Pytorch plugin usage example

pytorch/Dockerfile

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+FROM pytorch/pytorch:1.5-cuda10.1-cudnn7-runtime
+
+RUN pip install awscli
+
+RUN pip install tensorboardX==2.0.0 flytekit[pytorch]==0.8.1
+
+WORKDIR /app
+
+COPY ./workflows ./workflows
+COPY ./flyte.config ./
+
+ARG IMAGE_TAG
+ENV FLYTE_INTERNAL_IMAGE "$IMAGE_TAG"
+ENV LC_ALL C.UTF-8
+ENV LANG C.UTF-8
+ENV PYTHONPATH /app
+
+ENTRYPOINT []

pytorch/Makefile

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+docker_build:
+	IMAGE_NAME=flytesnacks-pytorch scripts/docker_build.sh

pytorch/README.rst

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+PyTorch plugin usage example
+============================
+
+This demo is built on top of this `example`_
+
+#############
+Prerequisites
+#############
+
+Before running this make sure that
+    - pytorch plugin is enabled in flytepropeller's config
+    - `Kubeflow pytorch operator`_ is installed in your k8s cluster (you can use `base`_ and configure it in your deploy)
+    - [if using GPU] `GPU device plugin`_ is deployed as well
+
+#####
+Usage
+#####
+
+Build container and push it to the registry
+
+.. code-block::
+
+    cd pytorch
+    make docker_build
+
+Create flyte project
+
+.. code-block::
+
+    curl -v -X POST http://127.0.0.1:30081/api/v1/projects -d '{"project": {"id": "pytorch-mnist", "name": "pytorch-mnist"} }'
+
+Register workflow
+
+.. code-block::
+
+    docker run --network host flytesnacks-pytorch:<TAG> pyflyte -p pytorch-mnist -d development -c flyte.config register workflows
+
+Navigate to https://127.0.0.1:30081/console/projects/pytorch-mnist/workflows?domain=development and launch workflow.
+
+#####
+Notes
+#####
+
+In order to disable GPU usage for the MNIST task just comment out ``per_replica_gpu_limit="1"`` in ``pytorch_task`` decorator
+
+.. code-block::
+
+    @pytorch_task(
+        workers_count=2,
+        per_replica_cpu_request="500m",
+        per_replica_memory_request="4Gi",
+        per_replica_memory_limit="8Gi",
+        #per_replica_gpu_limit="1",
+    )
+
+.. _`example`: https://github.com/kubeflow/pytorch-operator/blob/b7fef224fef1ef0117f6e74961b557270fcf4b04/examples/mnist/mnist.py
+.. _`Kubeflow pytorch operator`: https://github.com/kubeflow/pytorch-operator
+.. _`base`: https://github.com/lyft/flyte/blob/master/kustomize/base/operators/kfoperators/pytorch/kustomization.yaml
+.. _`GPU device plugin`: https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/#deploying-nvidia-gpu-device-plugin

pytorch/flyte.config

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+[sdk]
+workflow_packages=workflows
+
+[platform]
+url=127.0.0.1:30081
+insecure=True
+
+[auth]
+assumable_iam_role=arn:aws:iam::123:role/test-role
+
+[aws]
+s3_shard_formatter=s3://my-s3-bucket/{}
+s3_shard_string_length=2
+endpoint=http://minio:9000
+access_key_id=minio
+secret_access_key=miniostorage

pytorch/scripts/docker_build.sh

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+#!/usr/bin/env bash
+
+# WARNING: THIS FILE IS MANAGED IN THE 'BOILERPLATE' REPO AND COPIED TO OTHER REPOSITORIES.
+# ONLY EDIT THIS FILE FROM WITHIN THE 'LYFT/BOILERPLATE' REPOSITORY:
+#
+# TO OPT OUT OF UPDATES, SEE https://github.com/lyft/boilerplate/blob/master/Readme.rst
+
+set -e
+
+echo ""
+echo "------------------------------------"
+echo "           DOCKER BUILD"
+echo "------------------------------------"
+echo ""
+
+if [ -n "$REGISTRY" ]; then
+  # Do not push if there are unstaged git changes
+  CHANGED=$(git status --porcelain)
+  if [ -n "$CHANGED" ]; then
+    echo "Please commit git changes before pushing to a registry"
+    exit 1
+  fi
+fi
+
+
+GIT_SHA=$(git rev-parse HEAD)
+
+IMAGE_TAG_WITH_SHA="${IMAGE_NAME}:${GIT_SHA}"
+
+RELEASE_SEMVER=$(git describe --tags --exact-match "$GIT_SHA" 2>/dev/null) || true
+if [ -n "$RELEASE_SEMVER" ]; then
+  IMAGE_TAG_WITH_SEMVER="${IMAGE_NAME}:${RELEASE_SEMVER}${IMAGE_TAG_SUFFIX}"
+fi
+
+# build the image
+docker build -t "$IMAGE_TAG_WITH_SHA" --build-arg IMAGE_TAG="${IMAGE_TAG_WITH_SHA}" .
+echo "${IMAGE_TAG_WITH_SHA} built locally."
+
+# if REGISTRY specified, push the images to the remote registy
+if [ -n "$REGISTRY" ]; then
+
+  if [ -n "${DOCKER_REGISTRY_PASSWORD}" ]; then
+    docker login --username="$DOCKER_REGISTRY_USERNAME" --password="$DOCKER_REGISTRY_PASSWORD"
+  fi
+
+  docker tag "$IMAGE_TAG_WITH_SHA" "${REGISTRY}/${IMAGE_TAG_WITH_SHA}"
+
+  docker push "${REGISTRY}/${IMAGE_TAG_WITH_SHA}"
+  echo "${REGISTRY}/${IMAGE_TAG_WITH_SHA} pushed to remote."
+
+  # If the current commit has a semver tag, also push the images with the semver tag
+  if [ -n "$RELEASE_SEMVER" ]; then
+
+    docker tag "$IMAGE_TAG_WITH_SHA" "${REGISTRY}/${IMAGE_TAG_WITH_SEMVER}"
+
+    docker push "${REGISTRY}/${IMAGE_TAG_WITH_SEMVER}"
+    echo "${REGISTRY}/${IMAGE_TAG_WITH_SEMVER} pushed to remote."
+
+  fi
+fi

pytorch/workflows/mnist.py

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+#https://github.com/kubeflow/pytorch-operator/blob/b7fef224fef1ef0117f6e74961b557270fcf4b04/examples/mnist/mnist.py
+from __future__ import absolute_import
+from __future__ import print_function
+
+import argparse
+import os
+
+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
+
+from flytekit.sdk.tasks import inputs, outputs
+from flytekit.sdk.types import Types
+from flytekit.sdk.workflow import workflow_class, Input, Output
+
+from flytekit.sdk.tasks import pytorch_task
+
+WORLD_SIZE = int(os.environ.get('WORLD_SIZE', 1))
+
+
+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)
+
+    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)
+
+def train(model, device, train_loader, optimizer, epoch, writer, log_interval):
+    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 % 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)
+
+def test(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)))
+    accuracy = float(correct) / len(test_loader.dataset)
+    writer.add_scalar('accuracy', accuracy, epoch)
+    return (epoch, accuracy)
+
+def epoch_step(model, device, train_loader, test_loader, optimizer, epoch, writer, log_interval):
+    train(model, device, train_loader, optimizer, epoch, writer, log_interval)
+    return test(model, device, test_loader, writer, epoch)
+
+def should_distribute():
+    return dist.is_available() and WORLD_SIZE > 1
+
+
+def is_distributed():
+    return dist.is_available() and dist.is_initialized()
+
+@inputs(
+    no_cuda=Types.Boolean,
+    batch_size=Types.Integer,
+    test_batch_size=Types.Integer,
+    epochs=Types.Integer,
+    learning_rate=Types.Float,
+    sgd_momentum=Types.Float,
+    seed=Types.Integer,
+    log_interval=Types.Integer,
+    save_model=Types.Boolean,
+    dir=Types.String)
+@outputs(out=Types.String)
+@pytorch_task(
+    workers_count=2,
+    per_replica_cpu_request="500m",
+    per_replica_memory_request="4Gi",
+    per_replica_memory_limit="8Gi",
+    per_replica_gpu_limit="1",
+)
+def mnist_pytorch_job(workflow_params, no_cuda, batch_size, test_batch_size, epochs, learning_rate, sgd_momentum, seed, log_interval, save_model, dir, out):
+    backend_type = dist.Backend.GLOO
+
+    writer = SummaryWriter(dir)
+
+    torch.manual_seed(seed)
+
+    use_cuda = not no_cuda
+    device = torch.device('cuda' if use_cuda and torch.cuda.is_available else 'cpu')
+
+    print('Using device: {}, world size: {}'.format(device, WORLD_SIZE))
+
+    if should_distribute():
+        print('Using distributed PyTorch with {} backend'.format(backend_type))
+        dist.init_process_group(backend=backend_type)
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+    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=batch_size, shuffle=True, **kwargs)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('../data', train=False, transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))
+        ])),
+        batch_size=test_batch_size, shuffle=False, **kwargs)
+
+    model = Net().to(device)
+
+    if is_distributed():
+        Distributor = nn.parallel.DistributedDataParallel if use_cuda and torch.cuda.is_available \
+            else nn.parallel.DistributedDataParallelCPU
+        model = Distributor(model)
+
+    optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=sgd_momentum)
+
+    accuracies = [epoch_step(model, device, train_loader, test_loader, optimizer, epoch, writer, log_interval) for epoch in range(1, epochs + 1)]
+
+    if (save_model):
+        torch.save(model.state_dict(),"mnist_cnn.pt")
+
+    out.set(str(accuracies))
+
+
+@workflow_class
+class MNISTTest(object):
+    no_cuda = Input(Types.Boolean, default=False, help="disables CUDA training")
+    batch_size = Input(Types.Integer, default=64, help='input batch size for training (default: 64)')
+    test_batch_size = Input(Types.Integer, default=1000, help='input batch size for testing (default: 1000)')
+    epochs = Input(Types.Integer, default=1, help='number of epochs to train (default: 10)')
+    learning_rate = Input(Types.Float, default=0.01, help='learning rate (default: 0.01)')
+    sgd_momentum = Input(Types.Float, default=0.5, help='SGD momentum (default: 0.5)')
+    seed = Input(Types.Integer, default=1, help='random seed (default: 1)')
+    log_interval = Input(Types.Integer, default=10, help='how many batches to wait before logging training status')
+    save_model = Input(Types.Boolean, default=False, help='For Saving the current Model')
+    dir = Input(Types.String, default='logs', help='directory where summary logs are stored')
+
+    mnist_result = mnist_pytorch_job(
+        no_cuda=no_cuda,
+        batch_size=batch_size,
+        test_batch_size=test_batch_size,
+        epochs=epochs,
+        learning_rate=learning_rate,
+        sgd_momentum=sgd_momentum,
+        seed=seed,
+        log_interval=log_interval,
+        save_model=save_model,
+        dir=dir
+    )
+
+    accuracies = Output(mnist_result.outputs.out, sdk_type=Types.String)