摘要(Abstract)
缓存(cache),原始意义是指访问速度比一般随机存取存储器(RAM)快的一种高速存储器,通常它不像系统主存那样使用DRAM技术,而使用昂贵但较快速的SRAM技术。缓存的设置是所有现代计算机系统发挥高性能的重要因素之一
静态缓存
一、浏览器缓存
二、磁盘缓存
三、内存缓存
四、Nginx 的内存缓存
五、CDN
动态内容缓存
1 缓存与速度
包括整页缓存、局部缓存、数据缓存等。
2 页面缓存
3 局部无缓存
在流行的模板框架中,在整页缓存的基础上,都提供了局部无缓存的支持,它允许在页面中指定一块包含动态数据的HTML代码段,每次这些动态数据进行实时计算,然后和其余的缓存合成最终网页。
4 静态化内容
主题(Topic)
项目(Project)
LukeShirnia/out-of-memory
daphnis-kau/XMemory
vrmiguel/bustd
l13t/icinga2_check_oom
trailbehind/TBOOMDetector
petrbouchal/purrrow
hagary/processor-simulator
Saruspete/oom_manager
AutomataLab/Subway
robertdebock/ansible-role-earlyoom
unhammer/diff-large-files
WindomZ/shmcache
shared-memory
clickyotomy/de-swap
jollheef/out-of-tree
Vladimir-Novick/Monitoring-Memory-Linux
bittersweetshimmer/purescript-brainfuck
jboy/oomps-oomtables
rumd3x/delphi-hacker-tool
wx-component
master801/Out-of-Translation
spencermountain/out-of-character
y2bd/out-of-sorts
MrSimsek/out-of-home
InfiniteIntel/Out-Of-Body
out-of-cheese-error/astrochelys
test_main() File "cnn_test_auto.py", line 119, in test_main loss,acuracy = test(data_path,generate_test, model_path) File "cnn_test_auto.py", line 76, in test loss, accuracy = my_spatial_model.evaluate_generator(generate_test, steps=test_step) #98需要才能重新确定值的大小 File "D:\python 3.6.4\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper return func(*args, **kwargs) File "D:\python 3.6.4\lib\site-packages\keras\engine\training.py", line 1472, in evaluate_generator verbose=verbose) File "D:\python 3.6.4\lib\site-packages\keras\engine\training_generator.py", line 346, in evaluate_generator outs = model.test_on_batch(x, y, sample_weight=sample_weight) File "D:\python 3.6.4\lib\site-packages\keras\engine\training.py", line 1256, in test_on_batch outputs = self.test_function(ins) File "D:\python 3.6.4\lib\site-packages\keras\backend\tensorflow_backend.py", line 2715, in __call__ return self._call(inputs) File "D:\python 3.6.4\lib\site-packages\keras\backend\tensorflow_backend.py", line 2675, in _call fetched = self._callable_fn(*array_vals) File "D:\python 3.6.4\lib\site-packages\tensorflow\python\client\session.py", line 1439, in __call__ run_metadata_ptr) File "D:\python 3.6.4\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 528, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[128,64,1,1] and type float on /job :localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[{{node block2_sepconv1_1/separable_conv2d}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. [[{{node metrics_33/acc/Mean_1}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info." class="topic-tag topic-tag-link">
out-of-GPU-memoery
dextero/oomalloc
nathanjcochran/lru
ananthbhat94/DDR4MemoryController
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