Pytorch v1alpha2 api implementation

[johnugeorge]

This contains v1alpha2 version of Pytorch. It contains the following changes

1. PytorchJob v1alpha2 spec consistent with TFJob v1alpha2 . Fixes v1alpha2 pytorch API should try to be consistent with TFJob #49
2. Shares implementation with TF operator for common libraries like JobController, logger, control
3. Moved glide to dep for better dependency management.

Examples have to be added in separate PRs

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This change is 

[coveralls]

Coverage increased (+16.4%) to 62.553% when pulling 3df7fdc on johnugeorge:v1alpha2 into 12ce79b on kubeflow:master.

[johnugeorge]

/retest

[johnugeorge]

@jose5918

[jose5918]

@johnugeorge LGTM I just want to verify since I'm not familiar with the v1alpha2 implementation. What is the retry logic like in this operator?

[johnugeorge]

@jose5918 I am not sure if I understand your question. Retry logic if the pod failed?

[johnugeorge]

For v1alpha2 implementation, there are two steps involved.

1. Refactored the tf-operator code to create a base job controller class(for sharing functionalities across operators. https://github.com/kubeflow/tf-operator/tree/master/pkg/controller.v2/jobcontroller

Specs are kept different for every operator. More code can be shared in the future.

2. Add operator specific code in individual repos.

[jose5918]

I guess I was wondering if this is true
https://github.com/johnugeorge/pytorch-operator/blob/v1alpha2/pkg/apis/pytorch/v1alpha2/types.go#L109

I remember some cases where there were retryable errors (like connecting to the master) and the exit codes were below 127 for pytorch distributed

[johnugeorge]

Exit codes in https://github.com/kubeflow/tf-operator/blob/master/pkg/util/train/train_util.go is used to check if it has to be retried after a failure

[johnugeorge]

I will add a separate issue to track this.

[jose5918]

/approve

[jose5918]

/lgtm

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[jose5918]

have verified with @johnugeorge that restart behavior is similar to before