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pytorch.pb.go
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pytorch.pb.go
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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: flyteidl/plugins/pytorch.proto
package plugins
import (
fmt "fmt"
proto "github.com/golang/protobuf/proto"
math "math"
)
// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf
// This is a compile-time assertion to ensure that this generated file
// is compatible with the proto package it is being compiled against.
// A compilation error at this line likely means your copy of the
// proto package needs to be updated.
const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package
// Custom proto for torch elastic config for distributed training using
// https://github.com/kubeflow/training-operator/blob/master/pkg/apis/kubeflow.org/v1/pytorch_types.go
type ElasticConfig struct {
RdzvBackend string `protobuf:"bytes,1,opt,name=rdzv_backend,json=rdzvBackend,proto3" json:"rdzv_backend,omitempty"`
MinReplicas int32 `protobuf:"varint,2,opt,name=min_replicas,json=minReplicas,proto3" json:"min_replicas,omitempty"`
MaxReplicas int32 `protobuf:"varint,3,opt,name=max_replicas,json=maxReplicas,proto3" json:"max_replicas,omitempty"`
NprocPerNode int32 `protobuf:"varint,4,opt,name=nproc_per_node,json=nprocPerNode,proto3" json:"nproc_per_node,omitempty"`
MaxRestarts int32 `protobuf:"varint,5,opt,name=max_restarts,json=maxRestarts,proto3" json:"max_restarts,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *ElasticConfig) Reset() { *m = ElasticConfig{} }
func (m *ElasticConfig) String() string { return proto.CompactTextString(m) }
func (*ElasticConfig) ProtoMessage() {}
func (*ElasticConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_4df8a9374b28b766, []int{0}
}
func (m *ElasticConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_ElasticConfig.Unmarshal(m, b)
}
func (m *ElasticConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_ElasticConfig.Marshal(b, m, deterministic)
}
func (m *ElasticConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_ElasticConfig.Merge(m, src)
}
func (m *ElasticConfig) XXX_Size() int {
return xxx_messageInfo_ElasticConfig.Size(m)
}
func (m *ElasticConfig) XXX_DiscardUnknown() {
xxx_messageInfo_ElasticConfig.DiscardUnknown(m)
}
var xxx_messageInfo_ElasticConfig proto.InternalMessageInfo
func (m *ElasticConfig) GetRdzvBackend() string {
if m != nil {
return m.RdzvBackend
}
return ""
}
func (m *ElasticConfig) GetMinReplicas() int32 {
if m != nil {
return m.MinReplicas
}
return 0
}
func (m *ElasticConfig) GetMaxReplicas() int32 {
if m != nil {
return m.MaxReplicas
}
return 0
}
func (m *ElasticConfig) GetNprocPerNode() int32 {
if m != nil {
return m.NprocPerNode
}
return 0
}
func (m *ElasticConfig) GetMaxRestarts() int32 {
if m != nil {
return m.MaxRestarts
}
return 0
}
// Custom proto for plugin that enables distributed training using https://github.com/kubeflow/pytorch-operator
type DistributedPyTorchTrainingTask struct {
// number of worker replicas spawned in the cluster for this job
Workers int32 `protobuf:"varint,1,opt,name=workers,proto3" json:"workers,omitempty"`
// config for an elastic pytorch job
//
ElasticConfig *ElasticConfig `protobuf:"bytes,2,opt,name=elastic_config,json=elasticConfig,proto3" json:"elastic_config,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *DistributedPyTorchTrainingTask) Reset() { *m = DistributedPyTorchTrainingTask{} }
func (m *DistributedPyTorchTrainingTask) String() string { return proto.CompactTextString(m) }
func (*DistributedPyTorchTrainingTask) ProtoMessage() {}
func (*DistributedPyTorchTrainingTask) Descriptor() ([]byte, []int) {
return fileDescriptor_4df8a9374b28b766, []int{1}
}
func (m *DistributedPyTorchTrainingTask) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_DistributedPyTorchTrainingTask.Unmarshal(m, b)
}
func (m *DistributedPyTorchTrainingTask) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_DistributedPyTorchTrainingTask.Marshal(b, m, deterministic)
}
func (m *DistributedPyTorchTrainingTask) XXX_Merge(src proto.Message) {
xxx_messageInfo_DistributedPyTorchTrainingTask.Merge(m, src)
}
func (m *DistributedPyTorchTrainingTask) XXX_Size() int {
return xxx_messageInfo_DistributedPyTorchTrainingTask.Size(m)
}
func (m *DistributedPyTorchTrainingTask) XXX_DiscardUnknown() {
xxx_messageInfo_DistributedPyTorchTrainingTask.DiscardUnknown(m)
}
var xxx_messageInfo_DistributedPyTorchTrainingTask proto.InternalMessageInfo
func (m *DistributedPyTorchTrainingTask) GetWorkers() int32 {
if m != nil {
return m.Workers
}
return 0
}
func (m *DistributedPyTorchTrainingTask) GetElasticConfig() *ElasticConfig {
if m != nil {
return m.ElasticConfig
}
return nil
}
func init() {
proto.RegisterType((*ElasticConfig)(nil), "flyteidl.plugins.ElasticConfig")
proto.RegisterType((*DistributedPyTorchTrainingTask)(nil), "flyteidl.plugins.DistributedPyTorchTrainingTask")
}
func init() { proto.RegisterFile("flyteidl/plugins/pytorch.proto", fileDescriptor_4df8a9374b28b766) }
var fileDescriptor_4df8a9374b28b766 = []byte{
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}