for i, num in enumerate(input_list): result[index + i] = num ** 2 def square_list_parallel_threads(input_list): result = [0] * len(input_list) num_threads = 4 # 设置线程数量 threads = [] chunk_size = len(input_list) // num_threads for i in range(num_threads): start = ...
enumerate()等。 9 1 2 foriinrange(0,len(data),chunk_size):process_chunk(data[i:i+chunk_size])这样，数据被分成小块，每次只加载一小部分到内存中，提高了内存利用率。2. 并行处理：利用Python的多线程或多进程机制，可以并行处理数据，加速循环遍历的过程。concurrent.futures 库中的 ThreadPool...
for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise ...
chunks through the pipeline sequentially. Doing# this requires that the data and operations be _separable_, i.e.# there should be at least one dimension along which data can be# split such that the program does not have interactions across# this dimension. PiPPy provides `chunk_spec` ...
for rindex, row in enumerate(rows): for cindex , col in enumerate(row.find_all('td')): try: cell = table.cell(rindex, cindex) cell.text = col.text except: pass #纯文字,直接写入word文件 elif child.p: para = child.p.text.replace('\n', '').replace('_', '_') ...
while (SUCCEEDED(hr)) { hr = pIRowset->GetNextRows(NULL, 0, NUMROWS_CHUNK, &cRowsObtained, &pRows); if( FAILED(hr)) { // process error } if (cRowsObtained == 0 || FAILED(hr)) break; for (iRow = 0 ; iRow < cRowsObtained ; iRow++) { // Get the rowset data. if...
When you call the IXmlReader's Read method, it automatically stores any node attributes in an internal collection. This allows you to move the reader to a specific attribute by name using the MoveToAttributeByName method. However, it is usually more efficient to enumerate the attributes and st...
It builds models based on an incoming data chunk, but it also takes previously arrived instances into account. The proposed approach has been evaluated based on a wide range of computer experiments carried out on real and artificially generated data streams with various imbalance ratios, label ...
# Code cell 8 embedding = AzureOpenAIEmbeddings( deployment=DEPLOYMENT_NAME, model=MODEL_NAME, azure_endpoint=RESOURCE_ENDPOINT, openai_api_type="azure", openai_api_key=API_KEY, openai_api_version="2023-05-15", show_progress_bar=True, chunk_size=16 # current limit with Azure OpenAI service...
We also provide a command line interface (esm-fold) that efficiently predicts structures in bulk from a FASTA file using ESMFold: usage: esm-fold [-h] -i FASTA -o PDB [--num-recycles NUM_RECYCLES] [--max-tokens-per-batch MAX_TOKENS_PER_BATCH] [--chunk-size CHUNK_SIZE] [--cpu-on...