Tensorboard and VSCode is already well-integrated. However, there is a slightly problem when running it using the latest version.
Tensorboard is installed, prompted to install repeatedly. It keep re-appearing to ask installing Tensorboard session package with the same result :
Could not install tensorboard. If pip is not available, please use the package manager of your choice to manually install this library into your Python environment
Apparently, the major culprit is VSCode using different Python interpreter than the selected kernel in notebook. In this case, I’m using Anaconda with specific environment that already have tensorboard installed. To solve this, the solution is very straightfoward.
This is the most frustated problem I have encountered when using Asus Pro WS WRX80E-SAGE SE WIFI Motherboard Pro WS WRX80E-SAGE SE WIFI. The issue appeared when I changed the BIOS settings to enable the “SR-IOV” feature with the hope of solving USB devices not detected and avoiding adding “pci=nommconf” in GRUB.
Once, I rebooted, it suddenly its showing AMI Megatrend where everything was being initialized properly, and the last message was “ensure to connect the 8-pin power please enter setup to recover bios setting fatal error”. There is BIOS page at beginning to press F2 or Del, however it was not responsive and back to AMI page.
The problem when adjusting my dual RTX 4090 in Ubuntu Server 23.10 is, when running nvidia-settings its trigger error
ERROR: The control display is undefined; please run `nvidia-settings --help`
for usage information.
I’ve been search on internet and not found a better solution, then I decided try to install X and attached, while running nvidia-setting. The result its works. Here are the step by step on how to enable GPU fan speed in Ubuntu Server 23.10
sudo apt install -y xorg xinit
2. Give permission to run X for user
Edit the file and change console into anybody to give permission to running X
sudo vim /etc/X11/Xwrapper.config
If you don’t change this, it will trigger error
/usr/lib/xorg/Xorg.wrap: Only console users are allowed to run the X server
If you got this error when running your notebook with warning
contains a task of very large size. The maximum recommended task size is 1000 KiB
This mean PySpark warning you to increase the partition or parallelism (and might memory as well).
Example code to configure it, where you can adjust based on your workstation memory. In my case, is 192GB is my max memory
os.environ['PYSPARK_SUBMIT_ARGS'] = '--driver-memory 192g --executor-memory 16g pyspark-shell'
# add this one in your spark configuration
To solve Driver or CUDA 11 installation error in Ubuntu 23.10, the answer is to ensure its using the compatible GCC version. By default installation, it will using GCC 13 which is not working when compiling CUDA or NVIDIA Drivers (required GCC 10). Installing CUDA 11 is important to run Tensorflow that haven’t fully adapted with CUDA 12.
Failed to verify gcc version. See log at /var/log/cuda-installer.log for details.
First step to fixing this problem is to uninstall any nvidia and cuda installation made previously