metrics

If I'm going to put a program into production, there are several things I need that program to do in order to consider it "operationalized" – that is, running and maintainable in a measurable and verifiable way by both engineers and operations staff. For my purposes, an operationalized program must: Be able to log at multiple levels (ex: debug, warning, etc.). Be able to collect and share metrics/statistics about the types of work the program is doing and how long that work is taking. Ideally, the collected metrics are available in a format that's compatible with commonly-used monitoring tools

I have guest-level metrics enable for an Azure Virtual Machine and am trying to get the history for the [Guest]\Memory\Committed Bytes property using Get-AzureRMMetric . $endTime = Get-Date $startTime = $endTime.AddMinutes(-540) $timeGrain = '00:05:00' $metricName = '\Memory\Committed Bytes' $history=(Get-AzureRmMetric -ResourceId $resourceId ` -TimeGrain $timeGrain -StartTime $startTime ` -EndTime $endTime ` -MetricNames $metricName) $history.data | Format-table -wrap Average,Timestamp,Maxiumim,Minimum,Total I get the following error: This code works fine if I change the $metricname to any of

hystrix.command.default和hystrix.threadpool.default中的default为默认CommandKey Command Properties Execution相关的属性的配置： hystrix.command.default.execution.isolation.strategy 隔离策略，默认是Thread, 可选Thread｜Semaphore hystrix.command.default.execution.isolation.thread.timeoutInMilliseconds 命令执行超时时间，默认1000ms hystrix.command.default.execution.timeout.enabled 执行是否启用超时，默认启用true hystrix.command.default.execution.isolation.thread.interruptOnTimeout 发生超时是是否中断，默认true hystrix.command.default.execution.isolation.semaphore.maxConcurrentRequests 最大并发请求数，默认10，该参数当使用ExecutionIsolationStrategy.SEMAPHORE策略时才有效。如果达到最大并发请求数

　　目标检测模型主要分为two-stage和one-stage， one-stage的代表主要是yolo系列和ssd。简单记录下学习yolo系列的笔记。 1 yolo V1 　　　yolo v1是2015年的论文 you only look once：unified，real-time object detection 中提出，为one-stage目标检测的开山之作。其网络架构如下：（24个卷积层和两个全连接层，注意最后一个全连接层可以理解为1*4096到1*1470（7*7*30）的线性变换） 　　yolo v1的理解主要在于三点： 　　 1.1 网格划分 ： 输入图片为448*448，yolo将其划为为49(7*7)个cell, 每个cell只负责预测一个物体框， 如果这个物体的中心点落在了这个cell中，这个cell就负责预测这个物体 　　 　　 1.2 预测结果 ：最后网络的输出为7*7*30， 也可以看做49个1*30的向量，每个向量的组成如下： (x, y, w, h, confidence) *2 + 20; 即 每一个向量预测两个bounding box及对应的置信度，还有物体属于20个分类（VOC数据集包括20分类）的概率。 　 1.3 Loss 函数理解 ：loss函数如下图所示，下面几个概念需要理清楚 　　　　　　　s2：最后网络的输出为7*7*30，

I have linear regression model that seems to be working fine, but I want to display the accuracy of the model. First, I initialize the variables and placeholders... X_train, X_test, Y_train, Y_test = train_test_split( X_data, Y_data, test_size=0.2 ) n_rows = X_train.shape[0] X = tf.placeholder(tf.float32, [None, 89]) Y = tf.placeholder(tf.float32, [None, 1]) W_shape = tf.TensorShape([89, 1]) b_shape = tf.TensorShape([1]) W = tf.Variable(tf.random_normal(W_shape)) b = tf.Variable(tf.random_normal(b_shape)) pred = tf.add(tf.matmul(X, W), b) cost = tf.reduce_sum(tf.pow(pred-Y, 2)/(2*n_rows-1))

How can I get the size in bytes of the data returned from the database when executing a query? The reason for this is to compare load on the database server using two different techniques. We were running reports built from the same dataset which would load the entire dataset for every report. Now we are caching the dataset and running reports from the cache. We run reports per client, some datasets are significantly bigger than others, and I need some way to give a measurable metric for the database server load reduction. I have tried looking through DbConnection, DbDataReader and DbCommand

Let's assume that we have a code like this: switch(y) { case 1: case 2: case 3: function(); break; case 4: case 5: case 6: function_2(); break; } Can we get the CC value as 6+1 here? Why a value of 1 is added? If the CC value is considered as 7, is that the number of independent paths? What if a fall through scenario is considered above? As only possible two unique paths are there, 2 +1 =3 Which of the above are correct or are the both of them correct? As we know, CC = P+1. Here, P = number of predicate nodes (conditions) = 2 Number of conditions will be 2 because: Case branch can cover

OpenCensus Easily collect telemetry like metrics and distributed traces from your services OpenCensus and OpenTracing have merged to form OpenTelemetry , which serves as the next major version of OpenCensus and OpenTracing. OpenTelemetry will offer backwards compatibility with existing OpenCensus integrations, and we will continue to make security patches to existing OpenCensus libraries for two years. What is OpenCensus? OpenCensus is a set of libraries for various languages that allow you to collect application metrics and distributed traces, then transfer the data to a backend of your