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docker-compose.yml
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docker-compose.yml
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version: "2.3"
services:
play:
# stdin_open: true
# tty: true
command: lldb-server platform --server --listen "0.0.0.0:5000" --gdbserver-port 5001
user: "${WHO}"
privileged: true
build:
args:
# run the container as the current user so that permissions will work across the
# bindmount and so that the container doesn't have escalated privs.
HOST_UID: "${HOST_UID}"
HOST_GID: "${HOST_GID}"
WHO: "${WHO}"
context: "."
dockerfile: "play/nogpu.dockerfile"
ports:
- "5000:5000"
- "5001:5001"
volumes:
- '.:/seraphim'
- 'rust:/rust' # rust toolchain & build cache
- 'bash:/bash' # bash history
- 'data:/data' # model and game data
environment:
- SERAPHIM_DATA=/data # seraphim binaries read from and write to files in $SERAPHIM_DATA
- HISTFILE=/bash/play_history
- SERAPHIM_MODEL_NAME=${SERAPHIM_MODEL_NAME}
- CARGO_HOME=/rust/cargo
- CARGO_TARGET_DIR=/rust/target
- RUST_BACKTRACE=1
- RUST_LOG=seraphim,generate_games,hist,interactive,tfrecord_viewer
working_dir: /seraphim
train:
stdin_open: true
tty: true
command: /bin/bash
runtime: nvidia
user: "${WHO}"
# NOTE: The SHMEM allocation limit is set to the default of 64MB. This may be
# insufficient for TensorFlow. NVIDIA recommends the use of the following flags:
# nvidia-docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 ...
build:
args:
# run the container as the current user so that permissions will work across the
# bindmount and so that the container doesn't have escalated privs.
HOST_UID: "${HOST_UID}"
HOST_GID: "${HOST_GID}"
WHO: "${WHO}"
context: "."
dockerfile: "train/Dockerfile"
ports:
- "127.0.0.1:5006:5006"
volumes:
- '.:/seraphim'
- 'bash:/bash' # bash history
- 'data:/data' # model and game data
environment:
- SERAPHIM_DATA=/data # seraphim binaries read from and write to files in $SERAPHIM_DATA
- SERAPHIM_MODEL_NAME=${SERAPHIM_MODEL_NAME}
- HISTFILE=/bash/train_history
- NVIDIA_VISIBLE_DEVICES=all
working_dir: /seraphim
volumes:
rust:
bash:
data: