Commits on Jul 15, 2021 Collaborative training blog post: Improve the loading speed of animation (huggingface#131) SaulLu and severo authored collaborative training blog post: improve visibility in dark mode SaulLu committed Add organization references in collaborative training blog post (...
Another useful tool is deep-diving into the training dynamic and plot (in Tensorboard for instance) the evolution of multiple scalars through training. At the bare minimum, you should look at the dynamic of your loss(es), the parameters, and their gradients....
Though in theory it might be possible to combine the resources of multiple individuals, in practice, such distributed training methods have previously seen limited success because connection speeds over the Internet are way slower than in high-performance GPU supercomputers....
The output size of this layer corresponds to the number of tokens in the vocabulary, which does not depend on Wav2Vec2's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at Timit and define a vocab...
Each token is attending some global tokens, sliding tokens, & random tokens instead of attending to all other tokens. The authors hardcoded the attention matrix for multiple query components separately; and used a cool trick to speed up training/inference on GPU and TPU....
sagemaker-distributed-training-seq2seq.md sentence-transformers-in-the-hub.md simple-considerations.md spacy.md summer-at-huggingface.md tf-serving.md the-partnership-amazon-sagemaker-and-hugging-face.md warm-starting-encoder-decoder.md zero-deepspeed-fairscale.mdBreadcrumbs huggingface-...
🤗 Accelerate even handles the device placement for you, so you can simplify the training loop above even further: import torch import torch.nn.functional as F from datasets import load_dataset + from accelerate import Accelerator + accelerator = Accelerator() - device = 'cpu' - ...
accelerate-library.md accelerated-inference.md bert-cpu-scaling-part-1.md big-bird.md collaborative-training.md deploy-hugging-face-models-easily-with-amazon-sagemaker.md encoder-decoder.md few-shot-learning-gpt-neo-and-inference-api.md fine-tune-wav2vec2-english.md ...
The output size of this layer corresponds to the number of tokens in the vocabulary, which does not depend on XLSR-Wav2Vec2's pretraining task, but only on the labeled dataset used for fine-tuning. So in the first step, we will take a look at Common Voice and defi...
The main drawback of the torch.distributed implementation for document retrieval was that it latched onto the same process group used for training and only the rank 0 training worker loaded the index into memory. As a result, this implementation had some limitations: Synchronization bottleneck:...