NerfAcc:Setting up CUDA - error could not find files for the given pattern

[jportolese]

Nerfacto works fine but I can’t run instant-ngp. Get the above error and CalledProcessError: Command ‘[‘where’, ‘cl’]’ returned non-zero exit status

(nerfstudio) D:\nerfstudio>ns-train instant-ngp --data outputs\data\Francos [19:27:56] Using --data alias for --data.pipeline.datamanager.dataparser.data train.py:222 ──────────────────────────────────────────────────────── Config ──────────────────────────────────────────────────────── TrainerConfig( _target=<class 'nerfstudio.engine.trainer.Trainer'>, output_dir=WindowsPath('outputs'), method_name='instant-ngp', experiment_name=None, timestamp='2023-01-16_192756', machine=MachineConfig(seed=42, num_gpus=1, num_machines=1, machine_rank=0, dist_url='auto'), logging=LoggingConfig( relative_log_dir=WindowsPath('.'), steps_per_log=10, max_buffer_size=20, local_writer=LocalWriterConfig( _target=<class 'nerfstudio.utils.writer.LocalWriter'>, enable=True, stats_to_track=( <EventName.ITER_TRAIN_TIME: 'Train Iter (time)'>, <EventName.TRAIN_RAYS_PER_SEC: 'Train Rays / Sec'>, <EventName.CURR_TEST_PSNR: 'Test PSNR'>, <EventName.VIS_RAYS_PER_SEC: 'Vis Rays / Sec'>, <EventName.TEST_RAYS_PER_SEC: 'Test Rays / Sec'>, <EventName.ETA: 'ETA (time)'> ), max_log_size=10 ), enable_profiler=True ), viewer=ViewerConfig( relative_log_filename='viewer_log_filename.txt', start_train=True, zmq_port=None, launch_bridge_server=True, websocket_port=7007, ip_address='127.0.0.1', num_rays_per_chunk=64000, max_num_display_images=512, quit_on_train_completion=False, skip_openrelay=False ), pipeline=DynamicBatchPipelineConfig( _target=<class 'nerfstudio.pipelines.dynamic_batch.DynamicBatchPipeline'>, datamanager=VanillaDataManagerConfig( _target=<class 'nerfstudio.data.datamanagers.base_datamanager.VanillaDataManager'>, dataparser=NerfstudioDataParserConfig( _target=<class 'nerfstudio.data.dataparsers.nerfstudio_dataparser.Nerfstudio'>, data=WindowsPath('outputs/data/Francos'), scale_factor=1.0, downscale_factor=None, scene_scale=1.0, orientation_method='up', center_poses=True, auto_scale_poses=True, train_split_percentage=0.9, depth_unit_scale_factor=0.001 ), train_num_rays_per_batch=8192, train_num_images_to_sample_from=-1, train_num_times_to_repeat_images=-1, eval_num_rays_per_batch=1024, eval_num_images_to_sample_from=-1, eval_num_times_to_repeat_images=-1, eval_image_indices=(0,), camera_optimizer=CameraOptimizerConfig( _target=<class 'nerfstudio.cameras.camera_optimizers.CameraOptimizer'>, mode='off', position_noise_std=0.0, orientation_noise_std=0.0, optimizer=AdamOptimizerConfig( _target=<class 'torch.optim.adam.Adam'>, lr=0.0006, eps=1e-15, weight_decay=0 ), scheduler=SchedulerConfig( _target=<class 'nerfstudio.engine.schedulers.ExponentialDecaySchedule'>, lr_final=5e-06, max_steps=10000 ), param_group='camera_opt' ), camera_res_scale_factor=1.0 ), model=InstantNGPModelConfig( _target=<class 'nerfstudio.models.instant_ngp.NGPModel'>, enable_collider=False, collider_params=None, loss_coefficients={'rgb_loss_coarse': 1.0, 'rgb_loss_fine': 1.0}, eval_num_rays_per_chunk=8192, max_num_samples_per_ray=24, grid_resolution=128, contraction_type=<ContractionType.UN_BOUNDED_SPHERE: 2>, cone_angle=0.004, render_step_size=0.01, near_plane=0.05, far_plane=1000.0, use_appearance_embedding=False, background_color='random' ), target_num_samples=262144, max_num_samples_per_ray=1024 ), optimizers={ 'fields': { 'optimizer': AdamOptimizerConfig( _target=<class 'torch.optim.adam.Adam'>, lr=0.01, eps=1e-15, weight_decay=0 ), 'scheduler': None } }, vis='viewer', data=WindowsPath('outputs/data/Francos'), relative_model_dir=WindowsPath('nerfstudio_models'), steps_per_save=2000, steps_per_eval_batch=500, steps_per_eval_image=500, steps_per_eval_all_images=25000, max_num_iterations=30000, mixed_precision=True, save_only_latest_checkpoint=True, load_dir=None, load_step=None, load_config=None ) ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── [19:27:56] Saving config to: experiment_config.py:124 outputs\outputs\data\Francos\instant-ngp\2023-01-16_192756\config.yml [19:27:56] Saving checkpoints to: trainer.py:120 outputs\outputs\data\Francos\instant-ngp\2023-01-16_192756\nerfstudio_models Using ZMQ port: 62035

======================================================================================================================== [Public] Open the viewer at https://viewer.nerf.studio/versions/22-12-16-0/?websocket_url=ws://localhost:7007

Sending ping to the viewer Bridge Server... Successfully connected. Sending ping to the viewer Bridge Server... Successfully connected. [NOTE] Not running eval iterations since only viewer is enabled. Use --vis wandb or --vis tensorboard to run with eval instead. Disabled tensorboard/wandb event writers [19:27:56] Auto image downscale factor of 4 nerfstudio_dataparser.py:314 Skipping 0 files in dataset split train. nerfstudio_dataparser.py:165 Skipping 0 files in dataset split val. nerfstudio_dataparser.py:165 Setting up training dataset... Caching all 135 images. Setting up evaluation dataset... Caching all 15 images. None No checkpoints to load, training from scratch C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py:346: UserWarning: Error checking compiler version for cl: [WinError 2] The system cannot find the file specified warnings.warn(f'Error checking compiler version for {compiler}: {error}') ( ● ) NerfAcc: Setting up CUDA (This may take a few minutes the first time)INFO: Could not find files for the given pattern(s). Printing profiling stats, from longest to shortest duration in seconds Traceback (most recent call last): File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "C:\Users\jport\anaconda3\envs\nerfstudio\Scripts\ns-train.exe__main.py", line 7, in File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\scripts\train.py", line 247, in entrypoint main( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\scripts\train.py", line 233, in main launch( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\scripts\train.py", line 172, in launch main_func(local_rank=0, world_size=world_size, config=config) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\scripts\train.py", line 87, in train_loop trainer.train() File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfstudio\engine\trainer.py", line 194, in train callback.run_callback_at_location( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfstudio\engine\callbacks.py", line 103, in run_callback_at_location self.run_callback(step=step) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfstudio\engine\callbacks.py", line 90, in run_callback self.func(*self.args, self.kwargs, step=step) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfstudio\models\instant_ngp.py", line 148, in update_occupancy_grid self.occupancy_grid.every_n_step( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, *kwargs) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfacc\grid.py", line 271, in every_n_step self._update( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(args, kwargs) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfacc\grid.py", line 224, in _update x = contract_inv( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfacc\contraction.py", line 101, in contract_inv ctype = type.to_cpp_version() File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfacc\contraction.py", line 62, in to_cpp_version return _C.ContractionTypeGetter(self.value) File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfacc\cuda\init__.py", line 11, in call_cuda from ._backend import _C File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\nerfacc\cuda_backend.py", line 85, in _C = load( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1202, in load return _jit_compile( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1425, in _jit_compile _write_ninja_file_and_build_library( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1524, in _write_ninja_file_and_build_library _write_ninja_file_to_build_library( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1963, in _write_ninja_file_to_build_library _write_ninja_file( File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 2090, in _write_ninja_file cl_paths = subprocess.check_output(['where', File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\subprocess.py", line 415, in check_output return run(*popenargs, stdout=PIPE, timeout=timeout, check=True, File "C:\Users\jport\anaconda3\envs\nerfstudio\lib\subprocess.py", line 516, in run raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command '['where', 'cl']' returned non-zero exit status 1.

[pbaumann-cloudscape]

same here

[tancik]

Maybe give this a try - https://github.com/HRNet/HRNet-Semantic-Segmentation/issues/39#issuecomment-984003344

[liruilong940607]

Starting from this version we will have pre-built wheels provided so that you can directly install it:

https://github.com/KAIR-BAIR/nerfacc/releases/tag/v0.3.4

[liruilong940607]

Close as the pre-built wheels should be able to fix this build issue