GPU runs out of memory for VGG19 & VGG16 but not for ResNet50

Below I provide a toy example which throws an out-of-memory exception while training VGG19 or VGG16 models on GPU. The batch-size and the dataset used here are tiny and my graphics card should handle them. If I use a ResNet50 architecture instead, I get no error and I am able to use really big datasets & batch-sizes. Training the models on CPU works fine for all network architectures.
The problem first appeared on a more complex pipeline that runs on p2.16xlarge AWS instances. I can reproduce the problem using Ubuntu 14.04, Keras 1.2.1 and Tensorflow 0.12.1. Can anyone reproduce the problem? Any thoughts?
For the shake of completeness I also uploaded the dataset here.
import keras.applications
from keras.models import Sequential
from keras.layers import Flatten, Dense, Input
from keras.preprocessing import image

architecture = 'VGG19'

model = Sequential()
if architecture == 'VGG19': #This fails
    print('Using VGG19')
    model.add(keras.applications.vgg19.VGG19(weights='imagenet', include_top=False, input_tensor=Input(shape=(224, 224, 3), name='input')))
    model.add(Flatten(name='flatten'))
    model.add(Dense(4096, activation='relu', name='fc1'))
    model.add(Dense(4096, activation='relu', name='fc2'))
    model.add(Dense(2, activation='softmax', name='predictions'))
else: #This works
    print('Using ResNet50')
    model.add(keras.applications.resnet50.ResNet50(weights='imagenet', include_top=False, input_tensor=Input(shape=(224, 224, 3), name='input')))
    model.add(Flatten(name='flatten'))
    model.add(Dense(2, activation='softmax', name='predictions'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
generator = image.ImageDataGenerator().flow_from_directory('./data/cifar2tiny-train', target_size=(224, 224), batch_size=4, class_mode='categorical', shuffle=True) #100 images in 2 classes
model.fit_generator(generator, samples_per_epoch=100, nb_epoch=1)

Error message using VGG19:
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
Using VGG19
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: Quadro K2200
major: 5 minor: 0 memoryClockRate (GHz) 1.124
pciBusID 0000:03:00.0
Total memory: 3.95GiB
Free memory: 3.17GiB
W tensorflow/stream_executor/cuda/cuda_driver.cc:590] creating context when one is currently active; existing: 0x3ed1b30
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 1 with properties:
name: Quadro K2200
major: 5 minor: 0 memoryClockRate (GHz) 1.124
pciBusID 0000:81:00.0
Total memory: 3.95GiB
Free memory: 3.92GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 0 and 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 1 and 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y N
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 1:   N Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K2200, pci bus id: 0000:03:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Quadro K2200, pci bus id: 0000:81:00.0)
Found 100 images belonging to 2 classes.
Epoch 1/1
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 2.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 1.05GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 2.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 1.05GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 2.10GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 1.08GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 561.27MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 2.15GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 1.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:217] Ran out of memory trying to allocate 589.50MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256): 	Total Chunks: 2, Chunks in use: 0 512B allocated for chunks. 16B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096): 	Total Chunks: 1, Chunks in use: 0 7.5KiB allocated for chunks. 8B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768): 	Total Chunks: 1, Chunks in use: 0 32.0KiB allocated for chunks. 32.0KiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072): 	Total Chunks: 1, Chunks in use: 0 128.0KiB allocated for chunks. 16.0KiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432): 	Total Chunks: 1, Chunks in use: 0 49.73MiB allocated for chunks. 16.0KiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864): 	Total Chunks: 2, Chunks in use: 0 128.02MiB allocated for chunks. 128.00MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456): 	Total Chunks: 1, Chunks in use: 0 366.36MiB allocated for chunks. 64.0KiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 392.00MiB was 256.00MiB, Chunk State:
I tensorflow/core/common_runtime/bfc_allocator.cc:666]   Size: 366.36MiB | Requested Size: 64.0KiB | in_use: 0, prev:   Size: 64.00MiB | Requested Size: 64.00MiB | in_use: 1
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980000 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980700 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980900 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980b00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980c00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305980d00 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305981100 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305981300 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305981b00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305981c00 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305981f00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305982000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305982100 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305982a00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305982b00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305982c00 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305982e00 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305983200 of size 3328
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305983f00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305984000 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305988000 of size 6912
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305989b00 of size 32768
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305991b00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305991c00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305991d00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305991e00 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305995e00 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305999e00 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599a500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599a600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599a800 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599aa00 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599c700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599e600 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599e800 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599ea00 of size 1024
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599f200 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599f600 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230599fa00 of size 2048
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a0a00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a1200 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a1a00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a2200 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a2a00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a3200 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a3a00 of size 17920
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a8000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059a8100 of size 147456
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc100 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc200 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc300 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc400 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cc900 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cca00 of size 6912
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059ce500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059ce600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059ce700 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059ce900 of size 512
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059ceb00 of size 1024
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cef00 of size 1024
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059cfb00 of size 2048
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059e3b00 of size 256
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23059f0300 of size 294912
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305a38300 of size 147456
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2305a80500 of size 589824
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23066e1d00 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2306fe1d00 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23078e1d00 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23081e1d00 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2308ae1d00 of size 4718592
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2308f61d00 of size 2359296
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23093e1d00 of size 2359296
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2309621d00 of size 7077888
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2309ce1d00 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e1d00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e2500 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e2d00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e3500 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e3d00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e4500 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e4d00 of size 2048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230a5e5500 of size 4578048
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230aa43000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230b343000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230bc43000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x230c543000 of size 411041792
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2324d43000 of size 67108864
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2328d43000 of size 67108864
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x232cd43000 of size 53788672
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233008f000 of size 2359296
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23302cf000 of size 2359296
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233050f000 of size 2359296
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233074f000 of size 4718592
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2330bcf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23314cf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2331dcf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23326cf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2332fcf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23338cf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23341cf000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2338ad3000 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2338ad7000 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2338adb000 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233971b000 of size 25690112
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233af9b000 of size 25690112
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233c81b000 of size 6422528
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233ce3b000 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233da7b000 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233e6bb000 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233f2fb000 of size 12845056
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x233ff3b000 of size 3211264
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x234024b000 of size 6422528
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x234086b000 of size 6422528
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2340e8b000 of size 6422528
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23414ab000 of size 6422528
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2341acb000 of size 1605632
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2341c53000 of size 1605632
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2341ddb000 of size 1605632
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2341f63000 of size 1605632
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I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2342273000 of size 401408
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23422d5000 of size 16384
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23422d9000 of size 49152
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23422ed000 of size 32768
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2342315000 of size 65536
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2342325000 of size 401408
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2345543000 of size 2359296
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2345783000 of size 4718592
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2345c03000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2346503000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2346e03000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2347703000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2348003000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2348903000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2349203000 of size 9437184
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2349b03000 of size 411041792
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2362303000 of size 67108864
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2366303000 of size 2408448
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x236654f000 of size 64700416
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x236a303000 of size 411041792
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x2382b03000 of size 411041792
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x239f303000 of size 67108864
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x23a3303000 of size 67108864
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x230599a700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x230599c600 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x230599c800 of size 7680
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x2334acf000 of size 67125248
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x23422e5000 of size 32768
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x23422f5000 of size 131072
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x2342387000 of size 52150272
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x239b303000 of size 67108864
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x23a7303000 of size 384159744
I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size:
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 55 Chunks of size 256 totalling 13.8KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 512 totalling 4.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 14 Chunks of size 1024 totalling 14.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 25 Chunks of size 2048 totalling 50.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 3328 totalling 3.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 6912 totalling 27.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 16384 totalling 128.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 17920 totalling 17.5KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 32768 totalling 96.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 49152 totalling 48.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 50432 totalling 49.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 65536 totalling 64.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 147456 totalling 576.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 294912 totalling 1.12MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 401408 totalling 784.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 589824 totalling 2.25MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 1179648 totalling 3.38MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1320192 totalling 1.26MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 1605632 totalling 7.66MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 9 Chunks of size 2359296 totalling 20.25MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 2408448 totalling 2.30MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 3211264 totalling 3.06MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 4578048 totalling 4.37MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 4718592 totalling 13.50MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 6422528 totalling 30.62MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 7077888 totalling 6.75MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 22 Chunks of size 9437184 totalling 198.00MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 12845056 totalling 61.25MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 2 Chunks of size 25690112 totalling 49.00MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 53788672 totalling 51.30MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 64700416 totalling 61.70MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 67108864 totalling 320.00MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 411041792 totalling 1.53GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 2.35GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit:<PHONE_NUMBER>
InUse:<PHONE_NUMBER>
MaxInUse:<PHONE_NUMBER>
NumAllocs:                     553
MaxAllocSize:            585486336

W tensorflow/core/common_runtime/bfc_allocator.cc:274] **************************_******_************************************************_*****____________
W tensorflow/core/common_runtime/bfc_allocator.cc:275] Ran out of memory trying to allocate 392.00MiB.  See logs for memory state.
W tensorflow/core/framework/op_kernel.cc:975] Resource exhausted: OOM when allocating tensor with shape[25088,4096]
Traceback (most recent call last):
  File "./example.py", line 24, in <module>
    model.fit_generator(generator, samples_per_epoch=100, nb_epoch=1)
  File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 935, in fit_generator
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1553, in fit_generator
    class_weight=class_weight)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1316, in train_on_batch
    outputs = self.train_function(ins)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 1900, in __call__
    feed_dict=feed_dict)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[25088,4096]
	 [[Node: gradients/MatMul_grad/MatMul_1 = MatMul[T=DT_FLOAT, _class=["loc:@MatMul"], transpose_a=true, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape_16, gradients/add_16_grad/Reshape)]]

Caused by op u'gradients/MatMul_grad/MatMul_1', defined at:
  File "./example.py", line 24, in <module>
    model.fit_generator(generator, samples_per_epoch=100, nb_epoch=1)
  File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 935, in fit_generator
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1450, in fit_generator
    self._make_train_function()
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 761, in _make_train_function
    self.total_loss)
  File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 227, in get_updates
    grads = self.get_gradients(loss, params)
  File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 80, in get_gradients
    grads = K.gradients(loss, params)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 1925, in gradients
    return tf.gradients(loss, variables, colocate_gradients_with_ops=True)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 482, in gradients
    in_grads = grad_fn(op, *out_grads)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 731, in _MatMulGrad
    math_ops.matmul(op.inputs[0], grad, transpose_a=True))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1729, in matmul
    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1442, in _mat_mul
    transpose_b=transpose_b, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

...which was originally created as op u'MatMul', defined at:
  File "./example.py", line 13, in <module>
    model.add(Dense(4096, activation='relu', name='fc1'))
  File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 332, in add
    output_tensor = layer(self.outputs[0])
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 572, in __call__
    self.add_inbound_node(inbound_layers, node_indices, tensor_indices)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 635, in add_inbound_node
    Node.create_node(self, inbound_layers, node_indices, tensor_indices)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 166, in create_node
    output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
  File "/usr/local/lib/python2.7/dist-packages/keras/layers/core.py", line 813, in call
    output = K.dot(x, self.W)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 814, in dot
    out = tf.matmul(x, y)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1729, in matmul
    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1442, in _mat_mul
    transpose_b=transpose_b, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[25088,4096]
	 [[Node: gradients/MatMul_grad/MatMul_1 = MatMul[T=DT_FLOAT, _class=["loc:@MatMul"], transpose_a=true, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape_16, gradients/add_16_grad/Reshape)]]

Process finished with exit code 1

Successful execution with ResNet50:
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
Using ResNet50
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
name: Quadro K2200
major: 5 minor: 0 memoryClockRate (GHz) 1.124
pciBusID 0000:03:00.0
Total memory: 3.95GiB
Free memory: 3.16GiB
W tensorflow/stream_executor/cuda/cuda_driver.cc:590] creating context when one is currently active; existing: 0x4444090
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 1 with properties:
name: Quadro K2200
major: 5 minor: 0 memoryClockRate (GHz) 1.124
pciBusID 0000:81:00.0
Total memory: 3.95GiB
Free memory: 3.92GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 0 and 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:777] Peer access not supported between device ordinals 1 and 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y N
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 1:   N Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K2200, pci bus id: 0000:03:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Quadro K2200, pci bus id: 0000:81:00.0)
Found 100 images belonging to 2 classes.
Epoch 1/1
  4/100 [>.............................] - ETA: 308s - loss: 0.6807 - acc: 0.7500I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2668 get requests, put_count=2592 evicted_count=1000 eviction_rate=0.385802 and unsatisfied allocation rate=0.44078
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 100 to 110
 92/100 [==========================>...] - ETA: 1s - loss: 2.8755 - acc: 0.5326I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2696 get requests, put_count=2955 evicted_count=1000 eviction_rate=0.338409 and unsatisfied allocation rate=0.283383
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 256 to 281
100/100 [==============================] - 22s - loss: 2.6731 - acc: 0.5500

Process finished with exit code 0

Hello,
The out of memory error is caused by (which you don't add btw in case of resnet) :
model.add(Flatten(name='flatten'))
model.add(Dense(4096, activation='relu', name='fc1'))
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[25088,4096]
25088 = 77512
This is a huge matrix (102760448 parameters) , and I remember having some problem in the past with VGG and memory consumption with smaller GPU.

@unrealwill Thanks so much for your reply. So effectively what you say is that the model is too big to live in my GPU. Makes sense.
I noticed that I get the same error in GPUs with more memory IF I have earlier initialised other models (in the same session). Possibly the were not garbage collected? Is there a way to ensure that a model defined earlier is removed completely from the GPU? Will a simple "del model" do the trick?

I have not tried del model. You can monitor GPU usage with nvidia-smi (if not using CNMeM).
Usually I start a fresh python session. Maybe there is a trick do release the model, but I don't know it.

I tried del and forcing gc but does not work. I am going to close this ticket and it is not a bug and ask how to release memory of previous models on the different issue.
Thanks so much @unrealwill for your help. You were spot on.

@datumbox: The del keyword in Python only releases main memory allocated by your Python script. In order to release the memory on the GPU, you could use K.clear_session() where K refers to your Keras backend.

@unrealwill Is there something fundamentally different in the way memory is implemented on Tensorflow vs Theano?  The Theano vgg16 model has no problem running on my 4GB graphics card wheras the TF model runs out of memory and I saw another thread talking about how it allocates 12GB of memory?
I understand they're different libraries and implemented entirely differently, but if this really is a memory issue why would one framework be able to handle it and the other not?  I'm not saying you're wrong, I'm just genuinely curious.  I haven't heard that Theano is that much more efficient that Tensorflow at memory and if that's the case it changes the choice of platform I'm going to focus on.

@EvenOldridge Yes, Theano only reserved the amount of memory it needed for its variables, so running multiple Theano "sessions" in parallel was fine if your GPU had the RAM. Tensorflow greedily reserves all the RAM on all the GPU's when you start a session (check out nvidia-smi when you launch). That said, Theano is officially dying soon, and I've actually seen pretty substantial performance increases by switching from it to TF (not to mention absurdly faster launch times due to no runtime compilation), so you're probably best sticking with TF and trying to work with its design decisions.
@phobrain Good point, and I've also been learning, if doing keras/TF in Jupyter, make sure to kill one notebook before trying to run another. I was getting some very strange, non-deterministic bugs when two notebooks were both alive and had TF running.

Is there a concrete answer to this question yet? @EvenOldridge @scnerd


vtt format error:  second must be in 0..59

vtt format error:
second must be in 0..59

source
https://www.youtube.com/watch?v=Jkdnn6klg7w

Hi @zxl777, thanks for reporting!

Fixed in v0.5.0 thanks to @liamrs222!


F #-: Update vmm_exec_kvm defaults

I would suggest enabling machine = pc by default for qemu, to avoid situations where it is not possible to migrate virtual machines from ubnutu and centos to another OS, since they has own non standardized machine models defined to use by default, eg:
# qemu-system-x86_64 -machine help
Supported machines are:
microvm              microvm (i386)
pc-i440fx-zesty      Ubuntu 17.04 PC (i440FX + PIIX, 1996)
pc-i440fx-yakkety    Ubuntu 16.10 PC (i440FX + PIIX, 1996)
pc-i440fx-xenial     Ubuntu 16.04 PC (i440FX + PIIX, 1996)
pc-i440fx-wily       Ubuntu 15.04 PC (i440FX + PIIX, 1996)
pc-i440fx-trusty     Ubuntu 14.04 PC (i440FX + PIIX, 1996)
ubuntu               Ubuntu 20.04 PC (i440FX + PIIX, 1996) (alias of pc-i440fx-focal)
pc-i440fx-focal      Ubuntu 20.04 PC (i440FX + PIIX, 1996) (default)
pc-i440fx-focal-hpb  Ubuntu 20.04 PC (i440FX + PIIX +host-phys-bits=true, 1996)
pc-i440fx-eoan       Ubuntu 19.10 PC (i440FX + PIIX, 1996)
pc-i440fx-eoan-hpb   Ubuntu 19.10 PC (i440FX + PIIX +host-phys-bits=true, 1996)
pc-i440fx-disco      Ubuntu 19.04 PC (i440FX + PIIX, 1996)
pc-i440fx-disco-hpb  Ubuntu 19.04 PC (i440FX + PIIX +host-phys-bits=true, 1996)
pc-i440fx-cosmic     Ubuntu 18.10 PC (i440FX + PIIX, 1996)
pc-i440fx-cosmic-hpb Ubuntu 18.10 PC (i440FX + PIIX +host-phys-bits=true, 1996)
pc-i440fx-bionic     Ubuntu 18.04 PC (i440FX + PIIX, 1996)
pc-i440fx-bionic-hpb Ubuntu 18.04 PC (i440FX + PIIX, +host-phys-bits=true, 1996)
pc-i440fx-artful     Ubuntu 17.10 PC (i440FX + PIIX, 1996)
pc                   Standard PC (i440FX + PIIX, 1996) (alias of pc-i440fx-4.2)
pc-i440fx-4.2        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-4.1        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-4.0        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-3.1        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-3.0        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.9        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.8        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.7        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.6        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.5        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.4        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.3        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.2        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.12       Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.11       Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.10       Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.1        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-2.0        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-1.7        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-1.6        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-1.5        Standard PC (i440FX + PIIX, 1996)
pc-i440fx-1.4        Standard PC (i440FX + PIIX, 1996)
pc-1.3               Standard PC (i440FX + PIIX, 1996)
pc-1.2               Standard PC (i440FX + PIIX, 1996)
pc-1.1               Standard PC (i440FX + PIIX, 1996)
pc-1.0               Standard PC (i440FX + PIIX, 1996)
pc-0.15              Standard PC (i440FX + PIIX, 1996) (deprecated)
pc-0.14              Standard PC (i440FX + PIIX, 1996) (deprecated)
pc-0.13              Standard PC (i440FX + PIIX, 1996) (deprecated)
pc-0.12              Standard PC (i440FX + PIIX, 1996) (deprecated)
pc-q35-zesty         Ubuntu 17.04 PC (Q35 + ICH9, 2009)
pc-q35-yakkety       Ubuntu 16.10 PC (Q35 + ICH9, 2009)
pc-q35-xenial        Ubuntu 16.04 PC (Q35 + ICH9, 2009)
ubuntu-q35           Ubuntu 20.04 PC (Q35 + ICH9, 2009) (alias of pc-q35-focal)
pc-q35-focal         Ubuntu 20.04 PC (Q35 + ICH9, 2009)
pc-q35-focal-hpb     Ubuntu 20.04 PC (Q35 + ICH9, +host-phys-bits=true, 2009)
pc-q35-eoan          Ubuntu 19.10 PC (Q35 + ICH9, 2009)
pc-q35-eoan-hpb      Ubuntu 19.10 PC (Q35 + ICH9, +host-phys-bits=true, 2009)
pc-q35-disco         Ubuntu 19.04 PC (Q35 + ICH9, 2009)
pc-q35-disco-hpb     Ubuntu 19.04 PC (Q35 + ICH9, +host-phys-bits=true, 2009)
pc-q35-cosmic        Ubuntu 18.10 PC (Q35 + ICH9, 2009)
pc-q35-cosmic-hpb    Ubuntu 18.10 PC (Q35 + ICH9, +host-phys-bits=true, 2009)
pc-q35-bionic        Ubuntu 18.04 PC (Q35 + ICH9, 2009)
pc-q35-bionic-hpb    Ubuntu 18.04 PC (Q35 + ICH9, +host-phys-bits=true, 2009)
pc-q35-artful        Ubuntu 17.10 PC (Q35 + ICH9, 2009)
q35                  Standard PC (Q35 + ICH9, 2009) (alias of pc-q35-4.2)
pc-q35-4.2           Standard PC (Q35 + ICH9, 2009)
pc-q35-4.1           Standard PC (Q35 + ICH9, 2009)
pc-q35-4.0.1         Standard PC (Q35 + ICH9, 2009)
pc-q35-4.0           Standard PC (Q35 + ICH9, 2009)
pc-q35-3.1           Standard PC (Q35 + ICH9, 2009)
pc-q35-3.0           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.9           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.8           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.7           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.6           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.5           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.4           Standard PC (Q35 + ICH9, 2009)
pc-q35-2.12          Standard PC (Q35 + ICH9, 2009)
pc-q35-2.11          Standard PC (Q35 + ICH9, 2009)
pc-q35-2.10          Standard PC (Q35 + ICH9, 2009)
isapc                ISA-only PC
none                 empty machine

I have doubts that this solves anything.

pc is just an alias to some latest i440fx machine model
if chosen (manually or set as default), the real model to which alias points to is used. E.g., from example above:

pc                   Standard PC (i440FX + PIIX, 1996) (alias of pc-i440fx-4.2)

virtual will be of type pc-i440fx-4.2 at the end, not pc. When choosing pc on CentOS 7, the alias points to
pc                   RHEL 7.6.0 PC (i440FX + PIIX, 1996) (alias of pc-i440fx-rhel7.6.0)

and pc-i440fx-4.2 is not model compatible with pc-i440fx-rhel7.6.0.

Cold migration, when VM is powered-off on one HV and resumed on different HV, works now.

Live or checkpoint based based migration should usually work within same distribution from lower to higher versions.

I'm not convinced that it should be (by default) supported to migrated across any random distributions and random versions. User need to evaluate if the combination is supported, what are the risks, etc.

Maybe I'm not getting it and this machine models work different than I think, but I don't see any benefit of this PR for now.
Can you please elaborate, @kvaps  ?

The proposed change could help migrate from Ubuntu to Debian or Fedora, which uses pc as a default.
But I suppose Ubuntu has a reason, why they use their own machine model, and using the 'pc' may impact performance.
It's hard to find differences between these machine models to tell which one is better.
I would prefer using the default machine type for the OS. If the user needs migration between different OS, he should know what he is doing and he can adjust the vmm_exec_kvm.conf.

Agree, let's leave this up to users' consideration.


Removed unneeded Default constraint

I was trying to use MutableListArray with a type that doesn't implement Default, and ran into an error caused by this constraint. It looks unnecessary, and removing it doesn't seem to break anything.

Thanks a lot, @hohav !


How to Set a Cookie

I need to set a cookie in the header before making an API call/request.  I would like to use the static methods on the generated code to make the API call, but I'm not sure how to set the request headers.  I suspect that the --request parameter would need to point to a custom request file, but I am not exactly sure what should go in that file.  Can someone elaborate on this?

Please disregard.  I just found the answer at https://github.com/ferdikoomen/openapi-typescript-codegen/wiki/Custom-request-file.

@ballcoach12 hey, I'm going to re-open this because I don't agree with writing custom requests as a solution. We should offer a middleware interface for this. Do you need to set this cookie for every request?
cc @jordanshatford

@ballcoach12 are you using WITH_CREDENTIALS in your openapi config? And what client at you using? Axios?

Hey guys.  I appreciate the proactive approach here.  I need to be able to set a cookie on very request, and I am using the fetch client. I am using WITH_CREDENTIALS=true in my OpenApiConfig. I also had to set CREDENTIALS=same-origin in order for things to work correctly.

Hey guys.  I appreciate the proactive approach here.  I need to be able to set a cookie on every request, and I am using the fetch client. I am using WITH_CREDENTIALS=true in my OpenApiConfig. I also had to set CREDENTIALS=same-origin in order for things to work correctly.
And to put a finer point on things, I got it to work with the generated client and didn't have to use a custom request file.  My solution looks something like this (the code runs in a Web Worker):

const headers= {

        "Cookie": `auth=${this.services.session.TokenService.getToken()}`,

      };

    OpenAPI.HEADERS = headers;

    OpenAPI.BASE = `https://${this.services.session.TokenService.getHost()}/function-registry/v1`;

    OpenAPI.CREDENTIALS = "same-origin";

    OpenAPI.WITH_CREDENTIALS = true;

    OpenAPI.ORIGIN = `https://${this.services.session.TokenService.getHost()}`;

    try {

     const data = {

      majorVersion: (fqn.version as Version).majorVersion,

      minorVersion: (fqn.version as Version).minorVersion,

      packageName: fqn.packageName,

      revision: (fqn.version as Version).revision

     }

      const modelsPackage = await PackageService.getPackage(data);

     // do something with the result

}  catch (e) {

  // handle the error

}

...

Would something like this work for your use case https://github.com/ferdikoomen/openapi-typescript-codegen/wiki/Authorization this value will get put in the 'Authorization' header in the format 'Bearer '. See: https://github.com/hey-api/openapi-ts/blob/main/src/templates/core/fetch/getHeaders.hbs#L21

In our case, we use JWT for authorization and claims, so the simple bearer token would not do the job.

In our case, we need it stored as a Cookie.

Are you able to share the generated code for one of your services? Might be useful in providing better support for cookies

@ballcoach12 are you using WITH_CREDENTIALS in your openapi config? And what client at you using? Axios?

where do you place the
OpenAPI.CREDENTIALS = "include"
OpenAPI.WITH_CREDENTIALS = true

@ballcoach12 are you using WITH_CREDENTIALS in your openapi config? And what client at you using? Axios?

where do you place the
OpenAPI.CREDENTIALS = "include"
OpenAPI.WITH_CREDENTIALS = true

You can place it pretty much wherever. For example I set my OpenAPI.X values in my layout file.

awesome its working!

I am setting it just prior to making my request.

@donkey-donkey @ballcoach12 we now support interceptors which allow you to modify requests/responses before they're sent/returned, would you mind giving them a try? They're a better abstraction for what you're trying to achieve and eventually we plan to remove options such as OpenAPI.CREDENTIALS, so please try them!


App Thinning Size Report.txt not generating when passing <thin-for-all-variants>

New Issue Checklist

[X] Updated fastlane to the latest version
[X] I read the Contribution Guidelines
[X] I read docs.fastlane.tools
[X] I searched for existing GitHub issues

Issue Description
XCode instructions say plist "thinning key, with <thin-for-all-variants> as the value" will generate App Thinning Size Report.txt. Export_options is the plist for fastlane. (more technical docs and additional FastLane docs)
In mid July, FastLane stopped producing App Thinning Size Report.txt . We've reviewed our version changes for XCode, Ruby, and TestFlight and any changes in the nearby code and haven't been able to track down what change caused the issue.
Command executed
Executed in a FastFile.
Redacted Command
build_app(
      scheme: xxxxx,
      workspace: xxxx,
      configuration: xxxx,
      xcargs: xxxxx,
      export_method: xxxx,
      export_options: {
        signingStyle: xxxx,
        signingCertificate: xxxx,
        destination: xxxx,
        method: xxxx,
        teamID: xxxx,
        manageAppVersionAndBuildNumber: false,
        thinning: '<thin-for-all-variants>',
        provisioningProfiles: {
          xxxx => xxxx,
          xxxx => xxxx
        }
      },
      output_directory: xxxx,
      output_name: xxxxx
    )

Complete output when running fastlane, including the stack trace
Note: when following the instructions for --capture_output I kept receiving errors, instead I have a shortened version of --verbose
Reviewing our logs, a plist file is generated with the correct thinning options and the XCode 7 build safe option is receiving a plist file for -exportOptionsPlist
xcodebuild -exportArchive -exportOptionsPlist /var/folders/xxxxx.plist -archivePath '/Users/distiller/Library/Developer/Xcode/Archives/xxxxxx.xcarchive' -exportPath /var/folders/xxxx BUNDLE_IDENTIFIER=xxxxx 'PROVISIONING_PROFILE_SPECIFIER=xxxxx' DEVELOPMENT_TEAM=xxxxx 'CODE_SIGN_IDENTITY=iPhone Distribution' PRODUCT_BUNDLE_IDENTIFIER=xxxxx

But we are missing Successfully exported the App Thinning Size Report.txt file: in the logs. (related code)
Successfully exported and signed the ipa file:
INFO [xxxxx]: /Users/distiller/xxxxx/ios/artifacts/xxxxx.ipa
INFO [xxxxx]: ---------------------------------------
INFO [xxxxx]: --- Step: xxxxx ---

Environment

 | Key                         | Value                                                  |
| --------------------------- | ------------------------------------------------------ |
| OS                          | 12.6.3                                                 |
| Ruby                        | 3.0.5                                                  |
| Bundler?                    | true                                                   |
| Git                         | git version 2.37.3                                     |
| Installation Source         | **** |
| Host                        | macOS 12.6.3 (21G419)                                  |
| Ruby Lib Dir                | ~/.rvm/rubies/ruby-3.0.5/lib                           |
| OpenSSL Version             | OpenSSL 1.1.1s  1 Nov 2022                             |
| Is contained                | false                                                  |
| Is homebrew                 | false                                                  |
| Is installed via Fabric.app | false                                                  |
| Xcode Path                  | /Applications/Xcode.app/Contents/Developer/            |
| Xcode Version               | 14.2                                                   |
| Swift Version               | 5.7.2                                                  |

@maria-jackson-shipt have you found a solution for the issue? we are facing the same problem with app-thinning report

I'm also running into this issue

It seems broken at Apple side. I cannot generate directly with xcodebuild command neither. Might be related with Bitcode changes.
https://developer.apple.com/documentation/xcode/reducing-your-app-s-size


set(option, value) is not working with time related options.

set(option, value) is not working with time related options like enableTime, time_24h:
https://jsfiddle.net/eusougz/vu8Lh0de/24/
The buildTime internal function shoud be called in redraw?!
Your Environment

flatpickr version used: latest
Browser name and version: Chrome Version 108.0.5359.98 (Official Build) (64-bit)
OS and version: Ubuntu 20.04 LTS

Any updates on this?


Added insertion sort

I am a beginner to open source contribution . Thank you and I have added insertion sort.cpp under the label hacktoferbest. Please accept the PR. Will also star your repo!!

@pavithra-064
Wohoo!! Congrats🥳 your PR has been merged.

Thank you so much


Add support for data type like timestamp(x) with/without time zone

What's this PR do?
Add support for data type like timestamp(x) with/without time zone
Where should the reviewer start?
2 File has been updated:

core/datatype_mapper.go: add an extra case condition on capture data type like timestamp(x) with/without time zone
README.md: Based on the test, the latest mbp package is not compatible with the code, I try to use mbp v3.0.4 and it works, so update readme accordingly

How should this be manually tested?

create a table like below:

create table test (
    day1 timestamp(0) without time zone,
    day2 timestamp(1) without time zone,
    day3 timestamp(2) without time zone,
    day4 timestamp(3) without time zone,
    day5 timestamp(4) without time zone,
    day6 timestamp(5) without time zone,
    day7 timestamp(6) without time zone,
    day8 timestamp(0) with time zone,
    day9 timestamp(1) with time zone,
    day10 timestamp(2) with time zone,
    day11 timestamp(3) with time zone,
    day12 timestamp(4) with time zone,
    day13 timestamp(5) with time zone,
    day14 timestamp(6) with time zone
);

Run the code to generate data

 ./mockd-linux_v1.2 greenplum -p 4987 -u gpadmin -t public.test -d gpadmin -n 10000
...
 4s [==================================================================================================] 100 %  (Mocking Table: public.test)

Any background context you want to provide?
nope
What are the relevant tickets?
nope
Screenshots (if appropriate)
nope
Questions:

Is there a blog post?
Does the knowledge base need an update (eg.s README)?
added some comment, the mbp package had to be v3.0.4
Does this add new (go) dependencies which need to be added?
nope

looks good, thanks :)


Tiebreaker in _TypeResolver

_TypeResolver should default to calling something a string if there is a tie between string and another type.
Currently I am experiencing inconsistent behaviour for calls like
import unittest
from io import BytesIO

from tabulator import Stream
from tableschema import infer

class BooleanTestCase(unittest.TestCase):
    def test_check_type(self):

        data = BytesIO(b"foo\tbaraz\t\n1\ttst\tm tst\n\tf\tother test")

        with Stream(data, format="tsv") as stream:
            sample = stream.sample
        schema = infer(sample[0], sample[1:])
        assert schema["fields"][1]["type"] == "string"

Here there is a tie between boolean and string. If the test is run on its own then 1/2 the time it fails. I think that the best way to follow "in the face of ambiguity refuse the temptation to guess" would be for the typeresolver to just stick to string.
Or perhaps even to have a configurable threshold % of strings at which string would be the default.

I agree we should have it deterministic.


DBFlow 4.0 doesn't generate ContentProvider

DBFlow Version: 4.0.0-beta7
Issue Kind (Bug, Question, Feature): Bug
Please note if you are using Instant Run, there may be bugs where generated classes are not created. Ensure you are using
the apt or kapt plugins and that incremental compilation is off.
Description: Am I missing something, but I think DBFlow 4.0 doesn't generate ContentProvider.
@Database(name = MyDatabase.NAME,
        version = MyDatabase.VERSION)
@ContentProvider(authority = MyDatabase.AUTHORITY,
        database = MyDatabase.class,
        baseContentUri = MyDatabase.BASE_CONTENT_URI)
public class MyDatabase {
    public static final String NAME = "mydb";
    public static final int VERSION = 1;
    public static final String AUTHORITY = "jd.omg.myapp.authority";
    public static final String BASE_CONTENT_URI = "content://";
}

@Table(name = "tracks", database = MyDatabase.class)
@TableEndpoint(name = "DbFlowTrack", contentProvider = MyDatabase.class)
public class DbFlowTrack extends BaseModel {

    public static final String ENDPOINT = "DbFlowTrack";

    @ContentUri(path = DbFlowTrack.ENDPOINT,
            type = ContentUri.ContentType.VND_MULTIPLE + ENDPOINT)
    public static final Uri CONTENT_URI = buildUri(ENDPOINT);

    private static Uri buildUri(String... paths) {
        Uri.Builder builder = Uri.parse(MyDatabase.BASE_CONTENT_URI + MyDatabase.AUTHORITY).buildUpon();
        for (String path : paths) {
            builder.appendPath(path);
        }
        return builder.build();
    }

    @PrimaryKey(autoincrement = false)
    @Column(name = "id")
    String id;

    @Column(name = "title")
    String title;

    @Column(name = "author")
    String author;
}

        <provider
            android:authorities="jd.omg.myapp.authority"
            android:name="jd.omg.myapp.dbflow.MyDatabase_Provider"
            android:exported="false"/>

    // DBFlow
    // https://github.com/Raizlabs/DBFlow
    annotationProcessor "com.github.Raizlabs.DBFlow:dbflow-processor:$dbflowVersion"
    compile "com.github.Raizlabs.DBFlow:dbflow-core:$dbflowVersion"
    compile "com.github.Raizlabs.DBFlow:dbflow:$dbflowVersion"
    compile "com.github.Raizlabs.DBFlow:dbflow-kotlinextensions:$dbflowVersion"
    compile "com.github.Raizlabs.DBFlow:dbflow-rx2-kotlinextensions:$dbflowVersion"

But the MyDatabase_Provider doesn't get generated, it's red as Android Studio can't find it and it crashes with ClassNotFound exception. I tried clean project, rebuilt twice, no instant run. Am I missing some obvious point? Some of the documentation is confusing or outdated.
P.S. this issue template talks about using apt, kapt, but nowadays it is annotationProcessor. I use kotlin for this project, but I tested both kotlin classes and java classes.

Got it working on new empty project. I needed kapt line and
kapt {
    generateStubs = true
}

although in that new project I didn't use kotlin. In original project there was kapt (i.e. due to dagger) but no the generateStubs.
Sorry for the incovenience.


CD

[ ] Release process documented

https://github.com/Datateer/tap-clickcast/blob/main/.github/workflows/ci.yml


Add Support for SQL Server Analysis Services (SSAS) Connection with MDX Query Execution, Dimension Management, Pivot Tables, and Domain User Login

Apache Superset currently lacks native support for SQL Server Analysis Services (SSAS), a popular OLAP (Online Analytical Processing) platform. Integrating SSAS into Superset with support for MDX (Multidimensional Expressions) queries, dimension and database management, pivot table functionalities, and domain user login would greatly enhance its analytical capabilities.
Motivation
Adding these features to Superset will provide several benefits:

Advanced Query Execution: Allow users to execute MDX queries directly within Superset, facilitating complex analytical operations on multidimensional data.
Dimension and Database Management: Provide functionality to browse and manage SSAS cubes, dimensions, and databases, similar to how Excel interacts with SSAS.
Pivot Table Capabilities: Enable users to create and interact with pivot tables directly within Superset, offering a familiar interface for data analysis.
Domain User Login: Allow users to log in with domain credentials that have access to SSAS cubes. This feature will integrate domain authentication, simplifying user management and access control.
Streamlined Workflow: Integrate SSAS capabilities directly into Superset, reducing the need for additional BI tools and streamlining workflows.

Proposed Solution

Connection Support:

Implement support for connecting to SSAS using appropriate drivers and connection strings. Add configuration options for SSAS connections.

MDX Query Execution:

Develop and integrate a custom SQLAlchemy dialect to handle MDX queries. Enable users to execute and visualize MDX query results within Superset.

Dimension and Database Management:

Implement features for browsing SSAS cubes, dimensions, and databases. This functionality should be user-friendly and similar to Excel’s interface with SSAS.

Pivot Table Functionality:

Integrate drag-and-drop functionality for creating and manipulating pivot tables using SSAS cube data within Superset.

Domain User Login:

Add support for domain user authentication. This should allow users to log in with their domain credentials that have access to SSAS, simplifying the process of accessing and managing SSAS data.

Documentation and User Guide:

Update Superset’s documentation to include instructions on configuring SSAS connections, executing MDX queries, managing dimensions, using pivot table features, and setting up domain user logins.

Testing:

Conduct comprehensive testing to ensure that the SSAS connection, MDX query execution, dimension management, pivot table functionalities, and domain user login work seamlessly. Include unit tests and integration tests with SSAS.

Additional Context

Use Case: Many organizations rely on SSAS for complex analytics and multidimensional data analysis. Adding these features to Superset will provide a unified platform for advanced analytics and reporting, especially for users relying on domain credentials for access.
Related Issues: If there are any related issues or discussions, please link them here for reference.

Steps to Implement

Create a Custom SQLAlchemy Dialect: Develop and test the custom dialect for handling MDX queries and SSAS connections.
Develop Dimension and Database Management: Implement browsing and management features for SSAS cubes and dimensions.
Integrate Pivot Table Functionality: Enable drag-and-drop pivot table creation and manipulation.
Add Domain User Login: Implement support for domain user authentication with SSAS.
Update Documentation: Provide clear documentation and examples for users.
Perform Testing: Validate functionality through rigorous testing.

Thank you for considering this feature request. Implementing these enhancements will provide significant value to users who depend on SSAS for their business intelligence needs and wish to leverage Apache Superset for advanced analytics.

iman shirvani
Originally posted by @ishirvani in https://github.com/apache/superset/discussions/30182

Hello,
I am working on adding support for SQL Server Analysis Services (SSAS) to Apache Superset, with the goal of connecting via domain user authentication and ensuring that once connected, the user can view the full set of data, including databases and dimensions.
Here’s what I’ve done so far:
Creating a new DB engine spec: I’ve implemented an initial version of a custom engine spec for SSAS that includes MDX query execution and fetching metadata like dimensions and databases. Here’s the implementation I’ve started with:
python
Copy code
from superset.db_engine_specs.base import BaseEngineSpec
class SSASEngineSpec(BaseEngineSpec):
engine = 'ssas'
engine_name = 'SQL Server Analysis Services'
@classmethod
def execute(cls, cursor, query, **kwargs):
    cursor.execute(query)
    return cursor.fetchall()

@classmethod
def get_dimensions(cls, cursor):
    cursor.execute("SELECT * FROM $system.DISCOVER_DIMENSIONS")
    return cursor.fetchall()

@classmethod
def get_databases(cls, cursor):
    cursor.execute("SELECT * FROM $system.DISCOVER_CATALOGS")
    return cursor.fetchall()

Integrating domain user authentication: My goal is to connect to SSAS using domain user credentials (via Kerberos or LDAP). I want to ensure that once the domain user is authenticated, they can view the entire dataset (databases and dimensions) available in SSAS.
Testing and displaying data: After the successful connection, the user should be able to explore the SSAS databases and dimensions through Superset's interface, including using MDX queries.
I would like some guidance on:
How to correctly configure the SSAS connection in Superset for domain user authentication, ensuring that it works with Kerberos or LDAP.
Best practices or additional steps to fully register and make this SSAS connection selectable and usable for users.
How to ensure that, upon successful authentication, the entire SSAS data (including all databases and dimensions) is properly displayed to the user within Superset.
Any insights or existing implementations around SSAS in Superset, specifically for domain user logins, would be greatly appreciated!
Thank you for your support.

Here's the final version with a request for community help at the end:

Title: Issue with loading SSASEngineSpec - "Could not load database driver: SSASEngineSpec"
Hi Apache Superset team,
I am working on adding support for SQL Server Analysis Services (SSAS) to Superset. I have implemented a new class called SSASEngineSpec as needed, but I am encountering the following error:
ERROR: Could not load database driver: SSASEngineSpec

The code I implemented:
from typing import Any, Optional
from datetime import datetime
from superset.db_engine_specs.base import BaseEngineSpec

class SSASEngineSpec(BaseEngineSpec):
    engine = "ssas"
    engine_name = "SQL Server Analysis Services"

    @classmethod
    def execute(cls, cursor: Any, query: str, **kwargs: Any) -> Any:
        cursor.execute(query)
        return cursor.fetchall()

    @classmethod
    def get_dimensions(cls, cursor: Any) -> list[tuple[Any, ...]]:
        cursor.execute("SELECT * FROM $system.DISCOVER_DIMENSIONS")
        return cursor.fetchall()

    @classmethod
    def get_databases(cls, cursor: Any) -> list[tuple[Any, ...]]:
        cursor.execute("SELECT * FROM $system.DISCOVER_CATALOGS")
        return cursor.fetchall()

    @classmethod
    def convert_dttm(cls, target_type: str, dttm: datetime) -> Optional[str]:
        return f"'{dttm.isoformat()}'"

    @classmethod
    def fetch_data(cls, cursor: Any, limit: Optional[int] = None) -> list[tuple[Any, ...]]:
        if not cursor.description:
            return []
        return super().fetch_data(cursor, limit)

My questions:

Is there something I am missing regarding how to register or load this class?
Do any configuration changes need to be made (e.g., superset_config.py or similar) to load this new driver correctly?
Are there any guidelines or documentation for adding SSAS support?

Additionally, I am trying to connect to SSAS using the following connection string format:
ssas://ip/ssas_dbname

Does this format look correct for SSAS connections in Superset?
I would greatly appreciate any guidance or documentation on resolving this error.
Help from the community:
If anyone in the community has experience integrating SSAS with Superset, your input would be incredibly valuable. Any code samples, documentation, or suggestions to help resolve this issue would benefit not just me, but other developers looking to add SSAS support to Superset as well.
Thank you for your time and assistance!

Title: "Could not load database driver: SSASEngineSpec" after defining and registering custom engine for SSAS
Body:
Hi,
I am facing an issue with adding support for SQL Server Analysis Services (SSAS) to Apache Superset. Despite following the necessary steps to define and register the SSASEngineSpec class, I am encountering the following error:
Error:
Could not load database driver: SSASEngineSpec

What I've Done:

Defined the SSASEngineSpec Class:

Created the SSASEngineSpec class in superset/db_engine_specs/ssas.py, extending BaseEngineSpec with methods for executing MDX queries and managing dimensions and databases.

Registered SSASEngineSpec:

Updated superset/db_engine_specs/__init__.py to include SSASEngineSpec in the list of engine specs in the load_engine_specs() function.

Connection String:

Using the format ssas://username:password@hostname/ssas_dbname .

Logs:
Here are some relevant log entries when attempting to test the connection:
superset_app  | 2024-09-14 08:36:08,301:DEBUG:superset.stats_logger:[stats_logger] (incr) test_connection_attempt
superset_app  | 2024-09-14 08:36:08,319:DEBUG:superset.stats_logger:[stats_logger] (incr) test_connection_error.NoSuchModuleError
superset_app  | 2024-09-14 08:36:08,322:DEBUG:superset.stats_logger:[stats_logger] (incr) DatabaseRestApi.test_connection.warning
superset_app  | 2024-09-14 08:36:08,322:WARNING:superset.views.error_handling:CommandException
superset_app  | Traceback (most recent call last):
superset_app  |   File "/app/superset/commands/database/test_connection.py", line 143, in run
superset_app  |     with database.get_sqla_engine(override_ssh_tunnel=ssh_tunnel) as engine:
superset_app  |   File "/usr/local/lib/python3.10/contextlib.py", line 135, in __enter__
superset_app  |     return next(self.gen)
superset_app  |   File "/app/superset/models/core.py", line 446, in get_sqla_engine
superset_app  |     yield self._get_sqla_engine(
superset_app  |   File "/app/superset/models/core.py", line 465, in _get_sqla_engine
superset_app  |     self.db_engine_spec.validate_database_uri(sqlalchemy_url)
superset_app  |   File "/app/superset/db_engine_specs/base.py", line 2249, in validate_database_uri
superset_app  |     sqlalchemy_uri.get_driver_name(), set()
superset_app  |   File "/usr/local/lib/python3.10/site-packages/sqlalchemy/engine/url.py", line 625, in get_driver_name
superset_app  |     return self.get_dialect().driver
superset_app  |   File "/usr/local/lib/python3.10/site-packages/sqlalchemy/engine/url.py", line 680, in get_dialect
superset_app  |     entrypoint = self._get_entrypoint()
superset_app  |   File "/usr/local/lib/python3.10/site-packages/sqlalchemy/engine/url.py", line 662, in _get_entrypoint
superset_app  |     cls = registry.load(name)
superset_app  |   File "/usr/local/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py", line 343, in load
superset_app  |     raise exc.NoSuchModuleError(
superset_app  | sqlalchemy.exc.NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:ssas
superset_app  |
superset_app  | The above exception was the direct cause of the following exception:
superset_app  |
superset_app  | Traceback (most recent call last):
superset_app  |   File "/usr/local/lib/python3.10/site-packages/flask/app.py", line 1484, in full_dispatch_request
superset_app  |     rv = self.dispatch_request()
superset_app  |   File "/usr/local/lib/python3.10/site-packages/flask/app.py", line 1469, in dispatch_request
superset_app  |     return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
superset_app  |   File "/usr/local/lib/python3.10/site-packages/flask_appbuilder/security/decorators.py", line 95, in wraps
superset_app  |     return f(self, *args, **kwargs)
superset_app  |   File "/app/superset/views/base_api.py", line 119, in wraps
superset_app  |     duration, response = time_function(f, self, *args, **kwargs)
superset_app  |   File "/app/superset/utils/core.py", line 1364, in time_function
superset_app  |     response = func(*args, **kwargs)
superset_app  |   File "/app/superset/utils/log.py", line 303, in wrapper
superset_app  |     value = f(*args, **kwargs)
superset_app  |   File "/app/superset/views/base_api.py", line 91, in wraps
superset_app  |     return f(self, *args, **kwargs)
superset_app  |   File "/app/superset/databases/api.py", line 1219, in test_connection
superset_app  |     TestConnectionDatabaseCommand(item).run()
superset_app  |   File "/app/superset/commands/database/test_connection.py", line 193, in run
superset_app  |     raise DatabaseTestConnectionDriverError(
superset_app  | superset.commands.database.exceptions.DatabaseTestConnectionDriverError: Could not load database driver: SSASEngineSpec
superset_app  | 2024-09-14 08:36:08,329:INFO:werkzeug:<IP_ADDRESS> - - [14/Sep/2024 08:36:08] "POST /api/v1/database/test_connection/ HTTP/1.1" 422 -

Request:
I have confirmed that SSASEngineSpec is defined and registered according to the steps outlined in the documentation. However, the error persists. Could you provide any additional guidance or check if there might be something missed in the configuration or implementation? Any assistance would be greatly appreciated.
Thank you!


App Crashed While Opening The EventList of Bookmarks , Tracks Sections

Actual Behaviour
App Crashed when Opening the The Event List in the Section Like Bookmarks, Tracks, Schedule . The error which is given in the log is that there is no such column Thumbnail in the SQLite Database.
Expected Behaviour
There must be a Thumbnail column in the SQLite Database to avoid such crash or we should make some checks to avoid such crash.
LogCat for the issue

@RamolaWeb take a look at #1402


service/network_profiling_test/service now failing consistently on vm-win-debug-x64c

There are new test failures on [ddc] Fix default type args signature on native classes...[vm] Tweak handling of rare types with non-null instance type arguments..
The tests
service/network_profiling_test/service RuntimeError (expected Pass)

are failing on configurations
vm-win-debug-x64c

It started being flaky on 23. February, with 62 passes and 57 RTEs, but has finally failed consistently enough to get promoted to RTE. Excerpt from the log:
--- Command "vm" (took 54.000536s):
set DART_CONFIGURATION=DebugX64C & set DART_SUPPRESS_WER=1 & set DART_CRASHPAD_HANDLER=C:\b\s\w\ir\out\DebugX64C\crashpad_handler.exe & out\DebugX64C\dart.exe --sound-null-safety -Dtest_runner.configuration=vm-win-debug-x64c --ignore-unrecognized-flags --packages=C:\b\s\w\ir\.dart_tool\package_config.json C:\b\s\w\ir\runtime\observatory\tests\service\network_profiling_test.dart

exit code:
255

stdout:
00:00 +0: network_profiling_test.dart (VM Service)

** Launching SERVICE_TEST_TESTEE=true out\DebugX64C\dart.exe --disable-dart-dev --write-service-info=file:///C:/b/s/w/it_s3zehou/dart_service2be99803/service_info.json --profiler --sound-null-safety -Dtest_runner.configuration=vm-win-debug-x64c --ignore-unrecognized-flags --packages=C:\b\s\w\ir\.dart_tool\package_config.json --enable-vm-service:0 C:\b\s\w\ir\runtime\observatory\tests\service\network_profiling_test.dart

>testee>out> The Dart VM service is listening on http://<IP_ADDRESS>:62444/_Ng-DO__GPU=/

>testee>out>

** Signaled to run test queries on http://<IP_ADDRESS>:62444/_Ng-DO__GPU= (VM Service)

Loading VM...

For example, 'dart test --chain-stack-traces'.

stderr:
Unhandled exception:
Dummy exception to set exit code.

--- Re-run this test:
python3 tools/test.py -n vm-win-debug-x64c service/network_profiling_test/service

The .../dds version is similarly flaky, so wouldn't be surprised if it also goes full RTE soon.

It's meta-flaking: https://dart-ci.web.app/#showLatestFailures=false&test=service/network_profiling_test/dds&configurations=vm-win-debug-x64c
Flipped from flaky to failing again today.

flipped again: https://dart-ci.appspot.com/log/vm-win-debug-x64c/vm-win-debug-x64c/208/service/network_profiling_test/dds

Looking at the Dart VM Flakiness dashboard, this is something that happens on all Windows bots, though apparently the debug bots are the only ones that RTE enough for it to flip from flaky to RTE instead of back to Pass.

For example, 'dart test --chain-stack-traces'.

stderr:
Unhandled exception:
Dummy exception to set exit code.

--- Re-run this test:
python3 tools/test.py -n vm-win-debug-arm64 service/network_profiling_test/dds

The linked RTEs on the dashboard for the different configurations all seem to be consistent in sometimes getting 11 instead of 8 bytes written for https://github.com/dart-lang/sdk/blob/74eb7b38ea611007f09748102c4f0968bb75329c/runtime/observatory/tests/service/network_profiling_test.dart#L147
/cc @bkonyi


Displays remote URL for SSH and/or HTTP(s).

This pull request adds a button next to ZIP and TAR download buttons, which shows remote URL for SSH and/or HTTP(s). For this to work properly, #536 has to be merged with fixes I posted in review (there is a problem with nested repositories).
The idea is that if you are using gitolite as authorization layer, you can fill in the user under which gitolite is running. Also anonymous HTTP(s) cloning is supported. I have already made this working with gitolite via HTTP(s) but it's not yet polished enought for PR.
I hope this helps.

how to add sub dir in ssh clone?


cross-platform: Fix clang issues

remove -fno-inline-functions from build as it breaks CC / Clang3.8+
ATM we have all the tests are passing. So, we no longer need this compiler
switch in place.

enable Werror to prevent commits are breaking clang support

enable both debug and test-build tests on CI

fix clang 3.8/3.9 template issues

LGTM

@jianchun Thanks for the review


An argument with name "class" already exists.

Laravel Version: 8.32.1
PHP Version: 7.4.16
Database Driver & Version: -

Description:
After upgrade laravel/framework from v. 8.31.0 to 8.32.1 throw error in console An argument with name "class" already exists. after dump-autoload. Reverting to a previous version fixes the problem
Steps To Reproduce:
composer upgrade laravel/framework

I'm pretty sure that this is caused by https://github.com/laravel/framework/pull/36513 but I don't see how that's breaking. Are you modifying the command in any way? We'll need a lot more info here. How are you calling your seeder classes? Are there internal artisan calls in your code/tests?
/cc @jasonmccreary

@driesvints I didn't change anything. The only thing that might have to do with it is tenancy/multi-tenant. But this package was not updated at the same time

@code2prog ah I wasn't asking if you changed anything. I'm asking how you're calling the db:seed command.

https://github.com/tenancy/multi-tenant/issues/988
It seems that here is the reason

@driesvints, if you're worried about conflicts with community packages, we should try to rename it to seeder. But what's to say another package doesn't call it that. 🙃

Not entirely worried, was just wondering how your change broke certain use cases.

In general that wasn't part of the public API. If people are overwriting it in that way then we can't protect against that.

That is not true at all, rather this new change overwritten something and broke a package

@jonquintero we can't control the ways in how someone overwrites this. If you're overwriting a command and adding arguments of your own then you should be aware that an upstream change can break it. This is different from, for example, method signatures which are part of the public API of a codebase.

@driesvints I understand your point and it is totally valid, but my point is that this new change #36513 overwritten something that the mentioned package had been using for a long time and broke it, reverting to a previous version fixes the problem (8.31.0). What's great about all this? That forces developers to update their packages and not forget about them

@jonquintero you can't expect us to keep in mind every single way someone overwrites something in the framework. If we had to do that we had to do new major releases each week just to make sure that no small change breaks something in a third party app.

@driesvints I know and you are right, that what I said: What's great about all this? That forces developers to update their packages and not forget about them @ArlonAntonius

ah sorry, I misunderstood you there 👍

Tbh, I really hope Laravel is not gonna think too much about community packages and all the stuff they do; would be a mess and they would not be able to add anything anymore to the ecosystem.
The PR they did was perfect, it's simply up to us to fix the package, thanks for finding this problem and taking the time to investigate and discuss this topic 🙏

For the sake of reference, I'm adding here another constellation that breaks.
I'm having the same error, without having tenancy/multi-tenant installed.
I have another similar package though, stancl/tenancy - v3.4.0.
Loading composer repositories with package information
Updating dependencies
Lock file operations: 0 installs, 1 update, 0 removals
  - Upgrading laravel/framework (v8.29.0 => v8.32.1)
Writing lock file
Installing dependencies from lock file (including require-dev)
Package operations: 0 installs, 1 update, 0 removals
  - Downloading laravel/framework (v8.32.1)
  - Upgrading laravel/framework (v8.29.0 => v8.32.1): Extracting archive
Generating optimized autoload files
> Illuminate\Foundation\ComposerScripts::postAutoloadDump
> @php artisan package:discover --ansi

  An argument with name "class" already exists.

Script @php artisan package:discover --ansi handling the post-autoload-dump event returned with error code 1


Editor context menu doesn't scroll

Open TypeScript file
Decrease workbench height such that it can't fit the entire editor context menu
Open editor context menu

🐛 It doesn't provide scrolling behavior in order to reach the overflowing actions

This PR may be relevant here, https://github.com/microsoft/vscode/pull/91214.

Have the same issue and it would be really great if a solution could be found as soon as possible.
BTW: It is possible to go through all menu entries by using keyboard arrow buttons (up, down).


Dynup: First walkready pose is broken

When going into walkready after the power has been turned on, the IK solution for the walkready pose is bad, sometimes leading to the feet interlocking or joints colliding. This potentially causes damage to the robot. This seems more likely to happen when joints are outside of the accepted range, but this needs to be further evaluated.
Expected behavior

Directly move to the walkready pose.
Current behavior

Move to walkready through weird paths.
Steps to Reproduce

Turn off motor power and stop all software
Turn on motor power
Start motion_standalone.launch
...

Context (Environment)

[ ] RViz
[ ] Simulator
[ ] Robot
[ ] Local

Possible Solution

Partially fixed by #407


Release Cadence not documented in this repo.

The MAINTAINERS file says all decisions can be expressed as a pull request (https://github.com/docker/docker/blob/master/MAINTAINERS#L76). But I was unable to find any documentation explaining the release cadence (when is a minor as in semver's MAJOR.MINOR.PATCH). The closes thing I could find was https://github.com/docker/docker/blob/master/project/RELEASE-CHECKLIST.md which discusses what to do once you know you are working on making a release

Hi!
Please read this important information about creating issues.
If you are reporting a new issue, make sure that we do not have any duplicates already open. You can ensure this by searching the issue list for this repository. If there is a duplicate, please close your issue and add a comment to the existing issue instead.
If you suspect your issue is a bug, please edit your issue description to include the BUG REPORT INFORMATION shown below. If you fail to provide this information within 7 days, we cannot debug your issue and will close it. We will, however, reopen it if you later provide the information.
This is an automated, informational response.
Thank you.
For more information about reporting issues, see https://github.com/docker/docker/blob/master/CONTRIBUTING.md#reporting-other-issues

BUG REPORT INFORMATION
Use the commands below to provide key information from your environment:
docker version:
docker info:
uname -a:
Provide additional environment details (AWS, VirtualBox, physical, etc.):
List the steps to reproduce the issue:
1.
2.
3.
Describe the results you received:
Describe the results you expected:
Provide additional info you think is important:
----------END REPORT ---------
#ENEEDMOREINFO

its every two months

Thanks, it would be nice to have this documented somewhere the docker repo.

There's some initial work done to add a roadmap to the wiki; https://github.com/docker/docker/wiki, right now the information is quite limited, but the intention is to add more information once available (also for the longer term)

Thanks, closing as this is documented in the wiki now.