to(device) # Directly use generate() and tokenizer.decode() to get the output. # Use `max_new_tokens` to control the maximum output length. generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 to(device) # Directly use generate() and tokenizer.decode() to get the output. # Use `max_new_tokens` to control the maximum output length. generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 quantize_config) 但是，如果你想使用多 GPU 来读取模型，你需要使用 max_memory 而不是 device_map。下面是一段示例 代码： model = AutoGPTQForCausalLM.from_pretrained( model_path, quantize_config, max_memory={i:"20GB" for i in range(4)} ) 接下来，你需要准

September 6, 2023 14 / 57 Source of Training Data Provided random examples outside of the learner’s control. Negative examples available or only positive? Good training examples selected by a “benevolent” watching a given video on YouTube Predict the location in 3D space of a robot arm end effector, given control signals (torques) sent to its various motors Predict the amount of prostate specific antigen (PSA) Non-Parametric Models (Contd.) These two methods are opposite w.r.t. computation. NN-like methods are memory-based methods. We need to remember all the training data. Linear regression, after getting parameters

A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg 和 D. Hassabis, “Human-level control through deep reinforcement learning,” Nature, 卷 518, pp. 529-533, 2 2015. 预览版202112 除了具有空间结构的图片、视频等数据外，序列信号也是非常常见的一种数据类型， 其中一个最具代表性的序列信号就是文本数据。如何处理并理解文本数据是自然语言处理 的一个核心问题。卷积神经网络由于缺乏 Memory 机制和处理不定长序列信号的能力，并 不擅长序列信号的任务。循环神经网络(Recurrent Neural Network，简称 RNN)在 Yoshua Bengio、Jürgen Schmidhuber cuda.memory_allocated 函 数获取目前已分配显存大小，代码如下： # 获取 GPU 0 的总显存 t = torch.cuda.get_device_properties(0).total_memory # 获取保留显存 r = torch.cuda.memory_reserved(0) # 获取已分配显存 a = torch.cuda.memory_allocated(0)

multi-threaded data loaders, the default shared memory segment size with which the container runs might not be enough. Therefore, you should increase the shared memory size by issuing one of the following commands: commands: ‣ --ipc=host ‣ --shm-size=memory size> in the command line to docker run --gpus all To pull data and model descriptions from locations outside the container for use by PyTorch or (FP8) precision on Hopper GPUs which provides better training and inference performance with lower memory utilization. Transformer Engine also includes a collection of highly optimized modules for popular

bear, if we ever accidentally cross paths. We build an associative memory when about them over our lifetime. This associative memory helps us visualize the similarities or differences between a pair of model architecture of the downstream task. In essence, the embedding tables provide us a portable memory bank of knowledge about our domain of interest. This knowledge can be freely used by downstream tasks significant portion of the model size on disk and in memory. Although this comes with the cost of the table taking up significant disk space and memory, this issue can be a bottleneck if the model is going

Training Efficiency involves benchmarking the model training process in terms of computation cost, memory cost, amount of training data, and the training latency. It addresses questions like: ● How long the model take to train? ● How many devices are needed for the training? ● Can the model fit in memory? ● How much data would the model need to achieve the desired performance on the given task that embedding (known as the dimensionality). However, this also leads to a direct increase in model size and memory consumption. Figure 1-16: A regular embedding table on the left with an embedding for each token

example 8 squeeze 9 Expand / repeat ▪ Expand: broadcasting ▪ Repeat: memory copied 10 Expand/expand_as 11 repeat Memory touched 12 .t 13 Transpose 14 permute 15 Thank You.

Overcoming catastrophic forgetting in neural networks Memory-Augmented Neural Networks Source: Hybrid computing using a neural network with dynamic external memory Work Fun Play Hard

cs.toronto.edu/~fritz/absps/cvq.pdf probability distributions Meanwhile: Speech Sequence ▪ No Memory ▪ Time delay NN http://www.cs.toronto.edu/~fritz/absps/waibelTDNN.pdf Moving window ▪ Inspired kprop_old.pdf https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf LSTM: 1997 ▪ Long memory https://github.com/dzitkowskik/StockPredictionRNN/blob/master/docs/Hochreiter97_lst m.pdf http://www

#work数量 � --worker-memory 8192M \ #每个worker需要的内存� --worker-cores 1 \ #每个worker需要的CPU核数� --worker-gpus 2 \ #每个worker需要的GPU卡数� --ps-num 2 \ #ps数量� --ps-memory 1024M \ #每个ps需要的内存�