tqdm transformers>=4.31.0 # need Encodec there. xformers demucs librosa gradio torchmetrics encodec protobuf 1 change: 1 addition & 0 deletions 1 requirements_audiocraft_only.txt @@ -0,0 +1 @@ audiocraft @ git+https://git@github.com/facebookresearch/audiocraft@905371a779f608169353fe...
from tqdm import tqdm from mpi4py import MPI # Change this to reflect your cluster layout. # The GPU for a given rank is (rank % GPUS_PER_NODE). GPUS_PER_NODE = torch.cuda.device_count() SETUP_RETRY_COUNT = 3 def setup_dist(): """ Setup a distributed process group. """ if ...
centerline_phys_z[i] - label_phys_z])foriinrange(len(x_centerline_fit))]# get the index corresponding to the min distanceind_min_distance = np.argmin(distance_centerline)# get centerline coordinate (in physical space)[min_phy_x, min_phy_y, min_phy_z] = [centerline_phys_x[ind_min...
epoch = epoch_to_restorewhileTrue: g.train()for_inrange(self.nb_epochs_to_save): epoch +=1foridx_batch, current_batchinenumerate(tqdm(dataloader)): g.zero_grad() x = Variable(current_batch['x']).type(torch.FloatTensor).cuda() z = Variable(current_batch['z']).type(torch.FloatTensor...
tqdm:只需使用tqdm(iterable)包装任何可迭代对象,就能让你的循环生成一个智能进度条。 生成URL序列 通过指定的URL模板与max_id生成URL序列,这里添加了一个去重操作,如果之前已采集过高校信息,它会根据同目录下的文件,剔除已采集的高校ID,仅采集未获取过的高校信...
auto import tqdm, trange# %%# Due to a bug with tensorflow gradient tape, we cannot loop multiple model # training in the same python execution. # See https://github.com/tensorflow/tensorflow/issues/27120 for the problem and # https://github.com/tensorflow/tensorflow/issues/27120#issue...
pbar = tqdm(pbar) if rank == 0 else pbar total = 0 for _ in pbar: # Sample inputs: z = torch.randn(n, model.in_channels, latent_size, latent_size, device=device, dtype=dtype) if args.num_classes > 0: y = torch.randint(0, args.num_classes, (n,), device=device) if ...
from tqdm import tqdm from transformers import (AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase) from vllm.engine.arg_utils import EngineArgs from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs from vllm.entrypoints.openai.api_server import ( build_async_engine_client_from_e...
with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: while True: buffer = source.read(8192) if not buffer: break output.write(buffer) loop.update(len(buffer)) if hashlib.sha256(open(download_target, "rb").read()).hexdigest(...
# main loop for i in range(0, steps): # show progress if i % 100 == 0 and verbose: print("step", i, "of", steps) for i in tqdm(range(steps), disable=not verbose): # inverse fourier transform y = np.real(np.fft.ifft2(y_hat)) y = xp.real(xp.fft.ifft2(...