安装完成后我们可以通过运行以下命令打开Jupyter笔记本（在Window系统的命令行窗口中运行以下命令前， 需先将当前路径定位到刚下载的本书代码解压后的目录）： jupyter notebook 9 https://developer.nvidia.com/cuda‐downloads 10 目录 现在可以在Web浏览器中打开http://localhost:8888（通常会自动打开）。由此，我们可以运行这本书中每个 16160MiB | 55% Default | (continues on next page) 79 https://discuss.d2l.ai/t/1839 80 https://developer.nvidia.com/cuda‐downloads 5.6. GPU 211 (continued from previous page) | | | N/A | +-------- 这一点。 3. 设计一个实验，在CPU和GPU这两种设备上使用并行计算和通信。 4. 使用诸如NVIDIA的Nsight145之类的调试器来验证代码是否有效。 145 https://developer.nvidia.com/nsight‐compute‐2019_5 12.3. 自动并行 515 5. 设计并实验具有更加复杂的数据依赖关系的计算任务，以查看是否可以在提高性能的同时获得正确的

tensorflow.org/lite/examples TensorFlow Lite Examples 搭建 TensorFlow Lite 运行环境 (Android) https://developer.android.com/studio Step 1：下载 TensorFlow examples 项目 $ git clone https://github.com/tensorflow/examples

learning models today, how to think about it in terms of metrics that you care about, and finally the tools at your disposal to achieve what you want. The subsequent chapters will delve deeper into techniques pareto-frontier. Our goal with efficient deep learning is to have a collection of algorithms, techniques, tools, and infrastructure that work together to allow users to train and deploy pareto-optimal models that is illustrated in Figure 1-6. As mentioned earlier, with this book we’ll strive to build a set of tools and techniques that can help us make models pareto-optimal and let the user pick the right tradeoff

instruction" }, { "from": "gpt", "value": "model response" } ], "system": "system prompt (optional)", "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 json", "formatting": "sharegpt", "columns": { "messages": "conversations", "system": "system", "tools": "tools" }, "tags": { "role_tag": "from", "content_tag": "value", "user_tag": "user", "assistant_tag": import os import json5 import urllib.parse from qwen_agent.agents import Assistant from qwen_agent.tools.base import BaseTool, register_tool llm_cfg = { # Use the model service provided by DashScope: 'model':

flower from its picture. We have access to a flowers dataset (oxford_flowers102). As an application developer, with no experience with ML, we would like a model trained on the flowers dataset to integrate into

in those releases. To correct this, use the following command: $ pip install --index-urlhttps://developer.download.nvidia.com/compute/redist nvidia_tensorboard_plugin_dlprof==1.1.0 PyTorch RN-08516-001_v23 in those releases. To correct this, use the following command: $ pip install --index-urlhttps://developer.download.nvidia.com/compute/redist nvidia_tensorboard_plugin_dlprof==1.1.0 PyTorch RN-08516-001_v23

Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like • TensorboardX (monitor training) • PyTorchViz (visualise computation graph) • Various other

���� The vectorized version is useful and efficient when you’re working with numerical computing tools like Matlab/Octave. If you are familiar with matrices, you can prove to yourself that the two forms

Enhancement Input “Auto Enhance” on iPhone “Auto Tone” in Lightroom Ours Existing Photo Editing Tools Retinex-based Methods • LIME: [TIP 17] • WVM: [CVPR 16] • JieP: [ICCV 17] Learning-based Methods

Learning models: (a) lower model size, and (b) lower inference latency. We already have the necessary tools for achieving (a), the lower model size. Let us see how we can apply what we learnt for quantizing