def unregister(self): """ Unregister this system from the insights service """ machine_id = generate_machine_id() try: logger.debug("Unregistering %s", machine_id) url = self.api_url + "/v1/systems/" + machine_id net_logger.info("DELETE %s", url) self.session.delete(url) logger.info( "Successfully unregistered from the Red Hat Insights Service") write_unregistered_file() get_scheduler().remove_scheduling() return True except requests.ConnectionError as e: logger.debug(e) logger.error("Could not unregister this system") return False
def __init__(self, model, config, batchiter_acoustic, batchiter_train, batchiter_dev): self.config = config self.batchiter_acoustic = batchiter_acoustic self.batchiter_train = batchiter_train self.batchiter_dev = batchiter_dev self.model = model if config["multi_gpu"] == True: self.model_to_pack = self.model.module else: self.model_to_pack = self.model self.device = torch.device( 'cuda:0') if torch.cuda.is_available() else torch.device('cpu') self.num_epoch = config["num_epoch"] self.exp_dir = config["exp_dir"] self.print_inteval = config["print_inteval"] self.accumulate_grad_batch = config["accumulate_grad_batch"] self.init_lr = config["init_lr"] self.grad_max_norm = config["grad_max_norm"] self.label_smooth = config["label_smooth"] self.lambda_qua = config["lambda_qua"] self.lambda_ctc = config["lambda_ctc"] self.num_last_ckpt_keep = None if "num_last_ckpt_keep" in config: self.num_last_ckpt_keep = config["num_last_ckpt_keep"] self.lr_scheduler = schedule.get_scheduler(config["lr_scheduler"]) # Solver state self.epoch = 0 self.step = 0 self.tr_loss = [] self.cv_loss = [] self.lr = self.init_lr if config["optimtype"] == "sgd": self.optimizer = torch.optim.SGD(self.model_to_pack.parameters(), lr=self.lr, momentum=0.9) elif config["optimtype"] == "adam": self.optimizer = torch.optim.Adam(self.model_to_pack.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) else: raise ValueError("Unknown optimizer.") if not os.path.isdir(self.exp_dir): os.makedirs(self.exp_dir)
def __init__(self, model, config, tr_loader, cv_loader): self.config = config self.tr_loader = tr_loader self.cv_loader = cv_loader self.model = model if config['multi_gpu'] == True: self.model_to_pack = self.model.module else: self.model_to_pack = self.model self.device = torch.device( 'cuda:0') if torch.cuda.is_available() else torch.device('cpu') self.num_epoch = config['num_epoch'] self.exp_dir = config['exp_dir'] self.print_inteval = config['print_inteval'] self.accumulate_grad_batch = config['accumulate_grad_batch'] self.init_lr = config['init_lr'] self.grad_max_norm = config['grad_max_norm'] self.label_smooth = config['label_smooth'] self.num_last_ckpt_keep = None if "num_last_ckpt_keep" in config: self.num_last_ckpt_keep = config['num_last_ckpt_keep'] self.lr_scheduler = schedule.get_scheduler(config['lr_scheduler']) # Solver state self.epoch = 0 self.step = 0 self.tr_loss = [] self.cv_loss = [] self.lr = self.init_lr if config['optimtype'] == "sgd": self.optimizer = torch.optim.SGD(self.model_to_pack.parameters(), lr=self.lr, momentum=0.9) elif config['optimtype'] == "adam": self.optimizer = torch.optim.Adam(self.model_to_pack.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) else: raise ValueError("Unknown optimizer.") if not os.path.isdir(self.exp_dir): os.makedirs(self.exp_dir)
def save_model(self, request, obj, form, change): if obj.switch: sche = schedule.get_scheduler() try: sche.remove_job(str(obj.id)) except JobLookupError: pass sche.add_job(schedule.crawl_task, 'interval', id=str(obj.id), seconds=obj.seconds, max_instances=obj.thread_num, args=[obj], name=obj.task_name, jobstore="redis") obj.save()
def __init__(self, model, config, tr_loader, cv_loader): self.config = config self.tr_loader = tr_loader self.cv_loader = cv_loader self.model = model if config["multi_gpu"] == True: self.model_to_pack = self.model.module else: self.model_to_pack = self.model self.num_epoch = config["num_epoch"] self.exp_dir = config["exp_dir"] self.print_inteval = config["print_inteval"] self.accumulate_grad_batch = config["accumulate_grad_batch"] self.init_lr = config["init_lr"] self.grad_max_norm = config["grad_max_norm"] self.label_smooth = config["label_smooth"] self.num_last_ckpt_keep = None if "num_last_ckpt_keep" in config: self.num_last_ckpt_keep = config["num_last_ckpt_keep"] self.lr_scheduler = schedule.get_scheduler(config["lr_scheduler"]) self.metric_summarizer = metric.MetricSummarizer() self.metric_summarizer.register_metric("per_token_loss", display=True, visual=True, optim=True) self.metric_summarizer.register_metric("avg_token_loss", display=True, visual=True, optim=False) self.metric_summarizer.register_metric("per_token_acc", display=True, visual=True, optim=False) self.metric_summarizer.register_metric("avg_token_acc", display=True, visual=True, optim=False) self.metric_summarizer.register_metric("learning_rate", display=True, visual=True, optim=False) self.metric_summarizer.register_metric("token_per_sec", display=True, visual=True, optim=False) if utils.TENSORBOARD_LOGGING == 1: utils.visualizer.set_writer(os.path.join(self.exp_dir, "log")) # trainer state self.epoch = 0 self.step = 0 self.tr_loss = [] self.cv_loss = [] self.lr = self.init_lr if config["optimtype"] == "sgd": self.optimizer = torch.optim.SGD(self.model_to_pack.parameters(), lr=self.lr, momentum=0.9) elif config["optimtype"] == "adam": self.optimizer = torch.optim.Adam(self.model_to_pack.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) else: raise ValueError("Unknown optimizer.") if not os.path.isdir(self.exp_dir): os.makedirs(self.exp_dir) if utils.TENSORBOARD_LOGGING: (ids, labels, paddings) = next(iter(self.cv_loader)) # use a longer one if next(self.model_to_pack.parameters()).is_cuda: ids = ids.cuda() labels = labels.cuda() paddings = paddings.cuda() self.data_for_vis = (ids, labels, paddings)
def job_resume(request): id = request.GET.get("id_") sche = schedule.get_scheduler() sche.resume_job(id) return redirect(schedule_view)
import time import websocket import json import schedule import global_variable import core if __name__ == '__main__': if global_variable.get_slacker().rtm.connect(): response = global_variable.get_slacker().rtm.start() sock_endpoint = response.body['url'] slack_socket = websocket.create_connection(sock_endpoint) scheduler = schedule.get_scheduler() scheduler.start() schedule.set_scheduler(scheduler) schedule.get_scheduler().add_job(func=schedule.process_reserve, trigger='interval', seconds=30) while True: msg = json.loads(slack_socket.recv()) if (len(msg) > 0): res = core.parse_msg(msg) if (res is not None): global_variable.get_slacker().chat.post_message( channel=msg['channel'], text=res, username="******")