I'm trying to implement something with pytorch.
I have 2 GPUs and I want to train 2 models as below:
model0 = Mymodel().to('cuda:0')
model1 = Mymodel().to('cuda:1')
opt0 = torch.optim.Adam(model0.parameters(), lr=0.01)
opt1 = torch.optim.Adam(model0.parameters(), lr=0.01)
# 1.Forward data into model0 on GPU0
out = model0(x.to('cuda:0'))
# 2.Calculate the loss on model0, update model0's parameters
model0.loss.backward()
opt0.step()
opt0.zero_grad()
# 3.Use model0's output as input of model1 on GPU1
out = model1(out.to('cuda:1'))
# 4.Calculate the loss on model1, update model1's parameters
model1.loss.backward()
opt1.step()
opt1.zero_grad()
I want to train them simultaneously to speed up the whole procedure, but I think the code now will wait step 2(or 4) finished and finally do step 3(or 1). How can I implement my idea? Or which technique is I need(e.g. model parel, thread, multiprocessing...)?
I've consider some article like this, but I think there is some worng with the result, and I think it actually doesn't train models simultaneously.
You have a strong dependency between the 2 models, the 2nd one always needs the output from the previous one, so that part of the code will always be sequential.
I think you might need some sort of multiprocessing (take a look at torch.multiprocessing) or some kind of queue, where you can store the output from the first model.
Related
I try to construct a TFX pipeline with a trainer component with a Keras model defined like this:
def run_fn(fn_args: components.FnArgs):
transform_output = TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(fn_args.train_files,
fn_args.data_accessor,
transform_output,
num_batch)
eval_dataset = input_fn(fn_args.eval_files,
fn_args.data_accessor,
transform_output,
num_batch)
history = model.fit(train_dataset,
epochs=num_epochs,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps)
This works. However, if I change fitting to the following, this doesn't work:
history = model.fit(train_dataset,
epochs=num_epochs,
batch_size=num_batch,
validation_split=0.1)
Now, I have two questions:
Why does fitting work only with steps_per_epochs only? I couldn't find any explicit statement supporting this but this is the only way. Somehow I conclude that it must be something TFX specific (TFX handles input data only in a generator-like way?).
Let's say my train_dataset contains 100 instances and steps_per_epoch=1000 (with epochs=1). Is that mean that my 100 input instances are feed 10x each in order to reach the defined 1000 step? Isn't that counter-productive from training perspective?
I would like to know if Keras can be used as an interface to TensoFlow for only doing computation on my GPU.
I tested TF directly on my GPU. But for ML purposes, I started using Keras, including the backend. I would find it 'comfortable' to do all my stuff in Keras instead of Using two tools.
This is also a matter of curiosity.
I found some examples like this one:
http://christopher5106.github.io/deep/learning/2018/10/28/understand-batch-matrix-multiplication.html
However this example does not actually do the calculation.
It also does not get input data.
I duplicate the snippet here:
'''
from keras import backend as K
a = K.ones((3,4))
b = K.ones((4,5))
c = K.dot(a, b)
print(c.shape)
'''
I would simply like to know if I can get the result numbers from this snippet above, and how?
Thanks,
Michel
Keras doesn't have an eager mode like Tensorflow, and it depends on models or functions with "placeholders" to receive and output data.
So, it's a little more complicated than Tensorflow to do basic calculations like this.
So, the most user friendly solution would be creating a dummy model with one Lambda layer. (And be careful with the first dimension that Keras will insist to understand as a batch dimension and require that input and output have the same batch size)
def your_function_here(inputs):
#if you have more than one tensor for the inputs, it's a list:
input1, input2, input3 = inputs
#if you don't have a batch, you should probably have a first dimension = 1 and get
input1 = input1[0]
#do your calculations here
#if you used the batch_size=1 workaround as above, add this dimension again:
output = K.expand_dims(output,0)
return output
Create your model:
inputs = Input(input_shape)
#maybe inputs2 ....
outputs = Lambda(your_function_here)(list_of_inputs)
#maybe outputs2
model = Model(inputs, outputs)
And use it to predict the result:
print(model.predict(input_data))
I have created 2 different models using tensorflow and keras for image classification. Now I want to merge both the models and use both the models at the same time.
I am trying to send 1 video to each model and convert them to frames at 30 FPS. Then I want to check, say frame x from 1st model and frame x1 in 2nd model and then keep a simple if else statement like
if(frame x ==true && frame x1 == true)
print y
else
print z
So here I am getting the frames and all the information I need. But my only question is how shall I merge the two models. I want to merge them because I want frame x and frame x1 both at t seconds, thus helping me know both the image at take at same time.
it is explained nicely here. in short you need to define a function for initializing each model in its own unique variable scope that you use for both the pretraining and testing
something like
def create_model(session, FLAGS, forward_only, name):
with tf.variable_scope(name):
model = seq2seq_model.Seq2SeqModel(
FLAGS.en_vocab_size, FLAGS.fr_vocab_size, _buckets,
FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.learning_rate_decay_factor, forward_only=forward_only)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
return model
Assuming both models are in Keras, you can simply load them both at the start of your program with something like
model = load_model('my_model.h5')
taken from the Keras FAQ "How can I save a Keras model?".
So then you do
model1 = load_model( 'my_model1.h5' )
model2 = load_model( 'my_model2.h5' )
and then you can call predict on them separately and use the results.
I am new in Keras, but I worked with pure tensorflow before. I am trying to debug some of the following network (I will just copy a fragment. Loss function, optimizer, etc are unimportant to me for with this code)
#Block 1 (Conv,relu,batch) starts with 800 x 400
main_input = LNN.Input(shape=((800,400,5)),name='main_input')
enc_conv1 = LNN.Convolution2D(8,3,padding='same',activation='relu')(main_input)
enc_bn1 = LNN.BatchNormalization(axis=1)(enc_conv1)
#Block 2 (Conv,relu,batch) starts with 400 x 200
maxp1_4 = LNN.MaxPooling2D(strides=2)(enc_bn1)
enc_conv2 = LNN.Convolution2D(16,3,padding='same',activation='relu')(maxp1_4)
enc_bn2 = LNN.BatchNormalization(axis=1)(enc_conv2)
enc_conv3 = LNN.Convolution2D(16,3,padding='same',activation='relu')(enc_bn2)
enc_bn3 = LNN.BatchNormalization(axis=1)(enc_conv3)
concat1_5 = LNN.concatenate(axis=3,inputs=[enc_bn3,maxp1_4])
I have seen some examples of how to do it adding each operation to a Sequential() function (for example as the one explained here but with the add() function. Is there a way to check the output of each layer without adding them to a model itself (as it can be also done with Tensorflow, making a session)?
The best is to make a model that outputs those layers:
modelToOutputAll = Model(main_input, [enc_conv1, enc_bn1, maxp1_4, enc_conv2, enc_bn2, enc_conv3, enc_bn3, concat1_5])
For training, keep a model with only the final output:
modelForTraining = Model(main_input,concat1_5)
Both models are using the exact same weights, so training one changes the other. You use each one for doing what you need at the moment.
Train with modelForTraining.fit(xTrain,yTrain, ...)
See intermediate layers with modelToOutputAll.predict(xInput)
Assume I have a model like this. M1 and M2 are two layers linking left and right sides of the model.
The example model: Red lines indicate backprop directions
During training, I hope M1 can learn a mapping from L2_left activation to L2_right activation. Similarly, M2 can learn a mapping from L3_right activation to L3_left activation.
The model also needs to learn the relationship between two inputs and the output.
Therefore, I should have three loss functions for M1, M2, and L3_left respectively.
I probably can use:
model.compile(optimizer='rmsprop',
loss={'M1': 'mean_squared_error',
'M2': 'mean_squared_error',
'L3_left': mean_squared_error'})
But during training, we need to provide y_true, for example:
model.fit([input_1,input_2], y_true)
In this case, the y_true is the hidden layer activations and not from a dataset.
Is it possible to build this model and train it using it's hidden layer activations?
If you have only one output, you must have only one loss function.
If you want three loss functions, you must have three outputs, and, of course, three Y vectors for training.
If you want loss functions in the middle of the model, you must take outputs from those layers.
Creating the graph of your model: (if the model is already defined, see the end of this answer)
#Here, all "SomeLayer(blabla)" could be replaced by a "SomeModel" if necessary
#Example of using a layer or a model:
#M1 = SomeLayer(blablabla)(L12)
#M1 = SomeModel(L12)
from keras.models import Model
from keras.layers import *
inLef = Input((shape1))
inRig = Input((shape2))
L1Lef = SomeLayer(blabla)(inLef)
L2Lef = SomeLayer(blabla)(L1Lef)
M1 = SomeLayer(blablaa)(L2Lef) #this is an output
L1Rig = SomeLayer(balbla)(inRig)
conc2Rig = Concatenate(axis=?)([L1Rig,M1]) #Or Add, or Multiply, however you're joining the models
L2Rig = SomeLayer(nlanlab)(conc2Rig)
L3Rig = SomeLayer(najaljd)(L2Rig)
M2 = SomeLayer(babkaa)(L3Rig) #this is an output
conc3Lef = Concatenate(axis=?)([L2Lef,M2])
L3Lef = SomeLayer(blabla)(conc3Lef) #this is an output
Creating your model with three outputs:
Now you've got your graph ready and you know what the outputs are, you create the model:
model = Model([inLef,inRig], [M1,M2,L3Lef])
model.compile(loss='mse', optimizer='rmsprop')
If you want different losses for each output, then you create a list:
#example of custom loss function, if necessary
def lossM1(yTrue,yPred):
return keras.backend.sum(keras.backend.abs(yTrue-yPred))
#compiling with three different loss functions
model.compile(loss = [lossM1, 'mse','binary_crossentropy'], optimizer =??)
But you've got to have three different yTraining too, for training with:
model.fit([input_1,input_2], [yTrainM1,yTrainM2,y_true], ....)
If your model is already defined and you don't create it's graph like I did:
Then, you have to find in yourModel.layers[i] which ones are M1 and M2, so you create a new model like this:
M1 = yourModel.layers[indexForM1].output
M2 = yourModel.layers[indexForM2].output
newModel = Model([inLef,inRig], [M1,M2,yourModel.output])
If you want that two outputs be equal:
In this case, just subtract the two outputs in a lambda layer, and make that lambda layer be an output of your model, with expected values = 0.
Using the exact same vars as before, we'll just create two addictional layers to subtract outputs:
diffM1L1Rig = Lambda(lambda x: x[0] - x[1])([L1Rig,M1])
diffM2L2Lef = Lambda(lambda x: x[0] - x[1])([L2Lef,M2])
Now your model should be:
newModel = Model([inLef,inRig],[diffM1L1Rig,diffM2L2lef,L3Lef])
And training will expect those two differences to be zero:
yM1 = np.zeros((shapeOfM1Output))
yM2 = np.zeros((shapeOfM2Output))
newModel.fit([input_1,input_2], [yM1,yM2,t_true], ...)
Trying to answer to the last part: how to make gradients only affect one side of the model.
...well.... at first that sounds unfeasible to me. But, if that is similar to "train only a part of the model", then it's totally ok by defining models that only go to a certain point and making part of the layers untrainable.
By doing that, nothing will affect those layers. If that's what you want, then you can do it:
#using the previous vars to define other models
modelM1 = Model([inLef,inRig],diffM1L1Rig)
This model above ends in diffM1L1Rig. Before compiling, you must set L2Right untrainable:
modelM1.layers[??].trainable = False
#to find which layer is the right one, you may define then using the "name" parameter, or see in the modelM1.summary() the shapes, types etc.
modelM1.compile(.....)
modelM1.fit([input_1, input_2], yM1)
This suggestion makes you train only a single part of the model. You can repeat the procedure for M2, locking the layers you need before compiling.
You can also define a full model taking all layers, and lock only the ones you want. But you won't be able (I think) to make half gradients pass by one side and half the gradients pass by the other side.
So I suggest you keep three models, the fullModel, the modelM1, and the modelM2, and you cycle them in training. One epoch each, maybe....
That should be tested....