BVhSdZddlmZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z dd l mZd Zed d d g e j d GddZedGddZedGddZy)z3A Python interface for creating TensorFlow servers.) cluster_pb2)device_filters_pb2)tensorflow_server_pb2)pywrap_tf_session)errors)compat) deprecation) tf_exportctj}t|tjrK|j||||_|||_|||_||jj||S t|}|3t|jdk(r|jd}n td|0|j|}t|dk(r|d}n td|d}tj|j|||}||jj||S#t$r tdwxYw)aCreates a `tf.train.ServerDef` protocol buffer. Args: server_or_cluster_def: A `tf.train.ServerDef` or `tf.train.ClusterDef` protocol buffer, or a `tf.train.ClusterSpec` object, describing the server to be defined and/or the cluster of which it is a member. job_name: (Optional.) Specifies the name of the job of which the server is a member. Defaults to the value in `server_or_cluster_def`, if specified. task_index: (Optional.) Specifies the task index of the server in its job. Defaults to the value in `server_or_cluster_def`, if specified. Otherwise defaults to 0 if the server's job has only one task. protocol: (Optional.) Specifies the protocol to be used by the server. Acceptable values include `"grpc", "grpc+verbs"`. Defaults to the value in `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`. config: (Options.) A `tf.compat.v1.ConfigProto` that specifies default configuration options for all sessions that run on this server. Returns: A `tf.train.ServerDef`. Raises: TypeError: If the arguments do not have the appropriate type. ValueError: If an argument is not specified and cannot be inferred. z^Could not convert `server_or_cluster_def` to a `tf.train.ServerDef` or `tf.train.ClusterSpec`.rz$Must specify an explicit `job_name`.z&Must specify an explicit `task_index`.grpc)clusterjob_name task_indexprotocol)r ServerDef isinstance MergeFromrrrdefault_session_config ClusterSpec TypeErrorlenjobs ValueError task_indicesas_cluster_def)server_or_cluster_defrrrconfig server_def cluster_specrs U/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/training/server_lib.py_make_server_defr"sy4%..0*%'<'F'FG./$j(j$j ''11&98 5I !67l \   1 $$$Q'?@@!..x8l \ a !!_ ABBh&00++- J ''11&9 1 I H IIIs ; D77E zdistribute.Serverz train.Server)v1cfeZdZdZ d dZdZdZdZedZ edZ e d d Z y) ServeraAn in-process TensorFlow server, for use in distributed training. A `tf.distribute.Server` instance encapsulates a set of devices and a `tf.compat.v1.Session` target that can participate in distributed training. A server belongs to a cluster (specified by a `tf.train.ClusterSpec`), and corresponds to a particular task in a named job. The server can communicate with any other server in the same cluster. Nct||||||_tj|jj |_|r|j yy)aCreates a new server with the given definition. The `job_name`, `task_index`, and `protocol` arguments are optional, and override any information provided in `server_or_cluster_def`. Args: server_or_cluster_def: A `tf.train.ServerDef` or `tf.train.ClusterDef` protocol buffer, or a `tf.train.ClusterSpec` object, describing the server to be created and/or the cluster of which it is a member. job_name: (Optional.) Specifies the name of the job of which the server is a member. Defaults to the value in `server_or_cluster_def`, if specified. task_index: (Optional.) Specifies the task index of the server in its job. Defaults to the value in `server_or_cluster_def`, if specified. Otherwise defaults to 0 if the server's job has only one task. protocol: (Optional.) Specifies the protocol to be used by the server. Acceptable values include `"grpc", "grpc+verbs"`. Defaults to the value in `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`. config: (Options.) A `tf.compat.v1.ConfigProto` that specifies default configuration options for all sessions that run on this server. start: (Optional.) Boolean, indicating whether to start the server after creating it. Defaults to `True`. Raises: tf.errors.OpError: Or one of its subclasses if an error occurs while creating the TensorFlow server. N)r" _server_defc_api TF_NewServerSerializeToString_serverstart)selfrrrrrr,s r!__init__zServer.__init__ksQF((=x(2HfFD%%d&6&6&H&H&JKDL  jjl cttj}nt} tj|j d|_y#t $r Yd|_y|$r Yd|_ywxYwN)rUnimplementedError Exceptionr( TF_ServerStopr+AttributeError)r- exceptions r!__del__zServer.__del__si ++ii   $,,'DL   DL   DL sA A&A&%A&cBtj|jy)zStarts this server. Raises: tf.errors.OpError: Or one of its subclasses if an error occurs while starting the TensorFlow server. N)r(TF_ServerStartr+r-s r!r,z Server.starts &r/cBtj|jy)zBlocks until the server has shut down. This method currently blocks forever. Raises: tf.errors.OpError: Or one of its subclasses if an error occurs while joining the TensorFlow server. N)r( TF_ServerJoinr+r:s r!joinz Server.joins  %r/c|jS)zReturns the `tf.train.ServerDef` for this server. Returns: A `tf.train.ServerDef` protocol buffer that describes the configuration of this server. )r'r:s r!rzServer.server_defs   r/c@tj|jS)azReturns the target for a `tf.compat.v1.Session` to connect to this server. To create a `tf.compat.v1.Session` that connects to this server, use the following snippet: ```python server = tf.distribute.Server(...) with tf.compat.v1.Session(server.target): # ... ``` Returns: A string containing a session target for this server. )r(TF_ServerTargetr+r:s r!targetz Server.targets"   ..r/c&tddgid||S)adCreates a new single-process cluster running on the local host. This method is a convenience wrapper for creating a `tf.distribute.Server` with a `tf.train.ServerDef` that specifies a single-process cluster containing a single task in a job called `"local"`. Args: config: (Options.) A `tf.compat.v1.ConfigProto` that specifies default configuration options for all sessions that run on this server. start: (Optional.) Boolean, indicating whether to start the server after creating it. Defaults to `True`. Returns: A local `tf.distribute.Server`. localhostz localhost:0r )rrr,)r%)rr,s r!create_local_serverzServer.create_local_servers#( ;0! r/)NNNNT)NT) __name__ __module__ __qualname____doc__r.r7r,r=propertyrrA staticmethodrDr/r!r%r%^si 'R&' &   / /$r/r%ztrain.ClusterSpeccleZdZdZdZdZeZdZdZdZ dZ dZ e d Z d Zd Zd Zd ZdZy)raRepresents a cluster as a set of "tasks", organized into "jobs". A `tf.train.ClusterSpec` represents the set of processes that participate in a distributed TensorFlow computation. Every `tf.distribute.Server` is constructed in a particular cluster. To create a cluster with two jobs and five tasks, you specify the mapping from job names to lists of network addresses (typically hostname-port pairs). ```python cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222", "worker1.example.com:2222", "worker2.example.com:2222"], "ps": ["ps0.example.com:2222", "ps1.example.com:2222"]}) ``` Each job may also be specified as a sparse mapping from task indices to network addresses. This enables a server to be configured without needing to know the identity of (for example) all other worker tasks: ```python cluster = tf.train.ClusterSpec({"worker": {1: "worker1.example.com:2222"}, "ps": ["ps0.example.com:2222", "ps1.example.com:2222"]}) ``` clt|tri|_|jD]\}}t|tt frt |Dcic]\}}|| }}}nKt|tr-|jDcic]\}}t||}}}ntd|z||j|<|jyt|tjro||_ i|_|jjD]G}|jjDcic]\}}|| c}}|j|j<Iyt|t rtj|_ |jj#|j%i|_|jjD]G}|jjDcic]\}}|| c}}|j|j<Iytdcc}}wcc}}wcc}}wcc}}w)aCreates a `ClusterSpec`. Args: cluster: A dictionary mapping one or more job names to (i) a list of network addresses, or (ii) a dictionary mapping integer task indices to network addresses; or a `tf.train.ClusterDef` protocol buffer. Raises: TypeError: If `cluster` is not a dictionary mapping strings to lists of strings, and not a `tf.train.ClusterDef` protobuf. zMThe tasks for job %r must be a list or a dictionary from integers to strings.z}`cluster` must be a dictionary mapping one or more job names to lists of network addresses, or a `ClusterDef` protocol bufferN)rdict _cluster_specitemslisttuple enumerateintr_make_cluster_defr ClusterDef _cluster_defjobtasksnamerrr) r-rrrYitask job_tasksjob_defts r!r.zClusterSpec.__init__s'4 d$]]_1/(E edE] +.7.>?71dq$w?)? t $38;;=A4s1vt|A)A68@AB B'08$1  G[33 4!dd&&** '$]]002, QAqD, 7<<(  G[ )%002d !!'"8"8":;d&&** '$]]002, QAqD, 7<<( 5 661@A, , s HH$. H*( H0c,t|jSr1)boolrOr:s r!__bool__zClusterSpec.__bool__>s "" ##r/c |j|k(Sr1rOr-others r!__eq__zClusterSpec.__eq__D    &&r/c |j|k7Sr1rdres r!__ne__zClusterSpec.__ne__Grhr/c|j}t|Dcgc]}t|dzt||z!}}ddj|zdzScc}w)Nz: z ClusterSpec({z, z}))as_dictsortedreprr=)r- key_valuesk string_itemss r!__repr__zClusterSpec.__repr__JsbJ6>#&C:FGQAt((a00GC H JHs*Bc|jS)zFReturns a `tf.train.ClusterDef` protocol buffer based on this cluster.)rWr:s r!rzClusterSpec.as_cluster_defls   r/cHt|jjS)zReturns a list of job names in this cluster. Returns: A list of strings, corresponding to the names of jobs in this cluster. )rQrOkeysr:s r!rzClusterSpec.jobsps ""'') **r/cn |j|}t|S#t$rtd|zwxYw)aReturns the number of tasks defined in the given job. Args: job_name: The string name of a job in this cluster. Returns: The number of tasks defined in the given job. Raises: ValueError: If `job_name` does not name a job in this cluster. No such job in cluster: %r)rOKeyErrorrrr-rrXs r! num_taskszClusterSpec.num_tasksysG@   x (c s8O @ 3h> ??@s4c |j|}tt |j S#t$rtd|zwxYw)aVReturns a list of valid task indices in the given job. Args: job_name: The string name of a job in this cluster. Returns: A list of valid task indices in the given job. Raises: ValueError: If `job_name` does not name a job in this cluster, or no task with index `task_index` is defined in that job. r{)rOr|rrQrmryr}s r!rzClusterSpec.task_indicessT@   x (c sxxz" ## @ 3h> ??@s 3A c |j|} ||S#t$rtd|zwxYw#t$rtd|d|wxYw)aReturns the address of the given task in the given job. Args: job_name: The string name of a job in this cluster. task_index: A non-negative integer. Returns: The address of the given task in the given job. Raises: ValueError: If `job_name` does not name a job in this cluster, or no task with index `task_index` is defined in that job. r{zNo task with index z in job )rOr|r)r-rrrXs r!ruzClusterSpec.task_addresssr@   x (c/ _ @ 3h> ??@ / "H. ///s2/A c |j|}tt |j dzDcgc]}d}}|j D] \}}|||< |S#t$rtd|zwxYwcc}w)aReturns a mapping from task ID to address in the given job. NOTE: For backwards compatibility, this method returns a list. If the given job was defined with a sparse set of task indices, the length of this list may not reflect the number of tasks defined in this job. Use the `tf.train.ClusterSpec.num_tasks` method to find the number of tasks defined in a particular job. Args: job_name: The string name of a job in this cluster. Returns: A list of task addresses, where the index in the list corresponds to the task index of each task. The list may contain `None` if the job was defined with a sparse set of task indices. Raises: ValueError: If `job_name` does not name a job in this cluster. r{r N)rOr|rrangertryrP)r-rrX_rvr[r\s r!r]zClusterSpec.job_taskss(@   x (cs388:23 4A4 4C 499;4c!f J @ 3h> ??@ 4sA" A="A:ctj|_t|jj D]\}} t j|}|jjj}||_ t|j D]*\}} t j|}||j|<,y#t$rtd|zwxYw#t$rtd|zwxYw)zCreates a `tf.train.ClusterDef` based on the given `cluster_spec`. Raises: TypeError: If `cluster_spec` is not a dictionary mapping strings to lists of strings. $Job name %r must be bytes or unicodez(Task address %r must be bytes or unicodeN) rrVrWrmrOrPras_bytesrrXaddrZrY)r-rrYr^r[rus r!rUzClusterSpec._make_cluster_defs$..0D"$"4"4":":"<=(%K??8,!!%%))+ggl#EKKM2(/!\ (6,( a ((K>IJJK (D&'( ( (sC $C( C%(DN)rErFrGrHr.rb __nonzero__rgrjrrrlrrIrr~rrur]rUrKr/r!rrs_<*6X$+''<6 + +$$&/0:(r/rz(config.experimental.ClusterDeviceFiltersc(eZdZdZdZdZdZdZy)ClusterDeviceFiltersaaRepresent a collection of device filters for the remote workers in cluster. NOTE: this is an experimental API and subject to changes. Set device filters for selective jobs and tasks. For each remote worker, the device filters are a list of strings. When any filters are present, the remote worker will ignore all devices which do not match any of its filters. Each filter can be partially specified, e.g. "/job:ps", "/job:worker/replica:3", etc. Note that a device is always visible to the worker it is located on. For example, to set the device filters for a parameter server cluster: ```python cdf = tf.config.experimental.ClusterDeviceFilters() for i in range(num_workers): cdf.set_device_filters('worker', i, ['/job:ps']) for i in range(num_ps): cdf.set_device_filters('ps', i, ['/job:worker']) tf.config.experimental_connect_to_cluster(cluster_def, cluster_device_filters=cdf) ``` The device filters can be partically specified. For remote tasks that do not have device filters specified, all devices will be visible to them. c i|_d|_yr1)_device_filters_cluster_device_filtersr:s r!r.zClusterDeviceFilters.__init__ sD$(D r/ctd|DsJ|jj|i|Dcgc]}|c}|j||<d|_ycc}w)z6Set the device filters for given job name and task id.c3<K|]}t|tywr1)rstr).0dfs r! z:ClusterDeviceFilters.set_device_filters..s?2%K??8,  ( ( - - 1 1 3cch$*5;;=$92 ! & 2C O//#&C ))A, % % , ,S 1  222K>IJJK OG#MN N OsC-)D-DD N)rErFrGrHr.rrrrKr/r!rrs6(((2r/rN)rHtensorflow.core.protobufrrrtensorflow.python.clientrr(tensorflow.python.frameworkrtensorflow.python.utilrr tensorflow.python.util.tf_exportr r"deprecated_endpointsr%rrrKr/r!rs:07:?.).6@F $7#HI!!!.1OO2JOd y(y( y(x 56N2N27N2r/