ditto, win7 64 tensorflow v1.0.0 no gpu.

as implied by @enpasos, there are no error messages in console or web

ETA: with the --inspect flag tensorboard seems to be finding and reading the files fine... some but not all file reading methods are drive letter aware?

Unfortunately TensorBoard uses a colon as the separator between the optional "run name" and the path in the --logdir flag. That means that --logdir=E:\tmp\tensorflow\mnist\logs is interpreted as a run named E with path \tmp\tensorflow\mnist\logs, which explains why it's sensitive to the current working directory.

As a quick workaround, you could avoid this problem by always specifying an explicit run name as part of the --logdir flag, e.g.:

tensorboard --logdir=training:E:\tmp\tensorflow\mnist\logs

(Reassigning to @dandelionmane, who shows up on the blame for the relevant code.)

Migrated this to tensorflow/tensorboard.

It still can't load data though using the same driver's name

@mrry
yep ,that worked (Windows 7,64bit )
tensorboard --logdir=training:E:\tmp\tensorflow\mnist\logs

@enpasos Works on Windows 10, 64-bit with Python 3.5. Point to be noted, you should be in the drive only and not even inside the log folder.

Works only when inside D:> not when in the complete log path.

Only issue I had was to I had to execute it with python instead using tensorboard directly.
python -m tensorboard.main --logdir="D:/JupyterNotebooks/MultivariateTimeSeries/logs/"

I resolved the problem using double-quotes:
--logdir="E:\tmp\tensorflow\mnist\logs"

Windows 7, 64bit, cygwin, python 3

Windows 10, Pytorch 1.4.0 GPU,
I tried the method above mentioned, but did not work.
Is there any other way to solve it?

cd {logdir}
tensorboard --logdir ./
It works.

I resolved the problem using double-quotes:
--logdir="E:\tmp\tensorflow\mnist\logs"

Windows 7, 64bit, cygwin, python 3

Worked perfectly for me.

• Windows 10
• Intel Core i9-7920X
• 2x NVIDIA GeForce RTX 2080 Ti
• TensorFlow 2.4.1