10 8 import tqdm 11 9 from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, CHUNK_LENGTH, pad_or_trim, log_mel_spectrogram, load_audio 12 - from .alignment import get_trellis, backtrack, merge_repeats, merge_wo
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"multiple_tqdm": true, "print_hypernet_extra": false, "list_hidden_files": true, "disable_mmap_load_safetensors": false, "hide_ldm_prints": true, "dump_stacks_on_signal": false, "api_enable_requests": true, "api_forbid_local_requests": true, "api_useragent": "", "unload_models...
[2598/2598] WARNING:root:TPU has started up successfully with version pytorch-1.11 * CUDA: - GPU: - available: False - version: 10.2 * Packages: - numpy: 1.21.6 - pyTorch_debug: False - pyTorch_version: 1.11.0+cu102 - pytorch-lightning: 1.6.5 - tqdm: 4.64.0 * System: - OS: ...
while pending: p.join(interval) for t in pending[:]: if t.is_file(): # Could print(f"Done: {t}") pending.remove(t) progress.update(task, advance=1) if p.exitcode is not None: # Should be finished, but was it success or failure? pending = [] # to break the loop p.join...
in start_processes(fn, args, nprocs, join, daemon, start_method) 195 return context 197 # Loop on join until it returns True or raises an exception. --> 198 while not context.join(): 199 pass File ~/path/python3.8/site-packages/torch/multiprocessing/spawn.py:160, in ProcessContext.join...
试了下 que._loop.call_soon_threadsafe(que.put_nowait, (True, output)) 可以避免 pipeline 卡住,也保证性能(甚至提高了 8%)。但是没法 handle 住高并发的测试,会crash。 from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor from lmdeploy.serve.openai.api_client import APIClient que...
(X_trainFinal), img_width, img_height, img_channels), dtype=np.uint8) for n, path1 in tqdm(enumerate(X_trainFinal), total=len(X_trainFinal)): path = path1 img = imageio.imread(path)[:,:,:img_channels] img = resize(img, (img_height, img_width), mode='constant', preserve_...
Added inline copy of tqdm to make sure it's available for progress bar output for 2.7 or higher. Recommend having it in the Debian package. Added inline copy of colorama for use on Windows, where on some terminals it will give better results with the progress bar. Stop using old PyLint ...
["answers"]} for s_idx in range(len(dataset)) ] predicted_answers = [] for s_idx in tqdm(range(len(dataset))): example_id = dataset[s_idx]["id"] context = dataset[s_idx]["context"] answers = [] # Loop through all features associated with that example for feature_index in ...