..................................................................... 7 3. Keras ― Backend Configuration ............................................................................................. install using the below command: pip install TensorFlow Once we execute keras, we could see the configuration file is located at your home directory inside and go to .keras/keras.json. keras.json { folder name and add the above configuration inside keras.json file. We can perform some pre-defined operations to know backend functions. 3. Keras ― Backend Configuration Keras 10

errors. This approach is also called Configuration Selection because we are aiming to find optimal hyperparameter values. BOS is likely to reach the optimum configuration faster than Grid and Random searches configurations and adaptively allocates more resources to the promising ones. This is called Configuration Evaluation. Let's discuss it in detail in the next section. Figure 7-3: (a) Bayesian Optimization errors. (b) This plot shows the validation error as a function of resources allocated to each configuration. Promising configurations get more resources. Source: Hyperband2 2 Li, Lisha, et al. "Hyperband:

not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements

chat interface 来与 Qwen 进行交流： curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" - �→d '{ "model": "Qwen/Qwen1.5-7B-Chat", "messages": [ {"role": "system", "content": "You --model Qwen/Qwen1.5-7B-Chat-AWQ curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" - �→d '{ "model": "Qwen/Qwen1.5-7B-Chat-AWQ", "messages": [ {"role": "system", "content": Qwen/Qwen1.5-7B-Chat-GPTQ-Int8 curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" - �→d '{ "model": "Qwen/Qwen1.5-7B-Chat-GPTQ-Int8", "messages": [ {"role": "system", "content":

Max-Pool Conv3-32 Conv3-128 Conv3-64 Conv3-64 Max-Pool Max-Pool FC-512 Output ConvNet Configuration Stacked layers Previous input x F(x) y=F(x) Stacked layers Previous input x F(x) y=F(x)+x

natural language Mobile Hub Custom Connector 2: Invoke a SaaS application or an existing business application Business Application Firewall User Input 应用案例: Capital One “A highly scalable solution

edge devices. Let’s say you want to design a mobile application to highlight pets in a picture. A DSC model is a perfect choice for such an application because it has a smaller footprint than a regular convolution segmentation mask over an object in the input sample. This model will be used within a mobile application. Mobile devices are resource constrained. Let’s see if we can reduce the model footprint without model to produce a mask over a pet in an image. This model will be deployed with a pet filter application for mobile devices which would let you replace one pet with another. We will show you the first

model_content earlier. The converter object also supports weight and activation quantizations using configuration parameters. We are almost there. We have worked out the steps to create and train a model, load

downstream application (which is very reasonable), we only need to achieve that saving across 100 applications before it becomes profitable to pre-train BERT-Base rather than train each application from scratch

org/abs/1911.09723v1 3 https://github.com/google/XNNPACK Project: Lightweight model for pet filters application Recall that our regular CNN model in the pet filters project consisted of thirteen convolution can actually see latency benefits, apart from the size benefits we demonstrated. Another useful application for clustering (or any other compression technique for which there isn’t native support) is embedding