diff --git a/studio/install_python_stack.py b/studio/install_python_stack.py index a141c64425..20011a298a 100644 --- a/studio/install_python_stack.py +++ b/studio/install_python_stack.py @@ -140,15 +140,15 @@ def _bootstrap_uv() -> bool: global UV_NEEDS_SYSTEM if not shutil.which("uv"): return False - # Probe: try a dry-run install targeting the current Python explicitly. - # Without --python, uv can ignore the activated venv on some platforms. + # Probe: try a dry-run install without --system. + # If uv can't find a venv it exits with code 2. probe = subprocess.run( - ["uv", "pip", "install", "--dry-run", "--python", sys.executable, "pip"], + ["uv", "pip", "install", "--dry-run", "pip"], stdout = subprocess.PIPE, stderr = subprocess.STDOUT, ) if probe.returncode != 0: - # Retry with --system (some envs need it when uv can't find a venv) + # Retry with --system to confirm it works probe_sys = subprocess.run( ["uv", "pip", "install", "--dry-run", "--system", "pip"], stdout = subprocess.PIPE, @@ -204,10 +204,6 @@ def _build_uv_cmd(args: tuple[str, ...]) -> list[str]: cmd = ["uv", "pip", "install"] if UV_NEEDS_SYSTEM: cmd.append("--system") - # Always pass --python so uv targets the correct environment. - # Without this, uv can ignore an activated venv and install into - # the system Python (observed on Colab and similar environments). - cmd.extend(["--python", sys.executable]) cmd.extend(_translate_pip_args_for_uv(args)) cmd.append("--torch-backend=auto") return cmd diff --git a/studio/setup.sh b/studio/setup.sh index ee95e03966..ae47f7e362 100755 --- a/studio/setup.sh +++ b/studio/setup.sh @@ -41,26 +41,13 @@ if [[ "$keynames" == *$'\nCOLAB_'* ]]; then fi # ── Detect whether frontend needs building ── -# Skip if dist/ exists AND no tracked input is newer than dist/. -# Checks top-level config/entry files and src/, public/ recursively. -# This handles: PyPI installs (dist/ bundled), repeat runs (no changes), -# and upgrades/pulls (source newer than dist/ triggers rebuild). -_NEED_FRONTEND_BUILD=true -if [ -d "$SCRIPT_DIR/frontend/dist" ]; then - # Check all top-level files (package.json, bun.lock, vite.config.ts, index.html, etc.) - _changed=$(find "$SCRIPT_DIR/frontend" -maxdepth 1 -type f \ - -newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null) - # Check src/ and public/ recursively (|| true guards against set -e when dirs are missing) - if [ -z "$_changed" ]; then - _changed=$(find "$SCRIPT_DIR/frontend/src" "$SCRIPT_DIR/frontend/public" \ - -type f -newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null) || true - fi - if [ -z "$_changed" ]; then - _NEED_FRONTEND_BUILD=false - fi -fi -if [ "$_NEED_FRONTEND_BUILD" = false ]; then - echo "✅ Frontend already built and up to date -- skipping Node/npm check." +# Only skip when BOTH conditions are true: +# 1. We're inside site-packages (PyPI / pip install, not editable) +# 2. dist/ already exists (pre-built in the wheel) +# Otherwise always (re)build — handles upgrades, editable installs, and +# pip-from-source where dist/ was never built. +if [[ "$SCRIPT_DIR" == */site-packages/* ]] && [ -d "$SCRIPT_DIR/frontend/dist" ]; then + echo "✅ Frontend pre-built (PyPI) — skipping Node/npm check." else NEED_NODE=true if command -v node &>/dev/null && command -v npm &>/dev/null; then @@ -159,27 +146,12 @@ run_quiet "npm run build" npm run build _restore_gitignores trap - EXIT - -# Validate CSS output -- catch truncated Tailwind builds -_MAX_CSS=$(find "$SCRIPT_DIR/frontend/dist/assets" -name '*.css' -exec wc -c {} + 2>/dev/null | sort -n | tail -1 | awk '{print $1}') -if [ -z "$_MAX_CSS" ]; then - echo "⚠️ WARNING: No CSS files were emitted. The frontend build may have failed." -elif [ "$_MAX_CSS" -lt 100000 ]; then - echo "⚠️ WARNING: Largest CSS file is only $((_MAX_CSS / 1024))KB (expected >100KB)." - echo " Tailwind may not have scanned all source files. Check for .gitignore interference." -fi - +cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator" +run_quiet "npm install (oxc validator runtime)" npm install cd "$SCRIPT_DIR" echo "✅ Frontend built to frontend/dist" -fi # end frontend build check - -# ── oxc-validator runtime (needs npm -- skip if not available) ── -if [ -d "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator" ] && command -v npm &>/dev/null; then - cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator" - run_quiet "npm install (oxc validator runtime)" npm install - cd "$SCRIPT_DIR" -fi +fi # end frontend dist check # ── 6. Python venv + deps ── @@ -251,276 +223,188 @@ install_python_stack() { python "$SCRIPT_DIR/install_python_stack.py" } -# Create venv under ~/.unsloth/studio/ (shared location, not in repo). -# All platforms (including Colab) use the same isolated venv so that -# studio dependencies are never installed into the system Python. -STUDIO_HOME="$HOME/.unsloth/studio" -VENV_DIR="$STUDIO_HOME/.venv" -VENV_T5_DIR="$STUDIO_HOME/.venv_t5" -mkdir -p "$STUDIO_HOME" - -# Clean up legacy in-repo venvs if they exist -[ -d "$REPO_ROOT/.venv" ] && rm -rf "$REPO_ROOT/.venv" -[ -d "$REPO_ROOT/.venv_overlay" ] && rm -rf "$REPO_ROOT/.venv_overlay" -[ -d "$REPO_ROOT/.venv_t5" ] && rm -rf "$REPO_ROOT/.venv_t5" - -rm -rf "$VENV_DIR" -rm -rf "$VENV_T5_DIR" -# Try creating venv with pip; fall back to --without-pip + bootstrap -# (some environments like Colab have broken ensurepip) -if ! "$BEST_PY" -m venv "$VENV_DIR" 2>/dev/null; then - "$BEST_PY" -m venv --without-pip "$VENV_DIR" - source "$VENV_DIR/bin/activate" - curl -sS https://bootstrap.pypa.io/get-pip.py | python > /dev/null +if [ "$IS_COLAB" = true ]; then + # Colab: install packages directly without venv + install_python_stack else + # Local: create venv under studio home (shared location, not in repo) + # Configurable via UNSLOTH_STUDIO_HOME; defaults to ~/.unsloth/studio + STUDIO_HOME="${UNSLOTH_STUDIO_HOME:-$HOME/.unsloth/studio}" + echo " Studio home: $STUDIO_HOME" + # Persist for future `unsloth studio` runs (survives shell restarts) + mkdir -p "$HOME/.unsloth" + echo "$STUDIO_HOME" > "$HOME/.unsloth/studio_home" + VENV_DIR="$STUDIO_HOME/.venv" + VENV_T5_DIR="$STUDIO_HOME/.venv_t5" + mkdir -p "$STUDIO_HOME" + + # Clean up legacy in-repo venvs if they exist + [ -d "$REPO_ROOT/.venv" ] && rm -rf "$REPO_ROOT/.venv" + [ -d "$REPO_ROOT/.venv_overlay" ] && rm -rf "$REPO_ROOT/.venv_overlay" + [ -d "$REPO_ROOT/.venv_t5" ] && rm -rf "$REPO_ROOT/.venv_t5" + + rm -rf "$VENV_DIR" + rm -rf "$VENV_T5_DIR" + "$BEST_PY" -m venv "$VENV_DIR" source "$VENV_DIR/bin/activate" -fi + cd "$SCRIPT_DIR" + install_python_stack -# ── Ensure uv is available (much faster than pip) ── -USE_UV=false -if command -v uv &>/dev/null; then - USE_UV=true -elif curl -LsSf https://astral.sh/uv/install.sh | sh > /dev/null 2>&1; then - export PATH="$HOME/.local/bin:$PATH" - command -v uv &>/dev/null && USE_UV=true -fi - -# Helper: install a package, preferring uv with pip fallback -fast_install() { - if [ "$USE_UV" = true ]; then - uv pip install --python "$(command -v python)" "$@" && return 0 - fi - python -m pip install "$@" -} - -cd "$SCRIPT_DIR" -install_python_stack - -# ── 6b. Pre-install transformers 5.x into .venv_t5/ ── -# Models like GLM-4.7-Flash need transformers>=5.3.0. Instead of pip-installing -# at runtime (slow, ~10-15s), we pre-install into a separate directory. -# The training subprocess just prepends .venv_t5/ to sys.path -- instant switch. -echo "" -echo " Pre-installing transformers 5.x for newer model support..." -mkdir -p "$VENV_T5_DIR" -run_quiet "install transformers 5.x" fast_install --target "$VENV_T5_DIR" --no-deps "transformers==5.3.0" -run_quiet "install huggingface_hub for t5" fast_install --target "$VENV_T5_DIR" --no-deps "huggingface_hub==1.7.1" -run_quiet "install hf_xet for t5" fast_install --target "$VENV_T5_DIR" --no-deps "hf_xet==1.4.2" -# tiktoken is needed by Qwen-family tokenizers. Install with deps since -# regex/requests may be missing on Windows. -run_quiet "install tiktoken for t5" fast_install --target "$VENV_T5_DIR" "tiktoken" -echo "✅ Transformers 5.x pre-installed to $VENV_T5_DIR/" - -# ── 7. WSL: pre-install GGUF build dependencies ── -# On WSL, sudo requires a password and can't be entered during GGUF export -# (runs in a non-interactive subprocess). Install build deps here instead. -if grep -qi microsoft /proc/version 2>/dev/null; then + # ── 6b. Pre-install transformers 5.x into .venv_t5/ ── + # Models like GLM-4.7-Flash need transformers>=5.3.0. Instead of pip-installing + # at runtime (slow, ~10-15s), we pre-install into a separate directory. + # The training subprocess just prepends .venv_t5/ to sys.path — instant switch. echo "" - echo "⚠️ WSL detected -- installing build dependencies for GGUF export..." - _GGUF_DEPS="pciutils build-essential cmake curl git libcurl4-openssl-dev" + echo " Pre-installing transformers 5.x for newer model support..." + mkdir -p "$VENV_T5_DIR" + run_quiet "pip install transformers 5.x" pip install --target "$VENV_T5_DIR" --no-deps "transformers==5.3.0" + run_quiet "pip install huggingface_hub for t5" pip install --target "$VENV_T5_DIR" --no-deps "huggingface_hub==1.3.0" + echo "✅ Transformers 5.x pre-installed to $VENV_T5_DIR/" - # Try without sudo first (works when already root) - apt-get update -y >/dev/null 2>&1 || true - apt-get install -y $_GGUF_DEPS >/dev/null 2>&1 || true - - # Check which packages are still missing - _STILL_MISSING="" - for _pkg in $_GGUF_DEPS; do - case "$_pkg" in - build-essential) command -v gcc >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;; - pciutils) command -v lspci >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;; - libcurl4-openssl-dev) dpkg -s "$_pkg" >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;; - *) command -v "$_pkg" >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;; - esac - done - _STILL_MISSING=$(echo "$_STILL_MISSING" | sed 's/^ *//') - - if [ -z "$_STILL_MISSING" ]; then + # ── 7. WSL: pre-install GGUF build dependencies ── + # On WSL, sudo requires a password and can't be entered during GGUF export + # (runs in a non-interactive subprocess). Install build deps here instead. + if grep -qi microsoft /proc/version 2>/dev/null; then + echo "" + echo "⚠️ WSL detected — installing build dependencies for GGUF export..." + echo " You may be prompted for your password." + sudo apt-get update -y + sudo apt-get install -y build-essential cmake curl git libcurl4-openssl-dev echo "✅ GGUF build dependencies installed" - elif command -v sudo >/dev/null 2>&1; then - echo "" - echo " !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" - echo " WARNING: We require sudo elevated permissions to install:" - echo " $_STILL_MISSING" - echo " If you accept, we'll run sudo now, and it'll prompt your password." - echo " !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" - echo "" - printf " Accept? [Y/n] " - if [ -r /dev/tty ]; then - read -r REPLY /dev/null; then - echo "" - echo "⚠️ cmake not found — skipping llama-server build (GGUF inference won't be available)" - echo " Install cmake and re-run setup.sh to enable GGUF inference." - elif ! command -v git &>/dev/null; then - echo "" - echo "⚠️ git not found — skipping llama-server build (GGUF inference won't be available)" - else - echo "" - echo "Building llama-server for GGUF inference..." - - BUILD_OK=true - run_quiet "clone llama.cpp" git clone --depth 1 https://github.com/ggml-org/llama.cpp.git "$LLAMA_CPP_DIR" || BUILD_OK=false - - if [ "$BUILD_OK" = true ]; then - # Skip tests/examples we don't need (faster build) - CMAKE_ARGS="-DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_NATIVE=ON" - - # Use ccache if available (dramatically faster rebuilds) - if command -v ccache &>/dev/null; then - CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache" - echo " Using ccache for faster compilation" - fi - - # Detect CUDA: check nvcc on PATH, then common install locations - NVCC_PATH="" - if command -v nvcc &>/dev/null; then - NVCC_PATH="$(command -v nvcc)" - elif [ -x /usr/local/cuda/bin/nvcc ]; then - NVCC_PATH="/usr/local/cuda/bin/nvcc" - export PATH="/usr/local/cuda/bin:$PATH" - elif ls /usr/local/cuda-*/bin/nvcc &>/dev/null 2>&1; then - # Pick the newest cuda-XX.X directory - NVCC_PATH="$(ls -d /usr/local/cuda-*/bin/nvcc 2>/dev/null | sort -V | tail -1)" - export PATH="$(dirname "$NVCC_PATH"):$PATH" - fi - - if [ -n "$NVCC_PATH" ]; then - echo " Building with CUDA support (nvcc: $NVCC_PATH)..." - CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON" - - # Detect GPU compute capability and limit CUDA architectures - # Without this, cmake builds for ALL default archs (very slow) - # CUDA_ARCHS can be pre-set via env var (e.g. Docker ARG) to - # include targets not detectable at build time (no GPU access). - # We always include 86 (A10) and merge with detected GPUs. - _add_arch() { - local _a="$1" - case ";$CUDA_ARCHS;" in - *";$_a;"*) ;; - *) CUDA_ARCHS="${CUDA_ARCHS:+$CUDA_ARCHS;}$_a" ;; - esac - } - CUDA_ARCHS="" - # Merge any pre-set CUDA_ARCHS from environment - if [ -n "${CUDA_ARCHS_EXTRA:-}" ]; then - IFS=';' read -ra _env_archs <<< "$CUDA_ARCHS_EXTRA" - for _ea in "${_env_archs[@]}"; do - _ea=$(echo "$_ea" | tr -d '[:space:]') - [ -n "$_ea" ] && _add_arch "$_ea" - done - fi - # Always include sm_86 (A10) - _add_arch "86" - if command -v nvidia-smi &>/dev/null; then - # Read all GPUs, deduplicate (handles mixed-GPU hosts) - _raw_caps=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true) - while IFS= read -r _cap; do - _cap=$(echo "$_cap" | tr -d '[:space:]') - if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then - _add_arch "${BASH_REMATCH[1]}${BASH_REMATCH[2]}" - fi - done <<< "$_raw_caps" - fi - - if [ -n "$CUDA_ARCHS" ]; then - echo " GPU compute capabilities: ${CUDA_ARCHS//;/, } -- limiting build to detected archs" - CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHS}" - else - echo " Could not detect GPU arch -- building for all default CUDA architectures (slower)" - fi - - # Multi-threaded nvcc compilation (uses all CPU cores per .cu file) - CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0" - elif [ -d /usr/local/cuda ] || nvidia-smi &>/dev/null; then - echo " CUDA driver detected but nvcc not found — building CPU-only" - echo " To enable GPU: install cuda-toolkit or add nvcc to PATH" - else - echo " Building CPU-only (no CUDA detected)..." - fi - - NCPU=$(nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4) - - # Use Ninja if available (faster parallel builds than Make) - CMAKE_GENERATOR_ARGS="" - if command -v ninja &>/dev/null; then - CMAKE_GENERATOR_ARGS="-G Ninja" - fi - - run_quiet "cmake llama.cpp" cmake $CMAKE_GENERATOR_ARGS -S "$LLAMA_CPP_DIR" -B "$LLAMA_CPP_DIR/build" $CMAKE_ARGS || BUILD_OK=false - fi - - if [ "$BUILD_OK" = true ]; then - run_quiet "build llama-server" cmake --build "$LLAMA_CPP_DIR/build" --config Release --target llama-server -j"$NCPU" || BUILD_OK=false - fi - - # Also build llama-quantize (needed by unsloth-zoo's GGUF export pipeline) - if [ "$BUILD_OK" = true ]; then - run_quiet "build llama-quantize" cmake --build "$LLAMA_CPP_DIR/build" --config Release --target llama-quantize -j"$NCPU" || true - # Symlink to llama.cpp root — check_llama_cpp() looks for the binary there - QUANTIZE_BIN="$LLAMA_CPP_DIR/build/bin/llama-quantize" - if [ -f "$QUANTIZE_BIN" ]; then - ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize" - fi - fi - - if [ "$BUILD_OK" = true ]; then - if [ -f "$LLAMA_SERVER_BIN" ]; then - echo "✅ llama-server built at $LLAMA_SERVER_BIN" - else - echo "⚠️ llama-server binary not found after build — GGUF inference won't be available" - fi - if [ -f "$LLAMA_CPP_DIR/llama-quantize" ]; then - echo "✅ llama-quantize available for GGUF export" - fi - else - echo "⚠️ llama-server build failed — GGUF inference won't be available, but everything else works" - fi - fi -} -fi # end _SKIP_GGUF_BUILD check +# Disabled: llama.cpp build is commented out for now. +# UNCOMMENT the block below to re-enable. +# +# # Builds at ~/.unsloth/llama.cpp — a single shared location under the user's +# # home directory. This is used by both the inference server and the GGUF +# # export pipeline (unsloth-zoo). +# # - llama-server: for GGUF model inference +# # - llama-quantize: for GGUF export quantization (symlinked to root for check_llama_cpp()) +# UNSLOTH_HOME="$HOME/.unsloth" +# mkdir -p "$UNSLOTH_HOME" +# LLAMA_CPP_DIR="$UNSLOTH_HOME/llama.cpp" +# LLAMA_SERVER_BIN="$LLAMA_CPP_DIR/build/bin/llama-server" +# rm -rf "$LLAMA_CPP_DIR" +# { +# # Check prerequisites +# if ! command -v cmake &>/dev/null; then +# echo "" +# echo "⚠️ cmake not found — skipping llama-server build (GGUF inference won't be available)" +# echo " Install cmake and re-run setup.sh to enable GGUF inference." +# elif ! command -v git &>/dev/null; then +# echo "" +# echo "⚠️ git not found — skipping llama-server build (GGUF inference won't be available)" +# else +# echo "" +# echo "Building llama-server for GGUF inference..." +# +# BUILD_OK=true +# run_quiet "clone llama.cpp" git clone --depth 1 https://github.com/ggml-org/llama.cpp.git "$LLAMA_CPP_DIR" || BUILD_OK=false +# +# if [ "$BUILD_OK" = true ]; then +# # Skip tests/examples we don't need (faster build) +# CMAKE_ARGS="-DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_NATIVE=ON" +# +# # Use ccache if available (dramatically faster rebuilds) +# if command -v ccache &>/dev/null; then +# CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache" +# echo " Using ccache for faster compilation" +# fi +# +# # Detect CUDA: check nvcc on PATH, then common install locations +# NVCC_PATH="" +# if command -v nvcc &>/dev/null; then +# NVCC_PATH="$(command -v nvcc)" +# elif [ -x /usr/local/cuda/bin/nvcc ]; then +# NVCC_PATH="/usr/local/cuda/bin/nvcc" +# export PATH="/usr/local/cuda/bin:$PATH" +# elif ls /usr/local/cuda-*/bin/nvcc &>/dev/null 2>&1; then +# # Pick the newest cuda-XX.X directory +# NVCC_PATH="$(ls -d /usr/local/cuda-*/bin/nvcc 2>/dev/null | sort -V | tail -1)" +# export PATH="$(dirname "$NVCC_PATH"):$PATH" +# fi +# +# if [ -n "$NVCC_PATH" ]; then +# echo " Building with CUDA support (nvcc: $NVCC_PATH)..." +# CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON" +# +# # Detect GPU compute capability and limit CUDA architectures +# # Without this, cmake builds for ALL default archs (very slow) +# CUDA_ARCHS="" +# if command -v nvidia-smi &>/dev/null; then +# # Read all GPUs, deduplicate (handles mixed-GPU hosts) +# _raw_caps=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true) +# while IFS= read -r _cap; do +# _cap=$(echo "$_cap" | tr -d '[:space:]') +# if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then +# _arch="${BASH_REMATCH[1]}${BASH_REMATCH[2]}" +# # Append if not already present +# case ";$CUDA_ARCHS;" in +# *";$_arch;"*) ;; +# *) CUDA_ARCHS="${CUDA_ARCHS:+$CUDA_ARCHS;}$_arch" ;; +# esac +# fi +# done <<< "$_raw_caps" +# fi +# +# if [ -n "$CUDA_ARCHS" ]; then +# echo " GPU compute capabilities: ${CUDA_ARCHS//;/, } -- limiting build to detected archs" +# CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHS}" +# else +# echo " Could not detect GPU arch -- building for all default CUDA architectures (slower)" +# fi +# +# # Multi-threaded nvcc compilation (uses all CPU cores per .cu file) +# CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0" +# elif [ -d /usr/local/cuda ] || nvidia-smi &>/dev/null; then +# echo " CUDA driver detected but nvcc not found — building CPU-only" +# echo " To enable GPU: install cuda-toolkit or add nvcc to PATH" +# else +# echo " Building CPU-only (no CUDA detected)..." +# fi +# +# NCPU=$(nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4) +# +# # Use Ninja if available (faster parallel builds than Make) +# CMAKE_GENERATOR_ARGS="" +# if command -v ninja &>/dev/null; then +# CMAKE_GENERATOR_ARGS="-G Ninja" +# fi +# +# run_quiet "cmake llama.cpp" cmake $CMAKE_GENERATOR_ARGS -S "$LLAMA_CPP_DIR" -B "$LLAMA_CPP_DIR/build" $CMAKE_ARGS || BUILD_OK=false +# fi +# +# if [ "$BUILD_OK" = true ]; then +# run_quiet "build llama-server" cmake --build "$LLAMA_CPP_DIR/build" --config Release --target llama-server -j"$NCPU" || BUILD_OK=false +# fi +# +# # Also build llama-quantize (needed by unsloth-zoo's GGUF export pipeline) +# if [ "$BUILD_OK" = true ]; then +# run_quiet "build llama-quantize" cmake --build "$LLAMA_CPP_DIR/build" --config Release --target llama-quantize -j"$NCPU" || true +# # Symlink to llama.cpp root — check_llama_cpp() looks for the binary there +# QUANTIZE_BIN="$LLAMA_CPP_DIR/build/bin/llama-quantize" +# if [ -f "$QUANTIZE_BIN" ]; then +# ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize" +# fi +# fi +# +# if [ "$BUILD_OK" = true ]; then +# if [ -f "$LLAMA_SERVER_BIN" ]; then +# echo "✅ llama-server built at $LLAMA_SERVER_BIN" +# else +# echo "⚠️ llama-server binary not found after build — GGUF inference won't be available" +# fi +# if [ -f "$LLAMA_CPP_DIR/llama-quantize" ]; then +# echo "✅ llama-quantize available for GGUF export" +# fi +# else +# echo "⚠️ llama-server build failed — GGUF inference won't be available, but everything else works" +# fi +# fi +# } echo "" if [ "$IS_COLAB" = true ]; then @@ -529,9 +413,6 @@ if [ "$IS_COLAB" = true ]; then echo "╠══════════════════════════════════════╣" echo "║ Unsloth Studio is ready to start ║" echo "║ in your Colab notebook! ║" - echo "║ ║" - echo "║ from colab import start ║" - echo "║ start() ║" echo "╚══════════════════════════════════════╝" else echo "╔══════════════════════════════════════╗" @@ -539,6 +420,6 @@ else echo "╠══════════════════════════════════════╣" echo "║ Launch with: ║" echo "║ ║" - echo "║ unsloth studio -H 0.0.0.0 -p 8888 ║" + echo "║ unsloth studio -H 0.0.0.0 -p 8000 ║" echo "╚══════════════════════════════════════╝" fi