K8s Custom Resource and Operator For MXNet jobs
Custom Resources require Kubernetes 1.7
Distributed MXNet training jobs require managing multiple sets of MXNet replicas. There are three kinds of replicas sets which act as different roles in the job: Server (1-n instance) Worker (1-n instance) Scheduler(only one instance in a training cluster).
K8s makes it easy to configure and deploy each set of MXNet replicas. Various tools like helm and ksonnet can be used to simplify generating the configs for a MXNet job. Helm will be supported a few days later.
However, in addition to generating the configs we need some custom control logic because K8s built-in controllers (Jobs, ReplicaSets, StatefulSets, etc...) don't provide the semantics needed for managing MXNet jobs.
MxJob provides a K8s resource representing a single, distributed, mxnet training job. The Spec and Status (defined in mx_job.go are customized for MXNet. The spec allows specifying the Docker image and arguments to use for each MXNet replica (i.e. scheduler, worker, and parameter server). The status provides relevant information such as the number of replicas in various states.
Using a CRD gives users the ability to create and manage mxnet Jobs just like builtin K8s resources. For example to create a job
kubectl create -f examples/mx_job_local.yaml
To list jobs
kubectl get mxjobs NAME KINDS example-dist-job MxJob.v1beta1.mlkube.io
The code is closely modeled on Coreos's etcd-operator.
The MxJob Spec(defined in mx_job.go reuses the existing Kubernetes structure PodTemplateSpec to describe MXNet processes. We use PodTemplateSpec because we want to make it easy for users to configure the processes; for example setting resource requirements or adding volumes. We expect helm or ksonnet could be used to add syntactic sugar to create more convenient APIs for users not familiar with Kubernetes.
Leader election allows a K8s deployment resource to be used to upgrade the operator.
Installing the CRD and operator on your k8s cluster
Clone the repository
Deploy the operator
kubec create -f examples/mx_operator_deploy.yaml
- Create distributed mxjob demo.
kubectl create -f examples/mx_job_dist.yaml kubectl get job NAME DESIRED SUCCESSFUL AGE example-dist-job-scheduler-1kkf-0 1 1 1h example-dist-job-server-1kkf-0 1 1 1h example-dist-job-server-1kkf-1 1 1 1h example-dist-job-worker-1kkf-0 1 1 1h example-dist-job-worker-1kkf-1 1 1 1h example-dist-job-worker-1kkf-2 1 1 1h kubectl logs -f example-dist-job-worker-1kkf-2-hz22k INFO:root:start with arguments Namespace(batch_size=128, benchmark=0, data_nthreads=4, data_train='data/cifar10_train.rec', data_val='data/cifar10_val.rec', disp_batches=20, dtype='float32', gpus=None, image_shape='3,28,28', kv_store='dist_device_sync', load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='200,250', max_random_aspect_ratio=0, max_random_h=36, max_random_l=50, max_random_rotate_angle=0, max_random_s=50, max_random_scale=1, max_random_shear_ratio=0, min_random_scale=1, model_prefix='./wltest', mom=0.9, monitor=0, network='resnet', num_classes=10, num_epochs=10, num_examples=50000, num_layers=2, optimizer='sgd', pad_size=4, random_crop=1, random_mirror=1, rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001) [02:52:36] src/io/iter_image_recordio_2.cc:153: ImageRecordIOParser2: data/cifar10_train.rec, use 1 threads for decoding.. [02:52:37] src/io/iter_image_recordio_2.cc:153: ImageRecordIOParser2: data/cifar10_val.rec, use 1 threads for decoding.. INFO:root:Epoch Batch  Speed: 84.39 samples/sec accuracy=0.159970 INFO:root:Epoch Batch  Speed: 85.53 samples/sec accuracy=0.250391 INFO:root:Epoch Batch  Speed: 90.39 samples/sec accuracy=0.284375 INFO:root:Epoch Batch  Speed: 89.56 samples/sec accuracy=0.294531 INFO:root:Epoch Batch  Speed: 91.03 samples/sec accuracy=0.337500 INFO:root:Epoch Batch  Speed: 85.62 samples/sec accuracy=0.342187 INFO:root:Epoch Train-accuracy=0.363281 INFO:root:Epoch Time cost=190.507 INFO:root:Saved checkpoint to "./wltest-2-0001.params"
This is very much a prototype.
The status information reported by the operator is hacky and not well thought out. In particular, we probably need to figure out what the proper phases and conditions to report are.
The semantics for aggregating status of individual replicas into overall MxJob status needs to be thought out.
There is a lot of code from earlier versions (including the ETCD operator) that still needs to be cleaned up.
There is minimal testing.
There are some unittests.
The helm package provides some basic E2E tests.
Building the Operator
Resolve dependencies (if you don't have glide install, check how to do it here)
Runing the Operator Locally
Running the operator locally (as opposed to deploying it on a K8s cluster) is convenient for debugging/development.
We can configure the operator to run locally using the configuration available in your kubeconfig to communicate with a K8s cluster.
Set your environment
export USE_KUBE_CONFIG=$(echo ~/.kube/config) export MY_POD_NAMESPACE=default export MY_POD_NAME=my-pod
* MY_POD_NAMESPACE is used because the CRD is namespace scoped and we use the namespace of the controller to set the corresponding namespace for the resource.
TODO(jlewi): Do we still need to set MY_POD_NAME? Why?
On ubuntu the default go package appears to be gccgo-go which has problems see issue golang-go package is also really old so install from golang tarballs instead.
You may need to remove the vendor directory of dependencies that also vendor dependencies as these may produce conflicts with the versions vendored by mlkube; e.g.
rm -rf vendor/k8s.io/apiextensions-apiserver/vendor