replace setup.sh and install python stack files
This commit is contained in:
Roland Tannous
parent
a2c3f39e12
commit
4dbbc0945c
2 changed files with 188 additions and 311 deletions
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@ -140,15 +140,15 @@ def _bootstrap_uv() -> bool:
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global UV_NEEDS_SYSTEM
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if not shutil.which("uv"):
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return False
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# Probe: try a dry-run install targeting the current Python explicitly.
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# Without --python, uv can ignore the activated venv on some platforms.
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# Probe: try a dry-run install without --system.
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# If uv can't find a venv it exits with code 2.
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probe = subprocess.run(
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["uv", "pip", "install", "--dry-run", "--python", sys.executable, "pip"],
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["uv", "pip", "install", "--dry-run", "pip"],
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stdout = subprocess.PIPE,
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stderr = subprocess.STDOUT,
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)
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if probe.returncode != 0:
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# Retry with --system (some envs need it when uv can't find a venv)
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# Retry with --system to confirm it works
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probe_sys = subprocess.run(
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["uv", "pip", "install", "--dry-run", "--system", "pip"],
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stdout = subprocess.PIPE,
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@ -204,10 +204,6 @@ def _build_uv_cmd(args: tuple[str, ...]) -> list[str]:
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cmd = ["uv", "pip", "install"]
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if UV_NEEDS_SYSTEM:
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cmd.append("--system")
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# Always pass --python so uv targets the correct environment.
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# Without this, uv can ignore an activated venv and install into
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# the system Python (observed on Colab and similar environments).
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cmd.extend(["--python", sys.executable])
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cmd.extend(_translate_pip_args_for_uv(args))
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cmd.append("--torch-backend=auto")
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return cmd
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485
studio/setup.sh
485
studio/setup.sh
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@ -41,26 +41,13 @@ if [[ "$keynames" == *$'\nCOLAB_'* ]]; then
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fi
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# ── Detect whether frontend needs building ──
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# Skip if dist/ exists AND no tracked input is newer than dist/.
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# Checks top-level config/entry files and src/, public/ recursively.
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# This handles: PyPI installs (dist/ bundled), repeat runs (no changes),
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# and upgrades/pulls (source newer than dist/ triggers rebuild).
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_NEED_FRONTEND_BUILD=true
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if [ -d "$SCRIPT_DIR/frontend/dist" ]; then
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# Check all top-level files (package.json, bun.lock, vite.config.ts, index.html, etc.)
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_changed=$(find "$SCRIPT_DIR/frontend" -maxdepth 1 -type f \
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-newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null)
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# Check src/ and public/ recursively (|| true guards against set -e when dirs are missing)
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if [ -z "$_changed" ]; then
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_changed=$(find "$SCRIPT_DIR/frontend/src" "$SCRIPT_DIR/frontend/public" \
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-type f -newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null) || true
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fi
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if [ -z "$_changed" ]; then
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_NEED_FRONTEND_BUILD=false
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fi
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fi
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if [ "$_NEED_FRONTEND_BUILD" = false ]; then
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echo "✅ Frontend already built and up to date -- skipping Node/npm check."
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# Only skip when BOTH conditions are true:
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# 1. We're inside site-packages (PyPI / pip install, not editable)
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# 2. dist/ already exists (pre-built in the wheel)
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# Otherwise always (re)build — handles upgrades, editable installs, and
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# pip-from-source where dist/ was never built.
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if [[ "$SCRIPT_DIR" == */site-packages/* ]] && [ -d "$SCRIPT_DIR/frontend/dist" ]; then
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echo "✅ Frontend pre-built (PyPI) — skipping Node/npm check."
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else
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NEED_NODE=true
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if command -v node &>/dev/null && command -v npm &>/dev/null; then
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@ -159,27 +146,12 @@ run_quiet "npm run build" npm run build
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_restore_gitignores
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trap - EXIT
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# Validate CSS output -- catch truncated Tailwind builds
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_MAX_CSS=$(find "$SCRIPT_DIR/frontend/dist/assets" -name '*.css' -exec wc -c {} + 2>/dev/null | sort -n | tail -1 | awk '{print $1}')
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if [ -z "$_MAX_CSS" ]; then
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echo "⚠️ WARNING: No CSS files were emitted. The frontend build may have failed."
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elif [ "$_MAX_CSS" -lt 100000 ]; then
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echo "⚠️ WARNING: Largest CSS file is only $((_MAX_CSS / 1024))KB (expected >100KB)."
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echo " Tailwind may not have scanned all source files. Check for .gitignore interference."
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fi
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cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator"
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run_quiet "npm install (oxc validator runtime)" npm install
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cd "$SCRIPT_DIR"
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echo "✅ Frontend built to frontend/dist"
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fi # end frontend build check
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# ── oxc-validator runtime (needs npm -- skip if not available) ──
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if [ -d "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator" ] && command -v npm &>/dev/null; then
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cd "$SCRIPT_DIR/backend/core/data_recipe/oxc-validator"
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run_quiet "npm install (oxc validator runtime)" npm install
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cd "$SCRIPT_DIR"
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fi
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fi # end frontend dist check
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# ── 6. Python venv + deps ──
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@ -251,276 +223,188 @@ install_python_stack() {
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python "$SCRIPT_DIR/install_python_stack.py"
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}
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# Create venv under ~/.unsloth/studio/ (shared location, not in repo).
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# All platforms (including Colab) use the same isolated venv so that
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# studio dependencies are never installed into the system Python.
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STUDIO_HOME="$HOME/.unsloth/studio"
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VENV_DIR="$STUDIO_HOME/.venv"
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VENV_T5_DIR="$STUDIO_HOME/.venv_t5"
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mkdir -p "$STUDIO_HOME"
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# Clean up legacy in-repo venvs if they exist
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[ -d "$REPO_ROOT/.venv" ] && rm -rf "$REPO_ROOT/.venv"
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[ -d "$REPO_ROOT/.venv_overlay" ] && rm -rf "$REPO_ROOT/.venv_overlay"
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[ -d "$REPO_ROOT/.venv_t5" ] && rm -rf "$REPO_ROOT/.venv_t5"
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rm -rf "$VENV_DIR"
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rm -rf "$VENV_T5_DIR"
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# Try creating venv with pip; fall back to --without-pip + bootstrap
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# (some environments like Colab have broken ensurepip)
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if ! "$BEST_PY" -m venv "$VENV_DIR" 2>/dev/null; then
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"$BEST_PY" -m venv --without-pip "$VENV_DIR"
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source "$VENV_DIR/bin/activate"
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curl -sS https://bootstrap.pypa.io/get-pip.py | python > /dev/null
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if [ "$IS_COLAB" = true ]; then
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# Colab: install packages directly without venv
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install_python_stack
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else
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# Local: create venv under studio home (shared location, not in repo)
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# Configurable via UNSLOTH_STUDIO_HOME; defaults to ~/.unsloth/studio
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STUDIO_HOME="${UNSLOTH_STUDIO_HOME:-$HOME/.unsloth/studio}"
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echo " Studio home: $STUDIO_HOME"
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# Persist for future `unsloth studio` runs (survives shell restarts)
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mkdir -p "$HOME/.unsloth"
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echo "$STUDIO_HOME" > "$HOME/.unsloth/studio_home"
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VENV_DIR="$STUDIO_HOME/.venv"
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VENV_T5_DIR="$STUDIO_HOME/.venv_t5"
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mkdir -p "$STUDIO_HOME"
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# Clean up legacy in-repo venvs if they exist
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[ -d "$REPO_ROOT/.venv" ] && rm -rf "$REPO_ROOT/.venv"
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[ -d "$REPO_ROOT/.venv_overlay" ] && rm -rf "$REPO_ROOT/.venv_overlay"
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[ -d "$REPO_ROOT/.venv_t5" ] && rm -rf "$REPO_ROOT/.venv_t5"
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rm -rf "$VENV_DIR"
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rm -rf "$VENV_T5_DIR"
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"$BEST_PY" -m venv "$VENV_DIR"
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source "$VENV_DIR/bin/activate"
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fi
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cd "$SCRIPT_DIR"
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install_python_stack
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# ── Ensure uv is available (much faster than pip) ──
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USE_UV=false
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if command -v uv &>/dev/null; then
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USE_UV=true
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elif curl -LsSf https://astral.sh/uv/install.sh | sh > /dev/null 2>&1; then
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export PATH="$HOME/.local/bin:$PATH"
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command -v uv &>/dev/null && USE_UV=true
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fi
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# Helper: install a package, preferring uv with pip fallback
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fast_install() {
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if [ "$USE_UV" = true ]; then
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uv pip install --python "$(command -v python)" "$@" && return 0
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fi
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python -m pip install "$@"
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}
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cd "$SCRIPT_DIR"
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install_python_stack
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# ── 6b. Pre-install transformers 5.x into .venv_t5/ ──
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# Models like GLM-4.7-Flash need transformers>=5.3.0. Instead of pip-installing
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# at runtime (slow, ~10-15s), we pre-install into a separate directory.
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# The training subprocess just prepends .venv_t5/ to sys.path -- instant switch.
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echo ""
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echo " Pre-installing transformers 5.x for newer model support..."
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mkdir -p "$VENV_T5_DIR"
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run_quiet "install transformers 5.x" fast_install --target "$VENV_T5_DIR" --no-deps "transformers==5.3.0"
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run_quiet "install huggingface_hub for t5" fast_install --target "$VENV_T5_DIR" --no-deps "huggingface_hub==1.7.1"
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run_quiet "install hf_xet for t5" fast_install --target "$VENV_T5_DIR" --no-deps "hf_xet==1.4.2"
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# tiktoken is needed by Qwen-family tokenizers. Install with deps since
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# regex/requests may be missing on Windows.
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run_quiet "install tiktoken for t5" fast_install --target "$VENV_T5_DIR" "tiktoken"
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echo "✅ Transformers 5.x pre-installed to $VENV_T5_DIR/"
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# ── 7. WSL: pre-install GGUF build dependencies ──
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# On WSL, sudo requires a password and can't be entered during GGUF export
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# (runs in a non-interactive subprocess). Install build deps here instead.
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if grep -qi microsoft /proc/version 2>/dev/null; then
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# ── 6b. Pre-install transformers 5.x into .venv_t5/ ──
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# Models like GLM-4.7-Flash need transformers>=5.3.0. Instead of pip-installing
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# at runtime (slow, ~10-15s), we pre-install into a separate directory.
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# The training subprocess just prepends .venv_t5/ to sys.path — instant switch.
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echo ""
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echo "⚠️ WSL detected -- installing build dependencies for GGUF export..."
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_GGUF_DEPS="pciutils build-essential cmake curl git libcurl4-openssl-dev"
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echo " Pre-installing transformers 5.x for newer model support..."
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mkdir -p "$VENV_T5_DIR"
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run_quiet "pip install transformers 5.x" pip install --target "$VENV_T5_DIR" --no-deps "transformers==5.3.0"
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run_quiet "pip install huggingface_hub for t5" pip install --target "$VENV_T5_DIR" --no-deps "huggingface_hub==1.3.0"
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echo "✅ Transformers 5.x pre-installed to $VENV_T5_DIR/"
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# Try without sudo first (works when already root)
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apt-get update -y >/dev/null 2>&1 || true
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apt-get install -y $_GGUF_DEPS >/dev/null 2>&1 || true
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# Check which packages are still missing
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_STILL_MISSING=""
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for _pkg in $_GGUF_DEPS; do
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case "$_pkg" in
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build-essential) command -v gcc >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
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pciutils) command -v lspci >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
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libcurl4-openssl-dev) dpkg -s "$_pkg" >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
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*) command -v "$_pkg" >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
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esac
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done
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_STILL_MISSING=$(echo "$_STILL_MISSING" | sed 's/^ *//')
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if [ -z "$_STILL_MISSING" ]; then
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# ── 7. WSL: pre-install GGUF build dependencies ──
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# On WSL, sudo requires a password and can't be entered during GGUF export
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# (runs in a non-interactive subprocess). Install build deps here instead.
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if grep -qi microsoft /proc/version 2>/dev/null; then
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echo ""
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echo "⚠️ WSL detected — installing build dependencies for GGUF export..."
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echo " You may be prompted for your password."
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sudo apt-get update -y
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sudo apt-get install -y build-essential cmake curl git libcurl4-openssl-dev
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echo "✅ GGUF build dependencies installed"
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elif command -v sudo >/dev/null 2>&1; then
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echo ""
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echo " !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
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echo " WARNING: We require sudo elevated permissions to install:"
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echo " $_STILL_MISSING"
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echo " If you accept, we'll run sudo now, and it'll prompt your password."
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echo " !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
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echo ""
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printf " Accept? [Y/n] "
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if [ -r /dev/tty ]; then
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read -r REPLY </dev/tty || REPLY="y"
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else
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REPLY="y"
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fi
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case "$REPLY" in
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[nN]*)
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echo ""
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echo " Please install these packages first, then re-run Unsloth Studio setup:"
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echo " sudo apt-get update -y && sudo apt-get install -y $_STILL_MISSING"
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_SKIP_GGUF_BUILD=true
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;;
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*)
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sudo apt-get update -y
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sudo apt-get install -y $_STILL_MISSING
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echo "✅ GGUF build dependencies installed"
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;;
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esac
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else
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echo " sudo is not available on this system."
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echo " Please install as root, then re-run setup:"
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echo " apt-get install -y $_STILL_MISSING"
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_SKIP_GGUF_BUILD=true
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fi
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fi
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# ── 8. Build llama.cpp binaries for GGUF inference + export ──
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# Builds at ~/.unsloth/llama.cpp — a single shared location under the user's
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# home directory. This is used by both the inference server and the GGUF
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# export pipeline (unsloth-zoo).
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# - llama-server: for GGUF model inference
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# - llama-quantize: for GGUF export quantization (symlinked to root for check_llama_cpp())
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UNSLOTH_HOME="$HOME/.unsloth"
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mkdir -p "$UNSLOTH_HOME"
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LLAMA_CPP_DIR="$UNSLOTH_HOME/llama.cpp"
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LLAMA_SERVER_BIN="$LLAMA_CPP_DIR/build/bin/llama-server"
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if [ "${_SKIP_GGUF_BUILD:-}" = true ]; then
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echo ""
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echo "Skipping llama-server build (missing dependencies)"
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echo " Install the missing packages and re-run setup to enable GGUF inference."
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else
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rm -rf "$LLAMA_CPP_DIR"
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{
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# Check prerequisites
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if ! command -v cmake &>/dev/null; then
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echo ""
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echo "⚠️ cmake not found — skipping llama-server build (GGUF inference won't be available)"
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echo " Install cmake and re-run setup.sh to enable GGUF inference."
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elif ! command -v git &>/dev/null; then
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echo ""
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echo "⚠️ git not found — skipping llama-server build (GGUF inference won't be available)"
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else
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echo ""
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echo "Building llama-server for GGUF inference..."
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BUILD_OK=true
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run_quiet "clone llama.cpp" git clone --depth 1 https://github.com/ggml-org/llama.cpp.git "$LLAMA_CPP_DIR" || BUILD_OK=false
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if [ "$BUILD_OK" = true ]; then
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# Skip tests/examples we don't need (faster build)
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CMAKE_ARGS="-DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_NATIVE=ON"
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# Use ccache if available (dramatically faster rebuilds)
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if command -v ccache &>/dev/null; then
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CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
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echo " Using ccache for faster compilation"
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fi
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# Detect CUDA: check nvcc on PATH, then common install locations
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NVCC_PATH=""
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if command -v nvcc &>/dev/null; then
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NVCC_PATH="$(command -v nvcc)"
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elif [ -x /usr/local/cuda/bin/nvcc ]; then
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NVCC_PATH="/usr/local/cuda/bin/nvcc"
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export PATH="/usr/local/cuda/bin:$PATH"
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elif ls /usr/local/cuda-*/bin/nvcc &>/dev/null 2>&1; then
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# Pick the newest cuda-XX.X directory
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NVCC_PATH="$(ls -d /usr/local/cuda-*/bin/nvcc 2>/dev/null | sort -V | tail -1)"
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export PATH="$(dirname "$NVCC_PATH"):$PATH"
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fi
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if [ -n "$NVCC_PATH" ]; then
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echo " Building with CUDA support (nvcc: $NVCC_PATH)..."
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CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON"
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# Detect GPU compute capability and limit CUDA architectures
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# Without this, cmake builds for ALL default archs (very slow)
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# CUDA_ARCHS can be pre-set via env var (e.g. Docker ARG) to
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# include targets not detectable at build time (no GPU access).
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# We always include 86 (A10) and merge with detected GPUs.
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_add_arch() {
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local _a="$1"
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case ";$CUDA_ARCHS;" in
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*";$_a;"*) ;;
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*) CUDA_ARCHS="${CUDA_ARCHS:+$CUDA_ARCHS;}$_a" ;;
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esac
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}
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CUDA_ARCHS=""
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# Merge any pre-set CUDA_ARCHS from environment
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if [ -n "${CUDA_ARCHS_EXTRA:-}" ]; then
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IFS=';' read -ra _env_archs <<< "$CUDA_ARCHS_EXTRA"
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for _ea in "${_env_archs[@]}"; do
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_ea=$(echo "$_ea" | tr -d '[:space:]')
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[ -n "$_ea" ] && _add_arch "$_ea"
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done
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fi
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# Always include sm_86 (A10)
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_add_arch "86"
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if command -v nvidia-smi &>/dev/null; then
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# Read all GPUs, deduplicate (handles mixed-GPU hosts)
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_raw_caps=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true)
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while IFS= read -r _cap; do
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_cap=$(echo "$_cap" | tr -d '[:space:]')
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if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
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_add_arch "${BASH_REMATCH[1]}${BASH_REMATCH[2]}"
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fi
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done <<< "$_raw_caps"
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fi
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if [ -n "$CUDA_ARCHS" ]; then
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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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue