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How to use apply_parameter_changes method in localstack

Best Python code snippet using localstack_python

template_deployer.py

Source:template_deployer.py

...1180            ssm_client = aws_stack.connect_to_service("ssm")1181            param = ssm_client.get_parameter(Name=default_value)1182            return param["Parameter"]["Value"]1183        return None1184    def apply_parameter_changes(self, old_stack, new_stack) -> None:1185        parameters = {1186            p["ParameterKey"]: p1187            for p in old_stack.metadata["Parameters"]  # go through current parameter values1188        }1189        for logical_id, value in new_stack.template["Parameters"].items():1190            default = value.get("Default")1191            provided_param_value = parameters.get(logical_id)1192            param = {1193                "ParameterKey": logical_id,1194                "ParameterValue": provided_param_value if default is None else default,1195            }1196            if default is not None:1197                resolved_value = self.resolve_param(logical_id, value.get("Type"), default)1198                if resolved_value is not None:1199                    param["ResolvedValue"] = resolved_value1200            parameters[logical_id] = param1201        parameters.update({p["ParameterKey"]: p for p in new_stack.metadata["Parameters"]})1202        for change_set in new_stack.change_sets:1203            parameters.update({p["ParameterKey"]: p for p in change_set.metadata["Parameters"]})1204        # TODO: unclear/undocumented behavior in implicitly updating old_stack parameter here1205        old_stack.metadata["Parameters"] = [v for v in parameters.values() if v]1206    # TODO: fix circular import with cloudformation_api.py when importing Stack here1207    def construct_changes(1208        self,1209        existing_stack,1210        new_stack,1211        initialize=False,1212        change_set_id=None,1213        append_to_changeset=False,1214    ):1215        from localstack.services.cloudformation.cloudformation_api import StackChangeSet1216        old_resources = existing_stack.template["Resources"]1217        new_resources = new_stack.template["Resources"]1218        deletes = [val for key, val in old_resources.items() if key not in new_resources]1219        adds = [val for key, val in new_resources.items() if initialize or key not in old_resources]1220        modifies = [val for key, val in new_resources.items() if key in old_resources]1221        changes = []1222        for action, items in (("Remove", deletes), ("Add", adds), ("Modify", modifies)):1223            for item in items:1224                item["Properties"] = item.get("Properties", {})1225                change = self.get_change_config(action, item, change_set_id=change_set_id)1226                changes.append(change)1227        # append changes to change set1228        if append_to_changeset and isinstance(new_stack, StackChangeSet):1229            new_stack.changes.extend(changes)1230        return changes1231    def apply_changes(1232        self,1233        existing_stack,1234        new_stack,1235        stack_name,1236        change_set_id=None,1237        initialize=False,1238        action=None,1239    ):1240        old_resources = existing_stack.template["Resources"]1241        new_resources = new_stack.template["Resources"]1242        action = action or "CREATE"1243        self.init_resource_status(old_resources, action="UPDATE")1244        # apply parameter changes to existing stack1245        self.apply_parameter_changes(existing_stack, new_stack)1246        # construct changes1247        changes = self.construct_changes(1248            existing_stack,1249            new_stack,1250            initialize=initialize,1251            change_set_id=change_set_id,1252        )1253        # check if we have actual changes in the stack, and prepare properties1254        contains_changes = False1255        for change in changes:1256            res_action = change["ResourceChange"]["Action"]1257            resource = new_resources.get(change["ResourceChange"]["LogicalResourceId"])1258            if res_action != "Modify" or self.resource_config_differs(resource):1259                contains_changes = True...

trainer.py

Source:trainer.py

...98                modified_arg += char99        tensorboard_comment += modified_arg100    #params["tb_comment"] = tensorboard_comment101    return tensorboard_comment102def apply_parameter_changes(parameters, env, verbose):103     # NN architectures:104    hidden_size = parameters["hidden_size"]105    # Save in the sense of enough non-linearities per block:106    save_feature_block = [{"name": "linear", "neurons": hidden_size, "act_func": "relu"},107                              {"name": "linear", "neurons": hidden_size}]108    save_hidden_block = [{"name": "linear", "neurons": hidden_size, "act_func": "relu"},109                             {"name": "linear", "neurons": hidden_size, "act_func": "relu"}]110    111    thin_block = [{"name": "linear", "neurons": hidden_size, "act_func": "relu"}]112    test_block = [{"name": "linear", "neurons": 64, "act_func": "relu"}]113    standard_hidden_block = thin_block114    standard_feature_block = thin_block115    layers_feature_vector = standard_hidden_block116    layers_feature_merge = standard_feature_block117    layers_action = standard_feature_block118    layers_state_action_merge = standard_feature_block119    layers_r = standard_hidden_block120    layers_Q = standard_hidden_block121    layers_V = standard_hidden_block122    layers_actor = standard_hidden_block123    mnhi_early = [{"name": "conv", "filters": 32, "kernel_size": 8, "stride": 4, "act_func": "relu"},124                       {"name": "conv", "filters": 64, "kernel_size": 4, "stride": 2, "act_func": "relu"},125                       {"name": "conv", "filters": 64, "kernel_size": 2, "stride": 1, "act_func": "relu"}126                       ]127    mnhi_later = [{"name": "conv", "filters": 32, "kernel_size": 8, "stride": 4, "act_func": "relu"},128                       {"name": "conv", "filters": 64, "kernel_size": 4, "stride": 2, "act_func": "relu"},129                       {"name": "conv", "filters": 64, "kernel_size": 3, "stride": 1, "act_func": "relu"}130                       ]131    vizdoom_winner = [{"name": "conv", "filters": 16, "kernel_size": 3, "stride": 2, "act_func": "relu"},132                        {"name": "conv", "filters": 32, "kernel_size": 3, "stride": 2, "act_func": "relu"},133                        {"name": "conv", "filters": 64, "kernel_size": 3, "stride": 2, "act_func": "relu"},134                        {"name": "conv", "filters": 128, "kernel_size": 3, "stride": 1, "act_func": "relu"},135                        {"name": "conv", "filters": 256, "kernel_size": 3, "stride": 1, "act_func": "relu"}136                      ]137    own_arch = [{"name": "conv", "filters": 16, "kernel_size": 3, "stride": 2, "act_func": "relu"},138                {"name": "conv", "filters": 32, "kernel_size": 3, "stride": 2, "act_func": "relu"},139                {"name": "conv", "filters": 64, "kernel_size": 3, "stride": 2, "act_func": "relu"},140                {"name": "conv", "filters": 128, "kernel_size": 3, "stride": 2, "act_func": "relu"},141                {"name": "conv", "filters": 256, "kernel_size": 3, "stride": 1, "act_func": "relu"}142                ]143    # TODO: define R2D2 conv architecture! (IMPALA uses the same)144    layers_conv = standard_hidden_block145    parameters.update({146        # NN architecture setup:147        "layers_feature_vector": layers_feature_vector, "layers_state_action_merge": layers_state_action_merge,148        "layers_action": layers_action,149        "layers_feature_merge": layers_feature_merge, "layers_r": layers_r, "layers_Q": layers_Q,150        "layers_V": layers_V,151        "layers_actor": layers_actor152    })153        154    #Convert strings in hyperparams to objects:155    # optimizer:156    if parameters["optimizer"] == "RAdam":157        parameters["optimizer"] = RAdam158    elif parameters["optimizer"] == "Adam":159        parameters["optimizer"] = torch.optim.Adam160    # Conv layers:161    if parameters["layers_conv"] == "mnhi_early":162        parameters["layers_conv"] = mnhi_early163    elif parameters["layers_conv"] == "mnhi_later":164        parameters["layers_conv"] = mnhi_later165    elif parameters["layers_conv"] == "vizdoom_winner":166        parameters["layers_conv"] = vizdoom_winner167    elif parameters["layers_conv"] == "own":168        parameters["layers_conv"] = own_arch169    atari_envs = ['adventure', 'airraid', 'alien', 'amidar', 'assault', 'asterix', 'asteroids', 'atlantis',170                    'bank_heist', 'battlezone', 'beam_rider', 'berzerk', 'bowling', 'boxing', 'breakout', 'carnival',171                    'centipede', 'choppercommand', 'crazyclimber', 'defender', 'demonattack', 'doubledunk',172                    'elevatoraction', 'enduro', 'fishingderby', 'freeway', 'frostbite', 'gopher', 'gravitar',173                    'hero', 'icehockey', 'jamesbond', 'journey_escape', 'kangaroo', 'krull', 'kungfumaster',174                    'montezuma_revenge', 'ms_pacman', 'namethisgame', 'phoenix', 'pitfall', 'pong', 'pooyan',175                    'private_eye', 'qbert', 'riverraid', 'road_runner', 'robotank', 'seaquest', 'skiing',176                    'solaris', 'spaceinvaders', 'stargunner', 'tennis', 'timepilot', 'tutankham', 'upndown',177                    'venture', 'videopinball', 'wizardofwor', 'yars_revenge', 'zaxxon']178    if "MineRL" in env:179        if verbose:180            print("MineRL env!")181        use_hierarchical_action_wrapper = True182        parameters["convert_2_torch_wrapper"] = Convert2TorchWrapper183        if use_hierarchical_action_wrapper:184            parameters["action_wrapper"] = HierarchicalActionWrapper185        else:186            parameters["action_wrapper"] = SerialDiscreteActionWrapper187        parameters["use_MineRL_policy"] = True188        #if "Pickaxe" in env or "Diamond" in env:189        #    params["use_MineRL_policy"] = True190    elif any([atari_env in env.lower() for atari_env in atari_envs]):191        parameters["convert_2_torch_wrapper"] = AtariObsWrapper192        if verbose:193            print("Atari env!")194    else:195        parameters["convert_2_torch_wrapper"] = DefaultWrapper196    return parameters197class Trainer:198    def __init__(self, env_name, **kwargs):199        # Set up hyperparameters:200        hyperparameters = get_default_hyperparameters()201        hyperparameters.update(kwargs)202        verbose = hyperparameters["verbose"]203        hyperparameters = apply_parameter_changes(hyperparameters, env_name, verbose)204        self.hyperparameters = hyperparameters205        206        # Create tensorboard experiment comment:207        self.tb_comment = create_comment(hyperparameters["tb_comment"], env_name, kwargs)208        if verbose:209            print("Tensorboard comment: ", self.tb_comment)210        211        # Init logging:212        self.path = os.getcwd()213        self.do_log = hyperparameters["log"]214        self.log = Log(self.path + '/tb_log', self.do_log, self.tb_comment)215        self.verbose = verbose216        217        # Set device...

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