Training

The training pipeline resides in tf, this requires tensorflow running on linux (Ubuntu 16.04 in this case). (It can be made to work on windows too, but it takes more effort.)

Installation

Install the requirements under tf/requirements.txt. And call ./init.sh to compile the protobuf files.

Data preparation

In order to start a training session you first need to download trainingdata from http://lczero.org/training_data. This data is packed in tar.gz balls each containing 10'000 games or chunks as we call them. Preparing data requires the following steps:

tar -xzf games11160000.tar.gz
ls training.* | parallel gzip {}

This repacks each chunk into a gzipped file ready to be parsed by the training pipeline. Note that the parallel command uses all your cores and can be installed with apt-get install parallel.

Training pipeline

Now that the data is in the right format one can configure a training pipeline. This configuration is achieved through a yaml file, see training/tf/configs/example.yaml:

%YAML 1.2
---
name: 'kb1-64x6'                       # ideally no spaces
gpu: 0                                 # gpu id to process on

dataset: 
  num_chunks: 100000                   # newest nof chunks to parse
  train_ratio: 0.90                    # trainingset ratio
  # For separated test and train data.
  input_train: '/path/to/chunks/*/draw/' # supports glob
  input_test: '/path/to/chunks/*/draw/'  # supports glob
  # For a one-shot run with all data in one directory.
  # input: '/path/to/chunks/*/draw/'

training:
    batch_size: 2048                   # training batch
    total_steps: 140000                # terminate after these steps
    test_steps: 2000                   # eval test set values after this many steps
    # checkpoint_steps: 10000          # optional frequency for checkpointing before finish
    shuffle_size: 524288               # size of the shuffle buffer
    lr_values:                         # list of learning rates
        - 0.02
        - 0.002
        - 0.0005
    lr_boundaries:                     # list of boundaries
        - 100000
        - 130000
    policy_loss_weight: 1.0            # weight of policy loss
    value_loss_weight: 1.0             # weight of value loss
    path: '/path/to/store/networks'    # network storage dir

model:
  filters: 64
  residual_blocks: 6
...

The configuration is pretty self explanatory, if you're new to training I suggest looking at the machine learning glossary by google. Now you can invoke training with the following command:

./train.py --cfg configs/example.yaml --output /tmp/mymodel.txt

This will initialize the pipeline and start training a new neural network. You can view progress by invoking tensorboard:

tensorboard --logdir leelalogs

If you now point your browser at localhost:6006 you'll see the trainingprogress as the trainingsteps pass by. Have fun!

Restoring models

The training pipeline will automatically restore from a previous model if it exists in your training:path as configured by your yaml config. For initializing from a raw weights.txt file you can use training/tf/net_to_model.py, this will create a checkpoint for you.

Supervised training