first, let’s get ourselves familiar with label efficiency. Label Efficiency The number of labeled examples required for a model to reach the desired performance benchmark is another important metric to evaluate efficiently as possible. Extending the teaching-a-child analogy, consider the number of distinct examples of objects (labels) you must show a child before they can learn to identify them with high accuracy the same number of labeled training examples. Data Augmentation is a set of techniques which leverage the original training data to generate more training examples without having to label them. We’ll

your PyTorch image. For more information about PyTorch, including tutorials, documentation, and examples, see: ‣ PyTorch website ‣ PyTorch project This document provides information about the key features architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. ‣ A preview of Torch-TensorRT (1.4.0dev0) is now included. Torch-TRT is the TensorRT integration offers a number of advantages over APEX AMP. ‣ Guidance and examples demonstrating torch.cuda.amp can be found here. ‣ APEX AMP examples can be found here. For more information about AMP, see the Training

learning follow right after. The quantization section delves into the implementation details using code samples. We finish with a hands-on project that will walk you through the process of applying quantization contains the symbol-code mapping is transmitted along with the encoded data. Figure 2-1: Huffman Encoding & Huffman Tree. Source When decoding the encoded data, we look up the code from the lookup table back. Since the codes are unique for each symbol (in fact, they are prefix codes: no code is a prefix of some other code, which eliminates ambiguity when decoding), we can easily construct the original sequence

learning (SSL) helps with learning generalizable and robust representations without the need of labeled examples. The focus is to ensure that these representations are learnt without focusing narrowly on a label the model learns these representations it can then be fine-tuned with a small number of labeled examples over a reasonable number of training epochs to do well on the given task. We will go into details to predict if a pair of sentences are semantically equivalent. The dataset has only 5800 labeled examples of pairs, which would be incredibly small for this task if we were training a model using just the

required libraries to start with. We will use the gzip python module for demonstrating compression. The code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator been removed. Now that we have presented a general algorithm for pruning, we should go over some examples of different ways we implement them. Concretely, a practitioner might want to experiment with at the next section, we will discuss pruning at different granularities. Sparsity Granularities The examples of sparsity that we have considered so far are categorized under unstructured sparsity (also referred

/Miniconda3-latest-Linux-x86_64.sh • After Miniconda is installed: conda install pytorch -c pytorch Writing code • Up to you; feel free to use emacs, vim, PyCharm, etc. if you want. • Our recommendations: • Install: 1234:localhost:1234 __@__.cs.princeton.edu • First blank is username, second is hostname Jupyter Notebook VS Code • Install the Python extension. • ???????????? Install the Remote Development extension. • Python Jupyter notebooks by delimiting cells/sections with #%% • Debugging PyTorch code is just like debugging any other Python code: see Piazza @108 for info. Also try Jupyter Lab! Why talk about libraries

one of the two classes: ‘Suitable’ and ‘Not Suitable’. Since in this scenario we have only a few examples it is easy to manually assign them a label identifying which class a given animal belongs to. Puppies two classes (Suitable / Not Suitable), since there were very few examples. What if you have multiple classes / a large number of examples / more than two features? In those cases, we could use classical focus on Europa Ganymede and Jupiter's magnetosphere." Let's find the number of train and test examples. !wc -l dbpedia_csv/train.csv !wc -l dbpedia_csv/test.csv 560000 dbpedia_csv/train.csv 70000 dbpedia_csv/test

AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto" AutoTokenizer 借助 TextStreamer ，chat 的流式模式变得非常简单。下面我们将展示一个如何使用它的示例： ... # Reuse the code before `model.generate()` in the last code snippet from transformers import TextStreamer streamer = TextStreamer(tokenizer AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto"

age tuple constitutes one training example (x(i), y(i) in our dataset. There are m = 50 training examples, and you will use them to develop a linear regression model using gradient descent algorithm, based this in Matlab/Octave, the command is m = length (y ) ; % st or e the number of t r a i n i n g examples x = [ ones (m, 1) , x ] ; % Add a column of ones to x 2 From this point on, you will need to the best viewing results on your surface plot, use the range of theta values that we suggest in the code skeleton below. J v a l s = zeros (100 , 100); % i n i t i a l i z e Jvals to % 100∗100 matrix of

为了简化问题，我们将标准差设为0.01。下面的代码生成合成数据集。 def synthetic_data(w, b, num_examples): #@save """生成y=Xw+b+噪声""" X = torch.normal(0, 1, (num_examples, len(w))) y = torch.matmul(X, w) + b y += torch.normal(0, 0.01 data_iter(batch_size, features, labels): num_examples = len(features) indices = list(range(num_examples)) # 这些样本是随机读取的，没有特定的顺序 random.shuffle(indices) for i in range(0, num_examples, batch_size): batch_indices = = torch.tensor( indices[i: min(i + batch_size, num_examples)]) yield features[batch_indices], labels[batch_indices] 通常，我们利用GPU并行运算的优势，处理合理大小的“小批量”。每个样本都可以并行地进行模型计算，且 每个样本损失函数的梯度也可以被并行计算。GPU可以在处理几百个样本时，所花费的时间不比处理一个样