Category

NVIDIA

Optimizing Your Chia Farming Operation: A Guide to Using Chia Farming Scripts for GPU Plotting and Efficient Plot Management

By | Chia, Docker, NVIDIA | No Comments

Chia farming has become increasingly popular as people look for ways to earn rewards by utilizing their computer’s processing power. While Chia farming can be a lucrative endeavor, it also requires a lot of disk space and can be a time-consuming process. Fortunately, these Chia farming scripts can help you optimize your plotting process, farm your plots, and move them efficiently, making your Chia farming operation more streamlined and potentially more profitable.

https://github.com/88plug/chia-farming

The first step in the Chia farming process is to prepare your hard drives. The prepare-drives.sh script can help you automate this process by formatting your specified drives with an ext4 file system, setting reserve space to 0%, disabling write-cache, and optimizing read-ahead caching for performance. This can help you save time and ensure that your hard drives are optimized for Chia farming.

Once your hard drives are prepared, you can start creating Bladebit plots. The Bladebit algorithm is faster than the CPU-based plotting algorithms and doesn’t require CUDA. The bladebit-plotting.sh script runs a Docker container that is configured with the necessary environment variables to create Bladebit plots efficiently. With Bladebit plots, you can potentially earn more rewards and speed up your Chia farming operation.

If you have a compatible GPU, you can create C7 Bladebit plots with the bladebit-cuda-plotting.sh script. This script uses the bladebit_cuda command-line tool to create plots with your GPU. The plots are generated with a plot count of 50000 and a thread count of 16. With C7 plots, you can further accelerate your Chia farming and potentially earn more rewards.

Once you’ve created your Bladebit CUDA plots, you’ll need to farm them to earn rewards. The harvester-compose.yml file provides a convenient way to configure a Docker container to farm your Bladebit CUDA plots. The file specifies a service named chia_harvester that runs a Docker container based on the cryptoandcoffee/chia-node-cuda:1 image. The container is configured to run as a harvester, and it connects to your farmer using the specified farmer_address and farmer_port. The container also specifies the location of the CA folder from your farmer, which is mounted as a volume inside the container. This allows the container to access your Bladebit CUDA plots, which are stored in the /plots directory.

Efficiently moving your plots is also important when it comes to Chia farming. The plot-mover.sh script can help you do this by using a list of farming drives and shuffling it to find an available drive. If a drive is available, the script checks for new plot files in the specified source directory and moves the first available plot file to the farming drive. The script can handle multiple transfers simultaneously and waits for a few seconds before checking for new plot files or available drives again. This can help you manage your Chia farming operation and keep your plots organized.

In addition to these scripts, there are other things you can do to optimize your Chia farming operation. For example, you may want to consider using a RAM disk to speed up the plotting process or using a network-attached storage (NAS) device to store your plots. You can also use tools like Chia Plot Manager to manage and monitor your plots.

It’s important to keep in mind that Chia farming can require a lot of disk space, and it may take some time before you start seeing rewards. However, with the help of these scripts and other optimization techniques, you can potentially earn more rewards and make your Chia farming operation more efficient. Just be sure to use these scripts carefully and only run them on drives that are dedicated to Chia farming.

In conclusion, Chia farming can be a rewarding activity if done correctly. By using these Chia farming scripts and other optimization techniques

/usr/lib/libnvidia-gtk3.so.440.33.01: undefined symbol: gtk_widget_hide_on_delete

By | Linux, NVIDIA | No Comments

Fixes /usr/lib/libnvidia-gtk3.so.440.33.01: undefined symbol: gtk_widget_hide_on_delete

Step 1 : Check nvidia-smi for version first

Step 2: Locate nvidia-settings with same version as Step 1 at https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/ and download with wget to your system.

Step 3: Follow steps below replacing XXX with the version you downloaded.

sudo apt-get remove nvidia-modprobe nvidia-settings
sudo dpkg -i nvidia-settings_XXX.deb
sudo reboot now

nvidia-384 libEGL.so.1 is not a symbolic link solved

By | Linux, NVIDIA | No Comments

Fixes NVIDIA-384 problem with libEGL.so.1 is not a symbolic link
[raw]
sudo mv /usr/lib/nvidia-384/libEGL.so.1 /usr/lib/nvidia-384/libEGL.so.1.org
sudo mv /usr/lib32/nvidia-384/libEGL.so.1 /usr/lib32/nvidia-384/libEGL.so.1.org
sudo ln -s /usr/lib/nvidia-384/libEGL.so.384.90 /usr/lib/nvidia-384/libEGL.so.1
sudo ln -s /usr/lib32/nvidia-384/libEGL.so.384.90 /usr/lib32/nvidia-384/libEGL.so.1
[/raw]

(SOLVED) /sbin/ldconfig.real: /usr/lib/nvidia-375/libEGL.so.1 is not a symbolic link /sbin/ldconfig.real: /usr/lib32/nvidia-375/libEGL.so.1 is not a symbolic link

By | NVIDIA | No Comments

[raw]
sudo mv /usr/lib/nvidia-375/libEGL.so.1 /usr/lib/nvidia-375/libEGL.so.1.org
sudo mv /usr/lib32/nvidia-375/libEGL.so.1 /usr/lib32/nvidia-375/libEGL.so.1.org
sudo ln -s /usr/lib/nvidia-375/libEGL.so.375.39 /usr/lib/nvidia-375/libEGL.so.1
sudo ln -s /usr/lib32/nvidia-375/libEGL.so.375.39 /usr/lib32/nvidia-375/libEGL.so.1
[/raw]

Install ccminer sp-hash fork Ubuntu 16.04

By | ccminer, Crypto Mining, NVIDIA | No Comments

This guide will teach you how to install ccminer sp-hash on Ubuntu 16.04

You must first run Install NVIDIA Driver and CUDA on Ubuntu 16.04 before proceeding.

[raw]
#!/bin/bash
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib
export PATH=$PATH:/usr/local/cuda-8.0/bin
echo ‘export PATH=/usr/local/cuda-8.0/bin:$PATH’ >> ~/.bashrc
mkdir sphash
cd sphash
git clone https://github.com/sp-hash/ccminer.git
cd ccminer/
./autogen.sh
./configure CFLAGS=”-O3 -Wall -march=native”
make
[/raw]

To buy a pre-compiled Dockerfile with this image check my selection of miner friendly Dockerfiles at https://docker.cryptoandcoffee.com

sp-hash
9 repositories, 94 followers.

Install ccminer tpruvot fork Ubuntu 16.04

By | ccminer, Crypto Mining, NVIDIA | 5 Comments

This guide will teach you how to install ccminer tpruvot on Ubuntu 16.04

You must first run Install NVIDIA Driver and CUDA on Ubuntu 16.04 before proceeding.

[raw]
#!/bin/bash
mkdir tpruvot
cd tpruvot
git clone https://github.com/tpruvot/ccminer.git
cd ccminer/
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib
export PATH=$PATH:/usr/local/cuda-8.0/bin
echo ‘export PATH=/usr/local/cuda-8.0/bin:$PATH’ >> ~/.bashrc
./autogen.sh
./configure
make
echo “Finished”
[/raw]

To buy a pre-compiled Docker container with this image compatible with NVIDIA GPU’s check my selection of miner friendly Docker containers at https://cryptoandcoffee.com

tpruvot
157 repositories, 979 followers.

Install ccminer alexis78 fork Ubuntu 16.04

By | ccminer, Crypto Mining, NVIDIA | No Comments

This guide will teach you how to install ccminer alexis78 on Ubuntu 16.04

You must first run Install NVIDIA Driver and CUDA on Ubuntu 16.04 before proceeding.

[raw]
mkdir alexis78
cd alexis78
git clone https://github.com/alexis78/ccminer.git
cd ccminer/
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib
export PATH=$PATH:/usr/local/cuda-8.0/bin
./autogen.sh
./configure
make
echo “Finished”
exit
[/raw]

To buy a pre-compiled Dockerfile with this image check my selection of miner friendly Dockerfiles at https://docker.cryptoandcoffee.com

alexis78
2 repositories, 66 followers.

Install ccminer klaust fork Ubuntu 16.04

By | ccminer, Crypto Mining, NVIDIA | No Comments

The fastest algorithm to mine on NVIDIA is by far Skein.  This fork of ccminer from Klaust is definitely the fastest build of ccminer I have found to mine Skein on NVIDIA CUDA.

You must first run Install NVIDIA Driver and CUDA on Ubuntu 16.04 before proceeding.

[raw]
#!/bin/bash
mkdir klaust
cd klaust
wget https://github.com/KlausT/ccminer/archive/8.09.zip
apt-get install -y unzip
unzip 8.09.zip
cd ccminer-8.09
apt-get install -y python3-dev
echo ‘add_compile_options(-std=c++11)’ >> ./CMakeLists.txt
./autogen.sh
./configure
make
echo “Finished”
exit
[/raw]

To buy a pre-compiled Dockerfile with this image check my selection of miner friendly Dockerfiles at https://docker.cryptoandcoffee.com