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添加测试示例
This commit is contained in:
data-infra
parent
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2
.gitignore
vendored
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@ -6,7 +6,7 @@ myapp/static/file
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myapp/test
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#/myapp/static/appbuilder/vison/*
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/myapp/static/appbuilder/mnt
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/myapp/static/appbuilder/assets/
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# /myapp/static/appbuilder/assets/
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/images/web/target-detection/yolo/
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/install/docker/data/mysql/
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/myapp/visionPlus/node_modules
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58
README_CN.md
58
README_CN.md
@ -25,17 +25,17 @@ https://github.com/tencentmusic/cube-studio/wiki
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提示：
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- 1、可自由定制任务插件，更适用当前业务需求
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| 模块 | 模板 | 类型 | 文档地址 |
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| :----- | :---- | :---- |:---- |
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| 模块 | 模板 | 类型 | 文档地址 |
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|:-------| :---- | :---- |:---- |
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| 数据导入导出 | datax | 单机 | job-template/job/datax/README.md
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| 数据导入导出 | 数据集导入 | 单机 | job-template/job/dataset/README.md
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| 数据导入导出 | 模型导入 | 单机 | job-template/job/model_download/README.md
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| 数据预处理 | data-process | 单机 | job-template/job/data-process/README.md
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| 数据处理 | hadoop | 单机 | job-template/job/hadoop/README.md
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| 数据处理 | spark | 分布式 | job-template/job/spark/README.md
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| 数据处理 | ray | 分布式 | job-template/job/ray/README.md
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| 数据处理 | volcanojob | 分布式 | job-template/job/volcano/README.md
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| 特征工程 | feature-process | 单机 | job-template/job/feature-process/README.md
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| 数据预处理 | data-process | 单机 | job-template/job/data-process/README.md
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| 数据处理工具 | hadoop | 单机 | job-template/job/hadoop/README.md
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| 数据处理工具 | spark | 分布式 | job-template/job/spark/README.md
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| 数据处理工具 | ray | 分布式 | job-template/job/ray/README.md
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| 数据处理工具 | volcanojob | 分布式 | job-template/job/volcano/README.md
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| 特征处理 | feature-process | 单机 | job-template/job/feature-process/README.md
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| 机器学习框架 | ray-sklearn | 分布式 | job-template/job/ray_sklearn/README.md
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| 机器学习算法 | random_forest | 单机 | job-template/job/random_forest/README.md
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| 机器学习算法 | lr | 单机 | job-template/job/lr/README.md
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@ -48,27 +48,27 @@ https://github.com/tencentmusic/cube-studio/wiki
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| 机器学习算法 | decision-tree | 单机 | job-template/job/decision_tree/README.md
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| 机器学习算法 | bayesian | 单机 | job-template/job/bayesian/README.md
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| 机器学习算法 | adaboost | 单机 | job-template/job/adaboost/README.md
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| 深度学习 | tfjob | 分布式 | job-template/job/tf/README.md
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| 深度学习 | pytorchjob | 分布式 | job-template/job/pytorch/README.md
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| 深度学习 | paddle | 分布式 | job-template/job/paddle/README.md
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| 深度学习 | mxnet | 分布式 | job-template/job/mxnet/README.md
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| 深度学习 | mindspore | 分布式 | job-template/job/mindspore/README.md
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| 深度学习 | horovod | 分布式 | job-template/job/horovod/README.md
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| 深度学习 | mpi | 分布式 | job-template/job/mpi/README.md
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| 深度学习 | colossalai | 分布式 | job-template/job/colossalai/README.md
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| 深度学习 | deepspeed | 分布式 | job-template/job/deepspeed/README.md
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| 深度学习 | megatron | 分布式 | job-template/job/megatron/README.md
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| 模型处理 | model-evaluation | 单机 | job-template/job/model_evaluation/README.md
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| 模型服务化 | model-convert | 单机 | job-template/job/model_convert/README.md
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| 模型服务化 | model-register | 单机 | job-template/job/model_register/README.md
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| 模型服务化 | deploy-service | 单机 | job-template/job/deploy-service/README.md
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| 模型服务化 | model-offline-predict | 分布式 | job-template/job/model_offline_predict/README.md
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| 多媒体类 | media-download | 分布式 | job-template/job/video-audio/README.md
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| 多媒体类 | video-img | 分布式 | job-template/job/video-audio/README.md
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| 多媒体类 | video-audio | 分布式 | job-template/job/video-audio/README.md
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| 大模型 | llama2 | 单机多卡 | job-template/job/llama2/README.md
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| 大模型 | chatglm2 | 单机多卡 | job-template/job/chatglm2/README.md
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| 大模型 | baichuan2 | 单机多卡 | job-template/job/baichuan2/README.md
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| 深度学习 | tfjob | 分布式 | job-template/job/tf/README.md
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| 深度学习 | pytorchjob | 分布式 | job-template/job/pytorch/README.md
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| 深度学习 | paddle | 分布式 | job-template/job/paddle/README.md
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| 深度学习 | mxnet | 分布式 | job-template/job/mxnet/README.md
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| 深度学习 | mindspore | 分布式 | job-template/job/mindspore/README.md
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| 深度学习 | horovod | 分布式 | job-template/job/horovod/README.md
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| 深度学习 | mpi | 分布式 | job-template/job/mpi/README.md
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| 深度学习 | colossalai | 分布式 | job-template/job/colossalai/README.md
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| 深度学习 | deepspeed | 分布式 | job-template/job/deepspeed/README.md
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| 深度学习 | megatron | 分布式 | job-template/job/megatron/README.md
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| 模型处理 | model-evaluation | 单机 | job-template/job/model_evaluation/README.md
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| 模型服务化 | model-convert | 单机 | job-template/job/model_convert/README.md
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| 模型服务化 | model-register | 单机 | job-template/job/model_register/README.md
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| 模型服务化 | deploy-service | 单机 | job-template/job/deploy-service/README.md
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| 模型服务化 | model-offline-predict | 分布式 | job-template/job/model_offline_predict/README.md
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| 多媒体类 | media-download | 分布式 | job-template/job/video-audio/README.md
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| 多媒体类 | video-img | 分布式 | job-template/job/video-audio/README.md
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| 多媒体类 | video-audio | 分布式 | job-template/job/video-audio/README.md
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| 大模型 | llama2 | 单机多卡 | job-template/job/llama2/README.md
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| 大模型 | chatglm2 | 单机多卡 | job-template/job/chatglm2/README.md
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| 大模型 | baichuan2 | 单机多卡 | job-template/job/baichuan2/README.md
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# 公司
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@ -3,9 +3,9 @@ FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-runtime
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COPY . /SVC
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RUN python3 -m pip install pickle-mixin -i https://pypi.tuna.tsinghua.edu.cn/simple
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RUN python3 -m pip install FastAPI -i https://pypi.tuna.tsinghua.edu.cn/simple
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RUN python3 -m pip install uvicorn -i https://pypi.tuna.tsinghua.edu.cn/simple
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RUN python3 -m pip install pickle-mixin -i https://mirrors.aliyun.com/pypi/simple
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RUN python3 -m pip install FastAPI -i https://mirrors.aliyun.com/pypi/simple
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RUN python3 -m pip install uvicorn -i https://mirrors.aliyun.com/pypi/simple
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ENTRYPOINT ["python3", "/SVC/launcher.py"]
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38
myapp/example/pipeline/gpu/demo.py
Normal file
38
myapp/example/pipeline/gpu/demo.py
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# pip install torch -i https://mirrors.aliyun.com/pypi/simple
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import torch
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# 检查是否有可用的GPU设备
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(device)
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# 定义模型
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class MyModel(torch.nn.Module):
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def __init__(self):
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super(MyModel, self).__init__()
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self.linear = torch.nn.Linear(10, 1)
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def forward(self, x):
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return self.linear(x)
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model = MyModel().to(device)
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# 定义输入数据和目标标签
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inputs = torch.randn(100, 10).to(device)
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targets = torch.randn(100, 1).to(device)
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# 定义损失函数和优化器
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criterion = torch.nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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# 训练模型
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for epoch in range(1000000):
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# 前向传播
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outputs = model(inputs)
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loss = criterion(outputs, targets)
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# 反向传播和优化
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 100, loss.item()))
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671
myapp/example/pipeline/inference/onnx-client.ipynb
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myapp/example/pipeline/inference/onnx-client.ipynb
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myapp/example/pipeline/inference/tfserving-client.ipynb
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myapp/example/pipeline/inference/tfserving-client.ipynb
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myapp/example/pipeline/inference/torch-client.ipynb
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myapp/example/pipeline/inference/torch-client.ipynb
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myapp/example/pipeline/inference/triton-client.ipynb
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myapp/example/pipeline/media/imges_url.txt
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myapp/example/pipeline/media/imges_url.txt
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@ -0,0 +1,825 @@
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000000597.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000000797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000000897.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001397.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001997.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000002197.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000002697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000003197.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000003897.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000006197.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000007697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000008297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000008797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000009797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000010097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000011297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000011497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000011697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000012497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000012597.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000012797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000013397.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000013497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000013797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015197.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015897.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000016297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000016697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000017697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000017997.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000019397.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000019797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000020997.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000021097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000021197.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000021497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000022097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000023797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000024497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000025697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000025797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026997.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000028097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000028797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000030997.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000031497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000032997.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000033297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000033897.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000034097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000034397.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035097.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035297.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035397.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035497.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000036197.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000036397.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000036797.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000037697.jpg
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https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000038097.jpg
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000581797.jpg
|
||||
10
myapp/example/pipeline/media/video_audio.txt
Normal file
10
myapp/example/pipeline/media/video_audio.txt
Normal file
@ -0,0 +1,10 @@
|
||||
/mnt/admin/pipeline/example/media/ide.mp4 /mnt/admin/pipeline/example/media/ide.mp3
|
||||
/mnt/admin/pipeline/example/media/aigc.mp4 /mnt/admin/pipeline/example/media/aigc.np3
|
||||
/mnt/admin/pipeline/example/media/pipeline.mp4 /mnt/admin/pipeline/example/media/pipeline.mp3
|
||||
/mnt/admin/pipeline/example/media/vgpu.mp4 /mnt/admin/pipeline/example/media/vgpu.mp3
|
||||
/mnt/admin/pipeline/example/media/notebook.mp4 /mnt/admin/pipeline/example/media/notebook.mp3
|
||||
/mnt/admin/pipeline/example/media/aihub.mp4 /mnt/admin/pipeline/example/media/aihub.mp3
|
||||
/mnt/admin/pipeline/example/media/大模型.mp4 /mnt/admin/pipeline/example/media/大模型.mp3
|
||||
/mnt/admin/pipeline/example/media/自动化标注.mp4 /mnt/admin/pipeline/example/media/自动化标注.mp3
|
||||
/mnt/admin/pipeline/example/media/输入输出.mp4 /mnt/admin/pipeline/example/media/输入输出.mp3
|
||||
/mnt/admin/pipeline/example/media/aihub应用开发.mp4 /mnt/admin/pipeline/example/media/aihub应用开发.mp3
|
||||
10
myapp/example/pipeline/media/video_image.txt
Normal file
10
myapp/example/pipeline/media/video_image.txt
Normal file
@ -0,0 +1,10 @@
|
||||
/mnt/admin/pipeline/example/media/ide.mp4 /mnt/admin/pipeline/example/media/ide/ 2
|
||||
/mnt/admin/pipeline/example/media/aigc.mp4 /mnt/admin/pipeline/example/media/aigc/ 2
|
||||
/mnt/admin/pipeline/example/media/pipeline.mp4 /mnt/admin/pipeline/example/media/pipeline/ 2
|
||||
/mnt/admin/pipeline/example/media/vgpu.mp4 /mnt/admin/pipeline/example/media/vgpu/ 2
|
||||
/mnt/admin/pipeline/example/media/notebook.mp4 /mnt/admin/pipeline/example/media/notebook/ 2
|
||||
/mnt/admin/pipeline/example/media/aihub.mp4 /mnt/admin/pipeline/example/media/aihub/ 2
|
||||
/mnt/admin/pipeline/example/media/大模型.mp4 /mnt/admin/pipeline/example/media/大模型/ 2
|
||||
/mnt/admin/pipeline/example/media/自动化标注.mp4 /mnt/admin/pipeline/example/media/自动化标注/ 2
|
||||
/mnt/admin/pipeline/example/media/输入输出.mp4 /mnt/admin/pipeline/example/media/输入输出/ 2
|
||||
/mnt/admin/pipeline/example/media/aihub应用开发.mp4 /mnt/admin/pipeline/example/media/aihub应用开发/ 2
|
||||
10
myapp/example/pipeline/media/video_url.txt
Normal file
10
myapp/example/pipeline/media/video_url.txt
Normal file
@ -0,0 +1,10 @@
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/ide.mp4 /mnt/admin/pipeline/example/media/ide.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/aigc.mp4 /mnt/admin/pipeline/example/media/aigc.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/pipeline.mp4 /mnt/admin/pipeline/example/media/pipeline.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/vgpu.mp4 /mnt/admin/pipeline/example/media/vgpu.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/notebook.mp4 /mnt/admin/pipeline/example/media/notebook.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/aihub.mp4 /mnt/admin/pipeline/example/media/aihub.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/%E5%A4%A7%E6%A8%A1%E5%9E%8B.mp4 /mnt/admin/pipeline/example/media/大模型.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/%E8%87%AA%E5%8A%A8%E5%8C%96%E6%A0%87%E6%B3%A8.mp4 /mnt/admin/pipeline/example/media/自动化标注.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/%E8%BE%93%E5%85%A5%E8%BE%93%E5%87%BA.mp4 /mnt/admin/pipeline/example/media/输入输出.mp4
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/video/aihub%E5%BA%94%E7%94%A8%E5%BC%80%E5%8F%91.mp4 /mnt/admin/pipeline/example/media/aihub应用开发.mp4
|
||||
89
myapp/example/pipeline/ml/init.py
Executable file
89
myapp/example/pipeline/ml/init.py
Executable file
@ -0,0 +1,89 @@
|
||||
#!/usr/bin/env python
|
||||
# pip config set global.index-url https://mirrors.aliyun.com/pypi/simple
|
||||
# pip install psycopg2-binary pymysql
|
||||
|
||||
import os
|
||||
import shutil
|
||||
|
||||
import pysnooper
|
||||
|
||||
@pysnooper.snoop()
|
||||
def init_db(SQLALCHEMY_DATABASE_URI):
|
||||
if SQLALCHEMY_DATABASE_URI:
|
||||
import sqlalchemy.engine.url as url
|
||||
uri = url.make_url(SQLALCHEMY_DATABASE_URI)
|
||||
"""Inits the Myapp application"""
|
||||
sql = 'CREATE DATABASE IF NOT EXISTS example'
|
||||
if 'mysql' in SQLALCHEMY_DATABASE_URI:
|
||||
import pymysql
|
||||
# 创建连接
|
||||
conn = pymysql.connect(host=uri.host, port=uri.port, user=uri.username, password=uri.password, charset='utf8')
|
||||
sql = "CREATE DATABASE IF NOT EXISTS example DEFAULT CHARACTER SET utf8 DEFAULT COLLATE utf8_general_ci;"
|
||||
elif 'postgre' in SQLALCHEMY_DATABASE_URI:
|
||||
import psycopg2
|
||||
# 创建连接
|
||||
conn = psycopg2.connect(host=uri.host, port=uri.port, user=uri.username, password=uri.password)
|
||||
sql = "CREATE DATABASE example WITH ENCODING 'UTF8'"
|
||||
# 创建游标
|
||||
cursor = conn.cursor()
|
||||
conn.autocommit = True
|
||||
# 创建数据库的sql(如果数据库存在就不创建，防止异常)
|
||||
|
||||
print(sql)
|
||||
# 执行创建数据库的sql
|
||||
cursor.execute(sql)
|
||||
conn.commit()
|
||||
|
||||
|
||||
# 将文件导入到mysql数据库
|
||||
@pysnooper.snoop()
|
||||
def csv_table(csv_path,SQLALCHEMY_DATABASE_URI):
|
||||
# -*- coding:UTF-8 -*-
|
||||
|
||||
import pandas as pd
|
||||
from sqlalchemy import create_engine
|
||||
import sqlalchemy.engine.url as url
|
||||
uri = url.make_url(SQLALCHEMY_DATABASE_URI)
|
||||
# 数据库信息
|
||||
setting = {
|
||||
'host': uri.host,
|
||||
'port': uri.port,
|
||||
'user': uri.username,
|
||||
'passwd': uri.password,
|
||||
# 数据库名称
|
||||
'db': 'example',
|
||||
'charset': 'utf8'
|
||||
}
|
||||
# 表名
|
||||
# 如果不存在表，则自动创建
|
||||
table_name = os.path.basename(csv_path).replace('.csv','')
|
||||
# 文件路径
|
||||
path = csv_path
|
||||
|
||||
data = pd.read_csv(path, encoding='utf-8')
|
||||
print(data)
|
||||
if 'mysql' in SQLALCHEMY_DATABASE_URI:
|
||||
engine = create_engine("mysql+pymysql://{user}:{passwd}@{host}:{port}/{db}".format(**setting), max_overflow=5)
|
||||
elif 'postgre' in SQLALCHEMY_DATABASE_URI:
|
||||
engine = create_engine("postgresql+psycopg2://{user}:{passwd}@{host}:{port}/{db}".format(**setting), max_overflow=5)
|
||||
data.to_sql(table_name, engine, index=False, if_exists='replace', )
|
||||
print('导入成功...')
|
||||
|
||||
def init():
|
||||
mysql_sql_uri='mysql+pymysql://root:admin@mysql-service.infra:3306/example?charset=utf8'
|
||||
postgres_sql_uri = 'postgresql+psycopg2://postgres:postgres@postgresql.kubeflow:5432/example'
|
||||
current_work_dir = os.path.dirname(__file__)
|
||||
try:
|
||||
init_db(SQLALCHEMY_DATABASE_URI=mysql_sql_uri)
|
||||
csv_table(csv_path=os.path.join(current_work_dir, 'train.csv'), SQLALCHEMY_DATABASE_URI=mysql_sql_uri)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
try:
|
||||
init_db(SQLALCHEMY_DATABASE_URI=postgres_sql_uri)
|
||||
csv_table(csv_path=os.path.join(current_work_dir,'train.csv'),SQLALCHEMY_DATABASE_URI=postgres_sql_uri)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
init()
|
||||
|
||||
|
||||
70
myapp/example/pipeline/ml/mysql-csv.json
Normal file
70
myapp/example/pipeline/ml/mysql-csv.json
Normal file
@ -0,0 +1,70 @@
|
||||
{
|
||||
"job": {
|
||||
"setting": {
|
||||
"speed": {
|
||||
"channel": 1
|
||||
},
|
||||
"errorLimit": {
|
||||
"record": 0,
|
||||
"percentage": 0.02
|
||||
}
|
||||
},
|
||||
"content": [
|
||||
{
|
||||
"reader": {
|
||||
"name": "mysqlreader",
|
||||
"parameter": {
|
||||
"username": "root",
|
||||
"password": "admin",
|
||||
"column": [
|
||||
"age",
|
||||
"duration",
|
||||
"campaign",
|
||||
"pdays",
|
||||
"previous",
|
||||
"emp_var_rate",
|
||||
"cons_price_idx",
|
||||
"cons_conf_idx",
|
||||
"euribor3m",
|
||||
"nr_employed",
|
||||
"y"
|
||||
],
|
||||
"connection": [
|
||||
{
|
||||
"table": [
|
||||
"train"
|
||||
],
|
||||
"jdbcUrl": [
|
||||
"jdbc:mysql://mysql-service.infra:3306/example"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"writer": {
|
||||
"name": "txtfilewriter",
|
||||
"parameter": {
|
||||
"path": "/mnt/admin/pipeline/example/ml/",
|
||||
"fileName": "data-test.csv",
|
||||
"writeMode": "truncate",
|
||||
"fileFormat":"csv",
|
||||
"header": [
|
||||
"age",
|
||||
"duration",
|
||||
"campaign",
|
||||
"pdays",
|
||||
"previous",
|
||||
"emp_var_rate",
|
||||
"cons_price_idx",
|
||||
"cons_conf_idx",
|
||||
"euribor3m",
|
||||
"nr_employed",
|
||||
"y"
|
||||
],
|
||||
"dateFormat": "yyyy-MM-dd"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
70
myapp/example/pipeline/ml/pg-csv.json
Normal file
70
myapp/example/pipeline/ml/pg-csv.json
Normal file
@ -0,0 +1,70 @@
|
||||
{
|
||||
"job": {
|
||||
"setting": {
|
||||
"speed": {
|
||||
"channel": 1
|
||||
},
|
||||
"errorLimit": {
|
||||
"record": 0,
|
||||
"percentage": 0.02
|
||||
}
|
||||
},
|
||||
"content": [
|
||||
{
|
||||
"reader": {
|
||||
"name": "postgresqlreader",
|
||||
"parameter": {
|
||||
"username": "postgres",
|
||||
"password": "postgres",
|
||||
"column": [
|
||||
"age",
|
||||
"duration",
|
||||
"campaign",
|
||||
"pdays",
|
||||
"previous",
|
||||
"emp_var_rate",
|
||||
"cons_price_idx",
|
||||
"cons_conf_idx",
|
||||
"euribor3m",
|
||||
"nr_employed",
|
||||
"y"
|
||||
],
|
||||
"connection": [
|
||||
{
|
||||
"table": [
|
||||
"train"
|
||||
],
|
||||
"jdbcUrl": [
|
||||
"jdbc:postgresql://postgresql.kubeflow:5432/example"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"writer": {
|
||||
"name": "txtfilewriter",
|
||||
"parameter": {
|
||||
"path": "/mnt/admin/pipeline/example/ml/",
|
||||
"fileName": "data-pg.csv",
|
||||
"writeMode": "truncate",
|
||||
"fileFormat":"csv",
|
||||
"header": [
|
||||
"age",
|
||||
"duration",
|
||||
"campaign",
|
||||
"pdays",
|
||||
"previous",
|
||||
"emp_var_rate",
|
||||
"cons_price_idx",
|
||||
"cons_conf_idx",
|
||||
"euribor3m",
|
||||
"nr_employed",
|
||||
"y"
|
||||
],
|
||||
"dateFormat": "yyyy-MM-dd"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
47
myapp/example/pipeline/nni/demo.py
Normal file
47
myapp/example/pipeline/nni/demo.py
Normal file
@ -0,0 +1,47 @@
|
||||
import os
|
||||
import argparse
|
||||
import random,time,datetime
|
||||
import nni
|
||||
import logging
|
||||
from nni.utils import merge_parameter
|
||||
import pysnooper
|
||||
time.sleep(10)
|
||||
|
||||
@pysnooper.snoop()
|
||||
def main(args):
|
||||
test_acc=random.randint(30,50)
|
||||
for epoch in range(1, 11):
|
||||
test_acc_epoch= random.randint(3,5)
|
||||
time.sleep(3)
|
||||
# if os.path.exists('train')
|
||||
test_acc+=test_acc_epoch
|
||||
# 上报当前迭代目标值
|
||||
nni.report_intermediate_result(test_acc)
|
||||
# 上报最总目标值
|
||||
nni.report_final_result(test_acc)
|
||||
|
||||
|
||||
def get_params():
|
||||
# 必须接收超参数为输入参数
|
||||
parser = argparse.ArgumentParser(description='nni Example')
|
||||
parser.add_argument('--batch_size', type=int, default=64, help='input batch size for training (default: 64)')
|
||||
parser.add_argument('--hidden_size', type=int, default=128, help='hidden size for training (default: 128)')
|
||||
parser.add_argument('--lr', type=float, default=0.001, help='Learning rate for training (default: 0.001)')
|
||||
parser.add_argument('--momentum', type=float, default=0.0, help='momentum for training (default: 0)')
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
return args
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
try:
|
||||
# get parameters form tuner
|
||||
tuner_params = nni.get_next_parameter()
|
||||
params = vars(merge_parameter(get_params(), tuner_params))
|
||||
print(tuner_params, params)
|
||||
main(params)
|
||||
except Exception as exception:
|
||||
print(exception)
|
||||
raise
|
||||
|
||||
|
||||
63
myapp/example/pipeline/offline-inference/demo.py
Normal file
63
myapp/example/pipeline/offline-inference/demo.py
Normal file
@ -0,0 +1,63 @@
|
||||
# ===================用户代码=====================
|
||||
|
||||
|
||||
import os
|
||||
import numpy
|
||||
import requests
|
||||
from predict_model import Offline_Predict # 需要引入这个包里面的类
|
||||
|
||||
import pysnooper
|
||||
import torch
|
||||
import torchvision.transforms as transforms
|
||||
from torchvision import models
|
||||
from PIL import Image
|
||||
|
||||
class My_Offline_Predict(Offline_Predict):
|
||||
|
||||
def __init__(self):
|
||||
# 加载预训练的 ResNet-50 模型
|
||||
self.model = models.resnet50(pretrained=False)
|
||||
self.model.load_state_dict(torch.load('resnet50.pth'))
|
||||
# 切换到评估模式
|
||||
self.model.eval()
|
||||
|
||||
# 定义所有要处理的数据源，返回字符串列表
|
||||
def datasource(self):
|
||||
all_lines = open('images_url.txt', mode='r').readlines()
|
||||
return all_lines
|
||||
|
||||
# 定义一条数据的处理逻辑
|
||||
# @pysnooper.snoop()
|
||||
def predict(self,value):
|
||||
os.makedirs('download',exist_ok=True)
|
||||
# 加载图像并在 GPU 上进行推理
|
||||
image_path = value
|
||||
|
||||
if 'https://' in value or 'http://' in value:
|
||||
file_name = value[value.rindex("/")+1:]
|
||||
image_path = f'download/{file_name}'
|
||||
file=open(image_path,mode='wb')
|
||||
file.write(requests.get(value).content)
|
||||
file.close()
|
||||
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
transform = transforms.Compose([
|
||||
transforms.Resize(256),
|
||||
transforms.CenterCrop(224),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
])
|
||||
|
||||
image_tensor = transform(image).unsqueeze(0)
|
||||
with torch.no_grad():
|
||||
outputs = self.model(image_tensor)
|
||||
_, preds = torch.max(outputs, 1)
|
||||
result=preds.item()
|
||||
print(value)
|
||||
print(result)
|
||||
return result
|
||||
|
||||
|
||||
My_Offline_Predict().run()
|
||||
|
||||
|
||||
100
myapp/example/pipeline/offline-inference/images_url.txt
Normal file
100
myapp/example/pipeline/offline-inference/images_url.txt
Normal file
@ -0,0 +1,100 @@
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000000597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000000797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000000897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000001997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000002197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000002697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000003197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000003897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000006197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000007697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000008297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000008797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000009797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000010097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000011297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000011497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000011697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000012497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000012597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000012797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000013397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000013497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000013797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000015897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000016297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000016697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000017697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000017997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000019397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000019797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000020997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000021097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000021197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000021497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000022097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000023797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000024497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000025697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000025797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000026997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000028097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000028797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000030997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000031497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000032997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000033297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000033897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000034097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000034397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000035497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000036197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000036397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000036797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000037697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000038097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000038697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000039497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000039597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000041097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000041597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000041997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000042297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000043697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000045097.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000046997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000048797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000050597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000051197.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000052497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000052897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000053597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000053897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000055897.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000056397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000056997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000057697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000058397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000058497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000058597.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000058997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000059497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000059997.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000060297.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000061697.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000062397.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000063797.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000064497.jpg
|
||||
https://cube-studio.oss-cn-hangzhou.aliyuncs.com/pipeline/media-download/train2014/COCO_train2014_000000064697.jpg
|
||||
310
myapp/example/pipeline/offline-inference/predict_model.py
Normal file
310
myapp/example/pipeline/offline-inference/predict_model.py
Normal file
@ -0,0 +1,310 @@
|
||||
import os
|
||||
import time
|
||||
import numpy
|
||||
import threading
|
||||
import pysnooper
|
||||
import logging
|
||||
import time, json, datetime
|
||||
import pika
|
||||
import urllib
|
||||
import urllib.parse
|
||||
|
||||
VC_TASK_INDEX = int(os.environ.get('VC_TASK_INDEX', '0'))
|
||||
VC_WORKER_NUM = int(os.environ.get('VC_WORKER_NUM', '1')) - 1 # 0 作为生产者
|
||||
|
||||
print(VC_TASK_INDEX, VC_WORKER_NUM)
|
||||
|
||||
|
||||
class Rabbit_Producer():
|
||||
|
||||
def __init__(self, host=os.environ.get('RABBIT_HOST', '127.0.0.1'), port=5672, user='admin', password='admin',
|
||||
virtual_host='/'): # 默认端口5672，可不写
|
||||
credentials = pika.PlainCredentials(user, password)
|
||||
self.connection = pika.BlockingConnection(
|
||||
pika.ConnectionParameters(host=host, port=port, credentials=credentials, virtual_host=virtual_host, heartbeat=10)) # virtual_host='/'
|
||||
self.channel = self.connection.channel() # 声明一个管道
|
||||
self.properties = pika.BasicProperties( # 需要将消息发送到exchange，exchange会把消息分发给queue。queue会把消息分发给消费者
|
||||
delivery_mode=2, # 消息持久化
|
||||
# content_type='application/json',
|
||||
# content_encoding='UTF-8',
|
||||
# priority=0,
|
||||
# expiration = '60000000' # 有效时间，毫秒为单位
|
||||
)
|
||||
self.exchange_name = ""
|
||||
self.exchange_type = ""
|
||||
self.queue_name = ""
|
||||
|
||||
# 设置广播通道
|
||||
def set_exchange(self, exchange='predict-exchange', exchange_type='topic'):
|
||||
self.exchange_name = exchange
|
||||
self.exchange_type = exchange_type
|
||||
# 注意：这里是广播，不需要声明queue
|
||||
self.channel.exchange_declare(exchange=exchange, exchange_type=exchange_type, durable=True) # 声明广播管道
|
||||
if exchange_type == 'direct':
|
||||
self.set_queue()
|
||||
self.channel.basic_qos(prefetch_count=1) # 一个消费者只同时处理一个数据
|
||||
logging.info('set rabbit exchange %s, type %s' % (exchange, exchange_type))
|
||||
|
||||
# 注意：在直连模式下，设置队列，需要设置队列，此时不启动广播器exchange，其他情况不要设置队列
|
||||
def set_queue(self, queue_name='predict-queue'):
|
||||
self.queue_name = queue_name
|
||||
self.channel.queue_declare(queue=queue_name, durable=True)
|
||||
|
||||
# 发送消息，传递字节流
|
||||
def send_message(self, message, rout_key="predict-key"):
|
||||
# logging.info('send to rabbit: time is %s' % datetime.datetime.now())
|
||||
self.channel.basic_publish(exchange=self.exchange_name, routing_key=rout_key, body=message,
|
||||
properties=self.properties) # body消息内容
|
||||
|
||||
# 删除exchange, 因为有时exchange会弄错
|
||||
def delete_exchange(self, exchange_name=None):
|
||||
if not exchange_name:
|
||||
exchange_name = self.exchange_name
|
||||
self.channel.exchange_delete(exchange_name)
|
||||
|
||||
# 关闭，要记得关闭信道
|
||||
def close(self):
|
||||
self.channel.close()
|
||||
self.connection.close()
|
||||
|
||||
|
||||
class Rabbit_Consumer():
|
||||
|
||||
def __init__(self, host=os.environ.get('RABBIT_HOST', '127.0.0.1'), port=5672, user='admin', password='admin',
|
||||
virtual_host='/'): # 默认端口5672，可不写
|
||||
credentials = pika.PlainCredentials(user, password)
|
||||
self.connection = pika.BlockingConnection(
|
||||
pika.ConnectionParameters(host=host, port=port, credentials=credentials, virtual_host=virtual_host,
|
||||
heartbeat=10))
|
||||
self.channel = self.connection.channel() # 声明一个管道
|
||||
self.exchange_name = ""
|
||||
self.exchange_type = ""
|
||||
self.queue_name = ""
|
||||
|
||||
# 设置广播接收队列. exchange通过路由规则发送到符合路由规则的队列。用户订阅队列，就能收到该队列的消息。
|
||||
def set_queue(self, exchange='predict-exchange', exchange_type='topic', queue_name='predict-queue',
|
||||
rout_key="predict-key"):
|
||||
self.exchange_name = exchange
|
||||
self.exchange_type = exchange_type
|
||||
self.queue_name = queue_name
|
||||
if (exchange):
|
||||
# 声明转发器
|
||||
self.channel.exchange_declare(exchange=exchange, exchange_type=exchange_type, durable=True)
|
||||
# 发送方和接收方不知道谁首先连接到RabbitMQ，双方连接上来都先声明一个队列
|
||||
self.channel.queue_declare(queue=queue_name, durable=True)
|
||||
# queue绑定到转发器上
|
||||
self.channel.queue_bind(exchange=exchange, queue=queue_name,
|
||||
routing_key=rout_key) # 如果是fanout，rout_key要设置成""
|
||||
self.channel.basic_qos(prefetch_count=1) # 一个消费者只同时处理一个数据
|
||||
logging.info('set rabbit queue %s bing to exchange %s' % (queue_name, exchange))
|
||||
else:
|
||||
print('====', exchange)
|
||||
self.channel.queue_declare(queue=queue_name, durable=True)
|
||||
|
||||
# 设置消息接收配置
|
||||
def set_receive_config(self, callback, auto_ack=True):
|
||||
# 声明接收收消息变量。#callback收到消息后执行的回调函数。no_ack默认不执行回复ack，以便服务器宕机，能被其他消费者接收
|
||||
self.channel.basic_consume(on_message_callback=callback, queue=self.queue_name, auto_ack=auto_ack)
|
||||
|
||||
# 开始接收消息，不停循环接收，没有消息挂起等待
|
||||
def start_receive(self):
|
||||
logging.info('start listening')
|
||||
self.channel.start_consuming()
|
||||
|
||||
def close(self):
|
||||
self.channel.close()
|
||||
self.connection.close()
|
||||
|
||||
|
||||
class Rabbit_info():
|
||||
def __init__(self, host=os.environ.get('RABBIT_HOST', '127.0.0.1'), port=15672, user='admin', password='admin'):
|
||||
self.url = 'http://' + host + ':' + str(port) + '/api/'
|
||||
self.user = user
|
||||
self.password = password
|
||||
|
||||
def get_queue(self):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
response = requests.get(self.url + "queues", auth=HTTPBasicAuth(self.user, self.password))
|
||||
queues = json.loads(response.text)
|
||||
print(json.dumps(queues, indent=4))
|
||||
allqueue = []
|
||||
for queue in queues:
|
||||
queue_temp = {}
|
||||
queue_temp['name'] = queue['name'] # 队列名称
|
||||
queue_temp['auto_delete'] = queue['auto_delete'] # 是否自动删除
|
||||
queue_temp['consumers'] = queue['consumers'] # 队列后的消费者数目
|
||||
queue_temp['durable'] = queue['durable'] # 是否持久化
|
||||
queue_temp['memory'] = queue['memory'] / 1024 / 1024 # 占用内存MB
|
||||
queue_temp['state'] = queue['state'] # 状态
|
||||
queue_temp['vhost'] = queue['vhost'] # 命名空间
|
||||
queue_temp['message_count'] = queue['reductions'] # 消息数量
|
||||
if 'message_stats' in queue: # 没有消费者的话是没有message消息的
|
||||
queue_temp['message_stats:ack'] = queue['message_stats']['ack']
|
||||
queue_temp['message_stats:deliver'] = queue['message_stats']['deliver']
|
||||
queue_temp['message_stats:deliver_get'] = queue['message_stats']['deliver_get']
|
||||
queue_temp['message_stats:deliver_no_ack'] = queue['message_stats']['deliver_no_ack']
|
||||
queue_temp['message_stats:get'] = queue['message_stats']['get']
|
||||
queue_temp['message_stats:get_no_ack'] = queue['message_stats']['get_no_ack']
|
||||
queue_temp['message_stats:redeliver'] = queue['message_stats']['redeliver']
|
||||
|
||||
allqueue.append(queue_temp)
|
||||
return allqueue
|
||||
|
||||
# 不能获取历史数据，只能获取未被消费的数据
|
||||
def get_message(self, vhost, queue_name, count=50):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
# ack_requeue_true 让消息重新排队
|
||||
data = {"count": count, "ackmode": "ack_requeue_true", "encoding": "auto", "truncate": 50000}
|
||||
header = {
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
|
||||
vhost = urllib.parse.quote(vhost, 'utf-8')
|
||||
url = self.url + "queues/" + vhost + "/" + queue_name + "/get"
|
||||
# print(url)
|
||||
response = requests.post(url, json=data, headers=header, auth=HTTPBasicAuth(self.user, self.password))
|
||||
|
||||
message = json.loads(response.text)
|
||||
return message
|
||||
|
||||
def get_msg_count(self):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
url = self.url + "queues/%2f/predict-queue"
|
||||
response = requests.get(url, auth=HTTPBasicAuth(self.user, self.password))
|
||||
result = json.loads(response.text)
|
||||
# print(result)
|
||||
count = result.get('backing_queue_status', {}).get('len', 0)
|
||||
return count
|
||||
|
||||
def get_node(self):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
response = requests.get(self.url + "nodes", auth=HTTPBasicAuth(self.user, self.password))
|
||||
nodes = json.loads(response.text)
|
||||
return nodes
|
||||
|
||||
def get_exchange(self):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
response = requests.get(self.url + "exchanges", auth=HTTPBasicAuth(self.user, self.password))
|
||||
exchanges = json.loads(response.text)
|
||||
return exchanges
|
||||
|
||||
def get_channel(self):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
response = requests.get(self.url + "channels", auth=HTTPBasicAuth(self.user, self.password))
|
||||
channels = json.loads(response.text)
|
||||
return channels
|
||||
|
||||
def get_user(self):
|
||||
import requests, json
|
||||
from requests.auth import HTTPBasicAuth
|
||||
response = requests.get(self.url + "users", auth=HTTPBasicAuth(self.user, self.password))
|
||||
users = json.loads(response.text)
|
||||
return users
|
||||
|
||||
|
||||
class Offline_Predict():
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
# @pysnooper.snoop()
|
||||
def callback(self, ch, method, properties, body): # 四个参数为标准格式
|
||||
value = body.decode(encoding='utf-8')
|
||||
self.predict(value)
|
||||
ch.basic_ack(delivery_tag=method.delivery_tag) # 告诉生成者，消息处理完成
|
||||
|
||||
# @pysnooper.snoop()
|
||||
def consumer(self):
|
||||
consumer = Rabbit_Consumer()
|
||||
consumer.set_queue()
|
||||
consumer.set_receive_config(callback=self.callback, auto_ack=False)
|
||||
consumer.start_receive()
|
||||
|
||||
# @pysnooper.snoop()
|
||||
def producter(self):
|
||||
producer = Rabbit_Producer()
|
||||
producer.set_exchange()
|
||||
|
||||
all_lines = self.datasource()
|
||||
for line in all_lines:
|
||||
try:
|
||||
producer.send_message(line.replace('\n', '').encode(encoding='utf-8'))
|
||||
except Exception as e:
|
||||
print(e)
|
||||
producer.close()
|
||||
|
||||
# 不创建队列的话，exchange中的消息就会被流转走
|
||||
# @pysnooper.snoop()
|
||||
def create_queue(self):
|
||||
try:
|
||||
admin_client = Rabbit_Producer()
|
||||
admin_client.set_exchange()
|
||||
|
||||
consumer = Rabbit_Consumer()
|
||||
consumer.set_queue()
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
# exit(1)
|
||||
|
||||
# 计算升级数量
|
||||
def wait_finish(self):
|
||||
while (True):
|
||||
try:
|
||||
rabbit_client = Rabbit_info()
|
||||
left_msg_num = int(rabbit_client.get_msg_count())
|
||||
print("======================= left: {}".format(left_msg_num))
|
||||
time.sleep(60)
|
||||
if not left_msg_num:
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
def datasource(self):
|
||||
return []
|
||||
|
||||
def predict(self, value):
|
||||
return value
|
||||
|
||||
# @pysnooper.snoop()
|
||||
def start_predict(self, local_rank):
|
||||
VC_TASK_INDEX = os.environ.get('VC_TASK_INDEX', None)
|
||||
# 本地启动方式
|
||||
if not VC_TASK_INDEX:
|
||||
for item in self.datasource():
|
||||
self.predict(item)
|
||||
# 分布式启动方式
|
||||
else:
|
||||
self.create_queue()
|
||||
VC_TASK_INDEX = int(VC_TASK_INDEX)
|
||||
if VC_TASK_INDEX == 0 and local_rank == 0:
|
||||
t = threading.Thread(target=self.consumer)
|
||||
# 启动子线程
|
||||
t.start()
|
||||
# 主线程为监控消费是否结束
|
||||
self.producter()
|
||||
self.wait_finish()
|
||||
else:
|
||||
self.consumer()
|
||||
|
||||
# @pysnooper.snoop()
|
||||
def run(self):
|
||||
VC_TASK_INDEX = os.environ.get('VC_TASK_INDEX', None)
|
||||
local_rank = int(os.environ.get('LOCAL_RANK', 0))
|
||||
print('!!!!!!!! task_index=%s,local_rank=%s' % (VC_TASK_INDEX,local_rank))
|
||||
self.start_predict(local_rank)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
11
myapp/example/pipeline/offline-inference/start.sh
Normal file
11
myapp/example/pipeline/offline-inference/start.sh
Normal file
@ -0,0 +1,11 @@
|
||||
# 填过国内源
|
||||
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple
|
||||
# 安装基础包
|
||||
pip install pysnooper psutil requests numpy pyinstaller argparse pika torch==2.0.1 torchvision==0.15.2 Pillow
|
||||
# 下载模型
|
||||
if [ "$VC_TASK_INDEX" == "0" ] && [ ! -f "./resnet50.pth" ]; then
|
||||
wget -O resnet50.pth https://download.pytorch.org/models/resnet50-0676ba61.pth
|
||||
fi
|
||||
# 执行离线推理
|
||||
python demo.py
|
||||
|
||||
46
myapp/example/pipeline/offline-inference/test.py
Normal file
46
myapp/example/pipeline/offline-inference/test.py
Normal file
@ -0,0 +1,46 @@
|
||||
import torch
|
||||
import torchvision.transforms as transforms
|
||||
from torchvision import models
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def load_image(image_path):
|
||||
image = Image.open(image_path).convert('RGB')
|
||||
transform = transforms.Compose([
|
||||
transforms.Resize(256),
|
||||
transforms.CenterCrop(224),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
||||
])
|
||||
return transform(image).unsqueeze(0)
|
||||
|
||||
|
||||
def predict(image_path, model):
|
||||
# 加载图像并在 GPU 上进行推理
|
||||
image_tensor = load_image(image_path).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = model(image_tensor)
|
||||
_, preds = torch.max(outputs, 1)
|
||||
return preds.item()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 检查 GPU 可用性
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
print("Using device:", device)
|
||||
|
||||
# 加载预训练的 ResNet-18 模型
|
||||
# model = models.resnet50(pretrained=True).to(device)
|
||||
model = models.resnet50(pretrained=False)
|
||||
model.load_state_dict(torch.load('resnet50.pth'))
|
||||
model = model.to(device)
|
||||
# 切换到评估模式
|
||||
model.eval()
|
||||
# 指定图像路径
|
||||
image_path = "test.jpg"
|
||||
|
||||
# 对图像进行推理
|
||||
for image_path in open('images_url.txt').readlines():
|
||||
image_path = image_path.replace('\n','')
|
||||
prediction = predict(image_path, model)
|
||||
print("Predicted class:", prediction)
|
||||
186
myapp/example/pipeline/pytorch/demo.py
Normal file
186
myapp/example/pipeline/pytorch/demo.py
Normal file
@ -0,0 +1,186 @@
|
||||
from __future__ import print_function
|
||||
# pip install tensorboardX torch torchvision --index-url https://mirrors.aliyun.com/pypi/simple
|
||||
import argparse
|
||||
import os
|
||||
import datetime, time
|
||||
from tensorboardX import SummaryWriter
|
||||
from torchvision import datasets, transforms
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
import pysnooper
|
||||
|
||||
WORLD_SIZE = int(os.environ.get('WORLD_SIZE', 1))
|
||||
|
||||
|
||||
# 可以先下载数据到data目录
|
||||
class MyMNIST(datasets.MNIST):
|
||||
mirrors = ['https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/']
|
||||
# resources = [
|
||||
# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/train-images-idx3-ubyte.gz",
|
||||
# "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
|
||||
# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/train-labels-idx1-ubyte.gz",
|
||||
# "d53e105ee54ea40749a09fcbcd1e9432"),
|
||||
# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/t10k-images-idx3-ubyte.gz",
|
||||
# "9fb629c4189551a2d022fa330f9573f3"),
|
||||
# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/t10k-labels-idx1-ubyte.gz",
|
||||
# "ec29112dd5afa0611ce80d1b7f02629c")
|
||||
# ]
|
||||
|
||||
# 定义模型框架
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.conv1 = nn.Conv2d(1, 20, 5, 1)
|
||||
self.conv2 = nn.Conv2d(20, 50, 5, 1)
|
||||
self.fc1 = nn.Linear(4 * 4 * 50, 500)
|
||||
self.fc2 = nn.Linear(500, 10)
|
||||
|
||||
# 前向计算
|
||||
def forward(self, x):
|
||||
x = F.relu(self.conv1(x))
|
||||
x = F.max_pool2d(x, 2, 2)
|
||||
x = F.relu(self.conv2(x))
|
||||
x = F.max_pool2d(x, 2, 2)
|
||||
x = x.view(-1, 4 * 4 * 50)
|
||||
x = F.relu(self.fc1(x))
|
||||
x = self.fc2(x)
|
||||
return F.log_softmax(x, dim=1)
|
||||
|
||||
|
||||
# 训练过程
|
||||
def train(args, model, device, train_loader, optimizer, epoch, writer):
|
||||
model.train()
|
||||
for batch_idx, (data, target) in enumerate(train_loader):
|
||||
data, target = data.to(device), target.to(device)
|
||||
optimizer.zero_grad()
|
||||
output = model(data)
|
||||
loss = F.nll_loss(output, target)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if batch_idx % args.log_interval == 0:
|
||||
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}'.format(
|
||||
epoch, batch_idx * len(data), len(train_loader.dataset),
|
||||
100. * batch_idx / len(train_loader), loss.item()))
|
||||
niter = epoch * len(train_loader) + batch_idx
|
||||
writer.add_scalar('loss', loss.item(), niter)
|
||||
|
||||
|
||||
# 测试计算计算损失值
|
||||
def test(args, model, device, test_loader, writer, epoch):
|
||||
model.eval()
|
||||
test_loss = 0
|
||||
correct = 0
|
||||
with torch.no_grad():
|
||||
for data, target in test_loader:
|
||||
data, target = data.to(device), target.to(device)
|
||||
output = model(data)
|
||||
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
|
||||
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability
|
||||
correct += pred.eq(target.view_as(pred)).sum().item()
|
||||
|
||||
test_loss /= len(test_loader.dataset)
|
||||
print('\naccuracy={:.4f}\n'.format(float(correct) / len(test_loader.dataset)))
|
||||
writer.add_scalar('accuracy', float(correct) / len(test_loader.dataset), epoch)
|
||||
|
||||
|
||||
# 计算是不是分布式
|
||||
def should_distribute():
|
||||
return dist.is_available() and WORLD_SIZE > 1
|
||||
|
||||
|
||||
# 计算是不是分布式
|
||||
def is_distributed():
|
||||
return dist.is_available() and dist.is_initialized()
|
||||
|
||||
|
||||
@pysnooper.snoop()
|
||||
def main():
|
||||
# Training settings
|
||||
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
|
||||
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
|
||||
help='input batch size for training (default: 64)')
|
||||
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
|
||||
help='input batch size for testing (default: 1000)')
|
||||
parser.add_argument('--epochs', type=int, default=1, metavar='N',
|
||||
help='number of epochs to train (default: 10)')
|
||||
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
|
||||
help='learning rate (default: 0.01)')
|
||||
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
|
||||
help='SGD momentum (default: 0.5)')
|
||||
parser.add_argument('--no-cuda', action='store_true', default=False,
|
||||
help='disables CUDA training')
|
||||
parser.add_argument('--seed', type=int, default=1, metavar='S',
|
||||
help='random seed (default: 1)')
|
||||
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
|
||||
help='how many batches to wait before logging training status')
|
||||
parser.add_argument('--save-model', action='store_true', default=False,
|
||||
help='For Saving the current Model')
|
||||
parser.add_argument('--dir', default='logs', metavar='L',
|
||||
help='directory where summary logs are stored')
|
||||
if dist.is_available():
|
||||
parser.add_argument('--backend', type=str, help='Distributed backend',
|
||||
choices=[dist.Backend.GLOO, dist.Backend.NCCL, dist.Backend.MPI],
|
||||
default=dist.Backend.NCCL)
|
||||
args = parser.parse_args()
|
||||
print('reveice args:', args)
|
||||
|
||||
# args.no_cuda = True
|
||||
# args.backend = dist.Backend.GLOO
|
||||
|
||||
use_cuda = not args.no_cuda and torch.cuda.is_available()
|
||||
if use_cuda:
|
||||
print('Using CUDA')
|
||||
else:
|
||||
args.backend = dist.Backend.GLOO
|
||||
|
||||
writer = SummaryWriter(args.dir)
|
||||
|
||||
torch.manual_seed(args.seed)
|
||||
|
||||
device = torch.device("cuda" if use_cuda else "cpu")
|
||||
|
||||
if should_distribute():
|
||||
print('Using distributed PyTorch with {} backend'.format(args.backend))
|
||||
dist.init_process_group(backend=args.backend)
|
||||
|
||||
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
|
||||
print('bengin load train data %s' % str(datetime.datetime.now()))
|
||||
train_loader = torch.utils.data.DataLoader(
|
||||
datasets.MNIST('data', train=True, download=True,
|
||||
transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))
|
||||
])),
|
||||
batch_size=args.batch_size, shuffle=True, **kwargs)
|
||||
print('bengin load test data %s' % str(datetime.datetime.now()))
|
||||
test_loader = torch.utils.data.DataLoader(
|
||||
datasets.MNIST('data', train=False, transform=transforms.Compose([
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize((0.1307,), (0.3081,))
|
||||
])),
|
||||
batch_size=args.test_batch_size, shuffle=False, **kwargs)
|
||||
print('bengin make net model %s' % str(datetime.datetime.now()))
|
||||
model = Net().to(device)
|
||||
|
||||
if is_distributed():
|
||||
Distributor = nn.parallel.DistributedDataParallel if use_cuda \
|
||||
else nn.parallel.DistributedDataParallelCPU
|
||||
model = Distributor(model)
|
||||
|
||||
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
|
||||
print('bengin train model %s' % str(datetime.datetime.now()))
|
||||
for epoch in range(1, args.epochs + 1):
|
||||
train(args, model, device, train_loader, optimizer, epoch, writer)
|
||||
test(args, model, device, test_loader, writer, epoch)
|
||||
print('bengin save model %s' % str(datetime.datetime.now()))
|
||||
if (args.save_model):
|
||||
torch.save(model.state_dict(), "mnist_cnn.pt")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
print('begin python shell %s' % str(datetime.datetime.now()))
|
||||
main()
|
||||
print('end python shell %s' % str(datetime.datetime.now()))
|
||||
25
myapp/example/pipeline/pytorch/start.sh
Normal file
25
myapp/example/pipeline/pytorch/start.sh
Normal file
@ -0,0 +1,25 @@
|
||||
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple
|
||||
# pip install tensorboardX torch torchvision --no-cache-dir
|
||||
# pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2 tensorboardX --index-url https://download.pytorch.org/whl/cu118
|
||||
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 tensorboardX
|
||||
|
||||
pip install tensorboardX pysnooper requests
|
||||
|
||||
#export NCCL_IB_HCA=mlx5 # 需要适配
|
||||
#export NCCL_IB_TC=136
|
||||
# export NCCL_IB_SL=5 # 需要适配，可不填
|
||||
# export NCCL_IB_GID_INDEX=0 # 需要适配，可不填
|
||||
#export NCCL_IB_TIMEOUT=22
|
||||
# export NCCL_SOCKET_IFNAME=eth0 # 可不填，默认就是这个，有些协议无法走ib，会自动走以太网
|
||||
#export NCCL_DEBUG=INFO
|
||||
|
||||
mkdir -p data/MNIST/raw/
|
||||
if [ "$RANK" = "0" ]; then
|
||||
|
||||
wget -P data/MNIST/raw/ https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/train-images-idx3-ubyte.gz
|
||||
wget -P data/MNIST/raw/ https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/train-labels-idx1-ubyte.gz
|
||||
wget -P data/MNIST/raw/ https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/t10k-images-idx3-ubyte.gz
|
||||
wget -P data/MNIST/raw/ https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/t10k-labels-idx1-ubyte.gz
|
||||
fi
|
||||
python demo.py
|
||||
|
||||
31
myapp/example/pipeline/ray/demo.py
Normal file
31
myapp/example/pipeline/ray/demo.py
Normal file
@ -0,0 +1,31 @@
|
||||
import ray,os,time
|
||||
|
||||
|
||||
@ray.remote
|
||||
def fun1(arg):
|
||||
# 这里是耗时的任务，函数内不能引用全局变量，只能使用函数内的局部变量。
|
||||
print(arg)
|
||||
time.sleep(1)
|
||||
return 'back_data'
|
||||
|
||||
|
||||
def main():
|
||||
tasks=[]
|
||||
tasks_args = range(100)
|
||||
for arg in tasks_args:
|
||||
tasks.append(fun1.remote(arg)) # 建立远程函数
|
||||
result = ray.get(tasks) # 获取任务结果
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
head_service_ip = os.getenv('RAY_HOST', '')
|
||||
if head_service_ip:
|
||||
# 集群模式
|
||||
head_host = head_service_ip + ".pipeline" + ":10001"
|
||||
ray.util.connect(head_host)
|
||||
else:
|
||||
# 本地模式
|
||||
ray.init()
|
||||
|
||||
main()
|
||||
1
myapp/example/pipeline/sklearn/init.sh
Normal file
1
myapp/example/pipeline/sklearn/init.sh
Normal file
@ -0,0 +1 @@
|
||||
#pip install numpy
|
||||
4120
myapp/example/pipeline/sklearn/ml-label.csv
Normal file
4120
myapp/example/pipeline/sklearn/ml-label.csv
Normal file
File diff suppressed because it is too large
Load Diff
150
myapp/example/pipeline/sklearn/ml-nolabel.csv
Normal file
150
myapp/example/pipeline/sklearn/ml-nolabel.csv
Normal file
@ -0,0 +1,150 @@
|
||||
age,duration,campaign,pdays,previous,emp_var_rate,cons_price_idx,cons_conf_idx,euribor3m,nr_employed
|
||||
30,487,2,999,0,-1.8,92.893,-46.2,1.313,5099.1
|
||||
39,346,4,999,0,1.1,93.994,-36.4,4.855,5191
|
||||
25,227,1,999,0,1.4,94.465,-41.8,4.962,5228.1
|
||||
38,17,3,999,0,1.4,94.465,-41.8,4.959,5228.1
|
||||
47,58,1,999,0,-0.1,93.2,-42,4.191,5195.8
|
||||
32,128,3,999,2,-1.1,94.199,-37.5,0.884,4963.6
|
||||
32,290,4,999,0,-1.1,94.199,-37.5,0.879,4963.6
|
||||
41,44,2,999,0,-0.1,93.2,-42,4.191,5195.8
|
||||
31,68,1,999,1,-0.1,93.2,-42,4.153,5195.8
|
||||
35,170,1,999,0,1.1,93.994,-36.4,4.855,5191
|
||||
25,301,1,999,0,1.4,93.918,-42.7,4.958,5228.1
|
||||
36,148,1,999,0,1.4,93.918,-42.7,4.968,5228.1
|
||||
36,97,2,999,0,1.1,93.994,-36.4,4.859,5191
|
||||
47,211,2,999,0,1.4,94.465,-41.8,4.958,5228.1
|
||||
29,553,2,999,0,-1.8,92.893,-46.2,1.313,5099.1
|
||||
27,698,2,999,0,1.4,93.918,-42.7,4.963,5228.1
|
||||
44,191,6,999,0,1.4,93.918,-42.7,4.957,5228.1
|
||||
46,59,4,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
45,38,2,999,0,1.4,93.444,-36.1,4.965,5228.1
|
||||
50,849,1,999,0,1.4,93.918,-42.7,4.961,5228.1
|
||||
55,326,6,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
39,222,1,12,2,-1.8,93.369,-34.8,0.639,5008.7
|
||||
29,626,3,999,0,1.4,93.444,-36.1,4.967,5228.1
|
||||
40,119,1,999,0,1.4,93.444,-36.1,4.965,5228.1
|
||||
44,388,7,999,0,1.1,93.994,-36.4,4.864,5191
|
||||
38,479,1,999,0,1.4,93.444,-36.1,4.965,5228.1
|
||||
36,446,1,999,0,1.1,93.994,-36.4,4.856,5191
|
||||
28,68,2,999,1,-1.8,92.893,-46.2,1.299,5099.1
|
||||
47,127,1,999,0,1.1,93.994,-36.4,4.86,5191
|
||||
34,109,1,999,0,1.4,93.444,-36.1,4.963,5228.1
|
||||
38,113,1,999,1,-1.8,92.843,-50,1.687,5099.1
|
||||
33,393,3,999,0,1.4,94.465,-41.8,4.865,5228.1
|
||||
51,151,2,999,0,-2.9,92.963,-40.8,1.268,5076.2
|
||||
30,256,1,999,0,1.4,94.465,-41.8,4.864,5228.1
|
||||
48,42,7,999,0,1.4,93.444,-36.1,4.962,5228.1
|
||||
28,525,1,999,0,1.4,94.465,-41.8,4.864,5228.1
|
||||
36,57,1,999,0,1.1,93.994,-36.4,4.859,5191
|
||||
46,499,1,999,0,-0.1,93.2,-42,4.12,5195.8
|
||||
31,84,1,999,1,-1.8,92.893,-46.2,1.334,5099.1
|
||||
20,137,3,999,3,-1.1,94.601,-49.5,0.977,4963.6
|
||||
35,31,1,999,0,-1.8,92.893,-46.2,1.344,5099.1
|
||||
38,430,1,999,0,1.4,94.465,-41.8,4.967,5228.1
|
||||
76,126,1,999,1,-1.7,94.027,-38.3,0.899,4991.6
|
||||
45,211,3,999,0,1.4,93.918,-42.7,4.961,5228.1
|
||||
32,340,1,999,0,-1.8,92.893,-46.2,1.313,5099.1
|
||||
39,412,2,999,0,-1.8,92.893,-46.2,1.327,5099.1
|
||||
32,132,1,999,0,-0.1,93.2,-42,4.592,5195.8
|
||||
29,79,2,999,0,-1.8,92.893,-46.2,1.299,5099.1
|
||||
44,341,2,999,0,1.4,93.444,-36.1,4.97,5228.1
|
||||
32,157,3,999,0,1.4,93.918,-42.7,4.957,5228.1
|
||||
56,252,1,999,2,-2.9,92.963,-40.8,1.26,5076.2
|
||||
33,263,1,999,0,-1.8,92.893,-46.2,1.327,5099.1
|
||||
39,215,3,999,0,1.4,93.444,-36.1,4.966,5228.1
|
||||
24,89,2,3,1,-3.4,92.379,-29.8,0.77,5017.5
|
||||
33,143,1,999,0,-1.8,92.893,-46.2,1.327,5099.1
|
||||
58,40,2,999,0,1.4,94.465,-41.8,4.866,5228.1
|
||||
29,10,27,999,0,1.4,94.465,-41.8,4.959,5228.1
|
||||
31,113,1,999,0,1.4,93.444,-36.1,4.964,5228.1
|
||||
27,191,5,999,0,1.1,93.994,-36.4,4.857,5191
|
||||
60,481,1,999,1,-1.1,94.199,-37.5,0.886,4963.6
|
||||
55,233,1,999,2,-3.4,92.431,-26.9,0.739,5017.5
|
||||
37,204,2,999,0,-1.8,92.893,-46.2,1.334,5099.1
|
||||
52,403,1,6,1,-1.8,93.749,-34.6,0.654,5008.7
|
||||
46,180,1,999,0,-1.8,93.075,-47.1,1.405,5099.1
|
||||
42,16,2,999,0,-1.8,92.893,-46.2,1.281,5099.1
|
||||
35,447,3,999,0,1.4,93.918,-42.7,4.96,5228.1
|
||||
49,81,1,999,0,-3.4,92.431,-26.9,0.754,5017.5
|
||||
29,361,3,999,0,1.4,94.465,-41.8,4.865,5228.1
|
||||
32,1091,2,5,1,-0.1,93.2,-42,4.191,5195.8
|
||||
37,395,1,999,1,-1.8,92.893,-46.2,1.313,5099.1
|
||||
39,432,4,999,0,1.4,94.465,-41.8,4.865,5228.1
|
||||
51,596,3,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
28,59,3,999,0,-1.8,92.893,-46.2,1.291,5099.1
|
||||
34,77,4,999,0,-1.8,92.893,-46.2,1.281,5099.1
|
||||
31,768,1,999,0,1.4,93.444,-36.1,4.966,5228.1
|
||||
31,96,5,999,0,1.4,93.444,-36.1,4.962,5228.1
|
||||
31,357,2,999,0,1.4,94.465,-41.8,4.864,5228.1
|
||||
38,459,2,999,0,1.1,93.994,-36.4,4.855,5191
|
||||
33,11,6,999,0,1.4,94.465,-41.8,4.959,5228.1
|
||||
32,264,12,999,0,1.4,93.444,-36.1,4.966,5228.1
|
||||
54,698,1,999,0,-1.8,93.075,-47.1,1.405,5099.1
|
||||
41,204,2,999,0,1.4,94.465,-41.8,4.961,5228.1
|
||||
59,93,7,999,0,1.4,93.444,-36.1,4.963,5228.1
|
||||
57,374,1,2,1,-1.8,93.075,-47.1,1.365,5099.1
|
||||
38,252,1,999,0,-0.1,93.2,-42,4.076,5195.8
|
||||
46,158,1,999,0,1.1,93.994,-36.4,4.857,5191
|
||||
37,95,1,999,0,-0.1,93.2,-42,4.153,5195.8
|
||||
47,835,3,999,0,1.4,94.465,-41.8,4.961,5228.1
|
||||
45,505,1,999,0,1.4,93.444,-36.1,4.964,5228.1
|
||||
36,16,1,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
40,300,5,999,0,1.4,93.918,-42.7,4.96,5228.1
|
||||
38,180,2,999,1,-0.1,93.2,-42,4.153,5195.8
|
||||
44,390,2,999,0,1.4,94.465,-41.8,4.961,5228.1
|
||||
25,274,2,999,0,1.1,93.994,-36.4,4.857,5191
|
||||
30,135,5,999,0,1.4,93.918,-42.7,4.961,5228.1
|
||||
43,257,2,999,0,-0.1,93.2,-42,4.153,5195.8
|
||||
58,268,3,999,0,1.1,93.994,-36.4,4.857,5191
|
||||
31,157,6,999,0,1.1,93.994,-36.4,4.856,5191
|
||||
53,477,2,999,0,1.4,93.918,-42.7,4.961,5228.1
|
||||
52,91,1,999,0,1.4,93.444,-36.1,4.968,5228.1
|
||||
37,76,1,999,1,-1.8,93.075,-47.1,1.405,5099.1
|
||||
58,103,1,999,0,1.4,93.918,-42.7,4.961,5228.1
|
||||
37,436,3,999,0,-1.8,92.893,-46.2,1.266,5099.1
|
||||
38,191,1,999,0,-1.8,92.893,-46.2,1.313,5099.1
|
||||
33,483,2,999,0,-1.8,93.075,-47.1,1.41,5099.1
|
||||
45,250,1,999,0,-1.8,92.893,-46.2,1.25,5099.1
|
||||
44,259,1,999,0,1.1,93.994,-36.4,4.858,5191
|
||||
38,389,2,999,0,1.4,93.918,-42.7,4.966,5228.1
|
||||
34,7,1,999,0,-1.7,94.055,-39.8,0.702,4991.6
|
||||
47,123,1,999,0,-2.9,92.469,-33.6,1.029,5076.2
|
||||
59,92,1,999,0,-2.9,92.469,-33.6,1.085,5076.2
|
||||
51,297,2,999,0,1.4,93.444,-36.1,4.964,5228.1
|
||||
31,406,2,999,0,1.4,94.465,-41.8,4.864,5228.1
|
||||
56,104,1,999,1,-0.1,93.2,-42,4.12,5195.8
|
||||
39,135,2,999,0,1.4,94.465,-41.8,4.961,5228.1
|
||||
41,854,1,999,0,-0.1,93.2,-42,4.153,5195.8
|
||||
41,147,1,999,0,-0.1,93.2,-42,4.076,5195.8
|
||||
53,203,4,999,0,1.1,93.994,-36.4,4.86,5191
|
||||
35,149,4,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
58,144,1,999,0,-1.8,92.893,-46.2,1.334,5099.1
|
||||
25,394,2,999,0,1.4,94.465,-41.8,4.959,5228.1
|
||||
35,523,1,999,1,-1.8,92.893,-46.2,1.344,5099.1
|
||||
28,73,1,999,0,1.4,93.918,-42.7,4.963,5228.1
|
||||
55,197,3,999,1,-1.8,92.893,-46.2,1.25,5099.1
|
||||
54,108,1,999,0,1.1,93.994,-36.4,4.855,5191
|
||||
45,80,2,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
32,114,1,999,1,-1.8,93.075,-47.1,1.41,5099.1
|
||||
50,122,3,999,0,1.4,93.444,-36.1,4.963,5228.1
|
||||
75,109,1,999,1,-1.8,93.075,-47.1,1.405,5099.1
|
||||
33,1161,4,999,0,1.1,93.994,-36.4,4.858,5191
|
||||
29,181,2,999,0,1.4,93.444,-36.1,4.967,5228.1
|
||||
45,239,1,999,0,-1.8,92.893,-46.2,1.327,5099.1
|
||||
38,360,1,999,0,-1.8,93.075,-47.1,1.392,5099.1
|
||||
24,314,1,999,0,1.1,93.994,-36.4,4.858,5191
|
||||
41,984,1,999,0,1.4,93.444,-36.1,4.963,5228.1
|
||||
58,663,1,999,0,-2.9,92.963,-40.8,1.262,5076.2
|
||||
34,157,2,999,0,1.1,93.994,-36.4,4.857,5191
|
||||
34,141,2,999,0,-1.1,94.767,-50.8,1.05,4963.6
|
||||
37,374,1,999,0,1.1,93.994,-36.4,4.856,5191
|
||||
53,706,1,999,0,1.4,94.465,-41.8,4.959,5228.1
|
||||
54,797,14,999,0,1.4,93.918,-42.7,4.961,5228.1
|
||||
32,250,1,999,0,-0.1,93.2,-42,4.153,5195.8
|
||||
42,311,6,999,0,1.4,93.918,-42.7,4.962,5228.1
|
||||
28,63,2,3,1,-2.9,92.201,-31.4,0.851,5076.2
|
||||
33,111,1,999,0,1.1,93.994,-36.4,4.864,5191
|
||||
41,148,2,999,0,-1.8,93.075,-47.1,1.405,5099.1
|
||||
31,49,1,999,0,-0.1,93.2,-42,4.076,5195.8
|
||||
40,171,4,999,0,1.1,93.994,-36.4,4.86,5191
|
||||
53,242,1,999,1,-3.4,92.649,-30.1,0.716,5017.5
|
||||
|
34
myapp/example/pipeline/tensorboard/demo.py
Normal file
34
myapp/example/pipeline/tensorboard/demo.py
Normal file
@ -0,0 +1,34 @@
|
||||
# pip install tensorflow -i https://mirrors.aliyun.com/pypi/simple
|
||||
|
||||
# tensorboard --logdir=fit_logs --port=6007 --host 0.0.0.0 --debug
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
mnist = tf.keras.datasets.mnist
|
||||
|
||||
(x_train, y_train), (x_test, y_test) = mnist.load_data()
|
||||
x_train, x_test = x_train / 255.0, x_test / 255.0
|
||||
|
||||
|
||||
def create_model():
|
||||
return tf.keras.models.Sequential([
|
||||
tf.keras.layers.Flatten(input_shape=(28, 28)),
|
||||
tf.keras.layers.Dense(512, activation='relu'),
|
||||
tf.keras.layers.Dropout(0.2),
|
||||
tf.keras.layers.Dense(10, activation='softmax')
|
||||
])
|
||||
|
||||
|
||||
model = create_model()
|
||||
model.compile(optimizer='adam',
|
||||
loss='sparse_categorical_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
|
||||
# log_dir为日志存放文件
|
||||
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./fit_logs", histogram_freq=1)
|
||||
|
||||
model.fit(x=x_train,
|
||||
y=y_train,
|
||||
epochs=5,
|
||||
validation_data=(x_test, y_test),
|
||||
callbacks=[tensorboard_callback])
|
||||
1691
myapp/example/pipeline/tf/distributed_training.ipynb
Normal file
1691
myapp/example/pipeline/tf/distributed_training.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
212
myapp/example/pipeline/tf/mnist_with_summaries.py
Normal file
212
myapp/example/pipeline/tf/mnist_with_summaries.py
Normal file
@ -0,0 +1,212 @@
|
||||
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the 'License');
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an 'AS IS' BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""A simple MNIST classifier which displays summaries in TensorBoard.
|
||||
This is an unimpressive MNIST model, but it is a good example of using
|
||||
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
|
||||
naming summary tags so that they are grouped meaningfully in TensorBoard.
|
||||
It demonstrates the functionality of every TensorBoard dashboard.
|
||||
"""
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
from tensorflow.examples.tutorials.mnist import input_data
|
||||
|
||||
FLAGS = None
|
||||
|
||||
|
||||
def train():
|
||||
# Import data
|
||||
mnist = input_data.read_data_sets(FLAGS.data_dir,
|
||||
fake_data=FLAGS.fake_data)
|
||||
|
||||
sess = tf.InteractiveSession()
|
||||
# Create a multilayer model.
|
||||
|
||||
# Input placeholders
|
||||
with tf.name_scope('input'):
|
||||
x = tf.placeholder(tf.float32, [None, 784], name='x-input')
|
||||
y_ = tf.placeholder(tf.int64, [None], name='y-input')
|
||||
|
||||
with tf.name_scope('input_reshape'):
|
||||
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
|
||||
tf.summary.image('input', image_shaped_input, 10)
|
||||
|
||||
# We can't initialize these variables to 0 - the network will get stuck.
|
||||
def weight_variable(shape):
|
||||
"""Create a weight variable with appropriate initialization."""
|
||||
initial = tf.truncated_normal(shape, stddev=0.1)
|
||||
return tf.Variable(initial)
|
||||
|
||||
def bias_variable(shape):
|
||||
"""Create a bias variable with appropriate initialization."""
|
||||
initial = tf.constant(0.1, shape=shape)
|
||||
return tf.Variable(initial)
|
||||
|
||||
def variable_summaries(var):
|
||||
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
|
||||
with tf.name_scope('summaries'):
|
||||
mean = tf.reduce_mean(var)
|
||||
tf.summary.scalar('mean', mean)
|
||||
with tf.name_scope('stddev'):
|
||||
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
|
||||
tf.summary.scalar('stddev', stddev)
|
||||
tf.summary.scalar('max', tf.reduce_max(var))
|
||||
tf.summary.scalar('min', tf.reduce_min(var))
|
||||
tf.summary.histogram('histogram', var)
|
||||
|
||||
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
|
||||
"""Reusable code for making a simple neural net layer.
|
||||
It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
|
||||
It also sets up name scoping so that the resultant graph is easy to read,
|
||||
and adds a number of summary ops.
|
||||
"""
|
||||
# Adding a name scope ensures logical grouping of the layers in the graph.
|
||||
with tf.name_scope(layer_name):
|
||||
# This Variable will hold the state of the weights for the layer
|
||||
with tf.name_scope('weights'):
|
||||
weights = weight_variable([input_dim, output_dim])
|
||||
variable_summaries(weights)
|
||||
with tf.name_scope('biases'):
|
||||
biases = bias_variable([output_dim])
|
||||
variable_summaries(biases)
|
||||
with tf.name_scope('Wx_plus_b'):
|
||||
preactivate = tf.matmul(input_tensor, weights) + biases
|
||||
tf.summary.histogram('pre_activations', preactivate)
|
||||
activations = act(preactivate, name='activation')
|
||||
tf.summary.histogram('activations', activations)
|
||||
return activations
|
||||
|
||||
hidden1 = nn_layer(x, 784, 500, 'layer1')
|
||||
|
||||
with tf.name_scope('dropout'):
|
||||
keep_prob = tf.placeholder(tf.float32)
|
||||
tf.summary.scalar('dropout_keep_probability', keep_prob)
|
||||
dropped = tf.nn.dropout(hidden1, keep_prob)
|
||||
|
||||
# Do not apply softmax activation yet, see below.
|
||||
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
|
||||
|
||||
with tf.name_scope('cross_entropy'):
|
||||
# The raw formulation of cross-entropy,
|
||||
#
|
||||
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
|
||||
# reduction_indices=[1]))
|
||||
#
|
||||
# can be numerically unstable.
|
||||
#
|
||||
# So here we use tf.losses.sparse_softmax_cross_entropy on the
|
||||
# raw logit outputs of the nn_layer above, and then average across
|
||||
# the batch.
|
||||
with tf.name_scope('total'):
|
||||
cross_entropy = tf.losses.sparse_softmax_cross_entropy(
|
||||
labels=y_, logits=y)
|
||||
tf.summary.scalar('cross_entropy', cross_entropy)
|
||||
|
||||
with tf.name_scope('train'):
|
||||
train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
|
||||
cross_entropy)
|
||||
|
||||
with tf.name_scope('accuracy'):
|
||||
with tf.name_scope('correct_prediction'):
|
||||
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
|
||||
with tf.name_scope('accuracy'):
|
||||
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
|
||||
tf.summary.scalar('accuracy', accuracy)
|
||||
|
||||
# Merge all the summaries and write them out to
|
||||
# /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
|
||||
merged = tf.summary.merge_all()
|
||||
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
|
||||
test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
|
||||
tf.global_variables_initializer().run()
|
||||
|
||||
# Train the model, and also write summaries.
|
||||
# Every 10th step, measure test-set accuracy, and write test summaries
|
||||
# All other steps, run train_step on training data, & add training summaries
|
||||
|
||||
def feed_dict(train): # pylint: disable=redefined-outer-name
|
||||
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
|
||||
if train or FLAGS.fake_data:
|
||||
xs, ys = mnist.train.next_batch(FLAGS.batch_size, fake_data=FLAGS.fake_data)
|
||||
k = FLAGS.dropout
|
||||
else:
|
||||
xs, ys = mnist.test.images, mnist.test.labels
|
||||
k = 1.0
|
||||
return {x: xs, y_: ys, keep_prob: k}
|
||||
|
||||
for i in range(FLAGS.max_steps):
|
||||
if i % 10 == 0: # Record summaries and test-set accuracy
|
||||
summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
|
||||
test_writer.add_summary(summary, i)
|
||||
print('Accuracy at step %s: %s' % (i, acc))
|
||||
else: # Record train set summaries, and train
|
||||
if i % 100 == 99: # Record execution stats
|
||||
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
|
||||
run_metadata = tf.RunMetadata()
|
||||
summary, _ = sess.run([merged, train_step],
|
||||
feed_dict=feed_dict(True),
|
||||
options=run_options,
|
||||
run_metadata=run_metadata)
|
||||
train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
|
||||
train_writer.add_summary(summary, i)
|
||||
print('Adding run metadata for', i)
|
||||
else: # Record a summary
|
||||
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
|
||||
train_writer.add_summary(summary, i)
|
||||
train_writer.close()
|
||||
test_writer.close()
|
||||
|
||||
|
||||
def main(_):
|
||||
if tf.gfile.Exists(FLAGS.log_dir):
|
||||
tf.gfile.DeleteRecursively(FLAGS.log_dir)
|
||||
tf.gfile.MakeDirs(FLAGS.log_dir)
|
||||
train()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
|
||||
default=False,
|
||||
help='If true, uses fake data for unit testing.')
|
||||
parser.add_argument('--max_steps', type=int, default=1000,
|
||||
help='Number of steps to run trainer.')
|
||||
parser.add_argument('--learning_rate', type=float, default=0.001,
|
||||
help='Initial learning rate')
|
||||
parser.add_argument('--batch_size', type=int, default=100,
|
||||
help='Training batch size')
|
||||
parser.add_argument('--dropout', type=float, default=0.9,
|
||||
help='Keep probability for training dropout.')
|
||||
parser.add_argument(
|
||||
'--data_dir',
|
||||
type=str,
|
||||
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
|
||||
'tensorflow/mnist/input_data'),
|
||||
help='Directory for storing input data')
|
||||
parser.add_argument(
|
||||
'--log_dir',
|
||||
type=str,
|
||||
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
|
||||
'tensorflow/mnist/logs/mnist_with_summaries'),
|
||||
help='Summaries log directory')
|
||||
FLAGS, unparsed = parser.parse_known_args()
|
||||
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
|
||||
44
myapp/example/pipeline/volcano/demo.py
Normal file
44
myapp/example/pipeline/volcano/demo.py
Normal file
@ -0,0 +1,44 @@
|
||||
import time, datetime, json, requests, io, os
|
||||
from multiprocessing import Pool
|
||||
from functools import partial
|
||||
import os, random, sys
|
||||
|
||||
WORLD_SIZE = int(os.getenv('VC_WORKER_NUM', '1')) # 总worker的数目
|
||||
RANK = int(os.getenv("VC_TASK_INDEX", '0')) # 当前是第几个worker 从0开始
|
||||
|
||||
print(WORLD_SIZE, RANK)
|
||||
|
||||
|
||||
# 子进程要执行的代码
|
||||
def task(key):
|
||||
print(datetime.datetime.now(),'worker:', RANK, ', task:', key, flush=True)
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# if os.path.exists('./success%s' % RANK):
|
||||
# os.remove('./success%s' % RANK)
|
||||
|
||||
input = range(300) # 所有要处理的数据
|
||||
local_task = [] # 当前worker需要处理的任务
|
||||
for index in input:
|
||||
if index % WORLD_SIZE == RANK:
|
||||
local_task.append(index) # 要处理的数据均匀分配到每个worker
|
||||
|
||||
# 每个worker内部还可以用多进程，线程池之类的并发操作。
|
||||
pool = Pool(10) # 开辟包含指定数目线程的线程池
|
||||
pool.map(partial(task), local_task) # 当前worker，只处理分配给当前worker的任务
|
||||
pool.close()
|
||||
pool.join()
|
||||
|
||||
# 添加文件标识，当前worker结束
|
||||
# open('./success%s' % RANK, mode='w').close()
|
||||
# # rank0做聚合操作
|
||||
# while (RANK == 0):
|
||||
# success = [x for x in range(WORLD_SIZE) if os.path.exists('./success%s' % x)]
|
||||
# if len(success) != WORLD_SIZE:
|
||||
# time.sleep(5)
|
||||
# else:
|
||||
# # 所有worker全部结束，worker0开始聚合操作
|
||||
# print('begin reduce')
|
||||
# break
|
||||
11
myapp/example/pipeline/yolo/data-process.py
Executable file
11
myapp/example/pipeline/yolo/data-process.py
Executable file
@ -0,0 +1,11 @@
|
||||
import json
|
||||
import os
|
||||
base_dir = '/mnt/admin/coco'
|
||||
label_files = os.listdir(base_dir)
|
||||
|
||||
for file in label_files:
|
||||
file_path = os.path.join(base_dir,file)
|
||||
label = json.load(open(file_path))
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user