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docs/fr/docs/deployment/index.md
l'application efficacement et sans interruption ni problème. Ceci contraste avec les étapes de **développement**, où vous êtes constamment en train de modifier le code, de le casser et de le réparer, d'arrêter et de redémarrer le serveur de développement, _etc._ ## Stratégies de déploiement
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SECURITY.md
from anywhere, and executes the graphs it is sent without performing any checks. Therefore, if you run a `tf.train.Server` in your network, anybody with access to the network can execute arbitrary code with the privileges of the user running the `tf.train.Server`. ## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can
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docs/fr/docs/tutorial/index.md
Le **Guide d'utilisation avancé**, qui s'appuie sur cette base, utilise les mêmes concepts et vous apprend quelques fonctionnalités supplémentaires. Mais vous devez d'abord lire le **Tutoriel - Guide d'utilisation** (ce que vous êtes en train de lire en ce moment).
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docs/distributed/DESIGN.md
> http://host1/export5 > http://host2/export5 > http://host1/export6 > http://host2/export6 > http://host1/export7 > http://host2/export7 > http://host1/export8 > http://host2/export8 ``` *A noticeable trait of this expansion is that it chooses unique hosts such the setup provides maximum protection and availability.*
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docs/en/docs/features.md
* If you know Python types you know how to use Pydantic. * Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**: * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data. * Validate **complex structures**:
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docs/distributed/DECOMMISSION.md
# Decommissioning Decommissiong is a mechanism in MinIO to drain older pools (usually with old hardware) and migrate the content from such pools to a newer pools (usually better hardware). Decommissioning spreads the data across all pools - for example, if you decommission `pool1`, all the data from `pool1` spreads across `pool2` and `pool3`. ## Features
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docs/zh/docs/features.md
* **更简单**: * 没有新的模式定义 micro-language 需要学习。 * 如果你知道 Python types,你就知道如何使用 Pydantic。 * 和你 **<abbr title="集成开发环境,和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配: * 因为 pydantic 数据结构仅仅是你定义的类的实例;自动补全,linting,mypy 以及你的直觉应该可以和你验证的数据一起正常工作。 * 验证**复杂结构**: * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。 * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。
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common-protos/k8s.io/api/policy/v1/generated.proto
// +optional optional string unhealthyPodEvictionPolicy = 4; } // PodDisruptionBudgetStatus represents information about the status of a // PodDisruptionBudget. Status may trail the actual state of a system. message PodDisruptionBudgetStatus { // Most recent generation observed when updating this PDB status. DisruptionsAllowed and other
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common-protos/k8s.io/apimachinery/pkg/apis/testapigroup/v1/generated.proto
// If not specified, the carp will be dispatched by default scheduler. // +optional optional string schedulername = 19; } // CarpStatus represents information about the status of a carp. Status may trail the actual // state of a system. message CarpStatus { // Current condition of the carp. // More info: http://kubernetes.io/docs/user-guide/carp-states#carp-phase // +optional optional string phase = 1;
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