102_Python两大法宝.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Python两大法宝"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① Python3.6.3相当于一个package，package里面有不同的区域，不同的区域有不同的工具。\n",
    "\n",
    "② Python语法有两大法宝：dir()、help() 函数。\n",
    " - dir()：打开，看见里面有多少分区、多少工具。\n",
    " - help()：说明书。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "Help on function is_available in module torch.cuda:\n",
      "\n",
      "is_available() -> bool\n",
      "    Returns a bool indicating if CUDA is currently available.\n",
      "\n"
     ]
    },
    {
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       " 'fork',\n",
       " 'frac',\n",
       " 'frac_',\n",
       " 'frexp',\n",
       " 'frobenius_norm',\n",
       " 'from_dlpack',\n",
       " 'from_file',\n",
       " 'from_numpy',\n",
       " 'frombuffer',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'functional',\n",
       " 'fused_moving_avg_obs_fake_quant',\n",
       " 'futures',\n",
       " 'gather',\n",
       " 'gcd',\n",
       " 'gcd_',\n",
       " 'ge',\n",
       " 'geqrf',\n",
       " 'ger',\n",
       " 'get_autocast_cpu_dtype',\n",
       " 'get_autocast_gpu_dtype',\n",
       " 'get_default_dtype',\n",
       " 'get_device',\n",
       " 'get_file_path',\n",
       " 'get_num_interop_threads',\n",
       " 'get_num_threads',\n",
       " 'get_rng_state',\n",
       " 'glob',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'grid_sampler',\n",
       " 'grid_sampler_2d',\n",
       " 'grid_sampler_3d',\n",
       " 'group_norm',\n",
       " 'gru',\n",
       " 'gru_cell',\n",
       " 'gt',\n",
       " 'half',\n",
       " 'hamming_window',\n",
       " 'hann_window',\n",
       " 'hardshrink',\n",
       " 'has_cuda',\n",
       " 'has_cudnn',\n",
       " 'has_lapack',\n",
       " 'has_mkl',\n",
       " 'has_mkldnn',\n",
       " 'has_mlc',\n",
       " 'has_openmp',\n",
       " 'has_spectral',\n",
       " 'heaviside',\n",
       " 'hinge_embedding_loss',\n",
       " 'histc',\n",
       " 'histogram',\n",
       " 'hsmm',\n",
       " 'hsplit',\n",
       " 'hspmm',\n",
       " 'hstack',\n",
       " 'hub',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'i0_',\n",
       " 'igamma',\n",
       " 'igammac',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'import_ir_module',\n",
       " 'import_ir_module_from_buffer',\n",
       " 'index_add',\n",
       " 'index_copy',\n",
       " 'index_fill',\n",
       " 'index_put',\n",
       " 'index_put_',\n",
       " 'index_select',\n",
       " 'inf',\n",
       " 'inference_mode',\n",
       " 'init_num_threads',\n",
       " 'initial_seed',\n",
       " 'inner',\n",
       " 'instance_norm',\n",
       " 'int',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_repr',\n",
       " 'inverse',\n",
       " 'is_anomaly_enabled',\n",
       " 'is_autocast_cache_enabled',\n",
       " 'is_autocast_cpu_enabled',\n",
       " 'is_autocast_enabled',\n",
       " 'is_complex',\n",
       " 'is_conj',\n",
       " 'is_distributed',\n",
       " 'is_floating_point',\n",
       " 'is_grad_enabled',\n",
       " 'is_inference',\n",
       " 'is_inference_mode_enabled',\n",
       " 'is_loaded',\n",
       " 'is_neg',\n",
       " 'is_nonzero',\n",
       " 'is_same_size',\n",
       " 'is_signed',\n",
       " 'is_storage',\n",
       " 'is_tensor',\n",
       " 'is_vulkan_available',\n",
       " 'is_warn_always_enabled',\n",
       " 'isclose',\n",
       " 'isfinite',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'istft',\n",
       " 'jit',\n",
       " 'kaiser_window',\n",
       " 'kernel32',\n",
       " 'kl_div',\n",
       " 'kron',\n",
       " 'kthvalue',\n",
       " 'last_error',\n",
       " 'layer_norm',\n",
       " 'layout',\n",
       " 'lcm',\n",
       " 'lcm_',\n",
       " 'ldexp',\n",
       " 'ldexp_',\n",
       " 'le',\n",
       " 'legacy_contiguous_format',\n",
       " 'lerp',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lgamma',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'load',\n",
       " 'lobpcg',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log10_',\n",
       " 'log1p',\n",
       " 'log1p_',\n",
       " 'log2',\n",
       " 'log2_',\n",
       " 'log_',\n",
       " 'log_softmax',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logcumsumexp',\n",
       " 'logdet',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logit',\n",
       " 'logit_',\n",
       " 'logspace',\n",
       " 'logsumexp',\n",
       " 'long',\n",
       " 'lstm',\n",
       " 'lstm_cell',\n",
       " 'lstsq',\n",
       " 'lt',\n",
       " 'lu',\n",
       " 'lu_solve',\n",
       " 'lu_unpack',\n",
       " 'manual_seed',\n",
       " 'margin_ranking_loss',\n",
       " 'masked_fill',\n",
       " 'masked_scatter',\n",
       " 'masked_select',\n",
       " 'matmul',\n",
       " 'matrix_exp',\n",
       " 'matrix_power',\n",
       " 'matrix_rank',\n",
       " 'max',\n",
       " 'max_pool1d',\n",
       " 'max_pool1d_with_indices',\n",
       " 'max_pool2d',\n",
       " 'max_pool3d',\n",
       " 'maximum',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memory_format',\n",
       " 'merge_type_from_type_comment',\n",
       " 'meshgrid',\n",
       " 'min',\n",
       " 'minimum',\n",
       " 'miopen_batch_norm',\n",
       " 'miopen_convolution',\n",
       " 'miopen_convolution_transpose',\n",
       " 'miopen_depthwise_convolution',\n",
       " 'miopen_rnn',\n",
       " 'mkldnn_adaptive_avg_pool2d',\n",
       " 'mkldnn_convolution',\n",
       " 'mkldnn_convolution_backward_weights',\n",
       " 'mkldnn_linear_backward_weights',\n",
       " 'mkldnn_max_pool2d',\n",
       " 'mkldnn_max_pool3d',\n",
       " 'mm',\n",
       " 'mode',\n",
       " 'moveaxis',\n",
       " 'movedim',\n",
       " 'msort',\n",
       " 'mul',\n",
       " 'multinomial',\n",
       " 'multiply',\n",
       " 'multiprocessing',\n",
       " 'mv',\n",
       " 'mvlgamma',\n",
       " 'name',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nan_to_num_',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanquantile',\n",
       " 'nansum',\n",
       " 'narrow',\n",
       " 'narrow_copy',\n",
       " 'native_batch_norm',\n",
       " 'native_group_norm',\n",
       " 'native_layer_norm',\n",
       " 'native_norm',\n",
       " 'ne',\n",
       " 'neg',\n",
       " 'neg_',\n",
       " 'negative',\n",
       " 'negative_',\n",
       " 'nextafter',\n",
       " 'nn',\n",
       " 'no_grad',\n",
       " 'nonzero',\n",
       " 'norm',\n",
       " 'norm_except_dim',\n",
       " 'normal',\n",
       " 'not_equal',\n",
       " 'nuclear_norm',\n",
       " 'numel',\n",
       " 'nvtoolsext_dll_path',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'onnx',\n",
       " 'ops',\n",
       " 'optim',\n",
       " 'orgqr',\n",
       " 'ormqr',\n",
       " 'os',\n",
       " 'outer',\n",
       " 'overrides',\n",
       " 'package',\n",
       " 'pairwise_distance',\n",
       " 'parse_ir',\n",
       " 'parse_schema',\n",
       " 'parse_type_comment',\n",
       " 'path_patched',\n",
       " 'pca_lowrank',\n",
       " 'pdist',\n",
       " 'per_channel_affine',\n",
       " 'per_channel_affine_float_qparams',\n",
       " 'per_channel_symmetric',\n",
       " 'per_tensor_affine',\n",
       " 'per_tensor_symmetric',\n",
       " 'permute',\n",
       " 'pfiles_path',\n",
       " 'pi',\n",
       " 'pinverse',\n",
       " 'pixel_shuffle',\n",
       " 'pixel_unshuffle',\n",
       " 'platform',\n",
       " 'poisson',\n",
       " 'poisson_nll_loss',\n",
       " 'polar',\n",
       " 'polygamma',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'prelu',\n",
       " 'prepare_multiprocessing_environment',\n",
       " 'preserve_format',\n",
       " 'prev_error_mode',\n",
       " 'prod',\n",
       " 'profiler',\n",
       " 'promote_types',\n",
       " 'put',\n",
       " 'py_dll_path',\n",
       " 'q_per_channel_axis',\n",
       " 'q_per_channel_scales',\n",
       " 'q_per_channel_zero_points',\n",
       " 'q_scale',\n",
       " 'q_zero_point',\n",
       " 'qint32',\n",
       " 'qint8',\n",
       " 'qr',\n",
       " 'qscheme',\n",
       " 'quantile',\n",
       " 'quantization',\n",
       " 'quantize_per_channel',\n",
       " 'quantize_per_tensor',\n",
       " 'quantized_batch_norm',\n",
       " 'quantized_gru',\n",
       " 'quantized_gru_cell',\n",
       " 'quantized_lstm',\n",
       " 'quantized_lstm_cell',\n",
       " 'quantized_max_pool1d',\n",
       " 'quantized_max_pool2d',\n",
       " 'quantized_rnn_relu_cell',\n",
       " 'quantized_rnn_tanh_cell',\n",
       " 'quasirandom',\n",
       " 'quint4x2',\n",
       " 'quint8',\n",
       " 'rad2deg',\n",
       " 'rad2deg_',\n",
       " 'rand',\n",
       " 'rand_like',\n",
       " 'randint',\n",
       " 'randint_like',\n",
       " 'randn',\n",
       " 'randn_like',\n",
       " 'random',\n",
       " 'randperm',\n",
       " 'range',\n",
       " 'ravel',\n",
       " 'read_vitals',\n",
       " 'real',\n",
       " 'reciprocal',\n",
       " 'reciprocal_',\n",
       " 'relu',\n",
       " 'relu_',\n",
       " 'remainder',\n",
       " 'renorm',\n",
       " 'repeat_interleave',\n",
       " 'res',\n",
       " 'reshape',\n",
       " 'resize_as_',\n",
       " 'resize_as_sparse_',\n",
       " 'resolve_conj',\n",
       " 'resolve_neg',\n",
       " 'result_type',\n",
       " 'rnn_relu',\n",
       " 'rnn_relu_cell',\n",
       " 'rnn_tanh',\n",
       " 'rnn_tanh_cell',\n",
       " 'roll',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 'rrelu',\n",
       " 'rrelu_',\n",
       " 'rsqrt',\n",
       " 'rsqrt_',\n",
       " ...]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "print(torch.cuda.is_available())\n",
    "help(torch.cuda.is_available) # 查看 torch.cuda.is_available 的用法\n",
    "dir(torch)  # 查看torch包中有哪些区、有哪些工具"
   ]
  }
 ],
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