To work out bugs, you can follow the steps:
logdir
path set by VisualDL is correct. In the meanwhile, there must be a log file and its name (including vdlrecords
) should be correct. And then you can check the folder set by logdir
. If it is not okay, try the next step.--host
as 0.0.0.0
or 127.0.0.1
. And the latter one only supports the local address. If the server runs and you want to check by other addresses, please use 0.0.0.0
and ensure the end can be visited by outer net. If it is not okay, try the next step.We apply random sampling algorithm to display sampled data, when using modules of Image, Audio and Text. In this way, the front-end page will not be stuck or will not crash because of too much data, and then we can ensure users’ experience.
Though data shown in the front-page are sampled, all the data are saved in the log file. You can obtain all the data by using VisualDL.LogReader
. For more details, please refer to our LogReader tutorial.
Because there are two or more values in a certain step, you will find the curves you draw are like the following picture. Please check your script and find whether you add several values to one step, when usingadd_scalar
.
Please check the version of VisualDL you use (which visualdl). According to the error, it is most likely that you are using the VisualDL 1.3, which attributes to Python 2 you use. Python 2 will install the old version of VisualDL automatically.
At present, VisualDL does not support Python 2 any more. And the instructions of existing official documents ae based on VisualDL 2.0, which also will not support Python 2. We suggest upgrading the Python's version to Python 3 and installing the latest version of VisualDL. In this way, the problem will not appear again.
Different needs have different solutions
In order to minimize the CPU resource occupation for sampling, we improve the efficiency of data transmission and make the sampling uniform by using a reservoir sampling algorithm. In this way, all the data will be sampled in the back-end and then transmitted to the front-end. Reservoir Sampling can avoid loading all data through streaming sampling at once. For more details of the sampling theory, please refer to Reservoir Sampling.
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