Author: yodi

Categories
Deep Learning

Increase Fan Speed Nvidia RTX 4090 to reduce high Temperature

RTX 4090 at Full Load under Machine Learning training can produce high-temperature heat. Its can go 80-85 degree celsius. Using big industrial fans to cooling the GPU and open the PC case can reduce to 70 Celcius.

However, before going to that path, you can adjust your NVIDIA GPU fans speed from 30% to 90% or even 100%. Here are the steps to do in in Ubuntu

First, you need to configure the X11 

sudo vim /etc/X11/xorg.conf

Add add Option "Coolbits" "4" in the Section Device Nvidia

Section "Device"
     Identifier      "Device0"
     Driver          "nvidia"
     VendorName      "NVIDIA"
     Option          "Coolbits" "4"
EndSection

Reboot your PC to apply the new changes

The second steps, its to adjust its fans speed. I’m usually using Psensor to detect the fan speed. RTX 4090 have two fans, so you need to tuning both of them

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Categories
Deep Learning

Solve ImportError: cannot import name ‘Linear8bitLt’ from quantization

When running –quantize llm.int8 in adapter for LitLLama, I got this error

ImportError: cannot import name 'Linear8bitLt' from 'lit_llama.quantization'

First step, we need to make sure if bitsandbytes is running well by 

python -m bitsandbytes

And I received 

===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please run

python -m bitsandbytes

 and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
...
packages/bitsandbytes/functional.py", line 12, in <module>
    from scipy.stats import norm
ModuleNotFoundError: No module named 'scipy'

Now, I know the problem is scipy is not installed. To solve this is installing scipy

pip install scipy

And I re-run again for bitsandbytes

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Categories
Ubuntu

Solving File Folder Directories Suddenly Disappeared on Ubuntu

I’m using Ubuntu 23.04 Cinnamon the latest in 2023 and after moving files, suddenly the Nautilus explorer show error

Couldn't open file. No program to open the file

And suddenly the file, folder and all directories inside is gone.

If you have this problem, don’t panic. To solve this problem: Please reboot the ubuntu. Once you get login, you can go check on Trash and the file will be there!

Categories
Machine Learning

Install Transformers Pytorch Tensorflow Ubuntu 2023

To install transformers, Pytorch and Tensorflow works with GPU for the latest Ubuntu, several steps are required. This is how I successfully setup it and running several models with it.

Please make sure to install the latest NVIDIA drivers. I use RTX 4090 in this case. This is the link https://www.nvidia.com/download/driverResults.aspx/200481/en-us/

If you are using nouveau, you can disable it via

sudo bash -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf"
sudo bash -c "echo options nouveau modeset=0 >> /etc/modprobe.d/blacklist-nvidia-nouveau.conf"

sudo update-initramfs -u
sudo reboot
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Categories
Machine Learning

Install Stable Difussion Automatic111, Torch 2.0 and Fix RTX 4090 Performance

I use a clean installation of Ubuntu 23.04 Lunar Lobster and Nvidia driver 525. If you already have the driver installed, here are the steps to improve Automatic111 Stable Diffusion performance to 40-44 it/s

  1. Install required Anaconda

Ubuntu 23.04 default Python version is 3.11 version. In this case, I will using Anaconda to provide Python 3.10. Download Anaconda and install

chmod a+x /Anaconda3-2023.03-1-Linux-x86_64.sh
./Anaconda3-2023.03-1-Linux-x86_64.sh
Continue reading “Install Stable Difussion Automatic111, Torch 2.0 and Fix RTX 4090 Performance”
Categories
Machine Learning

Solve Pandas Drop Duplicates still not unique in Value Counts

When using pandas drop duplicates, we may encountered rows that still have duplicating by checking via 

df.column_name.value_counts()

Not sure why Pandas drop duplicates performance showing inconsistent result. However, to remove duplicate row, produce 100% unique based on index or key column, you can use this

df_unique = df_unique.drop(df_unique[df_unique["key_column_name"].duplicated()].index)
df_unique.temp_id.value_counts()
Categories
Machine Learning

Install LightGBM use GPU in Linux Ubuntu

LightGBM can work faster in GPU. In PyCaret, I’m passing parameter use_gpu=True in TSForecastingExperiment() and got errors:

[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.
Please recompile with CMake option -DUSE_GPU=1
[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.
Please recompile with CMake option -DUSE_GPU=1
[LightGBM] [Fatal] GPU Tree Learner was not enabled in this build.
Please recompile with CMake option -DUSE_GPU=1

To enable this, we need to uninstall the current LightGBM and re-install the LightGBM with GPU. For Linux Ubuntu, its better to install pre-requisite packages

sudo apt install cmake build-essential libboost-all-dev

Make sure you already have Nvidia Toolkit installed

sudo apt install nvidia-cuda-toolkit

The first option, is installation inside conda environment

pip uninstall lightgbm -y

conda install -c conda-forge gcc=12.1.0
pip install lightgbm --config-settings=cmake.define.USE_GPU=ON --config-settings=cmake.define.OpenCL_INCLUDE_DIR="/usr/local/cuda/include/" --config-settings=cmake.define.OpenCL_LIBRARY="/usr/local/cuda/lib64/libOpenCL.so"

Second options, installation without environment

# Get LightGBM source.
git clone --recursive https://github.com/Microsoft/LightGBM.git
cd LightGBM/python-package/
# cmake specifying locations of OpenCL files.
sudo cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda-8.0/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda-8.0/include/ ..
# Compile.
sudo make
# Install for Python, using what we just compiled.
python setup.py install --precompile
Categories
Windows

Install Stable Diffusion Windows and Fix RTX performance 2023

Many feedback about performance NVIDIA RTX after installing Stable Diffusion Automatic1111. I will explained a simple way to install and fix the RTX 4090 performance within 5 minutes

First, make sure you have Python 3.10 in your Windows. You can use Anaconda or native Python installation.

  1. Clone stable diffusion git repository to your local directory 
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git

2. Install Stable Diffusion with xformers

This part is tricky. By default, it will install Torch 2.1.0, however the latest xformers will required to use torch 2.0. Which later you will encountered the problems like : 

AssertionError: Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check

The solution for installation both xformers and torch inside stable difussion is to pass the arguments in installation

./webui.bat --xformers
Continue reading “Install Stable Diffusion Windows and Fix RTX performance 2023”
Categories
Windows

Fix Install Tensorflow 2 with GPU Simple Version 2023

Tensorflow running use GPU in Windows sometimes is difficult to do, where many articles not pointing exactly which Tensorflow version, NVIDIA drivers and other requirement needed to achieve it in Windows 11. Especially when you have NVIDIA RTX Graphic card like 4090 or 3090 or similar version.

I will help to explain on how to install it properly and make it run. Here are the steps to do:

1.Installing the Latest Anaconda.

Go to Anaconda website and download its community distribution. You can try to activate in your terminal windows with “conda activate” and you will enter your base.

If you are using powershelll, you can try “conda init powershell” to load the environment by default

2.Installing Microsoft Visual Studio 2022 (not to confuse with VSCode editor).

Choose the community version at https://visualstudio.microsoft.com/downloads/ and install Desktop Environment with C++

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Categories
Networking

Install Numpy with OneAPI MKL for AMD in Ubuntu

NumPy uses libraries like BLAS, LAPACK, BLIS, or MKL to execute vector, matrix, and linear algebra operations. It’s acknowledged that Intel with MKL (Math Kernel Library) is quite more mature in this math operation than other libraries due to resources and experiences.

If you want to leverage Intel OneAPI MKL as backend for your Numpy, especially on Intel chip (or AMD if you want to try), here are a quick step for installation in Ubuntu (I use the latest ubuntu 23.04 Lunar Lobster).

First, download the required softwares 

sudo apt install build-essential python3-pip python3 python3-dev libomp-dev

A context, libomp-dev will help you to avoid the error “Solve bmkl_intel_thread.so.2: undefined symbol: omp_get_num_procs” when importing the numpy libraries in the python interpreter.

Second, we will install the Intel Base-kit: https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?operatingsystem=linux&distributions=aptpackagemanager.

Or follow the commands below

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