synchronization is required in CUDA graphs workloads between graph replays. ‣ The DLProf TensorBoard plugin included with the 21.04 and 21.05 releases is an incorrect version with respect to the DLProf command $ pip install --index-urlhttps://developer.download.nvidia.com/compute/redist nvidia_tensorboard_plugin_dlprof==1.1.0 PyTorch RN-08516-001_v23.07 | 198 Chapter 31. PyTorch Release 21.04 The NVIDIA synchronization is required in CUDA graphs workloads between graph replays. ‣ The DLProf TensorBoard plugin included with the 21.04 release is an incorrect version with respect to the DLProf command line tool

Kubernetes对NUMA、异构计算、存储设备的调度能力待加强 1.6 nvidia/gpu custom scheduler 1.8 local-volume 1.10 CPU manager Device plugin 1.9 volume-awared scheduling Go语言在高性能系统中的实践经验 • 为什么用Go - 比起C++，更易于实践各种并发模式 - 比起Java，更加简洁，更易于与C/C++交互

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logging logging.basicConfig( format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO,␣ �→datefmt="%Y-%m-%d %H:%M:%S" ) model.quantize(data, cache_examples_on_gpu=False) 最后，保存量化模型： /gui.yaml --env ENDPOINT=$(sky serve status --endpoint qwen) 2. 随后，我们可以通过返回的 gradio 链接来访问 GUI ： | INFO | stdout | Running on public URL: https://6141e84201ce0bb4ed.gradio.live 你可以通过使用不同的温度和 top_p 值来尝试取得更好的结果。 "tools": "tool description (optional)" } ] 2. 在 data/dataset_info.json 文件中提供您的数据集定义，并采用以下格式： 1.12. 有监督微调 35 Qwen • 对于 alpaca 格式的数据集，其 dataset_info.json 文件中的列应为： "dataset_name": { "file_name": "dataset_name

sparse_categorical_accuracy: 0.9862 INFO:tensorflow:Assets written to: /tmp/tmp6ouf8al7/assets Model Name: mnist_model_float.tflite, Quantized: False Model Size: 138.71 KB Accuracy: 0.9862 INFO:tensorflow:Assets written written to: /tmp/tmpljlefd2f/assets INFO:tensorflow:Assets written to: /tmp/tmpljlefd2f/assets Model Name: mnist_model_quantized.tflite, Quantized: True Model Size: 39.65 KB Accuracy: 0.9861 The training

Training Neural Networks Training Define Neural Network Loss Function Optimization Algorithm More info about the training process in last year's lecture video. Training & Testing Neural Networks Validation multiprocessing ● dataset = MyDataset(file) ● dataloader = DataLoader(dataset, batch_size, shuffle=True) More info about batches and shuffling here. Dataset & Dataloader from torch.utils.data import Dataset, DataLoader

(train500_ds, train1000_ds, val_ds), ds_info = tfds.load( name='imdb_reviews', split=['train[:500]', 'train[:1000]', 'train[60%:]'], as_supervised=True, with_info=True, read_config=tfds.ReadConfig(try_autocache=False)

https://www.cs.cmu.edu/~epxing/Class/10715/reading/Kornick_et_al.pdf CPU? http://processortimeline.info/proc1980.htm Now theoretically solved:1989 http://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf

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? • Advantage of various deep learning frameworks

’ ) % remember that x i s now a matrix % with 2 columnsand the second % column contains the time info legend ( ’ Training data ’ , ’ Linear r e g r e s s i o n ’ ) Note that for most machine learning