether to install the runtime environment on the cluster at `ray.init()` time, before the workers are leased. This flag is set to `True` by default. > log_files eager_installsetup_timeout_seconds known_fieldsTrrr_default_configNrrrcTtt|tst dt ||dkr|dkrt d|||d<t|tst dt |||d<|bt|ts#t d|d t |d |D]&}t|tst d 'n |j d }||d <dS) Nz0setup_timeout_seconds must be of type int, got: rzFsetup_timeout_seconds must be greater than zero or equals to -1, got: rz%eager_install must be a boolean. got rz1log_files must be a list of strings or None, got z with type .z(Each item in log_files must be a string.r) super__init__ isinstanceint TypeErrortype ValueErrorboolliststrr)selfrrr file_name __class__s o/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/runtime_env/runtime_env.pyr$zRuntimeEnvConfig.__init__7s /55 623366 #a ' ',AR,G,GA)>AA )> $%-.. M]8K8KMM !._  i.. @ @@-1)__@@@' P P !)S11P#$NOOOP P,[9I%[configreturnct|tr|St|trt|tjz }t |rtd|dt}tjD]}||vr ||||<tdi|Stdt|)NzTThe following unknown entries in the runtime_env_config dictionary will be ignored: r"zEruntime_env['config'] must be of type dict or RuntimeEnvConfig, got: ) r%rrsetkeysrlenloggerwarningdictr'r()r2unknown_fields config_dictfields r0%parse_and_validate_runtime_env_configz6RuntimeEnvConfig.parse_and_validate_runtime_env_config^s f. / / M  % %  //2B2OON>"" E3AEEE&&K)6 7 7F??)/K&#22k22 2'V '' r1c$tdi|jS)Nr5)rr)clss r0default_configzRuntimeEnvConfig.default_configvs66#"5666r1ct}|d|_|d|_|d |j|d|S)Nrrr)ProtoRuntimeEnvConfigrrrextend)r-runtime_env_configs r0build_proto_runtime_env_configz/RuntimeEnvConfig.build_proto_runtime_env_configzsX244378O3P0+/+@(   (  ( / /[0A B B B!!r1rFc|j}|dkr |jd}|||jt|jS)Nrrr)rrrr+r)rArFrs r0 from_protozRuntimeEnvConfig.from_protosX 2 H !A % %$'$78O$P !s"7,:-788    r1c:tt|SN)r;rr-s r0to_dictzRuntimeEnvConfig.to_dictsHTNN###r1)__name__ __module__ __qualname____doc__rr r,__annotations__rrrr&r*r r r$ staticmethodr r? classmethodrBrDrGrIrM __classcell__r/s@r0rrs{"UTTL#c(TTT"EOT&I")- %&%&"%&%&DI& %&%&%&%&%&%&Nd../ \.77[7"0E""""  ,A    [  $$$$$$$$$r1rr2ceZdZUdZhdZeeed<hdZeeed<dddddddddddddddd e e ed e ed e ed e e ed e e e eefefde e eefde e eefde e e efde e ee eeffde e ee ee eefffde e e efdede ede e effdZdededdffd ZdededdfdZd@dZededdfd Zdefd!Zde fd"Zdefd#Zde efd$Zde efd%Zde efd&Zde efd'Zde efd(Zde efd)Zdefd*Z de efd+Z!de efd,Z"de efd-Z#de e ee eefffd.Z$de e ee ee eeffffd/Z%de fd0Z&defd1Z'defd2Z(defd3Z)defd4Z*defd5Z+de efd6Z,de fd7Z-de fd8Z.de efd9Z/defd:Z0de efd;Z1de efd<Z2de fd=Z3de efd>Z4de e5eeffd?Z6xZ7S)A RuntimeEnva'This class is used to define a runtime environment for a job, task, or actor. See :ref:`runtime-environments` for detailed documentation. This class can be used interchangeably with an unstructured dictionary in the relevant API calls. Can specify a runtime environment whole job, whether running a script directly on the cluster, using Ray Job submission, or using Ray Client: .. code-block:: python from ray.runtime_env import RuntimeEnv # Starting a single-node local Ray cluster ray.init(runtime_env=RuntimeEnv(...)) .. code-block:: python from ray.runtime_env import RuntimeEnv # Connecting to remote cluster using Ray Client ray.init("ray://123.456.7.89:10001", runtime_env=RuntimeEnv(...)) Can specify different runtime environments per-actor or per-task using ``.options()`` or the ``@ray.remote`` decorator: .. code-block:: python from ray.runtime_env import RuntimeEnv # Invoke a remote task that runs in a specified runtime environment. f.options(runtime_env=RuntimeEnv(...)).remote() # Instantiate an actor that runs in a specified runtime environment. actor = SomeClass.options(runtime_env=RuntimeEnv(...)).remote() # Specify a runtime environment in the task definition. Future invocations via # `g.remote()` use this runtime environment unless overridden by using # `.options()` as above. @ray.remote(runtime_env=RuntimeEnv(...)) def g(): pass # Specify a runtime environment in the actor definition. Future instantiations # via `MyClass.remote()` use this runtime environment unless overridden by # using `.options()` as above. @ray.remote(runtime_env=RuntimeEnv(...)) class MyClass: pass Here are some examples of RuntimeEnv initialization: .. code-block:: python # Example for using conda RuntimeEnv(conda={ "channels": ["defaults"], "dependencies": ["codecov"]}) RuntimeEnv(conda="pytorch_p36") # Found on DLAMIs # Example for using container RuntimeEnv( container={"image": "anyscale/ray-ml:nightly-py38-cpu", "run_options": ["--cap-drop SYS_ADMIN","--log-level=debug"]}) # Example for set env_vars RuntimeEnv(env_vars={"OMP_NUM_THREADS": "32", "TF_WARNINGS": "none"}) # Example for set pip RuntimeEnv( pip={"packages":["tensorflow", "requests"], "pip_check": False, "pip_version": "==22.0.2;python_version=='3.8.11'"}) # Example for using image_uri RuntimeEnv( image_uri="rayproject/ray:2.39.0-py312-cu123") Args: py_modules: List of local paths or remote URIs (either in the GCS or external storage), each of which is a zip file that Ray unpacks and inserts into the PYTHONPATH of the workers. working_dir: Local path or remote URI (either in the GCS or external storage) of a zip file that Ray unpacks in the directory of each task/actor. pip: Either a list of pip packages, a string containing the path to a pip requirements.txt file, or a Python dictionary that has three fields: 1) ``packages`` (required, List[str]): a list of pip packages, 2) ``pip_check`` (optional, bool): whether enable pip check at the end of pip install, defaults to False. 3) ``pip_version`` (optional, str): the version of pip, Ray prepends the package name "pip" in front of the ``pip_version`` to form the final requirement string, the syntax of a requirement specifier is defined in full in PEP 508. uv: Either a list of pip packages, or a Python dictionary that has one field: 1) ``packages`` (required, List[str]). conda: Either the conda YAML config, the name of a local conda env (e.g., "pytorch_p36"), or the path to a conda environment.yaml file. Ray automatically injects the dependency into the conda env to ensure compatibility with the cluster Ray. Ray may automatically mangle the conda name to avoid conflicts between runtime envs. This field can't be specified at the same time as the 'pip' field. To use pip with conda, specify your pip dependencies within the conda YAML config: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually container: Require a given (Docker) container image, The Ray worker process runs in a container with this image. This parameter only works alone, or with the ``config`` or ``env_vars`` parameters. The `run_options` list spec is here: https://docs.docker.com/engine/reference/run/ env_vars: Environment variables to set. worker_process_setup_hook: (Experimental) The setup hook that's called after workers start and before Tasks and Actors are scheduled. A module name (string type) or callable (function) can be passed. When a module name is passed, Ray worker should be able to access the module name. When a callable is passed, callable should be serializable. When a runtime env is specified by job submission API, only a module name (string) is allowed. nsight: Dictionary mapping nsight profile option name to it's value. rocprof_sys: Dictionary mapping rocprof-sys profile option name and environment variables to it's value. config: config for runtime environment. Either a dict or a RuntimeEnvConfig. Field: (1) setup_timeout_seconds, the timeout of runtime environment creation, timeout is in seconds. image_uri: URI to a container image. The Ray worker process runs in a container with this image. This parameter only works alone, or with the ``config`` or ``env_vars`` parameters. >uvpipcondar2_nsightenv_varsexcludes container image_uri java_jars py_modules _ray_commit working_dir _ray_release _rocprof_sys py_executable_inject_current_rayworker_process_setup_hookr>rcrerhextensions_fieldsNT)rbrgrdrZr[r_r]rinsight rocprof_sysr2 _validater`rYrbrgrdrZr[r_r]rirkrlr2rmr`rYc t|}|||d<|||d<|||d<|||d<|||d<|||d<| | |d<| | |d<|||d <|||d <| | |d <|||d <| | |d <||| sdS|ddu|dduz|dduzdkrRt d|dd|dd|dd|d rat |hdz }t|rt d|t d|d ret}t || z }t|rt d|tj D]$\}}||}|||=|||<%d|vr9|ds|drtj|d<d|vrdt jvrd|d<t%d|Dr|dSdS)NrbrgrdrZrYr[r\rfr_r]r2rir`zyThe 'pip' field, 'uv' field, and 'conda' field of runtime_env cannot be specified at the same time. specified pip field: z specified conda field: z specified uv field: z To use pip with conda, please only set the 'conda'field, and specify your pip dependencies within the conda YAML config dict: see https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually>r2r]r_zlThe 'container' field currently cannot be used together with other fields of runtime_env. Specified fields: zThe `container` runtime environment field is DEPRECATED and will be removed after July 31, 2025. Use `image_uri` instead. See https://docs.ray.io/en/latest/serve/advanced-guides/multi-app-container.html.zlThe 'image_uri' field currently cannot be used together with other fields of runtime_env. 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W4 ;L;L W W W    ?DFF$$  __V,,  $ K # # #$$r1Fparentchildoverrider3cd|i}|i}t|}t|}|di}|di}|st|t|rdSt|t|rdS|||||r||d<|S)aMerge the parent and child runtime environments. If override = True, the child's runtime env overrides the parent's runtime env in the event of a conflict. Merging happens per key (i.e., "conda", "pip", ...), but "env_vars" are merged per env var key. It returns None if Ray fails to merge runtime environments because of a conflict and `override = False`. Args: parent: Parent runtime env. child: Child runtime env. override: If True, the child's runtime env overrides conflicting fields. Returns: The merged runtime env's if Ray successfully merges them. None if the runtime env's conflict. Empty dict if parent and child are both None. Nr])rpopr6r7 intersectionru)rrrparent_env_varschild_env_varss r0_merge_runtime_envrxs4~ } f  F UOOEjjR00OYYz2..N  v{{}}   * *3uzz||+<+< = = 4 ##%% & & 3 3C8K8K8M8M4N4N O O 4 MM%>***-,z Mr1)F)2rloggingr{copyr dataclassesrrtypingrrrr r r r r ryray._private.ray_constantsrray._private.runtime_env.condarr%ray._private.runtime_env.default_implrray._private.runtime_env.pipr.ray._private.runtime_env.plugin_schema_managerrray._private.runtime_env.uvr#ray._private.runtime_env.validationrrray._private.thirdparty.daciter*ray.core.generated.runtime_environment_pb2rrDray.util.annotationsr getLoggerrNr9r;r?rXrr*rr5r1r0rs  ,,,,,,,,IIIIIIIIIIIIIIIIIIII JJJJJJCCCCCCJJJJJJ??????XXXXXX======544444+*****  8 $ $ Xv$v$v$v$v$tv$v$v$x:   LLLLLLL L^ $* $ $ $ $// Z / J //j //////r1