87 lines
5.3 KiB
Markdown
87 lines
5.3 KiB
Markdown
|
|
# Installation for MacOS users
|
|
|
|
## <a name="MacPrereq"></a>Installation pre-requisites
|
|
* Download and install [Python 3.7.5](https://www.python.org/downloads/release/python-375/)
|
|
* Install [Homebrew](https://brew.sh) by running the following command in terminal:
|
|
* ```
|
|
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"
|
|
```
|
|
* Install Katago using [Homebrew](https://brew.sh/) by executing `brew install katago`
|
|
* You can also follow instructions [here](https://github.com/lightvector/KataGo) to compile KataGo yourself.
|
|
|
|
## Installation and running KaTrain from PyPi
|
|
* Run `pip3 install katrain`
|
|
* Run the program by executing `katrain` in a terminal.
|
|
* If you see an error about initializing KataGo:
|
|
* Open the settings dialog by clicking on the gear icon at the bottom right of the window and change the path of the 'katago'
|
|
setting under 'engine' to `/usr/local/bin/katago`, or the path where you compiled KataGo.
|
|
|
|
## Installation from sources
|
|
* This is largely the same as for linux, see [here](#LinuxSources).
|
|
|
|
# Installation from sources for Windows users
|
|
* Download the repository by clicking the green *Clone or download* on this page and *Download zip*. Extract the contents.
|
|
* Make sure you have a python installation, I will assume Anaconda (Python 3.7), available [here](https://www.anaconda.com/products/individual#download-section).
|
|
* Open 'Anaconda prompt' from the start menu and navigate to where you extracted the zip file using the `cd <folder>` command.
|
|
* Execute the command `pip3 install .`
|
|
* Start the app by running `katrain` in the directory where you downloaded the scripts.
|
|
|
|
# <a name="LinuxSources"></a>Installation from sources for Linux users
|
|
|
|
* This assumed you have a working Python 3.6/3.7 installation as a default. If your default is python 2, use pip3/python3.
|
|
Kivy currently does not have a release for Python 3.8.
|
|
* Open a terminal.
|
|
* Run the command `git clone https://github.com/sanderland/katrain.git` to download the repository.
|
|
* Changing directory using `cd katrain`
|
|
* Run the command `pip3 install .` to install the package globally, or use `--user` to install locally, then run the program by typing `katrain` in the terminal.
|
|
* If you prefer not to install, run without installing using `python3 -m katrain`
|
|
* A binary for KataGo is included, but if you have compiled your own, point the 'engine/katago' setting to the relevant KataGo v1.4+ binary.
|
|
|
|
## Troubleshooting
|
|
|
|
Older linux machines may have trouble installing, you can try to manually install dependencies to resolve some issues.
|
|
|
|
* `sudo apt-get install pkg-config libgl-dev opencl-headers ocl-icd-opencl-dev `
|
|
* `pip3 install -U cython wheel setuptools`
|
|
* `pip3 install kivy==2.0.0rc2 kivymd==1.104.1`
|
|
|
|
In case KataGo does not start, an alternative is to go [here](https://github.com/lightvector/KataGo) and compile KataGo yourself.
|
|
|
|
# Configuring the GPU(s) KataGo uses
|
|
|
|
In most cases KataGo detects your configuration correctly, automatically searching for OpenCL devices and select the highest scoring device.
|
|
However, if you have multiple GPUs or want to force a specific device you will need to edit the 'analysis_config.cfg' file in the KataGo folder.
|
|
|
|
To see what devices are available and which one KataGo is using. Look for the following lines in the terminal after starting KaTrain:
|
|
```
|
|
Found 3 device(s) on platform 0 with type CPU or GPU or Accelerator
|
|
Found OpenCL Device 0: Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Intel) (score 102)
|
|
Found OpenCL Device 1: Intel(R) UHD Graphics 630 (Intel Inc.) (score 6000102)
|
|
Found OpenCL Device 2: AMD Radeon Pro 5500M Compute Engine (AMD) (score 11000102)
|
|
Using OpenCL Device 2: AMD Radeon Pro 5500M Compute Engine (AMD) OpenCL 1.2
|
|
```
|
|
|
|
The above devices were found on a 2019 MacBook Pro with both an on-motherboard graphics chip, and a separate AMD Radeon Pro video card.
|
|
As you can see it scores about twice as high as the Intel UHD chip and KataGo has selected
|
|
it as it's sole device. You can configure KataGo to use *both* the AMD and the Intel devices to get the best performance out of the system.
|
|
|
|
* Open the 'analysis_config.cfg' file in the KataGo folder.
|
|
* Search for `numNNServerThreadsPerModel` (~line 75), uncomment the line by deleting the # and set the value to 2. The line should read `numNNServerThreadsPerModel = 2`.
|
|
* Search for `openclDeviceToUseThread` (~line 117), uncomment by deleting the # and set the values to the device ID numbers identified in the terminal.
|
|
From the example above, we would want to use devices 1 and 2, for the Intel and AMD GPU's, but not device 0 (the CPU). In our case, the lines should read:
|
|
```
|
|
openclDeviceToUseThread0 = 1
|
|
openclDeviceToUseThread1 = 2
|
|
```
|
|
* Run `python3 katrain.py` and confirm that KataGo is now using both devices, by
|
|
checking the output from the terminal, which should indicate two devices being used. For example:
|
|
```
|
|
Found 3 device(s) on platform 0 with type CPU or GPU or Accelerator
|
|
Found OpenCL Device 0: Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz (Intel) (score 102)
|
|
Found OpenCL Device 1: Intel(R) UHD Graphics 630 (Intel Inc.) (score 6000102)
|
|
Found OpenCL Device 2: AMD Radeon Pro 5500M Compute Engine (AMD) (score 11000102)
|
|
Using OpenCL Device 1: Intel(R) UHD Graphics 630 (Intel Inc.) OpenCL 1.2
|
|
Using OpenCL Device 2: AMD Radeon Pro 5500M Compute Engine (AMD) OpenCL 1.2
|
|
```
|