diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch.md b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch.md new file mode 100644 index 000000000..3109ada24 --- /dev/null +++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch.md @@ -0,0 +1,159 @@ +# KFServing PyTorch models + +## Building a model and running inference on it. + +You can run inferencing using pytorchserver, a part of Kubeflow KFServing GitHub reposotory. +See [KFServing PyTorch demo](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pytorch) for more information if needed. + +You need to have the `pytorchserver` installed. You may need to install the prerequisites manually, specifying +versions and hardware nuances(CUDA version, etc.) + +In simple case: + + $ pip install torch torchvision + +Clone KFServing repository and install the pre-requisites. See KFServing's +[python/pytorchserver](https://github.com/kubeflow/kfserving/tree/master/python/pytorchserver) +if you have any issues. + + $ git clone https://github.com/kubeflow/kfserving.git + $ cd kfserving/python/pytorchserver + $ pip install -e . + +Verify that it works: + + /kfserving/python/pytorchserver$ python3 -m pytorchserver -h + usage: __main__.py [-h] [--http_port HTTP_PORT] [--grpc_port GRPC_PORT] + [--max_buffer_size MAX_BUFFER_SIZE] [--workers WORKERS] + --model_dir MODEL_DIR [--model_name MODEL_NAME] + [--model_class_name MODEL_CLASS_NAME] + + optional arguments: + -h, --help show this help message and exit + --http_port HTTP_PORT + The HTTP Port listened to by the model server. + --grpc_port GRPC_PORT + The GRPC Port listened to by the model server. + --max_buffer_size MAX_BUFFER_SIZE + The max buffer size for tornado. + --workers WORKERS The number of works to fork + --model_dir MODEL_DIR + A URI pointer to the model directory + --model_name MODEL_NAME + The name that the model is served under. + --model_class_name MODEL_CLASS_NAME + The class name for the model. + + +You can create a model: + + $ python3 pytorch_cifar10.py + Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz + 100.0%Extracting ./data/cifar-10-python.tar.gz to ./data + Files already downloaded and verified + [1, 2000] loss: 2.170 + [1, 4000] loss: 1.893 + [1, 6000] loss: 1.695 + [1, 8000] loss: 1.594 + [1, 10000] loss: 1.532 + [1, 12000] loss: 1.456 + [2, 2000] loss: 1.397 + [2, 4000] loss: 1.393 + [2, 6000] loss: 1.367 + [2, 8000] loss: 1.342 + [2, 10000] loss: 1.320 + [2, 12000] loss: 1.322 + Finished Training + + +And run the pytorchserver: + + $ python3 -m pytorchserver --model_dir `pwd` --model_name pytorchmodel --model_class_name Net + [I 201008 17:15:32 storage:35] Copying contents of /home/azureuser/kfserving/docs/samples/pytorch to local + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/model.pt to pytorchmodel/model.pt + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/pytorch.yaml to pytorchmodel/pytorch.yaml + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/pytorchmodel to pytorchmodel/pytorchmodel + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/README.md to pytorchmodel/README.md + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/data to pytorchmodel/data + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/input.json to pytorchmodel/input.json + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/pytorch_gpu.yaml to pytorchmodel/pytorch_gpu.yaml + [I 201008 17:15:32 storage:205] Linking: /home/azureuser/kfserving/docs/samples/pytorch/cifar10.py to pytorchmodel/cifar10.py + [I 201008 17:15:34 kfserver:88] Registering model: pytorchmodel + [I 201008 17:15:34 kfserver:77] Listening on port 8080 + [I 201008 17:15:34 kfserver:79] Will fork 0 workers + [I 201008 17:15:34 process:126] Starting 6 processes + [E 201008 17:18:28 web:2250] 200 POST /v1/models/pytorchmodel:predict (127.0.0.1) 21.34ms + +In a separate terminal, you can run the client script, it will make the request: + + $ python3 pytorch_pytorchserver_client.py + Files already downloaded and verified + + ... + +## Deploying model + +We have a .json with `inferenceservice` defined: + + $ kubectl create -f pytorch_cifar10.yaml -n kfserving-test + inferenceservice.serving.kubeflow.org/pytorch-cifar10 created + +Wait until the pods are running and the service is 'ready' and has URL: + + $ kubectl get po -n kfserving-test + NAME READY STATUS RESTARTS AGE + pytorch-cifar10-predictor-default-x4597-deployment-6dd9d4bfnmqs 2/2 Running 0 119s + + $ k get inferenceservices -n kfserving-test + NAME URL READY DEFAULT TRAFFIC CANARY TRAFFIC AGE + pytorch-cifar10 http://pytorch-cifar10.kfserving-test.example.com/v1/models/pytorch-cifar10 True 100 3m16s + +Define the parameters you will be using in your requests: + + $ export MODEL_NAME=pytorch-cifar10 + $ export INPUT_PATH=@./pytorch_input.json + $ export SERVICE_HOSTNAME=$(kubectl get inferenceservice pytorch-cifar10 -n kfserving-test -o jsonpath='{.status.url}' | cut -d "/" -f 3) + +Depending on your environment, if you run on KFServing that is part of Kubeflow instalation(this is what we do thuought this lab): + + $ export INGRESS_HOST=$(kubectl -n istio-system get service kfserving-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}') + $ export INGRESS_PORT=$(kubectl -n istio-system get service kfserving-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}') + +Or for more generic case: + + $ export INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}') + $ export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}') + +The `curl` call: + + $ curl -v -H "Host: ${SERVICE_HOSTNAME}" -d $INPUT_PATH http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict + * Trying 12.34.56.78... + * Connected to 12.34.56.78 (12.34.56.78) port 80 (#0) + > POST /v1/models/pytorch-cifar10:predict HTTP/1.1 + > Host: pytorch-cifar10.kfserving-test.example.com + > User-Agent: curl/7.47.0 + > Accept: */* + > Content-Length: 110681 + > Content-Type: application/x-www-form-urlencoded + > Expect: 100-continue + > + < HTTP/1.1 100 Continue + * We are completely uploaded and fine + < HTTP/1.1 200 OK + < content-length: 225 + < content-type: application/json; charset=UTF-8 + < date: Tue, 06 Oct 2020 21:43:45 GMT + < server: istio-envoy + < x-envoy-upstream-service-time: 14 + < + * Connection #0 to host 12.34.56.78 left intact + {"predictions": [[-1.6099601984024048, -2.6461071968078613, 0.3284444212913513, 2.4825074672698975, 0.4352457523345947, 2.3108041286468506, 1.0005676746368408, -0.42327627539634705, -0.5100944638252258, -1.7978390455245972]]} + +For troubleshooting, see [Kubeflow website](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pytorch) + + +## Links + +- https://github.com/kubeflow/kfserving/tree/master/docs/samples/pytorch + +[Back](Readme.md) diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10.py b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10.py new file mode 100644 index 000000000..c8c761ea7 --- /dev/null +++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10.py @@ -0,0 +1,84 @@ +# +# Originally from +# https://github.com/kubeflow/kfserving/tree/master/docs/samples +# +# See https://github.com/kubeflow/kfserving +# + +import torch +import torchvision +import torchvision.transforms as transforms +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 5 * 5) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + +if __name__ == "__main__": + + transform = transforms.Compose( + [transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + + trainset = torchvision.datasets.CIFAR10(root='./data', train=True, + download=True, transform=transform) + trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, + shuffle=True, num_workers=2) + + testset = torchvision.datasets.CIFAR10(root='./data', train=False, + download=True, transform=transform) + testloader = torch.utils.data.DataLoader(testset, batch_size=4, + shuffle=False, num_workers=2) + + classes = ('plane', 'car', 'bird', 'cat', + 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') + + net = Net() + + criterion = nn.CrossEntropyLoss() + optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) + + for epoch in range(2): # loop over the dataset multiple times + + running_loss = 0.0 + for i, data in enumerate(trainloader, 0): + # get the inputs; data is a list of [inputs, labels] + inputs, labels = data + + # zero the parameter gradients + optimizer.zero_grad() + + # forward + backward + optimize + outputs = net(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + + # print statistics + running_loss += loss.item() + if i % 2000 == 1999: # print every 2000 mini-batches + print('[%d, %5d] loss: %.3f' % + (epoch + 1, i + 1, running_loss / 2000)) + running_loss = 0.0 + + print('Finished Training') + + # Save model + torch.save(net.state_dict(), "model.pt") diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10.yaml b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10.yaml new file mode 100644 index 000000000..3f2db2995 --- /dev/null +++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10.yaml @@ -0,0 +1,28 @@ +# +# originally from https://github.com/kubeflow/kfserving/tree/master/docs/samples +# see the repository for model changes. +# + +# +# This is how it works: +# +# $ kubectl apply -f pytorch_cifar10.yaml -n kfserving-test +# inferenceservice.serving.kubeflow.org/pytorch-cifar10 configured +# +# $ kubectl get inferenceservice -n kfserving-test +# NAME URL READY DEFAULT TRAFFIC CANARY TRAFFIC AGE +# pytorch-cifar10 http://flowers-sample.kfserving-test.example.com/v1/models/pytorch-cifar10 True 100 2m23s +# + +apiVersion: "serving.kubeflow.org/v1alpha2" +kind: "InferenceService" +metadata: + name: "pytorch-cifar10" +spec: + default: + parallelism: 1 + predictor: + pytorch: + storageUri: "gs://kfserving-samples/models/pytorch/cifar10/" + modelClassName: "Net" + diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10_gpu.yaml b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10_gpu.yaml new file mode 100644 index 000000000..f0ee8a372 --- /dev/null +++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_cifar10_gpu.yaml @@ -0,0 +1,36 @@ +# +# originally from https://github.com/kubeflow/kfserving/tree/master/docs/samples +# see the repository for model changes. +# + +# +# This is how it works: +# +# $ kubectl apply -f pytorch_cifar10_gpu.yaml -n kfserving-test +# inferenceservice.serving.kubeflow.org/pytorch-cifar10-gpu configured +# +# $ kubectl get inferenceservice -n kfserving-test +# NAME URL READY DEFAULT TRAFFIC CANARY TRAFFIC AGE +# pytorch-cifar10-gpu http://flowers-sample.kfserving-test.example.com/v1/models/pytorch-cifar10-gpu True 100 2m23s +# + +apiVersion: "serving.kubeflow.org/v1alpha2" +kind: "InferenceService" +metadata: + name: "pytorch-cifar10-gpu" +spec: + default: + parallelism: 1 + predictor: + pytorch: + storageUri: "gs://kfserving-samples/models/pytorch/cifar10/" + modelClassName: "Net" + resources: + limits: + cpu: 100m + memory: 1Gi + nvidia.com/gpu: "1" + requests: + cpu: 100m + memory: 1Gi + nvidia.com/gpu: "1" diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_input.json b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_input.json new file mode 100644 index 000000000..ac9f2f139 --- /dev/null +++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_input.json @@ -0,0 +1,3277 @@ +{ + "instances":[ + [ + [ + [ + 0.23921573162078857, + 0.24705886840820312, + 0.29411768913269043, + 0.301960825920105, + 0.2549020051956177, + 0.22352945804595947, + 0.2705882787704468, + 0.24705886840820312, + 0.23921573162078857, + 0.24705886840820312, + 0.2627451419830322, + 0.2549020051956177, + 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transforms.Compose([transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) +testset = torchvision.datasets.CIFAR10(root='./data', train=False, + download=True, transform=transform) +testloader = torch.utils.data.DataLoader(testset, batch_size=4, + shuffle=False, num_workers=2) +dataiter = iter(testloader) +images, labels = dataiter.next() +formData = { + 'instances': images[0:1].tolist() + } +res = requests.post('http://localhost:8080/v1/models/pytorchmodel:predict', json=formData) +print(res) +print(res.text) diff --git a/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_runme.sh b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_runme.sh new file mode 100644 index 000000000..b73d541d8 --- /dev/null +++ b/Research/kubeflow-on-azure-stack-lab/04-KFServing/pytorch_runme.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +kubectl create -f pytorch_cifar10.yaml -n kfserving-test + +echo give the inferenceservice time to create the pods... +sleep 5 + +export INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}') +export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}') + +export MODEL_NAME=pytorch_cifar10 +export INPUT_PATH=@./pytorch_input.json +export SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -n kfserving-test -o jsonpath='{.status.url}' | cut -d "/" -f 3) + +echo curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH +curl -v -H "Host: ${SERVICE_HOSTNAME}" http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/$MODEL_NAME:predict -d $INPUT_PATH