How to fix "ResourceExhaustedError: OOM when allocating tensor"

Question

I wanna make a model with multiple inputs. So, I try to build a model like this.

# define two sets of inputs
inputA = Input(shape=(32,64,1))
inputB = Input(shape=(32,1024))
 
# CNN
x = layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu')(inputA)
x = layers.Conv2D(32, (3,3), activation='relu')(x)
x = layers.MaxPooling2D(pool_size=(2,2))(x)
x = layers.Dropout(0.2)(x)
x = layers.Flatten()(x)
x = layers.Dense(500, activation = 'relu')(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(500, activation='relu')(x)
x = Model(inputs=inputA, outputs=x)
 
# DNN
y = layers.Flatten()(inputB)
y = Dense(64, activation="relu")(y)
y = Dense(250, activation="relu")(y)
y = Dense(500, activation="relu")(y)
y = Model(inputs=inputB, outputs=y)
 
# Combine the output of the two models
combined = concatenate([x.output, y.output])
 

# combined outputs
z = Dense(300, activation="relu")(combined)
z = Dense(100, activation="relu")(combined)
z = Dense(1, activation="softmax")(combined)

model = Model(inputs=[x.input, y.input], outputs=z)

model.summary()

opt = Adam(lr=1e-3, decay=1e-3 / 200)
model.compile(loss = 'sparse_categorical_crossentropy', optimizer = opt,
    metrics = ['accuracy'])

and the summary : _

But, when i try to train this model,

history = model.fit([trainimage, train_product_embd],train_label,
    validation_data=([validimage,valid_product_embd],valid_label), epochs=10, 
    steps_per_epoch=100, validation_steps=10)

the problem happens.... :

 ResourceExhaustedError                    Traceback (most recent call
 last) <ipython-input-18-2b79f16d63c0> in <module>()
 ----> 1 history = model.fit([trainimage, train_product_embd],train_label,
 validation_data=([validimage,valid_product_embd],valid_label),
 epochs=10, steps_per_epoch=100, validation_steps=10)

 4 frames
 /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py
 in __call__(self, *args, **kwargs)    1470         ret =
 tf_session.TF_SessionRunCallable(self._session._session,    1471      
 self._handle, args,
 -> 1472                                                run_metadata_ptr)    1473         if run_metadata:    1474          
 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
 
 ResourceExhaustedError: 2 root error(s) found.   (0) Resource
 exhausted: OOM when allocating tensor with shape[800000,32,30,62] and
 type float on /job:localhost/replica:0/task:0/device:GPU:0 by
 allocator GPU_0_bfc     [[{{node conv2d_1/convolution}}]] Hint: If you
 want to see a list of allocated tensors when OOM happens, add
 report_tensor_allocations_upon_oom to RunOptions for current
 allocation info.
 
     [[metrics/acc/Mean_1/_185]] Hint: If you want to see a list of
 allocated tensors when OOM happens, add
 report_tensor_allocations_upon_oom to RunOptions for current
 allocation info.
 
   (1) Resource exhausted: OOM when allocating tensor with
 shape[800000,32,30,62] and type float on
 /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc    
 [[{{node conv2d_1/convolution}}]] Hint: If you want to see a list of
 allocated tensors when OOM happens, add
 report_tensor_allocations_upon_oom to RunOptions for current
 allocation info.
 
 0 successful operations. 0 derived errors ignored.

Thanks for reading and hopefully helping me :)

Answer 1

OOM stands for "out of memory". Your GPU is running out of memory, so it can't allocate memory for this tensor. There are a few things you can do:

• Decrease the number of filters in your Dense, Conv2D layers
• Use a smaller batch_size (or increase steps_per_epoch and validation_steps)
• Use grayscale images (you can use tf.image.rgb_to_grayscale)
• Reduce the number of layers
• Use MaxPooling2D layers after convolutional layers
• Reduce the size of your images (you can use tf.image.resize for that)
• Use smaller float precision for your input, namely np.float32
• If you're using a pre-trained model, freeze the first layers (like this)

There is more useful information about this error:

OOM when allocating tensor with shape[800000,32,30,62]

This is a weird shape. If you're working with images, you should normally have 3 or 1 channel. On top of that, it seems like you are passing your entire dataset at once; you should instead pass it in batches.