dockerfile参考，已跑通模型 #7
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<?php
define('BASE_PATH', __DIR__ . '/..');
require BASE_PATH . '/vendor/autoload.php';
require __DIR__ . '/utils.php';
use Dotenv\Dotenv;
use Dotenv\Repository\Adapter;
use Dotenv\Repository\RepositoryBuilder;
use function Laravel\Prompts\text;
use function Laravel\Prompts\info;
use function Laravel\Prompts\error;
load_env();
$os = PyCore::import('os');
$platform = PyCore::import('platform');
$transformers = PyCore::import('transformers');
$AutoModel = $transformers->AutoModel;
$AutoTokenizer = $transformers->AutoTokenizer;
$torch = PyCore::import('torch');
$MODEL_PATH = getenv('MODEL_PATH') ?: 'THUDM/chatglm3-6b';
$TOKENIZER_PATH = getenv("TOKENIZER_PATH") ?: $MODEL_PATH;
$DEVICE = $torch->cuda->is_available() ? 'cuda' : 'cpu';
$tokenizer = $AutoTokenizer->from_pretrained($TOKENIZER_PATH, trust_remote_code: true);
if ($DEVICE == 'cuda') {
# AMD, NVIDIA GPU can use Half Precision
// $model = $AutoModel->from_pretrained($MODEL_PATH, trust_remote_code: true)->to($DEVICE)->eval();
$model = load_model_on_gpus($MODEL_PATH, $torch->cuda->device_count());
} else {
# CPU, Intel GPU and other GPU can use Float16 Precision Only
$model = $AutoModel->from_pretrained($MODEL_PATH, trust_remote_code: true)->float()->to($DEVICE)->eval();
}
$welcome = '欢迎使用 ChatGLM3-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序';
$past_key_values = null;
$history = [];
$stop_stream = false;
info($welcome);
while (true) {
$query = text('用户：');
if (trim($query) == 'stop') {
break;
} elseif (trim($query) == 'clear') {
$past_key_values = null;
$history = [];
info("\033c");
info($welcome);
continue;
}
info('ChatGLM: ');
try {
$current_length = 0;
$rs = $model->stream_chat($tokenizer,
$query,
history: $history,
top_p: 1,
temperature: 0.01,
past_key_values: $past_key_values,
return_past_key_values: true
);
$it = PyCore::iter($rs);
echo " \e[32m";
while ($next = PyCore::next($it)) {
if ($stop_stream) {
$stop_stream = false;
break;
} else {
list($response, $history, $past_key_values) = PyCore::scalar($next);
echo mb_substr($response, $current_length);
$current_length = mb_strlen($response);
}
}
echo "\e[39m\n";
} catch (\Throwable $e) {
error($e->getMessage() ?: '执行出错了');
}
}
function load_env()
{
$repository = RepositoryBuilder::createWithNoAdapters()
->addAdapter(Adapter\PutenvAdapter::class)
->immutable()
->make();
Dotenv::create($repository, [BASE_PATH])->safeLoad();
} |
运行examples/paddlenlp/test.php教程
|
10分钟快速上手飞浆： 手写数字识别任务
<?php
ini_set('memory_limit', '2G');
$paddle = PyCore::import('paddle');
$np = PyCore::import('numpy');
$Normalize = PyCore::import('paddle.vision.transforms')->Normalize;
$transform = $Normalize(mean: [127.5], std: [127.5], data_format: 'CHW');
# 下载数据集并初始化 DataSet
$train_dataset = $paddle->vision->datasets->MNIST(mode: 'train', transform: $transform);
$test_dataset = $paddle->vision->datasets->MNIST(mode: 'test', transform: $transform);
# 模型组网并初始化网络
$lenet = $paddle->vision->models->LeNet(num_classes: 10);
$model = $paddle->Model($lenet);
# 模型训练的配置准备，准备损失函数，优化器和评价指标
$model->prepare(
$paddle->optimizer->Adam(parameters: $model->parameters()),
$paddle->nn->CrossEntropyLoss(),
$paddle->metric->Accuracy()
);
# 模型训练
$model->fit($train_dataset, epochs: 5, batch_size: 64, verbose: 1);
# 模型评估
$model->evaluate($test_dataset, batch_size: 64, verbose: 1);
# 保存模型
$model->save('./output/mnist');
# 加载模型
$model->load('output/mnist');
# 从测试集中取出一张图片
list($img, $label) = $test_dataset->__getitem__(0);
# 将图片shape从1*28*28变为1*1*28*28，增加一个batch维度，以匹配模型输入格式要求
$img_batch = $np->expand_dims($img->astype('float32'), axis: 0);
# 执行推理并打印结果，此处predict_batch返回的是一个list，取出其中数据获得预测结果
$out = $model->predict_batch($img_batch)[0];
$pred_label = $out->argmax();
PyCore::print(PyCore::str('true label: {}, pred label: {}')->format($label->__getitem__(0), $pred_label));
# 可视化图片
$plt = PyCore::import('matplotlib.pyplot');
$plt->imshow($img->__getitem__(0));
# 容器没有gui
$plt->imsave('./output/img.png', $img->__getitem__(0));
|
你可以基于 modelscope （推荐）、huggingface 、
|
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能弄个cpu的的吗，只跑验证码识别 |
示例代码 <?php
/**
* @link https://modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-general_damo/summary
*/
$pipeline = PyCore::import('modelscope.pipelines')->pipeline;
$Tasks = PyCore::import('modelscope.utils.constant')->Tasks;
$os = PyCore::import('os');
// 模型可以换成 xiaolv/ocr_small
$pipe = $pipeline($Tasks->ocr_recognition, model: 'damo/cv_convnextTiny_ocr-recognition-general_damo');
$file = './captcha.png';
file_put_contents($file, file_get_contents('https://business.swoole.com/page/captcha_register'));
echo '识别结果：' . $pipe($file)['text'][0], PHP_EOL;环境 pip config set global.index-url https://mirrors.aliyun.com/pypi/simple
pip config set install.trusted-host mirrors.aliyun.com
pip install -U pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install modelscope transformers SentencePiece opencv-python |
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@he426100 示例可以加入到 phpy 的 examples 中 |
用下面这个，又快又准 |
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docker-php的安装脚本 基于飞桨官方进行内安装运行后报错 容器环境是ubuntu20的 ++ dpkg-architecture --query DEB_BUILD_GNU_TYPE |
paddle-2.6.0 需要先执行 编译phpy命令需改成 phpize && \
./configure --with-python-config=/usr/bin/python3.10-config && \
make install && \
echo "extension=phpy.so" > /usr/local/etc/php/conf.d/20_phpy.ini && \
php --ri phpy |
|
docker-php的安装脚本 基于飞桨官方进行内安装运行后报错 容器环境是ubuntu20的 ++ dpkg-architecture --query DEB_BUILD_GNU_TYPE
嗯已安装dpkg-dev pkg-config 在测试编译谢谢 |
环境
Ubuntu 22.04.3 LTS
NVIDIA-SMI 520.61.05 Driver Version: 520.61.05 CUDA Version: 11.8
3080x2
效果
Dockefile
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