Frequently asked questions and support resources.
import torchfrom esm3 import MultiNodeESM3Model Configurationworld_size = torch.cuda.device_count()batch_size = 128model = MultiNodeESM3Model(“esm3-large”).cuda() Dataset and Dataloaderdef load_large_dataset():# Simulate large datasetreturn [“Sample sequence”] * batch_size * 10 data_loader = torch.utils.data.DataLoader(load_large_dataset(), batch_size=batch_size) Benchmark Functiondef multi_gpu_benchmark():model = torch.nn.DataParallel(model)total_time = 0total_samples = 0for batch in data_loader:batch = batch.to(“cuda”)start_time = time.time()with torch.no_grad():outputs = model(batch)total_time += time.time() – start_timetotal_samples += len(batch)throughput =…
Abstract ESM3 (Evolutionary Scale Modeling 3) represents the latest advancement in protein language models developed by the Evolutionary Scale Modeling group. Building upon the foundations of its predecessors, ESM3 leverages deep learning techniques to analyze and predict protein structures and functions at an unprecedented scale. This article provides a comprehensive overview of ESM3, highlighting…
