Go to the corresponding file, For AMiner and MAG CUDA_VISIBLE_DEVICES={Device_Id} python main.py --train_dir {train dir} --test_dir {test_dir} For Alpha and Yelp CUDA_VISIBLE_DEVICES={Device_Id} python main.py --data_dir {data_dir}这是什么意思
时间: 2024-04-09 17:30:56 浏览: 37
这段指令是在命令行中运行的脚本命令,用于训练或测试 AMiner 和 MAG、Alpha 和 Yelp 数据集的模型。
其中 `{Device_Id}` 是指定 GPU 设备的编号,可以是一个整数,用于指定在多个 GPU 设备中使用的哪一个。
`{train dir}` 是指训练数据集的目录路径。
`{test_dir}` 是指测试数据集的目录路径。
`{data_dir}` 是指数据集的目录路径。
你需要将这些占位符替换为实际的路径和设备编号,然后在命令行中执行相应的命令。
相关问题
version: '3' services: kafka01: image: wurstmeister/kafka:latest ports: - 19092:9092 environment: - KAFKA_BROKER_ID=0 - KAFKA_ZOOKEEPER_CONNECT=192.168.233.129:12181 - KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://192.168.233.129:19092 - KAFKA_LISTENERS=PLAINTEXT://0.0.0.0:9092 container_name: kafka01 tty: true restart: always 给坐上持久化
To add persistence to the Kafka container, you can add a volume to the `docker-compose.yml` file. Here's an updated version with persistence:
```yaml
version: '3'
services:
kafka01:
image: wurstmeister/kafka:latest
ports:
- 19092:9092
environment:
- KAFKA_BROKER_ID=0
- KAFKA_ZOOKEEPER_CONNECT=192.168.233.129:12181
- KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://192.168.233.129:19092
- KAFKA_LISTENERS=PLAINTEXT://0.0.0.0:9092
container_name: kafka01
tty: true
restart: always
volumes:
- ./kafka/data:/kafka/data
- ./kafka/logs:/kafka/logs
```
In this updated configuration, two volumes are added: `./kafka/data` and `./kafka/logs`. These volumes will map the Kafka container's data and logs directories to the corresponding directories on the host machine, allowing for persistence of data and logs even if the container is recreated.
Make sure to create the `kafka/data` and `kafka/logs` directories in the same location as your `docker-compose.yml` file before running `docker-compose up -d`.
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
As an AI language model, I do not have the ability to access external files or libraries. However, I can explain what the above code does.
The code is using the Keras library to load the CIFAR-10 dataset. The CIFAR-10 dataset is a collection of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 test images.
The `load_data()` function is used to load the data from the dataset. It returns two tuples: `(x_train, y_train)` and `(x_test, y_test)`. The `x_train` and `x_test` variables contain the images as numpy arrays, while `y_train` and `y_test` contain the corresponding labels for each image. The training data is used to train the model, while the testing data is used to evaluate the performance of the model.