- A new kubelet metric `image_pull_duration_seconds` was added. The metric tracks the duration (in seconds) it takes for an image to be pulled, including the time spent in the waiting queue of image puller. The metric is broken down by bucketed image size. ([#121719](https://github.com/kubernetes/kubernetes/pull/121719), [@ruiwen-zhao](https://github.com/ruiwen-zhao))

# Bucket Metrics

MinIO collects the following metrics at the bucket level.
Each metric includes the ``bucket`` label to identify the corresponding bucket.
Metrics may include one or more additional labels, such as the server that calculated that metric.

These metrics can be obtained from any MinIO server once per collection by using the following URL:

go.opentelemetry.io/otel/metric v1.26.0 h1:7S39CLuY5Jgg9CrnA9HHiEjGMF/X2VHvoXGgSllRz30=
go.opentelemetry.io/otel/metric v1.26.0/go.mod h1:SY+rHOI4cEawI9a7N1A4nIg/nTQXe1ccCNWYOJUrpX4=
go.opentelemetry.io/otel/sdk v1.26.0 h1:Y7bumHf5tAiDlRYFmGqetNcLaVUZmh4iYfmGxtmz7F8=
go.opentelemetry.io/otel/sdk v1.26.0/go.mod h1:0p8MXpqLeJ0pzcszQQN4F0S5FVjBLgypeGSngLsmirs=

# Metrics Version 3

In metrics version 3, all metrics are available under the endpoint:

```
/minio/metrics/v3
```

however, a specific path under this is required.

Metrics are organized into groups at paths **relative** to the top-level endpoint above.

## Metrics Request Handling

Each endpoint below can be queried at different intervals as needed via a scrape configuration in Prometheus or a compatible metrics collection tool.

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=

### 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`.

   limitations under the License.

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

go.opentelemetry.io/otel/metric
https://go.opentelemetry.io/otel/metric
----------------------------------------------------------------
                                 Apache License
                           Version 2.0, January 2004

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

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。

---

* **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.