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docs/zh/docs/deployment/concepts.md
### 每个进程的内存 现在,当程序将内容加载到内存中时,例如,将机器学习模型加载到变量中,或者将大文件的内容加载到变量中,所有这些都会消耗服务器的一点内存 (RAM) 。 多个进程通常**不共享任何内存**。 这意味着每个正在运行的进程都有自己的东西、变量和内存。 如果您的代码消耗了大量内存,**每个进程**将消耗等量的内存。 ### 服务器内存 例如,如果您的代码加载 **1 GB 大小**的机器学习模型,则当您使用 API 运行一个进程时,它将至少消耗 1 GB RAM。 如果您启动 **4 个进程**(4 个工作进程),每个进程将消耗 1 GB RAM。 因此,您的 API 总共将消耗 **4 GB RAM**。 如果您的远程服务器或虚拟机只有 3 GB RAM,尝试加载超过 4 GB RAM 将导致问题。 🚨 ### 多进程 - 一个例子
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src/main/resources/fess_indices/fess/pt/stopwords.txt
hajam houvesse houvéssemos houvessem houver houvermos houverem houverei houverá houveremos houverão houveria houveríamos houveriam sou somos são era éramos eram fui foi fomos foram fora fôramos seja sejamos sejam fosse fôssemos fossem for formos forem serei será seremos serão seria seríamos
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docs/pl/docs/features.md
* Nawet zależności mogą mieć zależności, tworząc hierarchię lub **"graf" zależności**. * Wszystko jest **obsługiwane automatycznie** przez framework.
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tensorflow/c/BUILD
":env", ":logging", ":tf_status", ":tf_tensor", "//tensorflow/c/experimental/filesystem:modular_filesystem", "//tensorflow/cc:grad_ops", "//tensorflow/cc:gradients", "//tensorflow/cc:ops", "//tensorflow/cc:scope_internal", "//tensorflow/cc:while_loop",
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RELEASE.md
`RandomTranslation`, `RandomRotation`, `RandomHeight`, `RandomWidth`, `RandomZoom`, `RandomContrast` * Improved **`TextVectorization`** layer, which handles string tokenization, n-gram generation, and token encoding * The `TextVectorization` layer now accounts for the mask_token as part of the vocabulary size when output_mode='int'. This means that, if you have
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tensorflow/c/eager/BUILD
"//tensorflow/c:c_api", "//tensorflow/c:c_test_util", "//tensorflow/c:tf_status_helper", "//tensorflow/c/experimental/gradients:array_grad", "//tensorflow/c/experimental/gradients:math_grad", "//tensorflow/c/experimental/gradients:not_differentiable", "//tensorflow/c/experimental/gradients/tape:tape_context", "//tensorflow/c/experimental/ops",
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tensorflow/c/experimental/gradients/BUILD
], ) cc_library( name = "gradients", hdrs = [ "array_grad.h", "math_grad.h", "nn_grad.h", "not_differentiable.h", ], visibility = [ "//tensorflow:internal", ], deps = [ ":array_grad", ":math_grad", ":nn_grad", ":not_differentiable", "//tensorflow/c/eager:abstract_context",
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docs/tr/docs/async.md
Ardından, 🤖 bitirmek için ilk görevi alır ("slow-file" 📝) ve onunla ne yapması gerekiyorsa onu devam ettirir. Bu "başka bir şey için bekle" normalde, aşağıdakileri beklemek gibi (işlemcinin ve RAM belleğinin hızına kıyasla) nispeten "yavaş" olan <abbr title="Input ve Output (Giriş ve Çıkış)">I/O</abbr> işlemlerine atıfta bulunur: * istemci tarafından ağ üzerinden veri göndermek * ağ üzerinden istemciye gönderilen veriler
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okhttp/src/test/java/okhttp3/internal/cache/DiskLruCacheTest.kt
while (taskFaker.isIdle()) { set("a", "a", "a") set("b", "b", "b") } val commitEditor = cache.edit("c")!! val abortEditor = cache.edit("d")!! cache.edit("e") // Grab an editor, but don't do anything with it. // Cause the rebuild action to fail. filesystem.setFaultyRename(cacheDir / DiskLruCache.JOURNAL_FILE_BACKUP, true) taskFaker.runNextTask()
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tensorflow/c/experimental/filesystem/plugins/gcs/BUILD
deps = [ "//tensorflow/c:env", ], ) cc_library( name = "cleanup", hdrs = ["cleanup.h"], ) cc_library( name = "ram_file_block_cache", srcs = ["ram_file_block_cache.cc"], hdrs = ["ram_file_block_cache.h"], deps = [ ":cleanup", "//tensorflow/c:env", "//tensorflow/c:logging", "//tensorflow/c:tf_status",
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