How to save parameters on local server when using remote grpc session on Tensorflow?
I first start a grpc server in server A.
server = tf.train.Server.create_local_server()
server.join()
Then I execute the training process on server B:
sess = tf.Session("grpc://172.31.222.83:34217")
sess.run(init)
for i in range(1000):
_, l = sess.run([train_op, loss], feed)
saver.save(sess, './ckpts/model')
When the training process is finished, I find the checkpoints have saved on server A. But I want server A just used as computational node. That is to say, I want the parameters are all saved on server B, server A is used only to compute. How can I achieve this?
tensorflow
asked Nov 24 '18 at 13:15
alanalan
11
add a comment |
I first start a grpc server in server A.
server = tf.train.Server.create_local_server()
server.join()
Then I execute the training process on server B:
sess = tf.Session("grpc://172.31.222.83:34217")
sess.run(init)
for i in range(1000):
_, l = sess.run([train_op, loss], feed)
saver.save(sess, './ckpts/model')
When the training process is finished, I find the checkpoints have saved on server A. But I want server A just used as computational node. That is to say, I want the parameters are all saved on server B, server A is used only to compute. How can I achieve this?
tensorflow
asked Nov 24 '18 at 13:15
alanalan
11
add a comment |
I first start a grpc server in server A.
server = tf.train.Server.create_local_server()
server.join()
Then I execute the training process on server B:
sess = tf.Session("grpc://172.31.222.83:34217")
sess.run(init)
for i in range(1000):
_, l = sess.run([train_op, loss], feed)
saver.save(sess, './ckpts/model')
When the training process is finished, I find the checkpoints have saved on server A. But I want server A just used as computational node. That is to say, I want the parameters are all saved on server B, server A is used only to compute. How can I achieve this?
tensorflow
asked Nov 24 '18 at 13:15
alanalan
11
I first start a grpc server in server A.
server = tf.train.Server.create_local_server()
server.join()
Then I execute the training process on server B:
sess = tf.Session("grpc://172.31.222.83:34217")
sess.run(init)
for i in range(1000):
_, l = sess.run([train_op, loss], feed)
saver.save(sess, './ckpts/model')
When the training process is finished, I find the checkpoints have saved on server A. But I want server A just used as computational node. That is to say, I want the parameters are all saved on server B, server A is used only to compute. How can I achieve this?
tensorflow
tensorflow
asked Nov 24 '18 at 13:15
alanalan
11
asked Nov 24 '18 at 13:15
alanalan
11
asked Nov 24 '18 at 13:15
alanalan
11
asked Nov 24 '18 at 13:15
alanalan
11
asked Nov 24 '18 at 13:15
alanalan
11
11
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Here is one possibility.
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
return &pb.TrainResponse{tensorTrainer.Train(req.data)}
}
Here is another
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
rpc Results(ResultsRequest) returns (ResultsResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
session, err := NewTrainingSession(req.data)
if err != nil { panic() }
go session.Train()
return &pb.TrainResponse{session.id}
}
func (s *trainerServer) Results(..., req *pb.ResultsRequest) (resp *pb.ResultsResponse) {
results, err := GetResults(req.id)
if err != nil { panic() }
return &pb.ResultsResponse{results}
}
The client can call Train and poll Results until success. Perhaps TrainResponse returns an estimate.
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Here is one possibility.
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
return &pb.TrainResponse{tensorTrainer.Train(req.data)}
}
Here is another
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
rpc Results(ResultsRequest) returns (ResultsResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
session, err := NewTrainingSession(req.data)
if err != nil { panic() }
go session.Train()
return &pb.TrainResponse{session.id}
}
func (s *trainerServer) Results(..., req *pb.ResultsRequest) (resp *pb.ResultsResponse) {
results, err := GetResults(req.id)
if err != nil { panic() }
return &pb.ResultsResponse{results}
}
The client can call Train and poll Results until success. Perhaps TrainResponse returns an estimate.
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
add a comment |
Here is one possibility.
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
return &pb.TrainResponse{tensorTrainer.Train(req.data)}
}
Here is another
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
rpc Results(ResultsRequest) returns (ResultsResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
session, err := NewTrainingSession(req.data)
if err != nil { panic() }
go session.Train()
return &pb.TrainResponse{session.id}
}
func (s *trainerServer) Results(..., req *pb.ResultsRequest) (resp *pb.ResultsResponse) {
results, err := GetResults(req.id)
if err != nil { panic() }
return &pb.ResultsResponse{results}
}
The client can call Train and poll Results until success. Perhaps TrainResponse returns an estimate.
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
add a comment |
Here is one possibility.
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
return &pb.TrainResponse{tensorTrainer.Train(req.data)}
}
Here is another
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
rpc Results(ResultsRequest) returns (ResultsResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
session, err := NewTrainingSession(req.data)
if err != nil { panic() }
go session.Train()
return &pb.TrainResponse{session.id}
}
func (s *trainerServer) Results(..., req *pb.ResultsRequest) (resp *pb.ResultsResponse) {
results, err := GetResults(req.id)
if err != nil { panic() }
return &pb.ResultsResponse{results}
}
The client can call Train and poll Results until success. Perhaps TrainResponse returns an estimate.
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
Here is one possibility.
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
return &pb.TrainResponse{tensorTrainer.Train(req.data)}
}
Here is another
service TrainerService {
rpc Train(TrainRequest) returns (TrainResponse);
rpc Results(ResultsRequest) returns (ResultsResponse);
}
func (s *trainerServer) Train(..., req *pb.TrainRequest) (resp *pb.TrainResponse) {
session, err := NewTrainingSession(req.data)
if err != nil { panic() }
go session.Train()
return &pb.TrainResponse{session.id}
}
func (s *trainerServer) Results(..., req *pb.ResultsRequest) (resp *pb.ResultsResponse) {
results, err := GetResults(req.id)
if err != nil { panic() }
return &pb.ResultsResponse{results}
}
The client can call Train and poll Results until success. Perhaps TrainResponse returns an estimate.
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
answered Nov 24 '18 at 13:27
user2882597user2882597
389211
389211
add a comment |
add a comment |
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