Slovakia basketball predictions tomorrow

Risk Management Techniques

Effective risk management is essential for long-term betting success. Implement strategies to control your bankroll and minimize losses:

• Bet Sizing:** Allocate a fixed percentage of your bankroll per bet to avoid significant losses in unfavorable outcomes.
• Odds Evaluation:** Carefully assess odds before placing bets to ensure they offer value relative to predicted outcomes.yqzhen/nni<|file_sep|>/examples/algorithms/darts/train_darts.py # Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import argparse import os import torch from nni.algorithms.darts.darts_utils import get_model_from_config from nni.algorithms.darts.train_utils import create_exp_dir from nni.algorithms.darts.train_utils import create_logger from nni.algorithms.darts.train_utils import load_checkpoint from nni.algorithms.darts.train_utils import load_cfg from nni.algorithms.darts.train_utils import save_checkpoint from nni.algorithms.darts.train_utils import set_seed def main(): parser = argparse.ArgumentParser("PyTorch DARTS Training") parser.add_argument("--config", type=str) parser.add_argument("--data", type=str) parser.add_argument("--seed", type=int) parser.add_argument("--gpu", type=int) parser.add_argument("--path", type=str) parser.add_argument("--init_channels", type=int) parser.add_argument("--layers", type=int) parser.add_argument("--model_path", type=str) parser.add_argument("--cutout", action="store_true") parser.add_argument("--cutout_length", type=int) parser.add_argument('--save', '-s', type=str) args = parser.parse_args() # init seed set_seed(args.seed) # init logger if not os.path.exists(args.path): os.makedirs(args.path) print("Create path {}".format(args.path)) os.system("cp {} {}".format(__file__, args.path)) os.system("cp {}/*.py {}".format(os.path.dirname(__file__), args.path)) os.system("cp {}/*.sh {}".format(os.path.dirname(__file__), args.path)) os.system("cp -r {} {}".format(os.path.dirname(__file__), args.path)) print("Copy files from {} to {}".format(os.path.dirname(__file__), args.path)) # init config file if not exist. cfg_file = os.path.join(args.path, "config.yaml") if not os.path.exists(cfg_file): print("Create config file at {}".format(cfg_file)) with open(cfg_file, 'w') as f: f.write('# add config heren') f.write('dataset: cifar10n') f.write('batch_size: 64n') f.write('learning_rate: 0.025n') f.write('learning_rate_min: 0.001n') f.write('learning_rate_decay: 0.97n') f.write('warmup_epochs: 5n') f.write('momentum: 0.9n') f.write('weight_decay: 3e-4n') f.write('report_freq: 50n') f.write('epochs: 50n') f.write('grad_clip: 5n') f.write('train_portion: 0.5n') f.write('init_channels: 16n') f.write('layers: 8n') f.write('auxiliary: Falsen') print("The default config.yaml file is created at {}".format(cfg_file)) cfg = load_cfg(cfg_file) # set config params. cfg['init_channels'] = args.init_channels cfg['layers'] = args.layers cfg['auxiliary'] = False # save config file. with open(cfg_file, 'w') as f: yaml.dump(cfg, f) print("The config file at {} is created".format(cfg_file)) # create exp dir. create_exp_dir(args.save) logger = create_logger(args.save + "/log.txt") logger.info("==========nArgs:{}n==========".format(args)) logger.info(cfg) # save config file. shutil.copy(cfg_file, os.path.join(args.save, 'config.yaml')) logger.info("Copy config file from {} to {}".format( cfg_file, os.path.join(args.save, 'config.yaml'))) logger.info("==========nArgs:{}n==========".format(args)) logger.info(cfg) if __name__ == '__main__': main() <|repo_name|>yqzhen/nni<|file_sep|>/docs/en_US/Tutorial/QuickStart.md # Quick Start NNI is an open source AutoML toolkit developed by Microsoft Research. In this tutorial we will use NNI in two ways: 1. [Using pre-defined search algorithms](#using-pre-defined-search-algorithms) 2. [Developing custom algorithms](#developing-custom-algorithms) ## Using Pre-defined Search Algorithms This part will introduce how to use some pre-defined search algorithms provided by NNI. ### Prepare environment To use NNI we need first install Python>=3.6: bash pip install nni --user -i https://mirrors.aliyun.com/pypi/simple/ Note that we use `--user` because we may not have permission to install python package system wide. If you want to use Docker image provided by NNI instead of installing Python locally: bash docker pull nniorg/tutorials:nas_v1alpha101_cifar10_darts_pyt_1_0_0-gpu-py36-cuda10-cudnn7-devel-ubuntu18.04-pytorch1.0-tf1-gluon-cvm-latest or without GPU: bash docker pull nniorg/tutorials:nas_v1alpha101_cifar10_darts_pyt_1_0_0-gpu-py36-cuda10-cudnn7-devel-ubuntu18.04-pytorch1.0-tf1-gluon-cvm-latest Then you can run this docker image: bash docker run -it --rm -v $PWD:/workspace -w /workspace nniorg/tutorials:nas_v1alpha101_cifar10_darts_pyt_1_0_0-gpu-py36-cuda10-cudnn7-devel-ubuntu18.04-pytorch1.0-tf1-gluon-cvm-latest /bin/bash or without GPU: bash docker run -it --rm -v $PWD:/workspace -w /workspace nniorg/tutorials:nas_v1alpha101_cifar10_darts_pyt_1_0_0-no-gpu-py36-cuda10-cudnn7-devel-ubuntu18.04-pytorch1.0-tf1-gluon-cvm-latest /bin/bash ### Import packages Import packages used in this tutorial: python import json import nni from nni.nas.pytorch.fixed import NasModelSpaceWrapper as ModelSpaceWrapper ### Define model space For example we define model space based on PyTorch framework using following code: python class MyModelSpace(ModelSpaceWrapper): def __init__(self): super(MyModelSpace,self).__init__() self.input_size = [1] + list(self.cfg.data.shape[2:]) self.output_size = self.cfg.num_classes self.build() def build(self): self.input_node = InputNode(self.input_size,name='input') self.conv_node = Conv2dStaticSamePadding(32,[3]*self.input_node.output_shape[2:],name='conv',node=self.input_node) self.pool_node = PoolingStaticSamePadding(pool_type='max',kernel_size=2,stride=2,name='pool',node=self.conv_node) self.dropout_node = DropoutStatic(p=0.5,name='dropout',node=self.pool_node) self.fc_node = LinearStatic(in_features=32*8*8,out_features=self.output_size,name='fc',node=self.dropout_node) self.output_node = OutputNode(node=self.fc_node) my_model_space = MyModelSpace() print(my_model_space.model_str()) Output: text input -> conv -> pool -> dropout -> fc -> output conv : Conv2dStaticSamePadding(in_channels=1,out_channels=32,kernel_size=[3],stride=[1],padding=[1],groups=1,bias=True,name=conv) pool : PoolingStaticSamePadding(pool_type=max,kernel_size=2,stride=2,padding=[0],pool_padding=False,count_include_pad=False,dilation=1,data_format=NCHW,name=pool) dropout : DropoutStatic(p=0.5,name=dropout) fc : LinearStatic(in_features=2048,out_features=10,bias=True,name=fc) We can see that `MyModelSpace` defines a simple CNN model consisting of convolutional layer followed by max pooling layer followed by dropout layer followed by fully connected layer. For more details about how to define model space using PyTorch framework please refer [here](../UserGuide/NAS.md). ### Define trial code Define trial code which will be executed during each trial. python def train_mnist(trial): with ModelSpaceWrapper() as model_space: model_space.config['data'] = [28 * 28] model_space.config['num_classes'] = 10 model_space.build() trial.report(model_space.model_str(), step=0) model_space.search() params = { 'lr': trial.suggest_uniform('lr', 0., 10.) } accuracy = train(model_space.model, params['lr'], epochs=model_space.cfg.get('epochs'), log_step=model_space.cfg.get('log_step')) return accuracy def train(model, lr, epochs, log_step=None): model.to(device=device) optimizer = torch.optim.SGD(model.parameters(), lr=lr) criterion = nn.CrossEntropyLoss() model.train() total_loss = [] for epoch in range(epochs): epoch_loss = [] for batch_idx,(data,target) in enumerate(train_loader): data,target=data.to(device),target.to(device) optimizer.zero_grad() output=model(data) loss=criterion(output,target) loss.backward() optimizer.step() epoch_loss.append(loss.item()) if batch_idx % log_step == log_step - 1: print('[{}/{}][{}/{}] Loss:{:.6f}' .format(epoch+1, epochs, batch_idx+1, len(train_loader), np.mean(epoch_loss))) epoch_loss=[] total_loss.append(np.mean(epoch_loss)) return np.mean(total_loss) if __name__ == '__main__': # parse arguments. args,_ = parse_known_args() # define dataset. train_dataset=torchvision.datasets.MNIST(root='./data', train=True, download=True,