Python cloudpickle.dumps方法代碼示例

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def dumps(obj, protocol=None):
    """Serialize obj as a string of bytes allocated in memory

    protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
    pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed
    between processes running the same Python version.

    Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure
    compatibility with older versions of Python.
    """
    file = StringIO()
    try:
        cp = CloudPickler(file, protocol=protocol)
        cp.dump(obj)
        return file.getvalue()
    finally:
        file.close()


# including pickles unloading functions in this namespace 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def _run_on_cluster(
    experiment_fn: ExperimentFn,
    skein_cluster: SkeinCluster,
    eval_monitor_log_thresholds: Dict[str, Tuple[float, float]] = None,
    n_try: int = 0
) -> Optional[metrics.Metrics]:
    def _new_experiment_fn():
        return _add_monitor_to_experiment(experiment_fn())
    new_experiment_fn = _new_experiment_fn

    # Attempt serialization early to avoid allocating unnecesary resources
    serialized_fn = cloudpickle.dumps(new_experiment_fn)
    with skein_cluster.client:
        return _execute_and_await_termination(
            skein_cluster,
            serialized_fn,
            eval_monitor_log_thresholds,
            n_try=n_try
        ) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def get_safe_experiment_fn(full_fn_name: str, *args):
    """
    tf-yarn serializes the provided experiment function with cloudpickle.dumps.
    This is good for interactive experiments but can sometimes fail
    because the function is not serializable.
    You can use this wrapper function
    if you ship your experiment function (via conda, pex) manually to the workers.

    full_fn_name
        the name of the function ( with the full path to package and module)
        i.e. tf_yarn.my_module.my_experiment_fn

    args
        arguments to be provided to this function

    """
    module_name, fn_name = full_fn_name.rsplit('.', 1)
    module = importlib.import_module(module_name)
    experiment_fn = getattr(module, fn_name)
    return partial(experiment_fn, *args) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def test__prepare_container():
    with contextlib.ExitStack() as stack:
        # mock modules
        mocked_client_call = stack.enter_context(
            patch(f"{MODULE_TO_TEST}.skein.ApplicationClient.from_current"))
        mocked_logs = stack.enter_context(patch(f'{MODULE_TO_TEST}._setup_container_logs'))
        mocked_cluster_spec = stack.enter_context(patch(f'{MODULE_TO_TEST}.cluster.start_cluster'))

        # fill client mock
        mocked_client = mock.MagicMock(spec=skein.ApplicationClient)
        host_port = ('localhost', 1234)
        instances = [('worker', 10), ('chief', 1)]
        mocked_client.kv.wait.return_value = json.dumps(instances).encode()
        mocked_client_call.return_value = mocked_client
        (client, cluster_spec, cluster_tasks) = _prepare_container(host_port)

        # checks
        mocked_logs.assert_called_once()
        mocked_cluster_spec.assert_called_once_with(host_port, mocked_client, cluster_tasks)
        assert client == mocked_client
        assert cluster_tasks == list(iter_tasks(instances)) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def as_dict(self):
        """Convert this objet in a json seriable dict (can be use in __init__)
        """

        # Get the properties inherited from OptiGenAlg
        OptiGenAlgNsga2Deap_dict = super(OptiGenAlgNsga2Deap, self).as_dict()
        if self.toolbox is None:
            OptiGenAlgNsga2Deap_dict["toolbox"] = None
        else:  # Store serialized data (using cloudpickle) and str to read it in json save files
            OptiGenAlgNsga2Deap_dict["toolbox"] = {
                "__class__": str(type(self._toolbox)),
                "__repr__": str(self._toolbox.__repr__()),
                "serialized": dumps(self._toolbox).decode("ISO-8859-2"),
            }
        # The class name is added to the dict fordeserialisation purpose
        # Overwrite the mother class name
        OptiGenAlgNsga2Deap_dict["__class__"] = "OptiGenAlgNsga2Deap"
        return OptiGenAlgNsga2Deap_dict 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def test_pickle_meta_evaluator():
    set_seed(100)
    tasks = SetTaskSampler(lambda: GarageEnv(PointEnv()))
    max_path_length = 200
    env = GarageEnv(PointEnv())
    n_traj = 3
    with tempfile.TemporaryDirectory() as log_dir_name:
        runner = LocalRunner(
            SnapshotConfig(snapshot_dir=log_dir_name,
                           snapshot_mode='last',
                           snapshot_gap=1))
        meta_eval = MetaEvaluator(test_task_sampler=tasks,
                                  max_path_length=max_path_length,
                                  n_test_tasks=10,
                                  n_exploration_traj=n_traj)
        policy = RandomPolicy(env.spec.action_space)
        algo = MockAlgo(env, policy, max_path_length, n_traj, meta_eval)
        runner.setup(algo, env)
        log_file = tempfile.NamedTemporaryFile()
        csv_output = CsvOutput(log_file.name)
        logger.add_output(csv_output)
        meta_eval.evaluate(algo)
        meta_eval_pickle = cloudpickle.dumps(meta_eval)
        meta_eval2 = cloudpickle.loads(meta_eval_pickle)
        meta_eval2.evaluate(algo) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def tcp_send_object(sock, obj, compress=False, pre_pickled=False):
    """Sends any python object over TCP using cloud-pickle with optional LZ4
    compression. Returns True if sent, False if connection closed"""
    data = cloudpickle.dumps(obj) if not pre_pickled else obj
    if compress:
        import lz4.frame
        data = lz4.frame.compress(data)

    # Send metadata to receiver: Size of the data buffer and whether
    # compression is enabled
    sock.send(struct.pack("II",len(data), 1 if compress else 0))
    sent = sock.send(data)
    if not sent:
        return False
    # Assumed either connection closed and sent=0, or the full thing was sent?
    # Maybe not if XFR stopped in the middle??
    assert(sent == len(data))
    return True 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def __init__(self, history_cls, history_args):
        super().__init__()

        self._last_needed_feed_count = 0
        self.results = {}
        self.pending_counts = {}

        # Make sure to use 'spawn' and not 'fork' to allow shared CUDA tensors
        # on linux
        ctx = mp.get_context('spawn')
        self.close_event = ctx.Event()
        self.qevent = ctx.Event()
        # Queue for requests, such as getting training data
        self.request_queue = ctx.Queue(10)
        # Queue for updates like new acting samples and priority updates
        self.update_queue = ctx.Queue(10)
        # Queue for sending back request results
        self.result_queue = ctx.Queue()

        self._process = ctx.Process(
            target=self.run,
            args=(history_cls, cloudpickle.dumps(history_args)))

        self._process.start() 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def run_with_logger(thunk, logdir):
    from epg.launching import logger
    from mpi4py import MPI
    rank = MPI.COMM_WORLD.Get_rank()
    if rank == 0:
        os.makedirs(logdir, exist_ok=True)
    try:
        with logger.scoped_configure(dir=logdir, format_strs=None if rank == 0 else []):
            retval = thunk()
            if rank == 0:
                atomic_write(pickle.dumps(retval, protocol=-1), os.path.join(logdir, 'retval.pkl'))
            return retval
    except Exception as e:
        with open(os.path.join(logdir, "exception%i.txt" % rank), 'wt') as fh:
            fh.write(traceback.format_exc())
        raise e 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def __init__(self, make_env, observation_space):
        self.observation_space = observation_space
        if isinstance(observation_space, gym.spaces.Box):
            num_elems = len(np.array(observation_space.low).flatten())
            zeros = [0] * num_elems
            self._obs_buf = Array('b', zeros)
        else:
            self._obs_buf = None
        self._pipe, other_end = Pipe()
        self._proc = Process(target=self._worker,
                             args=(other_end,
                                   self._obs_buf,
                                   cloudpickle.dumps(make_env)),
                             daemon=True)
        self._proc.start()
        self._running_cmd = None
        other_end.close()
        self._pipe.send(('action_space', None))
        self.action_space = self._get_response() 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def create_worker(
        worker_id: int,
        step_queue: Queue,
        env_factory: Callable[[int, List[SideChannel]], BaseEnv],
        engine_configuration: EngineConfig,
    ) -> UnityEnvWorker:
        parent_conn, child_conn = Pipe()

        # Need to use cloudpickle for the env factory function since function objects aren't picklable
        # on Windows as of Python 3.6.
        pickled_env_factory = cloudpickle.dumps(env_factory)
        child_process = Process(
            target=worker,
            args=(
                child_conn,
                step_queue,
                pickled_env_factory,
                worker_id,
                engine_configuration,
                logger.level,
            ),
        )
        child_process.start()
        return UnityEnvWorker(child_process, worker_id, parent_conn) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def store(key, value, chunksize=950000):
    serialized = cloudpickle.dumps(value, 2)
    values = {}
    for i in xrange(0, len(serialized), chunksize):
        values['%s.%s' % (key, i // chunksize)] = serialized[i:i + chunksize]
    return memcache.set_multi(values) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def __getstate__(self):
        import cloudpickle
        return cloudpickle.dumps(self.x) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def _setup_cluster_spec(
    task_instances: List[Tuple[str, int]],
    app: skein.ApplicationClient
) -> tf.train.ClusterSpec:
    tasks_not_in_cluster = ['evaluator', 'tensorboard']
    cluster_instances = [t for t in task_instances if t[0] not in tasks_not_in_cluster]
    app.kv[constants.KV_CLUSTER_INSTANCES] = json.dumps(cluster_instances).encode()
    return tf.train.ClusterSpec(
        cluster.aggregate_spec(app, list(_internal.iter_tasks(cluster_instances)))
    ) 

# 需要導入模塊: import cloudpickle [as 別名]
# 或者: from cloudpickle import dumps [as 別名]
def _send_config_proto(
        skein_cluster: SkeinCluster,
        tf_session_config: tf.compat.v1.ConfigProto):
    serialized_fn = cloudpickle.dumps(tf_session_config)
    skein_cluster.app.kv[constants.KV_TF_SESSION_CONFIG] = serialized_fn