* **Machine Learning**: normalmente requiere muchas multiplicaciones de "matrices" y "vectores". Imagina en una enorme hoja de cálculo con números y tener que multiplicarlos todos al mismo tiempo.

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

### Major Features and Improvements

*   Added F-Score metrics `tf.keras.metrics.FBetaScore`, `tf.keras.metrics.F1Score`, and `tf.keras.metrics.R2Score`.
*   Added activation function `tf.keras.activations.mish`.

1. Start a distributed MinIO instance (4 nodes setup)
2. Start Prometheus server and AlertManager
3. Bring down couple of MinIO instances to bring down the Erasure Set tolerance to -1 and verify the same with `mc admin prometheus metrics ALIAS | grep minio_cluster_health_erasure_set_status`
4. Wait for 5 mins (as alert is configured to be firing after 5 mins), and verify that you see an entry in webhook for the alert as well as in Prometheus console as shown below

- [`KT-44833`](https://youtrack.jetbrains.com/issue/KT-44833) Gradle DSL: Add `languageSettings` accessor to `kotlin` extension that applies to all source sets
- [`KT-58315`](https://youtrack.jetbrains.com/issue/KT-58315) Add build metrics for Kotlin/Native task

#### Performance Improvements

- [`KT-62318`](https://youtrack.jetbrains.com/issue/KT-62318) Android Studio sync memory leak in 1.9.20-Beta

你还可能有**其他原因**，这将使你更容易拥有一个带有**多个进程**的**单个容器**，而不是拥有每个容器中都有**单个进程**的**多个容器**。

例如（取决于你的设置）你可以在同一个容器中拥有一些工具，例如 Prometheus exporter，该工具应该有权访问**每个请求**。

在这种情况下，如果你有**多个容器**，默认情况下，当 Prometheus 来**读取metrics**时，它每次都会获取**单个容器**的metrics（对于处理该特定请求的容器），而不是获取所有复制容器的**累积metrics**。

在这种情况， 这种做法会更加简单：让**一个容器**具有**多个进程**，并在同一个容器上使用本地工具（例如 Prometheus exporter）收集所有内部进程的 Prometheus 指标并公开单个容器上的这些指标。

---

 * Copyright (c) 2006- Facebook

---------------------------------------------------

================================================================

github.com/armon/go-metrics
https://github.com/armon/go-metrics
----------------------------------------------------------------
The MIT License (MIT)

Copyright (c) 2013 Armon Dadgar

github.com/armon/consul-api v0.0.0-20180202201655-eb2c6b5be1b6/go.mod h1:grANhF5doyWs3UAsr3K4I6qtAmlQcZDesFNEHPZAzj8=
github.com/armon/go-metrics v0.0.0-20190430140413-ec5e00d3c878/go.mod h1:3AMJUQhVx52RsWOnlkpikZr01T/yAVN2gn0861vByNg=
github.com/armon/go-metrics v0.4.0 h1:yCQqn7dwca4ITXb+CbubHmedzaQYHhNhrEXLYUeEe8Q=
github.com/armon/go-metrics v0.4.0/go.mod h1:E6amYzXo6aW1tqzoZGT755KkbgrJsSdpwZ+3JqfkOG4=

                "//tensorflow/c:ops",
                "//tensorflow/cc/saved_model:fingerprinting_impl",
                "//tensorflow/cc/saved_model:loader_lite_impl",
                "//tensorflow/cc/saved_model:metrics_impl",
                "//tensorflow/compiler/tf2tensorrt:op_converter_registry_impl",
                "//tensorflow/core/common_runtime:core_cpu_impl",
                "//tensorflow/core/common_runtime/gpu:gpu_runtime_impl",

* **Bilgisayar görüsü**: bir görüntü milyonlarca pikselden oluşur, her pikselin 3 değeri / rengi vardır, bu pikseller üzerinde aynı anda bir şeyler hesaplamayı gerektiren işleme.
* **Makine Öğrenimi**: Çok sayıda "matris" ve "vektör" çarpımı gerektirir. Sayıları olan ve hepsini aynı anda çarpan büyük bir elektronik tablo düşünün.