Question

我的构建服务器是基于Linux的。我需要protoc将它集成到我的基于ant的构建系统中。

我在build.xml中使用以下内容：

    <exec executable="tools/protoc.exe" failonerror="true">
        <arg value="--java_out=../protos/java/generated" />
        <arg value="--proto_path=../protos/proto" />
        <arg value="../protos/proto/*.proto" />
    </exec>

我找到了windows二进制文件，但是找不到用于protoc的linux二进制文件。

任何帮助找到一个或构建静态链接的protoc二进制文件都会很好。

Answer 1

您是否尝试过下载main protobuf project并关注installation instructions？我似乎记得，如果你只需要二进制文件，它就非常简单：

$ ./autogen.sh
$ ./configure
$ make

（在这种情况下，您可能不需要make install，如果您只需要protoc二进制文件。只需找出它的构建位置并复制它。）

Answer 2

最新版本的预编译二进制文件位于GitHub上的官方releases page。可以在Maven repository上找到以前的版本。

Answer 3

对于java，您还可以使用已包含不同平台的二进制文件的https://github.com/os72/protoc-jar。

Answer 4

在Ubuntu上（从12.04开始），您可以在存储库中找到protobuf-compiler。

sudo apt-get install protobuf-compiler

$> protoc --version
libprotoc 2.6.1

您可能还想安装头文件：

sudo aptitude install libprotobuf-dev

Answer 5

如果您只想为任何平台编译import tensorflow as tf import tensorflow_probability as tfp import matplotlib.pyplot as plt import numpy as np mnist = tf.keras.datasets.mnist tfe = tf.contrib.eager ed = tfp.edward2 tf.reset_default_graph() tf.enable_eager_execution() (x_train, y_train), (x_test, y_test) = mnist.load_data() pix_w = 28 pix_h = 28 def vis_pix(image): plt.imshow(image, cmap='Greys') plt.show() def scale(x, min_val=0.0, max_val=255.0): x = tf.cast(x, tf.float32) return tf.div(tf.subtract(x, min_val), tf.subtract(max_val, min_val)) def create_dataset(x, y): dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) dataset = dataset.map(lambda x, y: (scale(x), tf.one_hot(y, 10))) dataset = dataset.map(lambda x, y: (tf.reshape(x, [pix_w * pix_h]), y)) dataset = dataset.shuffle(10000).batch(30) return dataset train_ds = create_dataset(x_train, y_train) model = tf.keras.Sequential([ tfp.layers.DenseFlipout(10) ]) optimizer = tf.train.AdamOptimizer() def loss_fn(model, x, y): logits = model(x) neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits) kl = sum(model.losses) elbow_loss = neg_log_likelihood + kl return elbow_loss def get_accuracy(model, x, y): logits = model(x) yhat = tf.argmax(logits, 1) is_correct = tf.equal(yhat, tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32)) return accuracy epochs = 1000 for (batch, (x, y)) in enumerate(train_ds): optimizer.minimize(lambda: loss_fn(model, x, y), global_step=tf.train.get_or_create_global_step()) if batch % 10 == 0: acc = get_accuracy(model, x, y).numpy() * 100 loss = loss_fn(model, x, y).numpy().mean() print("Iteration {}, loss: {:.3f}, train accuracy: {:.2f}%".format(batch, loss, acc)) if batch > epochs: break个二进制文件，请按照以下步骤操作：

在protobuf项目目录下

/**
 *
 *
 */
public class Diamond {

    public static void main(String[] args) {

        int diamondHeight = 18;
        int diamondWidth = 21;
        String header = "";
        header = makeDiamondTemplate(diamondWidth, '+', '-', '-', '-');
        System.out.println(header);
        makeUpperHalf(diamondHeight / 2, diamondWidth);
        makeLowerHalf(diamondHeight / 2, diamondWidth);
        System.out.println(header);
        makeLowerHalf(diamondHeight / 2, diamondWidth);
        makeUpperHalf(diamondHeight / 2, diamondWidth);
        System.out.println(header);
    }

    public static String makeDiamondTemplate(int diamondWidth, char border, char mid, char fillerLeft,
            char fillerRight) {
        String result = "" + border;
        int midIndex = diamondWidth / 2;
        for (int i = 1; i < diamondWidth - 1; i++) {
            if (midIndex == i) {
                result = result + mid;
            } else if (i < midIndex) {
                result = result + fillerLeft;
            } else if (i > midIndex) {
                result = result + fillerRight;
            }
        }
        result = result + border;

        return result;
    }

    public static void makeUpperHalf(int diamondHeight, int diamondWidth) {
        StringBuilder template = new StringBuilder(makeDiamondTemplate(diamondWidth, '|', '*', ' ', ' '));
        int starIndex = diamondWidth / 2;
        System.out.println(template);
        for (int i = 1; i < diamondHeight; i++) {
            template.setCharAt(starIndex - i, '/');
            template.setCharAt(starIndex + i, '\\');
            System.out.println(template);
        }
    }

    public static void makeLowerHalf(int diamondHeight, int diamondWidth) {
        StringBuilder template = new StringBuilder(makeDiamondTemplate(diamondWidth, '|', '*', '\\', '/'));
        int starIndex = diamondWidth / 2;
        int replaceStart = starIndex - 2;
        if (template.length() > 1) {
            template.setCharAt(1, ' ');
            template.setCharAt(template.length() - 2, ' ');
        }
        System.out.println(template);
        for (int i = 1; i < diamondHeight; i++) {
            template.setCharAt(starIndex - replaceStart, ' ');
            template.setCharAt(starIndex + replaceStart, ' ');
            replaceStart--;
            System.out.println(template);

        }
    }

}

protoc内置于./autogen.sh cd protoc-artifacts ./build-protoc.sh linux x86_64 protoc中将其重命名为protoc.exe

protobuf/protoc-artifacts/target/linux/x86_64/protoc

Answer 6

1）从网址https://github.com/protocolbuffers/protobuf/releases

下载二进制文件

2）提取目录并将其保留在特定位置（/user/app/protoc）

3）在/usr//.bash_profile中将条目添加为

export PROTOC_HOME=/user/app/protoc
export PATH=$PROTOC_HOME/bin:$PATH

4）刷新文件$source /usr/<username>/.bash_profile

下载存储库后，其他选项将逐个运行以下命令：

sudo rm -rf ./protoc
unzip protoc-3.10.1-linux-x86_64.zip -d protoc
chmod 755 -R protoc
BASE=/usr/local
sudo rm -rf $BASE/include/google/protobuf/
sudo cp protoc/bin/protoc $BASE/bin
sudo cp -R protoc/include/* $BASE/include

如何为protoc（协议缓冲区）获取预编译的linux二进制文件？

6 个答案: