Hi:
perl -e 'print "hello world!\n"'
A simple filter:
perl -ne 'print if /REGEX/'
Filter out blank lines (in place):
yj infinex
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| z() { | |
| if [[ "$#" -ne 0 ]]; then | |
| cd $(autojump $@) | |
| return | |
| fi | |
| cd "$(autojump -s | sort -k1gr | awk '$1 ~ /[0-9]:/ && $2 ~ /^\// { for (i=2; i<=NF; i++) { print $(i) } }' | fzf --height 40% --reverse --inline-info)" | |
| } | |
| k() { |
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| from tensorflow_core.python.training.py_checkpoint_reader import NewCheckpointReader | |
| import tensorflow as tf | |
| bert_init_dir = '' | |
| latest = tf.train.latest_checkpoint(bert_init_dir) | |
| reader = NewCheckpointReader(latest) | |
| name_shape_map = reader.get_variable_to_shape_map() |
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| special_tok_ids = {} | |
| for tok_name, tok_symbol in tokenizer.special_tokens_map.items(): | |
| idx = tokenizer.all_special_tokens.index(tok_symbol) | |
| special_tok_ids[tok_name] = tokenizer.all_special_ids[idx] | |
| class JigSawDataset(Dataset): | |
| def __init__(self, data, special_tok_ids, is_training, targets=None): | |
| self.special_tok_ids = special_tok_ids | |
| self.is_training = is_training | |
| if self.is_training: |
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| DATA_DIR=./data | |
| field_keywords=$(wildcard $(DATA_DIR)/field_keywords_*) | |
| out_keywords=$(patsubst $(DATA_DIR)/field_keywords_%,out%.txt,$(field_keywords)) | |
| REAL_PATH=$(abspath $(DATA_DIR)) | |
| NN_FRAMEWORK_DATA=~/.nn_framework_Data | |
| field_keyword=$(patsubst $(DATA_DIR)/field_keywords_%,$(DATA_DIR)/field_keywords_\%,$(word 1,$(field_keywords))) | |
| ## all: run command | |
| all: $(out_keywords) |
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| {"lastUpload":"2020-12-30T06:47:43.145Z","extensionVersion":"v3.4.3"} |
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| mapkey('\\F', "Toggle fullscreen (YouTube)", ytFullscreen, { | |
| repeatIgnore: true, | |
| domain: /(youtube\.com)/i | |
| }); | |
| function ytFullscreen() { | |
| $('.ytp-fullscreen-button.ytp-button').click(); | |
| } | |
| // 优酷搜索 | |
| addSearchAliasX('yk', 'youku', 'http://www.soku.com/search_video/q_', 's', 'http://tip.soku.com/search_tip_1?query=', function(response) { |
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| clear mod1 | |
| clear mod4 | |
| keycode 64 =Super_L | |
| keycode 133 = Alt_L | |
| add mod1 = Alt_L | |
| add mod4 = Super_L | |
| ! Unmap capslock | |
| clear Lock | |
| keycode 66 = Hyper_L | |
| ! Set mod3 to capslock |
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| diff --git a/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc b/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc | |
| index 0f7adaf24a..934ccbada6 100644 | |
| --- a/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc | |
| +++ b/tensorflow/core/kernels/concat_lib_gpu_impl.cu.cc | |
| @@ -69,7 +69,7 @@ __global__ void concat_variable_kernel( | |
| IntType num_inputs = input_ptr_data.size; | |
| // verbose declaration needed due to template | |
| - extern __shared__ __align__(sizeof(T)) unsigned char smem[]; | |
| + extern __shared__ __align__(sizeof(T) > 16 ? sizeof(T) : 16) unsigned char smem[]; |
infinex
/ gist:1e0fda899d3192ac0c2dede34a9bf9ca
Last active
November 24, 2020 16:01
manjaro deep learning installation
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| sudo mhwd -a pci nonfree 0300 | |
| pacman -S cuda cudnn | |
| yaourt Ananconda | |
| git clone https://github.com/fastai/fastai | |
| cd fastai | |
| conda-env create -f environment.yaml | |
| build gcc 6 | |
| yay -S gcc6 |