Begin work on runpod configuration

This commit is contained in:
Aaron Po
2026-05-03 23:32:08 -04:00
parent 26635ace84
commit b05000c6fb
12 changed files with 457 additions and 87 deletions

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@@ -0,0 +1,9 @@
build/
cmake-build-debug/
.git/
.idea/
**/*.sqlite
**/*.log
**/*.sqlite3
**/*.db

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@@ -1,41 +1,45 @@
cmake_minimum_required(VERSION 3.31)
project(biergarten-pipeline)
# Set policy to allow FetchContent_Populate for header-only libraries
# that have outdated CMakeLists.txt files
cmake_policy(SET CMP0169 OLD)
# 1. Build Options
option(BIERGARTEN_MOCK_ONLY "Build with mock data generators only — skips llama.cpp" OFF)
if (BIERGARTEN_MOCK_ONLY)
message(STATUS "[biergarten] MOCK_ONLY build — llama.cpp will not be compiled.")
endif ()
if(BIERGARTEN_MOCK_ONLY)
message(STATUS "[biergarten] MOCK_ONLY build — llama.cpp will not be compiled.")
endif()
# 2. Platform & GPU Detection
if (NOT UNIX)
message(FATAL_ERROR "[biergarten] Windows is not supported. Please use Linux (Fedora 43) or macOS (M1 Pro).")
endif ()
if(NOT UNIX)
message(FATAL_ERROR "[biergarten] Windows is not supported. Please use Linux (Fedora 43) or macOS (M1 Pro).")
endif()
if (APPLE)
if (CMAKE_SYSTEM_PROCESSOR MATCHES "arm64")
message(STATUS "[biergarten] Apple Silicon detected — enabling Metal acceleration.")
set(GGML_METAL ON CACHE BOOL "Enable Metal for Apple Silicon" FORCE)
else ()
message(STATUS "[biergarten] Intel Mac detected — using CPU / Accelerate framework.")
set(GGML_METAL OFF CACHE BOOL "Disable Metal for Intel Macs" FORCE)
endif ()
else ()
find_package(CUDAToolkit QUIET)
find_package(hip CONFIG QUIET)
if(APPLE)
if(CMAKE_SYSTEM_PROCESSOR MATCHES "arm64")
message(STATUS "[biergarten] Apple Silicon detected — enabling Metal acceleration.")
set(GGML_METAL ON CACHE BOOL "Enable Metal for Apple Silicon" FORCE)
else()
message(STATUS "[biergarten] Intel Mac detected — using CPU / Accelerate framework.")
set(GGML_METAL OFF CACHE BOOL "Disable Metal for Intel Macs" FORCE)
endif()
else()
find_package(CUDAToolkit QUIET)
find_package(hip CONFIG QUIET)
if (CUDAToolkit_FOUND)
message(STATUS "[biergarten] NVIDIA GPU detected — enabling CUDA acceleration.")
set(GGML_CUDA ON CACHE BOOL "Enable CUDA for NVIDIA GPUs" FORCE)
set(CMAKE_CUDA_ARCHITECTURES native)
elseif (hip_FOUND OR DEFINED ENV{ROCM_PATH} OR EXISTS "/opt/rocm")
message(STATUS "[biergarten] AMD GPU detected — enabling HIP/ROCm acceleration.")
set(GGML_HIPBLAS ON CACHE BOOL "Enable HIP for AMD GPUs" FORCE)
else ()
message(STATUS "[biergarten] No NVIDIA or AMD GPU found — falling back to CPU.")
endif ()
endif ()
if(CUDAToolkit_FOUND)
message(STATUS "[biergarten] NVIDIA GPU detected — enabling CUDA acceleration.")
set(GGML_CUDA ON CACHE BOOL "Enable CUDA for NVIDIA GPUs" FORCE)
set(CMAKE_CUDA_ARCHITECTURES native)
elseif(hip_FOUND OR DEFINED ENV{ROCM_PATH} OR EXISTS "/opt/rocm")
message(STATUS "[biergarten] AMD GPU detected — enabling HIP/ROCm acceleration.")
set(GGML_HIPBLAS ON CACHE BOOL "Enable HIP for AMD GPUs" FORCE)
else()
message(STATUS "[biergarten] No NVIDIA or AMD GPU found — falling back to CPU.")
endif()
endif()
# 3. Project-wide Settings
set(CMAKE_CXX_STANDARD 20)
@@ -51,16 +55,23 @@ include(FetchContent)
find_package(Boost REQUIRED COMPONENTS json program_options)
# Boost.DI (unofficial Boost extension, must declare separately from main Boost dependency)
# Header-only library, so we only fetch without invoking its CMakeLists.txt
FetchContent_Declare(
boost-di
GIT_REPOSITORY https://github.com/boost-ext/di.git
GIT_TAG v1.3.0
GIT_SHALLOW TRUE
)
FetchContent_MakeAvailable(boost-di)
if (TARGET Boost.DI AND NOT TARGET boost::di)
add_library(boost::di ALIAS Boost.DI)
endif ()
FetchContent_GetProperties(boost-di)
if(NOT boost-di_POPULATED)
FetchContent_Populate(boost-di)
endif()
add_library(boost_di INTERFACE)
add_library(boost::di ALIAS boost_di)
target_include_directories(boost_di INTERFACE
$<BUILD_INTERFACE:${boost-di_SOURCE_DIR}/include>
)
# SQLite amalgamation
FetchContent_Declare(
sqlite_amalgamation
@@ -69,21 +80,38 @@ FetchContent_Declare(
EXCLUDE_FROM_ALL
)
FetchContent_MakeAvailable(sqlite_amalgamation)
if (NOT TARGET sqlite3)
add_library(sqlite3 STATIC ${sqlite_amalgamation_SOURCE_DIR}/sqlite3.c)
target_include_directories(sqlite3 PUBLIC ${sqlite_amalgamation_SOURCE_DIR})
target_compile_definitions(sqlite3 PUBLIC SQLITE_THREADSAFE=1)
endif ()
if(NOT TARGET sqlite3)
add_library(sqlite3 STATIC ${sqlite_amalgamation_SOURCE_DIR}/sqlite3.c)
target_include_directories(sqlite3 PUBLIC ${sqlite_amalgamation_SOURCE_DIR})
target_compile_definitions(sqlite3 PUBLIC SQLITE_THREADSAFE=1)
endif()
# llama.cpp — skipped for mock-only builds
if (NOT BIERGARTEN_MOCK_ONLY)
FetchContent_Declare(
llama-cpp
GIT_REPOSITORY https://github.com/ggml-org/llama.cpp.git
GIT_TAG b8742
)
FetchContent_MakeAvailable(llama-cpp)
endif ()
if(NOT BIERGARTEN_MOCK_ONLY)
find_library(LLAMA_LIB NAMES llama)
find_library(GGML_LIB NAMES ggml)
find_library(GGML_BASE_LIB NAMES ggml-base)
find_path(LLAMA_INC_DIR NAMES llama.h PATH_SUFFIXES include)
if(LLAMA_LIB AND GGML_LIB AND GGML_BASE_LIB AND LLAMA_INC_DIR)
message(STATUS "[biergarten] Found system llama.cpp — skipping FetchContent")
add_library(llama SHARED IMPORTED)
set_target_properties(llama PROPERTIES
IMPORTED_LOCATION "${LLAMA_LIB}"
INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INC_DIR}"
INTERFACE_LINK_LIBRARIES "${GGML_LIB};${GGML_BASE_LIB}"
)
else()
message(STATUS "[biergarten] System llama.cpp not found — fetching via FetchContent")
FetchContent_Declare(
llama-cpp
GIT_REPOSITORY https://github.com/ggml-org/llama.cpp.git
GIT_TAG b9012
)
FetchContent_MakeAvailable(llama-cpp)
endif()
endif()
# spdlog
FetchContent_Declare(
@@ -153,16 +181,16 @@ target_sources(${PROJECT_NAME} PRIVATE
)
# --- data_generation: llama (skipped for mock-only builds) ---
if (NOT BIERGARTEN_MOCK_ONLY)
target_sources(${PROJECT_NAME} PRIVATE
src/data_generation/llama/load.cc
src/data_generation/llama/helpers.cc
src/data_generation/llama/generate_brewery.cc
src/data_generation/llama/infer.cc
src/data_generation/llama/llama_generator.cc
src/data_generation/llama/generate_user.cc
)
endif ()
if(NOT BIERGARTEN_MOCK_ONLY)
target_sources(${PROJECT_NAME} PRIVATE
src/data_generation/llama/load.cc
src/data_generation/llama/helpers.cc
src/data_generation/llama/generate_brewery.cc
src/data_generation/llama/infer.cc
src/data_generation/llama/llama_generator.cc
src/data_generation/llama/generate_user.cc
)
endif()
# --- services: wikipedia ---
target_sources(${PROJECT_NAME} PRIVATE
@@ -189,8 +217,6 @@ target_sources(${PROJECT_NAME} PRIVATE
# 6. Include Directories, Link Libraries & Compile Definitions
target_include_directories(${PROJECT_NAME} PRIVATE
includes
$<$<NOT:$<BOOL:${BIERGARTEN_MOCK_ONLY}>>:${llama-cpp_SOURCE_DIR}/include>
$<$<NOT:$<BOOL:${BIERGARTEN_MOCK_ONLY}>>:${llama-cpp_SOURCE_DIR}/common>
)
target_link_libraries(${PROJECT_NAME} PRIVATE
@@ -225,4 +251,4 @@ add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy_directory
${CMAKE_SOURCE_DIR}/prompts
${CMAKE_BINARY_DIR}/prompts
)
)

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@@ -0,0 +1,57 @@
# Phase 1: Pull prebuilt binaries
FROM ghcr.io/ggml-org/llama.cpp:full-cuda AS llama-bin
# Phase 2: Building environment
FROM nvidia/cuda:12.6.3-devel-ubuntu24.04
ENV DEBIAN_FRONTEND=noninteractive \
CMAKE_GENERATOR=Ninja \
APP_ROOT=/workspace/app \
BUILD_DIR=/workspace/app/build
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
ca-certificates \
curl \
git \
libboost-json-dev \
libboost-program-options-dev \
libssl-dev \
ninja-build \
pkg-config \
zlib1g-dev \
&& rm -rf /var/lib/apt/lists/*
# Install modern CMake via curl (Ubuntu 24.04 'apt' version can be laggy)
RUN curl -L https://github.com/Kitware/CMake/releases/download/v3.31.0/cmake-3.31.0-linux-x86_64.sh -o cmake.sh && \
sh cmake.sh --skip-license --prefix=/usr/local && rm cmake.sh
# Copy and link backends
COPY --from=llama-bin /app/lib*.so* /usr/local/lib/
RUN ldconfig && \
find /usr/local/lib -name "libggml-cuda.so*" -exec ln -s {} /usr/local/lib/libggml-cuda.so \; 2>/dev/null || true && \
find /usr/local/lib -name "libggml-cpu.so*" -exec ln -s {} /usr/local/lib/libggml-cpu.so \; 2>/dev/null || true
# Set Environment for the loader
ENV GGML_BACKEND_PATH="/usr/local/lib"
ENV LD_LIBRARY_PATH="/usr/local/lib:$LD_LIBRARY_PATH"
# Headers for C++ Build
RUN git clone --depth 1 -b b9012 https://github.com/ggml-org/llama.cpp.git /tmp/llama-src && \
cp -r /tmp/llama-src/include/* /usr/local/include/ && \
cp -r /tmp/llama-src/ggml/include/* /usr/local/include/ && \
rm -rf /tmp/llama-src
WORKDIR /workspace/app
COPY . .
# Build the C++ pipeline
RUN cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && \
cmake --build build -j$(nproc)
# Setup Start Script
COPY runpod/start.sh /usr/local/bin/biergarten-start
RUN chmod +x /usr/local/bin/biergarten-start
WORKDIR /workspace/app/build
ENTRYPOINT ["/usr/local/bin/biergarten-start"]

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@@ -0,0 +1,66 @@
# RunPod Pod Template for Biergarten Pipeline
This folder contains a starter RunPod pod template for the C++ pipeline in the
repository root.
## What it does
- Builds `biergarten-pipeline` inside the container.
- Builds the binary on first pod start, then reuses a mode-specific build
directory (`build-mocked/` or `build-live/`).
- Runs from the repository root and lets the launcher switch into the active
build directory after CMake has copied `locations.json` and `prompts/`.
- Supports two runtime modes:
- `BIERGARTEN_MODE=mocked` — fast deterministic generation, no model required.
- `BIERGARTEN_MODE=live` — uses a mounted GGUF model and the prompt files.
- Writes generated SQLite exports and logs to writable volumes.
## Files
- `Dockerfile` — GPU-ready build image for the application.
- `start.sh` — runtime launcher that selects mocked or live mode.
- `pod-template.yaml` — starter pod template you can adapt to the exact RunPod
import/export schema.
## Build the image
```bash
docker build -t biergarten-pipeline:latest -f runpod/Dockerfile .
```
## Run locally in mocked mode
```bash
docker run --rm \
--gpus all \
-e BIERGARTEN_MODE=mocked \
-v "$PWD/output:/workspace/output" \
-v "$PWD/logs:/workspace/logs" \
biergarten-pipeline:latest
```
## Run locally in live mode
Mount your GGUF model at `/workspace/models/google_gemma-4-E4B-it-Q6_K.gguf`
and switch to `BIERGARTEN_MODE=live`.
```bash
docker run --rm \
--gpus all \
-e BIERGARTEN_MODE=live \
-v "$PWD/models:/workspace/models" \
-v "$PWD/output:/workspace/output" \
-v "$PWD/logs:/workspace/logs" \
biergarten-pipeline:latest
```
## Notes for RunPod
- Use a GPU pod for live inference.
- Mount persistent storage for `/workspace/models`, `/workspace/output`, and
`/workspace/logs`.
- If you only want deterministic seed generation, change the template's
`BIERGARTEN_MODE` to `mocked`.
- The launcher handles the build directory automatically; CMake still copies
`locations.json` and `prompts/` into the active build tree before execution.

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@@ -0,0 +1,39 @@
# Biergarten Pipeline — RunPod pod template
#
# This template is meant to be imported into RunPod or adapted to the exact
# schema used by your account/export format. It intentionally keeps the runtime
# contract simple:
# - the container boots into /workspace/app/build
# - prompts are available from build/prompts
# - generated SQLite exports and logs go to writable volumes
# - mocked mode works without a model file
# - live mode can be enabled by setting BIERGARTEN_MODE=live and mounting a GGUF model
name: biergarten-pipeline-live
image: biergarten-pipeline:latest
workingDir: /workspace/app
entrypoint:
- /usr/local/bin/biergarten-start
resources:
gpu: 1
containerDiskInGb: 50
volumeInGb: 50
environment:
BIERGARTEN_MODE: live
BIERGARTEN_MODEL_PATH: /workspace/models/google_gemma-4-E4B-it-Q6_K.gguf
BIERGARTEN_PROMPT_DIR: /workspace/app/build/prompts
BIERGARTEN_OUTPUT_DIR: /workspace/output
BIERGARTEN_LOG_PATH: /workspace/logs/pipeline.log
BIERGARTEN_TEMPERATURE: "1.0"
BIERGARTEN_TOP_P: "0.95"
BIERGARTEN_TOP_K: "64"
BIERGARTEN_N_CTX: "8192"
BIERGARTEN_SEED: "-1"
volumes:
- name: models
mountPath: /workspace/models
- name: output
mountPath: /workspace/output
- name: logs
mountPath: /workspace/logs

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@@ -0,0 +1,53 @@
#!/bin/bash
set -e
# Configuration / Defaults
MODEL_PATH="${BIERGARTEN_MODEL_PATH:-/workspace/models/google_gemma-4-E4B-it-Q6_K.gguf}"
OUTPUT_DIR="${BIERGARTEN_OUTPUT_DIR:-/workspace/output}"
LOG_PATH="${BIERGARTEN_LOG_PATH:-/workspace/logs/pipeline.log}"
EXECUTABLE="/workspace/app/build/biergarten-pipeline"
PROMPT_DIR="/workspace/app/build/prompts"
echo "--- Starting Biergarten Pipeline Environment Check ---"
# 1. Ensure Volume Mounts exist
mkdir -p "$OUTPUT_DIR"
mkdir -p "$(dirname "$LOG_PATH")"
# 2. Check for Model
if [ ! -f "$MODEL_PATH" ]; then
echo "ERROR: Model not found at $MODEL_PATH"
echo "Current /workspace/models contents:"
ls -lh /workspace/models
exit 1
fi
# 3. Check for Backends (Diagnostic)
echo "Loading backends from: $GGML_BACKEND_PATH"
ls -l /usr/local/lib/libggml*
# 4. Build the command arguments
ARGS=(
"--model" "$MODEL_PATH"
"--prompt-dir" "$PROMPT_DIR"
"--output" "$OUTPUT_DIR"
"--log-path" "$LOG_PATH"
)
# Optional Hyperparameters
[[ -n "$BIERGARTEN_TEMPERATURE" ]] && ARGS+=("--temperature" "$BIERGARTEN_TEMPERATURE")
[[ -n "$BIERGARTEN_TOP_P" ]] && ARGS+=("--top-p" "$BIERGARTEN_TOP_P")
[[ -n "$BIERGARTEN_TOP_K" ]] && ARGS+=("--top-k" "$BIERGARTEN_TOP_K")
[[ -n "$BIERGARTEN_N_CTX" ]] && ARGS+=("--n-ctx" "$BIERGARTEN_N_CTX")
[[ -n "$BIERGARTEN_SEED" ]] && ARGS+=("--seed" "$BIERGARTEN_SEED")
[[ -n "$BIERGARTEN_GL_LAYERS" ]] && ARGS+=("--n-gpu-layers" "$BIERGARTEN_GL_LAYERS")
# Append extra custom args
if [[ -n "$BIERGARTEN_EXTRA_ARGS" ]]; then
ARGS+=($BIERGARTEN_EXTRA_ARGS)
fi
echo "--- Executing: $EXECUTABLE ${ARGS[@]} ---"
# Execute the binary directly (replaces shell process)
exec "$EXECUTABLE" "${ARGS[@]}"

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@@ -12,6 +12,7 @@
#include <utility>
#include "data_generation/llama_generator.h"
#include "ggml-backend.h"
#include "llama.h"
// Maximum batch size for decode operations. Capping the batch prevents
@@ -22,6 +23,10 @@ void LlamaGenerator::Load(const std::string& model_path) {
context_.reset();
model_.reset();
// Specifically load dynamic ggml backends (like CUDA) that are provided
// externally before attempting to load a model.
ggml_backend_load_all();
const llama_model_params model_params = llama_model_default_params();
LlamaGenerator::ModelHandle loaded_model(
llama_model_load_from_file(model_path.c_str(), model_params));