Fix multi-GPU with lazy CuPy init + rendering improvements
GPU fix: - Revert to CUDA_VISIBLE_DEVICES approach but with lazy CuPy init - gpu.py: CuPy is no longer imported at module level; _init_gpu() imports it lazily on first to_gpu() call. This allows workers to set CUDA_VISIBLE_DEVICES before CuPy creates a CUDA context. - gpu.py: detect GPU count via nvidia-smi (no CUDA context needed) - pipeline.py: each worker sets CUDA_VISIBLE_DEVICES=N before CuPy init, so each process uses only its assigned GPU Rendering improvements: - Title: split into bold title (14pt) + italic description (10pt) instead of single 15pt bold block - North arrow: moved inside data area (top-right corner) with semi-transparent white background for readability over data - Colorbar: full height (no gap for compass rose), added ScalarFormatter(useOffset=False) to avoid scientific notation - Colorbar compass rose gap removed since north arrow is now inside the data area
This commit is contained in:
Antoine Jacquin
@ -7,8 +7,9 @@ operations fall back to numpy/scipy on CPU.
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GPU errors (e.g. in forked subprocesses) are caught gracefully and
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cause an automatic fallback to CPU for the current operation.
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Multi-GPU support: when multiple GPUs are available, each worker process
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can be assigned a different GPU via set_active_gpu() for balanced load.
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Multi-GPU support: each worker process sets CUDA_VISIBLE_DEVICES before
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CuPy is imported, so CuPy only sees its assigned GPU. This avoids kernel
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cache incompatibilities that occur with Device.use() switching.
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"""
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import logging
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@ -18,16 +19,13 @@ from scipy import ndimage
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logger = logging.getLogger("lidar")
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# Detect GPU count at import time WITHOUT importing CuPy.
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# We use nvidia-smi or CUDA_VISIBLE_DEVICES to count GPUs,
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# so that CUDA_VISIBLE_DEVICES can be set BEFORE CuPy context creation
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# in worker processes.
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# Detect total GPU count via nvidia-smi (no CUDA context created).
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# This must happen before any CUDA_VISIBLE_DEVICES manipulation.
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_NUM_GPUS = 0
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HAS_GPU = False
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_gpu_name = None
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_gpu_mem_gb = 0
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# Check if GPUs are available via nvidia-smi (no CUDA context created)
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try:
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import subprocess
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_result = subprocess.run(
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@ -49,9 +47,9 @@ try:
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except (FileNotFoundError, subprocess.TimeoutExpired, Exception):
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pass
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# Lazy-initialized GPU module references
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# CuPy is imported only when first needed, allowing CUDA_VISIBLE_DEVICES
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# to be set before CuPy context creation in worker processes.
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# Lazy CuPy initialization — imported only when first needed.
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# This allows CUDA_VISIBLE_DEVICES to be set before CuPy creates
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# a CUDA context, enabling per-process GPU assignment.
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_xp = np # Default: CPU
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_cp = None # cupy module (or None)
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_cp_ndimage = None # cupyx.scipy.ndimage (or None)
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@ -61,8 +59,8 @@ _gpu_initialized = False
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def _init_gpu():
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"""Lazily initialize CuPy on first GPU use.
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This allows CUDA_VISIBLE_DEVICES to take effect in worker processes
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before CuPy creates a CUDA context.
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Import CuPy only when needed, so CUDA_VISIBLE_DEVICES can be
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set before the CUDA context is created.
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"""
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global _xp, _cp, _cp_ndimage, _gpu_initialized
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if _gpu_initialized:
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@ -76,39 +74,36 @@ def _init_gpu():
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_xp = _real_cupy
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_cp = _real_cupy
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_cp_ndimage = _real_cupy_ndimage
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except (ImportError, Exception):
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except (ImportError, Exception) as e:
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logger.debug(f"CuPy non disponible: {e}")
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_xp = np
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_cp = None
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_cp_ndimage = None
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def num_gpus():
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"""Return the number of available CUDA GPUs."""
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"""Return the total number of CUDA GPUs in the system."""
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return _NUM_GPUS
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def set_active_gpu(gpu_id):
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"""Set the active GPU for the current process.
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"""Set the active GPU for the current process via CUDA_VISIBLE_DEVICES.
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Must be called BEFORE any GPU operation (to_gpu, etc.) to ensure
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the CUDA context is created on the correct device.
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MUST be called before any GPU operation (to_gpu, etc.) to ensure
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CuPy creates its CUDA context on the correct device. With lazy
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initialization, CuPy is imported AFTER this call, so it only
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sees the assigned GPU.
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Args:
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gpu_id: 0-based GPU index. Clamped to valid range.
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gpu_id: 0-based GPU index (referring to the system GPU numbering).
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"""
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if not HAS_GPU or _NUM_GPUS <= 1:
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return # Nothing to do for single GPU or no GPU
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gpu_id = gpu_id % _NUM_GPUS
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# Set CUDA_VISIBLE_DEVICES before CuPy context is created
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# This is the most reliable way in spawn processes
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# Set CUDA_VISIBLE_DEVICES before CuPy context creation
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os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
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# Reset lazy init so CuPy re-detects with the new env
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global _gpu_initialized, _cp, _cp_ndimage, _xp
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_gpu_initialized = False
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_cp = None
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_cp_ndimage = None
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_xp = np
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logger.info(f" GPU {gpu_id} sélectionnée pour ce worker")
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@ -127,8 +122,17 @@ def _gpu_available():
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def log_gpu_status():
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"""Log GPU detection result. Called after logging is configured."""
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if HAS_GPU:
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gpu_info = f"GPU détectée: {_gpu_name} ({_gpu_mem_gb} Go VRAM)"
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if _gpu_available():
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# Get actual device name from CuPy (after init)
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try:
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dev = _cp.cuda.Device()
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name = _cp.cuda.runtime.getDeviceProperties(0)['name']
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if isinstance(name, bytes):
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name = name.decode()
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mem_gb = _cp.cuda.runtime.getDeviceProperties(0)['totalGlobalMem'] // (1024 ** 3)
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gpu_info = f"GPU: {name} ({mem_gb} Go VRAM)"
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except Exception:
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gpu_info = f"GPU: {_gpu_name} ({_gpu_mem_gb} Go VRAM)"
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if _NUM_GPUS > 1:
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gpu_info += f" × {_NUM_GPUS}"
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logger.info(gpu_info)
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@ -537,10 +537,9 @@ def _process_file_standalone(laz_file_str, input_dir, output_dir, resolution, fo
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Each worker gets its own temp directory to avoid file conflicts.
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When multiple GPUs are available, each worker is assigned a GPU via
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CUDA_VISIBLE_DEVICES to balance load across GPUs.
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CuPy's Device API to balance load across GPUs.
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"""
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# Assign GPU FIRST — before any CuPy import happens
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# This sets CUDA_VISIBLE_DEVICES and resets lazy CuPy init
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# Assign GPU to this worker using CuPy's Device API
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if gpu_id is not None and gpu_id >= 0:
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from .gpu import set_active_gpu
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set_active_gpu(gpu_id)
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@ -30,6 +30,7 @@ import matplotlib.pyplot as plt
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from matplotlib import rcParams
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from matplotlib.patches import Polygon as MplPolygon, Rectangle as RectPatch, FancyBboxPatch
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from matplotlib.colors import ListedColormap
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from matplotlib.ticker import ScalarFormatter
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try:
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from cmcrameri import cm as cmc
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@ -495,15 +496,16 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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im = ax.imshow(data, cmap=cmap, aspect='equal', origin='upper',
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interpolation='bilinear')
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ax.set_title(f"{title}\n{description}", fontsize=15, fontweight='bold', pad=10)
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ax.set_title(f"{title}", fontsize=14, fontweight='bold', pad=8)
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ax.text(0.5, 1.01, description, transform=ax.transAxes,
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fontsize=10, fontstyle='italic', color='#555555',
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ha='center', va='bottom')
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# Colorbar/legend area — reduced height to leave room for compass rose above
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# Colorbar/legend area — full height alongside data
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cbar_left = data_left + data_width_frac + 0.02
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cbar_width = 0.04
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compass_height = 0.07
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compass_gap = 0.02
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cbar_bottom = data_bottom
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cbar_height = data_height_frac - compass_height - compass_gap
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cbar_height = data_height_frac
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if is_rgb:
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# RGB: descriptive text label instead of gradient colorbar
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cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
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@ -521,12 +523,14 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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sm.set_array([])
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cbar = plt.colorbar(sm, cax=cbar_ax)
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cbar.ax.tick_params(labelsize=9, width=1.5)
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cbar.ax.yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
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cbar.outline.set_linewidth(1.5)
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cbar.set_label(legend_label, fontsize=10, fontweight='bold')
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else:
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cbar_ax = fig.add_axes([cbar_left, cbar_bottom, cbar_width, cbar_height])
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cbar = plt.colorbar(im, cax=cbar_ax)
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cbar.ax.tick_params(labelsize=9, width=1.5)
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cbar.ax.yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
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cbar.outline.set_linewidth(1.5)
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cbar.set_label(legend_label, fontsize=10, fontweight='bold')
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@ -565,32 +569,35 @@ def tif_to_png(tif_file, vis_dir, resolution, keep_tif=False, source_info=None,
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spine.set_color('black')
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spine.set_linewidth(0.8)
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# North arrow — compass rose style, positioned above the colorbar
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compass_bottom = data_bottom + data_height_frac + 0.02
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compass_height = 0.07
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compass_width = cbar_width + 0.03
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north_ax = fig.add_axes([cbar_left, compass_bottom, compass_width, compass_height])
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# North arrow — compass rose style, inside the data area (top-right corner)
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# Semi-transparent background for readability over any data
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north_ax = fig.add_axes([data_left + data_width_frac - 0.06,
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data_bottom + data_height_frac - 0.12,
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0.05, 0.10],
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facecolor='none')
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north_ax.set_xlim(-1.2, 1.2)
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north_ax.set_ylim(-0.5, 1.5)
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north_ax.set_ylim(-0.3, 1.5)
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north_ax.axis('off')
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north_ax.set_aspect('equal')
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north_ax.set_facecolor('white')
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# Semi-transparent white background circle
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circle_bg = plt.Circle((0, 0.5), 0.85, facecolor='white', edgecolor='#888888',
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linewidth=0.5, alpha=0.7, zorder=1)
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north_ax.add_patch(circle_bg)
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# N arrow
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north_ax.annotate('N', xy=(0, 1.3), fontsize=11, fontweight='bold',
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ha='center', va='bottom', color='#b22222')
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north_ax.annotate('N', xy=(0, 1.3), fontsize=9, fontweight='bold',
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ha='center', va='bottom', color='#b22222', zorder=10)
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north_ax.plot([0, 0], [0.0, 1.0], color='#b22222', linewidth=2.0, zorder=10)
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north_ax.add_patch(MplPolygon([[0, 0.3], [-0.2, 0.7], [0, 1.0], [0.2, 0.7]],
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closed=True, facecolor='#b22222', edgecolor='#b22222', zorder=9))
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# Cardinal ticks
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for angle, label in [(90, ''), (0, 'E'), (180, 'O'), (270, 'S')]:
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rad = np.radians(angle)
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r_text = 1.25
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north_ax.plot([0.85*np.cos(rad), 1.05*np.cos(rad)],
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[0.85*np.sin(rad), 1.05*np.sin(rad)],
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color='#555555', linewidth=0.8, zorder=5)
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if label:
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north_ax.text(r_text*np.cos(rad), r_text*np.sin(rad), label,
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fontsize=7, ha='center', va='center', color='#555555')
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north_ax.text(1.15*np.cos(rad), 1.15*np.sin(rad), label,
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fontsize=6, ha='center', va='center', color='#555555', zorder=5)
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# Bottom info bar — enriched with source, method, date
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info_ax = fig.add_axes([data_left, 0.015, data_width_frac + cbar_width + 0.02, 0.09])
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