Six Steps to Take When a Labeling Machine Suddenly Slows Down

If the speed of data annotation suddenly drops, it might be due to server overload, task allocation, or configuration issues.The article covers six steps for troubleshooting, including hardware checks, optimizing task scheduling, and debugging code. It provides practical methods for helping webmasters quickly pinpoint and resolve bottlenecks.

Step 1: Check server load situation.

If the speed of the labeling slows down, first don't panic, but check to see if the server is "tired out.

Check the CPU and memory usage.

Open up a monitoring tool (such as htop or the task manager) and if the CPU is running at 90 % or higher for an extended period of time, or the memory is nearly full, there's a good chance that the hardware is unable to cope with the load.At this point, you can consider cleaning up the background processes or temporarily upgrading the server configuration.

Check the number of database connections.

An overloaded database can also slow things down.If you use command-line tools to check the number of active connections and find that a large number of idle connections haven't been released, then you should adjust the connection pool settings or try restarting the database service.

Step 2: Analyze the task assignment mechanism.

An uneven distribution of tasks can cause some nodes to be overloaded while others have little to do.

Confirm the status of the task queue.

Open the task scheduler and see if there are any tasks stuck in the line that have never been processed.Sometimes a task will be abnormally time consuming and cause a backlog of pending tasks. In these situations the operator may need to manually clear the offending task or adjust the priority of pending tasks.

Check node load balancing.

If a distributed labeling system is used, it is necessary to ensure that the load on each node is balanced.If one node is overloaded, quickly move the task to an idle node, so that one bad apple doesn't spoil the whole bunch.

Step 3: Debug the code for the annotation tool.

There are also many pitfalls on the software side.

Check the error log for abnormal errors.

I'd look through the log files from the past few hours, focusing on errors like memory leaks and null pointers.For example, if an "OutOfMemoryError" appears frequently, then there is an 80 % chance that there is a resource in the code that has not been released.

This tests the efficiency of single task execution.

Run a single task in isolation and use a performance analysis tool (such as cProfile for Python) to track the operations that take the most time.Previously, I encountered a situation where the lack of a cache in the image preprocessing function caused the same operation to be calculated repeatedly, slowing down the process.

Step 4: Test the transmission efficiency of the network.

If the data is transmitted slowly, it is only natural that the labeling process will be slow as well.

Testing internal network bandwidth and latency.

Using the iperf utility, we tested the transmission speed between the servers.One time, after a half-day of searching, we discovered that it was because the port on the switch was not making good contact, causing the transmission speed to drop below 100M. We immediately solved the problem by changing the network cable.

Checking storage I / O performance.

If the data is read directly from the hard drive, use iotop to see if the disk read/write is saturated.By switching to SSD or caching hot data in memory, speed can be doubled.

Step 5: Check the status of third-party services.

Are you using external APIs or cloud services? They could be the culprits.

Monitor the API response time.

Add a timer to the code to record the time it takes to call the third party interface.One time, Alibaba Cloud's OSS service went down, and the delay jumped from 200ms to five seconds, almost bringing the entire annotation pipeline to a halt.

Verify the validity of the authorization certificate.

Don't laugh! I once had the experience of an expired SSL certificate, which resulted in all outside requests being blocked.Regularly checking the validity of keys and certificates is a low-level error that can really throw a wrench into the works.

Step 6: Roll back the recent changes.

If you can't find the problem in the first five steps, it's probably because the most recent update has caused a problem.

Compare versions and keep a record of changes.

Each of the changes made in the past three days was rolled back for testing.Last week a team updated the TensorFlow version, and because of a compatibility problem the GPU utilization dropped by 50 %.

Gray-scale verification of restoration plans.

After finding the problem, we first test the solution on a small scale to make sure that it restores normal speeds.Don't get too excited about your success, as fixing one bug might create three new problems.