mirror of
https://github.com/tencentmusic/cube-studio.git
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初始化脚本目录变更
This commit is contained in:
data-infra
parent
1047044b21
commit
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[
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{
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"job_type": "Job",
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"project": "public",
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"name": "test",
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"namespace": "automl",
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"describe": "nni功能测试",
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"parallel_trial_count": 3,
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"max_trial_count": 12,
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"objective_type": "maximize",
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"objective_goal": 0.99,
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"objective_metric_name": "accuracy",
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"algorithm_name": "Random",
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"parameters": {
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"batch_size": {"_type":"choice", "_value": [16, 32, 64, 128]},
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"momentum":{"_type":"uniform","_value":[0, 1]}
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},
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"job_json": {
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},
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"job_worker_image": "ccr.ccs.tencentyun.com/cube-studio/nni:20230601",
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"working_dir": "/mnt/admin/pipeline/example/nni/",
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"job_worker_command": "python demo.py",
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"resource_memory": "1G",
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"resource_cpu": "1",
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"resource_gpu": "0"
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}
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]
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name,label,describe,source_type,source,industry,field,usage,research,storage_class,file_type,status,years,url,path,download_url,storage_size,entries_num,duration,price,status,icon,owner
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MNIST,手写数字数据集,"包含一组60,000个示例的训练集和一组10,000个示例的测试集。数字已经过尺寸标准化，以适合 20x20 像素框，同时保持其纵横比，并在固定尺寸的图像中居中",开源,github,图像处理,视觉,传统机器学习和深度学习入门,svm、分类,压缩,gz,正常,,http://yann.lecun.com/exdb/mnist/,,"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
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http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
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http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
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http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz",11M," 60,000 个示例的训练集和 10,000 个示例的测试集",,0,正常,/static/assets/images/dataset/mnist.png,admin
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Fashion-MNIST,时尚产品数据,"包含60,000个训练图像和10,000个测试图像。类似MNIST的时尚产品数据库。",开源,github,图像处理,视觉,传统机器学习和深度学习入门,图像分类,压缩,gz,正常,,https://github.com/zalandoresearch/fashion-mnist,,"http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
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http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
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http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
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http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz",5M,"60,000个训练图像和10,000个测试图像",,0,正常,/static/assets/images/dataset/fashion-mnist.png,admin
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{
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"tf-mnist": {
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"project_name": "public",
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"service_name": "mnist-202208011",
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"model_name": "mnist",
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"service_describe": "tf 图像分类",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/tfserving:2.3.4",
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"model_version": "v2022.08.01.1",
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"model_path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/tf-mnist.tar.gz",
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"service_type": "tfserving",
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"env": "TF_CPP_VMODULE=http_server=1\nTZ=Asia/Shanghai",
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"ports": "8501",
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"metrics": "8501:/metrics",
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"command": "wget https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/tf-mnist.tar.gz && tar -zxvf tf-mnist.tar.gz && mkdir -p /models/mnist/202207281/ && cp -r tf-mnist/* /models/mnist/202207281/ && /usr/bin/tf_serving_entrypoint.sh --model_config_file=/config/models.config --monitoring_config_file=/config/monitoring.config --platform_config_file=/config/platform.config",
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"health": "8501:/v1/models/mnist/versions/202207281/metadata",
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"volume_mount": "kubeflow-user-workspace(pvc):/mnt,kubeflow-archives(pvc):/archives",
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"resource_memory": "2G",
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"resource_cpu": "2",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/tree/master/images/serving/tfserving/example"
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}
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},
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"pytorch-resnet50": {
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"project_name": "public",
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"service_name": "resnet50-202208012",
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"model_name": "resnet50",
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"service_describe": "pytorch 图像分类",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/torchserve:0.5.3-cpu",
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"model_version": "v2022.08.01.2",
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"model_path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50.mar",
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"service_type": "torch-server",
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"env": "",
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"ports": "8080,8081",
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"metrics": "8082:/metrics",
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"workdir": "/models",
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"command": "wget https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50.mar && mkdir -p /models && cp /config/* /models/ && cp resnet50.mar /models/ && torchserve --start --model-store /models --models resnet50=resnet50.mar --foreground --ts-config=/config/config.properties",
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"health": "8080:/ping",
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"volume_mount": "kubeflow-user-workspace(pvc):/mnt,kubeflow-archives(pvc):/archives",
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"resource_memory": "5G",
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"resource_cpu": "5",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/tree/master/images/serving/torchserver/example"
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}
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},
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"torchscript-resnet50": {
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"project_name": "public",
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"service_name": "resnet50-202208013",
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"model_name": "resnet50",
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"service_describe": "torchscript 图像分类",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/tritonserver:22.07-py3",
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"model_version": "v2022.08.01.3",
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"model_path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50-torchscript.pt",
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"service_type": "triton-server",
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"env": "",
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"ports": "8000,8002",
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"metrics": "",
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"workdir": "",
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"command": "wget https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50-torchscript.pt && mkdir -p /models/resnet50/202208013/ && cp /config/* /models/resnet50/ && cp -r resnet50-torchscript.pt /models/resnet50/202208013/model.pt && tritonserver --model-repository=/models --strict-model-config=true --log-verbose=1",
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"health": "8000:/v2/health/ready",
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"volume_mount": "kubeflow-user-workspace(pvc):/mnt,kubeflow-archives(pvc):/archives",
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"resource_memory": "5G",
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"resource_cpu": "5",
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"inference_config": "\n---config.pbtxt\n\nname: \"resnet50\"\nplatform: \"pytorch_libtorch\"\nmax_batch_size: 0\ninput \n[\n {\n name: \"INPUT__0\"\n data_type: TYPE_FP32\n format: FORMAT_NCHW\n dims: [ 3, 224, 224 ]\n reshape: {\n shape: [ 1, 3, 224, 224 ]\n }\n }\n]\n \noutput \n[\n {\n name: \"OUTPUT__0\"\n data_type: TYPE_FP32\n dims: [ 1000 ]\n reshape: {\n shape: [ 1, 1000 ]\n }\n }\n]\n \n\nparameters: { key: \"DISABLE_OPTIMIZED_EXECUTION\" value: { string_value:\"true\" } }\nparameters: { key: \"INFERENCE_MODE\" value: { string_value: \"false\" } }\n",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/tree/master/images/serving/triton-server/example"
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}
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},
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"onnx-resnet50": {
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"project_name": "public",
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"service_name": "resnet50-202208014",
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"model_name": "resnet50",
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"service_describe": "onnx 图像分类",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/tritonserver:22.07-py3",
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"model_version": "v2022.08.01.4",
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"model_path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50.onnx",
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"service_type": "triton-server",
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"env": "",
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"ports": "8000,8002",
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"metrics": "",
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"workdir": "",
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"command": "wget https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50.onnx && mkdir -p /models/resnet50/202208014/ && cp /config/* /models/resnet50/ && cp -r resnet50.onnx /models/resnet50/202208014/model.onnx && tritonserver --model-repository=/models --strict-model-config=true --log-verbose=1",
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"health": "8000:/v2/health/ready",
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"volume_mount": "kubeflow-user-workspace(pvc):/mnt,kubeflow-archives(pvc):/archives",
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"resource_memory": "5G",
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"resource_cpu": "5",
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"inference_config": "---config.pbtxt\n\nname: \"resnet50\"\nplatform: \"onnxruntime_onnx\"\nbackend: \"onnxruntime\"\nmax_batch_size : 0\n\ninput [\n {\n name: \"input_name\"\n data_type: TYPE_FP32\n format: FORMAT_NCHW\n dims: [ 3, 224, 224 ]\n reshape { shape: [ 1, 3, 224, 224 ] }\n }\n]\noutput [\n {\n name: \"output_name\"\n data_type: TYPE_FP32\n dims: [ 1000 ]\n reshape { shape: [ 1, 1000 ] }\n }\n]\n\nparameters { key: \"intra_op_thread_count\" value: { string_value: \"10\" } }\nparameters { key: \"execution_mode\" value: { string_value: \"1\" } }\nparameters { key: \"inter_op_thread_count\" value: { string_value: \"10\" } }\n",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/tree/master/images/serving/triton-server/example"
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}
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}
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}
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{
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"mysql-ui": {
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"project_name": "public",
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"service_name": "mysql-ui",
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"service_describe": "可视化编辑mysql数据库",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/phpmyadmin",
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"command": "",
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"env": "PMA_HOST=mysql-service.infra\nPMA_PORT=3306\nPMA_USER=root\nPMA_PASSWORD=admin",
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"ports": "80",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/blob/master/docs/example/service.md"
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}
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},
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"redis-ui": {
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"project_name": "public",
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"service_name": "redis-ui",
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"service_describe": "可视化编辑redis数据库",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/patrikx3:latest",
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"command": "",
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"env": "REDIS_NAME=default\nREDIS_HOST=redis-master.infra\nREDIS_PORT=6379\nREDIS_PASSWORD=admin",
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"ports": "7843",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/blob/master/docs/example/service.md"
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}
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},
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"mongo-express": {
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"project_name": "public",
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"service_name": "mongo-express",
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"service_describe": "可视化编辑mongo数据库",
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"image_name": "mongo-express:0.54.0",
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"command": "",
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"env": "ME_CONFIG_MONGODB_SERVER=xx.xx.xx.xx\nME_CONFIG_MONGODB_PORT=xx\nME_CONFIG_MONGODB_ENABLE_ADMIN=true\nME_CONFIG_MONGODB_ADMINUSERNAME=xx\nME_CONFIG_MONGODB_ADMINPASSWORD=xx\nME_CONFIG_MONGODB_AUTH_DATABASE=xx\nME_CONFIG_MONGODB_AUTH_USERNAME=xx\nME_CONFIG_MONGODB_AUTH_PASSWORD=xx\nVCAP_APP_HOST=0.0.0.0\nVCAP_APP_PORT=8081\nME_CONFIG_OPTIONS_EDITORTHEME=ambiance",
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"ports": "8081",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/blob/master/docs/example/service.md"
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}
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},
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"neo4j": {
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"project_name": "public",
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"service_name": "neo4j",
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"service_describe": "可视化编辑图数据库neo4j",
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"image_name": "ccr.ccs.tencentyun.com/cube-studio/neo4j:4.4",
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"command": "",
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"env": "NEO4J_AUTH=neo4j/admin",
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"ports": "7474,7687",
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"volume_mount": "kubeflow-user-workspace(pvc):/mnt,/data/k8s/kubeflow/pipeline/workspace/admin/neo4j(hostpath):/var/lib/neo4j/data",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/blob/master/docs/example/service.md"
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}
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},
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"jaeger": {
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"project_name": "public",
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"service_name": "jaeger",
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"service_describe": "jaeger链路追踪",
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"image_name": "jaegertracing/all-in-one:1.29",
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"command": "",
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"env": "",
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"ports": "16686,5775",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/blob/master/docs/example/service.md"
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}
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},
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"postgresql-ui": {
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"project_name": "public",
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"service_name": "postgresql-ui",
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"service_describe": "可视化编辑postgresql数据库",
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"image_name": "dpage/pgadmin4",
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"command": "",
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"env": "PGADMIN_DEFAULT_EMAIL=admin@tencent.com\nPGADMIN_DEFAULT_PASSWORD=root",
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"ports": "80",
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"expand": {
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"help_url": "https://github.com/tencentmusic/cube-studio/blob/master/docs/example/service.md"
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}
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}
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}
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{
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"tf-mnist": {
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"project_name": "public",
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"name": "mnist",
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"version": "v2022.08.01.1",
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"describe": "tf mnist 图像分类 tfserving推理",
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"path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/tf-mnist.tar.gz",
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"framework": "tf",
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"api_type": "tfserving"
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},
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"pytorch-resnet50": {
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"project_name": "public",
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"name": "resnet50",
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"version": "v2022.08.01.2",
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"describe": "pytorch resnet50 图像分类 torch-server推理",
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"path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50.mar",
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"framework": "pytorch",
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"api_type": "torch-server"
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},
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"torchscript-resnet50": {
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"project_name": "public",
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"name": "resnet50",
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"version": "v2022.08.01.3",
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"describe": "torchscript resnet50 图像分类 triton推理",
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"path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50-torchscript.pt",
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"framework": "pytorch",
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"api_type": "triton-server"
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},
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"onnx-resnet50": {
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"project_name": "public",
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"name": "resnet50",
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"version": "v2022.08.01.4",
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"describe": "onnx resnet50 图像分类 triton推理",
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"path": "https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/inference/resnet50.onnx",
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"framework": "onnx",
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"api_type": "triton-server"
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}
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}
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"""Defines the templating context for SQL Lab"""
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from datetime import datetime, timedelta
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import inspect
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import json
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import random
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import time
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from typing import Any, List, Optional, Tuple
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import uuid
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from dateutil.relativedelta import relativedelta
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from flask import g, request
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from jinja2.sandbox import SandboxedEnvironment
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from myapp import app
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# template context
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conf = app.config
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BASE_CONTEXT = {
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"datetime": datetime,
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"random": random,
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"relativedelta": relativedelta,
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"time": time,
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"timedelta": timedelta,
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"uuid": uuid,
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}
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BASE_CONTEXT.update(conf.get("JINJA_CONTEXT_ADDONS", {}))
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def url_param(param: str, default: Optional[str] = None) -> Optional[Any]:
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"""Read a url or post parameter and use it in your SQL Lab query
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When in SQL Lab, it's possible to add arbitrary URL "query string"
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parameters, and use those in your SQL code. For instance you can
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alter your url and add `?foo=bar`, as in
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`{domain}/myapp/sqllab?foo=bar`. Then if your query is something like
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SELECT * FROM foo = '{{ url_param('foo') }}', it will be parsed at
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runtime and replaced by the value in the URL.
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||||
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As you create a visualization form this SQL Lab query, you can pass
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||||
parameters in the explore view as well as from the dashboard, and
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it should carry through to your queries.
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:param param: the parameter to lookup
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:param default: the value to return in the absence of the parameter
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"""
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if request.args.get(param):
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return request.args.get(param, default)
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# Supporting POST as well as get
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form_data = request.form.get("form_data")
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if isinstance(form_data, str):
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form_data = json.loads(form_data)
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url_params = form_data.get("url_params") or {}
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return url_params.get(param, default)
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return default
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def current_user_id() -> Optional[int]:
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"""The id of the user who is currently logged in"""
|
||||
if hasattr(g, "user") and g.user:
|
||||
return g.user.id
|
||||
return None
|
||||
|
||||
|
||||
def current_username() -> Optional[str]:
|
||||
"""The username of the user who is currently logged in"""
|
||||
if g.user:
|
||||
return g.user.username
|
||||
return None
|
||||
|
||||
|
||||
def filter_values(column: str, default: Optional[str] = None) -> List[str]:
|
||||
""" Gets a values for a particular filter as a list
|
||||
|
||||
This is useful if:
|
||||
- you want to use a filter box to filter a query where the name of filter box
|
||||
column doesn't match the one in the select statement
|
||||
- you want to have the ability for filter inside the main query for speed
|
||||
purposes
|
||||
|
||||
This searches for "filters" and "extra_filters" in ``form_data`` for a match
|
||||
|
||||
Usage example::
|
||||
|
||||
SELECT action, count(*) as times
|
||||
FROM logs
|
||||
WHERE action in ( {{ "'" + "','".join(filter_values('action_type')) + "'" }} )
|
||||
GROUP BY action
|
||||
|
||||
:param column: column/filter name to lookup
|
||||
:param default: default value to return if there's no matching columns
|
||||
:return: returns a list of filter values
|
||||
"""
|
||||
form_data = json.loads(request.form.get("form_data", "{}"))
|
||||
return_val = []
|
||||
for filter_type in ["filters", "extra_filters"]:
|
||||
if filter_type not in form_data:
|
||||
continue
|
||||
|
||||
for f in form_data[filter_type]:
|
||||
if f["col"] == column:
|
||||
if isinstance(f["val"], list):
|
||||
for v in f["val"]:
|
||||
return_val.append(v)
|
||||
else:
|
||||
return_val.append(f["val"])
|
||||
|
||||
if return_val:
|
||||
return return_val
|
||||
|
||||
if default:
|
||||
return [default]
|
||||
else:
|
||||
return []
|
||||
|
||||
|
||||
class CacheKeyWrapper:
|
||||
""" Dummy class that exposes a method used to store additional values used in
|
||||
calculation of query object cache keys"""
|
||||
|
||||
def __init__(self, extra_cache_keys: Optional[List[Any]] = None):
|
||||
self.extra_cache_keys = extra_cache_keys
|
||||
|
||||
def cache_key_wrapper(self, key: Any) -> Any:
|
||||
""" Adds values to a list that is added to the query object used for calculating
|
||||
a cache key.
|
||||
|
||||
This is needed if the following applies:
|
||||
- Caching is enabled
|
||||
- The query is dynamically generated using a jinja template
|
||||
- A username or similar is used as a filter in the query
|
||||
|
||||
Example when using a SQL query as a data source ::
|
||||
|
||||
SELECT action, count(*) as times
|
||||
FROM logs
|
||||
WHERE logged_in_user = '{{ cache_key_wrapper(current_username()) }}'
|
||||
GROUP BY action
|
||||
|
||||
This will ensure that the query results that were cached by `user_1` will
|
||||
**not** be seen by `user_2`, as the `cache_key` for the query will be
|
||||
different. ``cache_key_wrapper`` can be used similarly for regular table data
|
||||
sources by adding a `Custom SQL` filter.
|
||||
|
||||
:param key: Any value that should be considered when calculating the cache key
|
||||
:return: the original value ``key`` passed to the function
|
||||
"""
|
||||
if self.extra_cache_keys is not None:
|
||||
self.extra_cache_keys.append(key)
|
||||
return key
|
||||
|
||||
|
||||
class BaseTemplateProcessor:
|
||||
"""Base class for database-specific jinja context
|
||||
|
||||
There's this bit of magic in ``process_template`` that instantiates only
|
||||
the database context for the active database as a ``models.Database``
|
||||
object binds it to the context object, so that object methods
|
||||
have access to
|
||||
that context. This way, {{ hive.latest_partition('mytable') }} just
|
||||
knows about the database it is operating in.
|
||||
|
||||
This means that object methods are only available for the active database
|
||||
and are given access to the ``models.Database`` object and schema
|
||||
name. For globally available methods use ``@classmethod``.
|
||||
"""
|
||||
|
||||
engine: Optional[str] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
database=None,
|
||||
query=None,
|
||||
table=None,
|
||||
extra_cache_keys: Optional[List[Any]] = None,
|
||||
**kwargs
|
||||
):
|
||||
self.database = database
|
||||
self.query = query
|
||||
self.schema = None
|
||||
if query and query.schema:
|
||||
self.schema = query.schema
|
||||
elif table:
|
||||
self.schema = table.schema
|
||||
self.context = {
|
||||
"url_param": url_param,
|
||||
"current_user_id": current_user_id,
|
||||
"current_username": current_username,
|
||||
"cache_key_wrapper": CacheKeyWrapper(extra_cache_keys).cache_key_wrapper,
|
||||
"filter_values": filter_values,
|
||||
"form_data": {},
|
||||
}
|
||||
self.context.update(kwargs)
|
||||
self.context.update(BASE_CONTEXT)
|
||||
if self.engine:
|
||||
self.context[self.engine] = self
|
||||
self.env = SandboxedEnvironment()
|
||||
|
||||
def process_template(self, sql: str, **kwargs) -> str:
|
||||
"""Processes a sql template
|
||||
|
||||
>>> sql = "SELECT '{{ datetime(2017, 1, 1).isoformat() }}'"
|
||||
>>> process_template(sql)
|
||||
"SELECT '2017-01-01T00:00:00'"
|
||||
"""
|
||||
template = self.env.from_string(sql)
|
||||
kwargs.update(self.context)
|
||||
return template.render(kwargs)
|
||||
|
||||
|
||||
|
||||
template_processors = {}
|
||||
keys = tuple(globals().keys())
|
||||
for k in keys:
|
||||
o = globals()[k]
|
||||
if o and inspect.isclass(o) and issubclass(o, BaseTemplateProcessor):
|
||||
template_processors[o.engine] = o
|
||||
|
||||
|
||||
def get_template_processor(database, table=None, query=None, **kwargs):
|
||||
TP = template_processors.get(database.backend, BaseTemplateProcessor)
|
||||
return TP(database=database, table=table, query=query, **kwargs)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user