1. Deild stats & predictions

Il prossimo turno della 1. Deild Faroe Islands vedrà diverse partite cruciali che potrebbero determinare il corso della stagione. Ecco un elenco delle partite principali:

• B36 Tórshavn vs. KÍ Klaksvík
• Víkingur Gøta vs. NSÍ Runavík
• AB Argir vs. HB Tórshavn

B36 Tórshavn vs. KÍ Klaksvík

Questo match è uno dei più attesi della giornata, dato che B36 Tórshavn e KÍ Klaksvík sono due delle squadre più titolate del campionato. B36 Tórshavn, attualmente al primo posto in classifica, cerca di mantenere il vantaggio sui rivali storici del KÍ Klaksvík.

Predizione: Si prevede un match equilibrato, ma B36 Tórshavn potrebbe avere un leggero vantaggio grazie alla sua forma recente. Un risultato probabile potrebbe essere una vittoria per 2-1 a favore di B36 Tórshavn.

Víkingur Gøta vs. NSÍ Runavík

Víkingur Gøta è una squadra solida con un forte attacco, mentre NSÍ Runavík ha dimostrato una buona difesa nelle ultime partite. Questo incontro potrebbe essere deciso da pochi dettagli.

Predizione: Víkingur Gøta potrebbe avere la meglio grazie alla sua maggiore esperienza in situazioni decisive. Una vittoria per 1-0 o 2-1 sembra plausibile.

AB Argir vs. HB Tórshavn

AB Argir ha mostrato una crescita costante durante la stagione, mentre HB Tórshavn è sempre una minaccia grazie alla sua capacità di ribaltare le partite nei momenti cruciali.

Predizione: HB Tórshavn potrebbe sfruttare la sua esperienza per superare AB Argir in una partita che si preannuncia combattuta. Un possibile risultato potrebbe essere un pareggio o una vittoria per HB Tórshavn per 2-1.

Ogni squadra avrà le sue tattiche specifiche per cercare di ottenere i tre punti. Ecco alcune strategie che potrebbero essere adottate:

• B36 Tórshavn: Probabilmente cercherà di controllare il gioco con il possesso palla e di sfruttare le transizioni rapide per creare occasioni da gol.
• KÍ Klaksvík: Potrebbe adottare un approccio difensivo solido, cercando di colpire in contropiede e sfruttare le ripartenze veloci.
• Víkingur Gøta: Sarà importante mantenere alta la pressione offensiva e sfruttare la fisicità dei propri attaccanti per superare la difesa avversaria.
• NSÍ Runavík: Dovrà concentrarsi sulla solidità difensiva e cercare di capitalizzare sulle poche opportunità offensive.
• AB Argir: Potrebbe cercare di giocare con aggressività, mettendo pressione alta sugli avversari per recuperare il possesso palla rapidamente.
• Hb Tórshavn: Probabilmente adotterà una tattica equilibrata, cercando di sfruttare le proprie qualità tecniche individuali per creare superiorità numerica in attacco.

Ecco alcune statistiche chiave che possono aiutare a comprendere meglio le dinamiche delle partite in programma:

• B36 Tórshavn: Miglior attacco del campionato con una media di 2,5 gol a partita.
• KÍ Klaksvík: Seconda miglior difesa del campionato, subendo mediamente solo 1 gol a partita.
• Víkingur Gøta: Terza miglior squadra in termini di possesso palla, con una media del 55% nelle ultime partite.
• NSÍ Runavík: Ha mantenuto la porta inviolata in tre delle ultime cinque partite.
• AB Argir: Ha vinto quattro delle ultime sei partite casalinghe, dimostrando grande efficacia in casa.
• Hb Tórshavn: Miglior squadra nelle fasi finali delle partite, avendo segnato almeno un gol nei minuti finali in cinque delle ultime sette partite.

Oltre alle prestazioni sul campo, ci sono diversi fattori esterni che possono influenzare l'esito delle partite:

• Clima: Le condizioni meteorologiche possono avere un impatto significativo sulle prestazioni delle squadre, specialmente nelle Isole Fær Øer dove il tempo può cambiare rapidamente.
• Affluenza dei tifosi: La presenza dei tifosi può fornire un'importante spinta motivazionale alle squadre locali, aumentando la pressione sugli avversari ospiti.
• Infortuni e squalifiche: Le assenze chiave a causa di infortuni o squalifiche possono alterare le strategie delle squadre e influenzare l'esito delle partite.

Ecco alcuni suggerimenti utili per gli appassionati che seguono le partite e per i scommettitori che vogliono fare le loro scelte con maggiore consapevolezza:

• Tieniti aggiornato sulle formazioni ufficiali pubblicate dalle squadre prima del calcio d'inizio.
• Osserva le prestazioni recenti delle squadre nei campionati precedenti e nelle competizioni internazionali.
• Tieni d'occhio eventuali dichiarazioni dei tecnici o degli allenatori che potrebbero dare indicazioni sulle strategie adottate dalle squadre.
• Analizza le statistiche dettaglia<|file_sep|># Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from typing import Optional import torch import torch.nn as nn from nni.retiarii.nn.pytorch import ModuleWrapper from nni.retiarii.strategy import Strategy from nni.retiarii.strategy.strategy import StrategyCollection from nni.retiarii.utils import is_iterable class CudaStrategy(Strategy): """ Strategy to move module onto CUDA devices. This strategy will move the input tensor and the module onto the same CUDA device if the input tensor is on a CUDA device. The following environment variable controls the behavior of this strategy: NNI_RETIARII_CUDA_STRATEGY: A comma separated list of GPU device ids. - "auto": Auto choose devices based on availability and batch size. - "0": Use specific GPU device id. - "0,1": Use specific GPU device ids. - "": Do not use CUDA devices. For example: >>> @strategy(CudaStrategy()) >>> def add_relu(x: torch.Tensor) -> torch.Tensor: >>> x = x + x >>> return x.relu() >>> # Input tensor is on CPU >>> add_relu(torch.rand(64)) # Output tensor is on CPU >>> # Input tensor is on CUDA:0 >>> add_relu(torch.rand(64).cuda()) # Output tensor is on CUDA:0 >>> # Input tensor is on CUDA:0 but CUDA:0 is not available >>> add_relu(torch.rand(64).cuda()) # RuntimeError: CUDA error: invalid device ordinal >>> # Using specific GPU devices >>> os.environ["NNI_RETIARII_CUDA_STRATEGY"] = "0" >>> add_relu(torch.rand(64).cuda()) # Output tensor is on CUDA:0 >>> os.environ["NNI_RETIARII_CUDA_STRATEGY"] = "0,1" >>> add_relu(torch.rand(64).cuda()) # Output tensor is on CUDA:0 or CUDA:1 >>> os.environ["NNI_RETIARII_CUDA_STRATEGY"] = "" >>> add_relu(torch.rand(64).cuda()) # Output tensor is on CPU """ def __init__(self, batch_size: Optional[int] = None, device_ids: Optional[StrategyCollection[int]] = None, pin_memory: bool = False): """ Args: batch_size (int): Batch size used for auto choosing GPU devices. device_ids (list(int)): List of GPU device ids to use. If ``batch_size`` and ``device_ids`` are not specified, it will try to auto choose devices based on availability and batch size. pin_memory (bool): If True, the strategy will pin memory when moving tensors onto GPU devices. This can improve performance when transferring data between CPU and GPU. However, it may also increase memory usage and slow down training if the memory usage is high. Default value is False. .. note:: This option only affects the input tensor and does not affect any other tensors created by the module. Therefore it does not affect modules that create tensors internally (e.g., BatchNorm layers). It also does not affect tensors that are already pinned (e.g., tensors created with ``torch.tensor(..., pin_memory=True)``). Furthermore, this option only affects tensors that are moved onto GPU devices by this strategy. It does not affect tensors that are already on GPU devices or tensors that are moved onto GPU devices by other means (e.g., manually using ``tensor.to('cuda')``). .. note:: When using ``pin_memory=True``, it's recommended to set ``non_blocking=True`` when transferring data from CPU to GPU using functions like ``torch.cuda.Stream.copy_()`` or ``torch.utils.data.DataLoader`` with ``pin_memory=True`` option. This can further improve performance by allowing asynchronous data transfers. However, setting ``non_blocking=True`` may cause errors if the data transfer fails or if the data is not yet available on the GPU when it's needed for computation. Therefore it's important to ensure that the data transfer has completed before using the transferred data for computation. .. note:: When using ``pin_memory=True``, it's recommended to use a high-performance storage medium (e.g., SSD) for storing pinned memory buffers to minimize latency and maximize throughput during data transfers between CPU and GPU. Additionally, it's recommended to allocate enough pinned memory buffers to accommodate the maximum amount of data that needs to be transferred at any given time to avoid unnecessary overhead caused by frequent allocation and deallocation of pinned memory buffers. However, allocating too much pinned memory buffers can also cause excessive memory usage and slow down training, so it's important to find a balance between performance and memory usage. .. note:: When using ``pin_memory=True``, it's important to ensure that the system has enough available memory to store both pinned and non-pinned memory buffers simultaneously. If there is not enough available memory, the system may run out of memory and crash or become very slow. Therefore it's important to monitor memory usage and adjust pinned memory settings accordingly. .. note:: When using ``pin_memory=True``, it's recommended to use a high-performance storage medium (e.g., SSD) for storing pinned memory buffers to minimize latency and maximize throughput during data transfers between CPU and GPU. Additionally, it's recommended to allocate enough pinned memory buffers to accommodate the maximum amount of data that needs to be transferred at any given time to avoid unnecessary overhead caused by frequent allocation and deallocation of pinned memory buffers. However, allocating too much pinned memory buffers can also cause excessive memory usage and slow down training, so it's important to find a balance between performance and memory usage. .. note:: When using ``pin_memory=True``, it's important to ensure that the system has enough available memory to store both pinned and non-pinned memory buffers simultaneously. If there is not enough available memory, the system may run out of memory and crash or become very slow. Therefore it's important to monitor memory usage and adjust pinned memory settings accordingly. pin_memory (bool): If True, the strategy will pin memory when moving tensors onto GPU devices. This can improve performance when transferring data between CPU and GPU. However, it may also increase memory usage and slow down training if the memory usage is high. Default value is False. .. note:: This option only affects the input tensor and does not affect any other tensors created by the module. Therefore it does not affect modules that create tensors internally (e.g., BatchNorm layers). It also does not affect tensors that are already pinned (e.g., tensors created with ``torch.tensor(..., pin_memory=True)``). Furthermore, this option only affects tensors that are moved onto GPU devices by this strategy. It does not affect tensors that are already on GPU devices or tensors that are moved onto GPU devices by other means (e.g., manually using ``tensor.to('cuda')``). .. note:: When using ``pin_memory=True``, it's recommended to set ``non_blocking=True`` when transferring data from CPU to GPU using functions like ``torch.cuda.Stream.copy_()`` or ``torch.utils.data.DataLoader`` with ``pin_memory=True`` option. This can further improve performance by allowing asynchronous data transfers. However, setting ``non_blocking=True`` may cause errors if the data transfer fails or if the data is not yet available on the GPU when it's needed for computation. Therefore it's important to ensure that the data transfer has completed before using the transferred data for computation. .. note:: When using ``pin_memory=True``, it's recommended to use a high-performance storage medium (e.g., SSD) for storing pinned memory buffers to minimize latency and maximize throughput during data transfers between CPU and GPU. Additionally, it's recommended to allocate enough pinned memory buffers to accommodate the maximum amount of data that needs to be transferred at any given time to avoid unnecessary overhead caused by frequent allocation and deallocation of pinned memory buffers. However, allocating too much pinned memory buffers can also cause excessive memory usage and slow down training, so it's important to find a balance between performance and memory usage. .. note:: When using ``pin_memory=True``, it's important to ensure that the system has enough available memory to store both pinned and non-pinned memory buffers simultaneously. If there is not enough available memory, the system may run out of memory and crash or become very slow. Therefore it's important to monitor memory usage and adjust pinned memory settings accordingly. auto_choose_gpu_devices(bool): If True, automatically choose available GPUs based on batch size if no specific GPUs are specified via environment variables or function arguments. .. warning:: This option will be removed in future versions as NNI plans phase out support for automatically choosing GPUs based on batch size. Default value is True. .. warning:: This option will be removed in future versions as NNI plans phase out support for automatically choosing GPUs based on batch size. .. warning:: The environment variable NNI_RETIARII_CUDA_STRATEGY="auto" will be removed as well. Instead of relying on NNI_RETIARII_CUDA_STRATEGY="auto" which uses NNI_RETIARII_BATCH_SIZE as default batch size, users should explicitly specify batch_size argument when creating an instance of CudaStrategy(). For example: cuda_strategy = CudaStrategy(batch_size=64) By specifying batch_size explicitly when creating an instance of CudaStrategy(), users can have more control over how GPUs are selected based on