异构设备驱动的兼容性 • 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

anything related to the inner working of the layer. Once the custom functionality is done, we can call the base class build function. Our custom build function is as follows: 8. Keras ― Customized Layer  Line 2 creates the weight corresponding to input shape and set it in the kernel. It is our custom functionality of the layer. It creates the weight using ‘normal’ initializer.  Line 6 calls Implement call method call method does the exact working of the layer during training process. Our custom call method is as follows: def call(self, input_data): return K.dot(input_data, self.kernel)

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

Transfer Learning Step1.Load data ▪ Inherit from torch.utils.data.Dataset ▪ __len__ ▪ __getitem__ Custom Dataset Preprocessing ▪ Image Resize ▪ 224x224 for ResNet18 ▪ Data Argumentation ▪ Rotate ▪ details https://indico.io/blog/exploring-computer-vision-transfer-learning/ In Conclusion ▪ Load custom data ▪ Train from scratch ▪ Transfer learning 下一课时 Thank You.

如果要加载的模型包含自定义层或其他自定义类或函数，则可以通过 custom_objects 参数将 它们传递给加载机制： from keras.models import load_model # 假设你的模型包含一个 AttentionLayer 类的实例 model = load_model('my_model.h5', custom_objects={'AttentionLayer': AttentionLayer}) model_from_yaml 的工作方式相同： from keras.models import model_from_json model = model_from_json(json_string, custom_objects={'AttentionLayer': AttentionLayer}) 3.3.7 为什么训练误差比测试误差高很多？ Keras 模型有两种模式：训练和测试。正则化机制，如 MobileNet 模 型， 你 需 要 导 入 自 定 义 对 象 relu6 和 DepthwiseConv2D 并通过 custom_objects 传参。 下面是示例代码： model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6, 'DepthwiseConv2D': mobilenet

Jupyter Notebook VS Code • Install the Python extension. • ???????????? Install the Remote Development extension. • Python files can be run like Jupyter notebooks by delimiting cells/sections with

模型 训练 参数 调优 模型 部署 识别 服务 使用 Flask 快速搭建 验证码识别服务 使用 Flask 启动 验证码识别服务 $ export FLASK_ENV=development && flask run --host=0.0.0.0 打开浏览器访问测试 URL（http://localhost:5000/ping） 访问 验证码识别服务 $ curl -X POST

be expensive when using very large models. def distillation_loss_fn(y_true_combined, y_pred): """Custom distillation loss function.""" # We will split the y tensor to extract the ground-truth and the model_pred) opt = keras.optimizers.Adam(learning_rate=learning_rate) # Compile the model with the custom loss function and metric. model.compile( loss=distillation_loss_fn, metrics=[categorical_accuracy]

support Experimental support Experimental support Supported planned post 2.0 Supported Custom training loop Experimental support Experimental support Support planned post 2.0 Support

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