R418, R440, R460, and R520 drivers, which are not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Volta™, NVIDIA Turing™, NVIDIA Ampere architecture, and NVIDIA Hopper™ architecture families. For a list of GPUs to which this compute capability corresponds, see CUDA GPUs. For additional support details R418, R440, R460, and R520 drivers, which are not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA

"user", (续下页) 24 Chapter 1. 文档 Qwen (接上页) "content": "Show me the python code for quick sorting a list of integers." } ], "max_tokens": 512 }' | jq -r '.choices[0].message.content' 1.11.5 使用 Chat GUI LoraArguments: lora_r: int = 64 lora_alpha: int = 16 lora_dropout: float = 0.05 lora_target_modules: List[str] = field( default_factory=lambda: [ "q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj" # Check this issue https://github.com/huggingface/peft/issues/746 for more␣ �→information. if ( list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")) and not training_args.use_lora ): trainer

Keras in this chapter. Available modules Let us first see the list of modules available in the Keras.  Initializers: Provides a list of initializers function. We can learn it in details in Keras learning.  Regularizers: Provides a list of regularizers function. We can learn it in details in Keras Layers chapter.  Constraints: Provides a list of constraints function. We can learn it Layers chapter.  Activations: Provides a list of activator function. We can learn it in details in Keras Layers chapter.  Losses: Provides a list of loss function. We can learn it in details

cmd.exe。或者点击开始菜单，输入“cmd”也可搜索到 cmd.exe 程序，打开即可。输入 conda list 命令即可查看 Python 环境已安装的库，如果是新安装的 Python 环境，则列出的 库都是 Anaconda 自带的软件库，如图 1.24 所示。如果 conda list 能够正常弹出一系列的库 列表信息，说明 Anaconda 软件安装成功；如果 conda 命令不能被识别，则说明安装失败， 将 PyTorch 张量的数据导出为 numpy 数组格式 Out[3]: array([1. , 2. , 3.3], dtype=float32) 创建向量、矩阵、张量等，可以通过 List 容器传给 torch.tensor()函数。例如，创建一 个元素的向量，代码如下： In [4]: a = torch.tensor([1.2]) # 创建一个元素的向量 a, a Python List 列表是 Python 程序中非常重要的数据载体容器，很多 数据都是通过 Python 语言将数据加载至 Array 或者 List 容器，再转换到 Tensor 类型，通过 PyTorch 运算处理后导出到 Array 或者 List 容器，方便其他模块调用。 通过 tf.tensor()函数可以创建新 Tensor，并将保存在 Python List 对象或者 Numpy

the blocks which could produce more complex cells. For primitive operations, NASNet chooses from a list of 13 frequently used operations in convolution networks such as regular convolutions, max pooling cells=2, initial_width=1, initial_channels=4 ) STATE_SPACE = [ dict(name='hidden_state', values=list(range(2)), count=2), dict( name=primitives, values=['sep_3x3', 'sep_5x5', 'sep_7x7', 'avg_3x3', 'max_3x3' the input feature space using a convolution layer with stride 2 """ if self.stride == 2: inputs = list( map( lambda inp: layers.Conv2D( self.channels, 3, strides=(2,2), padding='same' )(inp), inputs

还可以将包含多个datetime对象的列表传给 index参数，同样能创建具有时间戳索引的 Series对象。 date_list = [datetime(2018, 1, 1), datetime(2018, 1, 15] time_se = pd.Series(np.arange(6), index=date_list) 12 创建时间序列 如果希望DataFrame对象具有时间戳索引， 也可以采用上述方式进行创建。 也可以采用上述方式进行创建。 data_demo = [[11, 22, 33], [44, 55, 66]] date_list = [datetime(2018, 1, 23), datetime(2018, 2, 15)] time_df = pd.DataFrame(data_demo, index=date_list) 13 通过时间戳索引选取子集 最简单的选取子集的方式，是直接使用位置 索引来获取具体的数据。 创建时期对象 除了使用上述方式创建PeriodIndex外，还 可以直接在PeriodIndex的构造方法中传入 一组日期字符串。 str_list = ['2010', '2011', '2012'] pd.PeriodIndex(str_list, freq='A-DEC') 39 创建时期对象 DatetimeIndex是用来指代一系列时间点 的一种索引结构，而PeriodIndex则是用

zero. def sparsify_smallest(w, sparsity_rate): w = w.copy() w_1d = np.reshape(w, (-1)) # Create a list of indices sorted by the absolute magnitude of the weights. w_1d_sorted_indices = np.argsort(np.abs(w_1d)) code prepares the input arguments to create a model for pruning. The prunable_blocks variable is the list of names of prunable convolution blocks. We prune all convolution blocks from second (zero indexed) to the pet segmentation model from chapter four. # Pruning start and end blocks prunable_blocks = list(map(lambda l: l.name, model.layers[2:13])) model_for_pruning = create_model_for_pruning(model, prunable_blocks)

data. We used an image of the whale to demonstrate the effects of transformations visually. The above list is not exhaustive, rather we have used it as a guide to help make better transformation choices. A LookupError as e: import nltk nltk.download('wordnet') """ It returns a list of synonyms of the input word. The output list may contain the original word. """ def synonyms(word): results = set() for syn in wordnet.synsets(word): for lemma in syn.lemmas(): results.add(lemma.name()) return list(results) """ It handles the cases when the synonyms for a word are unavailable. It returns the

预备知识 (continued from previous page) return (hasattr(X, "ndim") and X.ndim == 1 or isinstance(X, list) and not hasattr(X[0], "__len__")) if has_one_axis(X): X = [X] if Y is None: X, Y = [[]] * len(X) defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. Keyword arguments: 82 2. 预备知识 out (Tensor, optional): the output tensor. dtype (torch tensor([1., 1., 1., 1.]) 在Jupyter记事本中，我们可以使用?指令在另一个浏览器窗口中显示文档。例如，list?指令将创建 与help(list)指令几乎相同的内容，并在新的浏览器窗口中显示它。此外，如果我们使用两个问号，如list??， 将显示实现该函数的Python代码。 2.7. 查阅文档 83 小结 • 官方文档提供了本书之外的大量描述和示例。 •

returns a dictionary of trainable parameters with their current values • model.parameters() - returns a list of all trainable parameters in the model • model.train() or model.eval() Putting things together regular Python class that inherits from the Dataset class. You can think of it as a kind of a Python list of tuples, each tuple corresponding to one point (features, label) • 3 components: • __init__(self)