Badges 116 × Eureka!
there will be a tr but there will be a separate graph for top1 and loss, on your system then go into the same graph, since loss and train accuracy usually have very diffrent value ranges, it make it impossible to see the loss graph without starting go manipulate it
Thanks ! thats great, also can i some how make sure that no matter what results are not uploaded to the public demo server ?
Hi, yes its a docker on a VM
OK what solved it is increasing the RAM of the VM, do you specify minimum requirements anywhere ?
I did, but i will try again
It is a VM running Ubuntu 18.04, yes i ended up giving it 8 GB which seemed to solve the issue. Pretty common to run servers on VMs these days ... :)
which changes do i need to make to get elastic search to work ?
i have actually already tried to follow those instructions, after a fresh install of the OS
there is a funny issue with trains, one of the great features in our book is the fact that you pickup tensorboard logs automatically, but you group them in the opposite direction, i.e. if i have:
` File "/opt/conda/lib/python3.6/site-packages/trains/task.py", line 277, in init
not auto_connect_frameworks.get('detect_repository', True)) else True
File "/opt/conda/lib/python3.6/site-packages/trains/task.py", line 1163, in _create_dev_task
File "/opt/conda/lib/python3.6/site-packages/trains/task.py", line 111, in init
File "/opt/conda/lib/python3.6/site-packages/trains/backend_interface/task/task.py", line 10...
no, we have a vmware server, on it we run a bunch of servers. While I have your attention, I'm running into a new issue, most of our training sessions run from inside a docker. When i try to run such a training session, i get an error about the user:
i need to run the docker with my uid which is 10001 but the docker does not know or have the user, why does it need it ? to find the trains.conf ? is there any way to pass it manually ?
It worked ! took me a while to get the docker "user" to pick up trains.conf ...