Put the full PyTorch prototype in the jsonnet file. (kubeflow#1119)

* The current pattern in Kubeflow is to put the complete prototype in
  the jsonnet file. This way the result of ks generate is a .jsonnet
  file containing a full spec. This makes it easy for users to do
  complex modifications starting with the prototype as an example.

* Configure the prototype to do mnist by default.

Fix kubeflow#1114

kubeflow/pytorch-job/prototypes/pytorch-job.jsonnet

@@ -5,7 +5,7 @@
 // @param name string Name to give to each of the components
 // @optionalParam namespace string null Namespace to use for the components. It is automatically inherited from the environment if not set.
 // @optionalParam args string null Comma separated list of arguments to pass to the job
-// @optionalParam image string null The docker image to use for the job.
+// @optionalParam image string gcr.io/kubeflow-examples/pytorch-dist-mnist:v20180702-a57993c The docker image to use for the job.
 // @optionalParam image_gpu string null The docker image to use when using GPUs.
 // @optionalParam num_masters number 1 The number of masters to use
 // @optionalParam num_ps number 0 The number of ps to use
@@ -14,6 +14,75 @@
 
 local k = import "k.libsonnet";
 
-local all = import "kubeflow/pytorch-job/pytorch-job.libsonnet";
+local util = {
+  pytorchJobReplica(replicaType, number, args, image, numGpus=0)::
+    local baseContainer = {
+      image: image,
+      name: "pytorch",
+    };
+    local containerArgs = if std.length(args) > 0 then
+      {
+        args: args,
+      }
+    else {};
+    local resources = if numGpus > 0 then {
+      resources: {
+        limits: {
+          "nvidia.com/gpu": numGpus,
+        },
+      },
+    } else {};
+    if number > 0 then
+      {
+        replicas: number,
+        template: {
+          spec: {
+            containers: [
+              baseContainer + containerArgs + resources,
+            ],
+            restartPolicy: "OnFailure",
+          },
+        },
+        replicaType: replicaType,
+      }
+    else {},
+};
 
-std.prune(k.core.v1.list.new(all.pyTorchJobPrototype(params, env)))
+local namespace = env.namespace;
+local name = params.name;
+
+local argsParam = params.args;
+local args =
+  if argsParam == "null" then
+    []
+  else
+    std.split(argsParam, ",");
+
+local image = params.image;
+local imageGpu = params.image_gpu;
+local numMasters = params.num_masters;
+local numWorkers = params.num_workers;
+local numGpus = params.num_gpus;
+
+local workerSpec = if numGpus > 0 then
+  util.pytorchJobReplica("WORKER", numWorkers, args, imageGpu, numGpus)
+else
+  util.pytorchJobReplica("WORKER", numWorkers, args, image);
+
+local masterSpec = util.pytorchJobReplica("MASTER", numMasters, args, image);
+local replicas = [masterSpec, workerSpec];
+
+
+local job = {
+  apiVersion: "kubeflow.org/v1alpha1",
+  kind: "PyTorchJob",
+  metadata: {
+    name: name,
+    namespace: namespace,
+  },
+  spec: {
+    replicaSpecs: replicas,
+  },
+};
+
+std.prune(k.core.v1.list.new([job]))