代码空间


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 全部项目