text:
trt_convert error

Please make sure that this is a bug. As per our
GitHub Policy,
we only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub. tag:bug_template
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.3.0
Python version:
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: 10.2, 8.0
GPU model and memory: Nvidia Xavier

I have keras model that I'm trying to convert to tensorrt using TrtGraphConverterV2. The model takes image and classifies it.
def my_input_fn():
	img = cv2.imread('messi5.jpg',0)
	img = np.expand_dims(img, axis=0)
	return img

conversion_params = trt.DEFAULT_TRT_CONVERSION_PARAMS
conversion_params = conversion_params._replace(
    max_workspace_size_bytes=(1<<32))
conversion_params = conversion_params._replace(precision_mode="FP16")
conversion_params = conversion_params._replace(
    maximum_cached_engines=100)

converter = trt.TrtGraphConverterV2(
    input_saved_model_dir="input_model/", conversion_params=conversion_params)
converter.convert()
converter.build(input_fn=my_input_fn)
converter.save("tensorrt_model")

With this code I'm getting:
2020-10-17 21:47:52.620064: W tensorflow/compiler/tf2tensorrt/kernels/trt_engine_op.cc:588] Running native segment forTRTEngineOp_0_0 due to failure in verifying input shapes: Input shapes do not match input partial shapes stored in graph, for TRTEngineOp_0_0: [[480,768,3]] != [[1,480,768,3]]
2020-10-17 21:47:52.677691: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at transpose_op.cc:157 : Invalid argument: transpose expects a vector of size 3. But input(1) is a vector of size 4
2020-10-17 21:47:52.677916: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at trt_engine_op.cc:401 : Invalid argument: transpose expects a vector of size 3. But input(1) is a vector of size 4
[[{{node StatefulPartitionedCall/model_1/conv2d_1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer}}]]
Traceback (most recent call last):
File "convert_to_tensorrt.py", line 118, in
converter.build(input_fn=my_input_fn)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py", line 1174, in build
func(*map(ops.convert_to_tensor, inp))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1657, in call
return self._call_impl(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/wrap_function.py", line 247, in _call_impl
args, kwargs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1675, in _call_impl
return self._call_with_flat_signature(args, kwargs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1724, in _call_with_flat_signature
return self._call_flat(args, self.captured_inputs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1926, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 550, in call
ctx=ctx)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError:  transpose expects a vector of size 3. But input(1) is a vector of size 4
[[{{node StatefulPartitionedCall/model_1/conv2d_1/Conv2D-0-TransposeNHWCToNCHW-LayoutOptimizer}}]]
[[TRTEngineOp_0_0]] [Op:__inference_pruned_16623]

If I remove expand_dims I'm getting:
Traceback (most recent call last):
File "convert_to_tensorrt.py", line 118, in
converter.build(input_fn=my_input_fn)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py", line 1174, in build
func(*map(ops.convert_to_tensor, inp))
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1657, in call
return self._call_impl(args, kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/wrap_function.py", line 247, in _call_impl
args, kwargs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1675, in _call_impl
return self._call_with_flat_signature(args, kwargs, cancellation_manager)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py", line 1697, in _call_with_flat_signature
len(args)))
TypeError: pruned(x) takes 1 positional arguments but 480 were given

@PawelFaron
Can you please share colab link or simple standalone code with supporting files to reproduce the issue in our environment.It helps us in localizing the issue faster.Thanks!

authors: PawelFaron ravikyram


text:
WARNING: function Model.make_predict_function..predict_function

I'm facing the following warning message in tf 2.3 in my local machine. This error probably doesn't impact on the model performance (or do).

WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function..predict_function at 0x000002424DCD6678> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has an experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.

The issue is, I can't reproduce it in Colab or Kaggle either with the same version of tensorflow tf 2.3. What makes it prevent to show this warning message logs on colab or kaggle? I tried to disable the warning on my local machine but didn't work. Below is the reproducible code. In short, I've tried to train a subclassed model and saved a few checkpoints, and ended up with inference iteratively. This is actually a mimic version of my original code.
import tensorflow as tf
import numpy as np
from glob import glob
import gc

class Classifier(tf.keras.Model):
    def __init__(self, dim):
        super(CassavaClassifier, self).__init__()
        # define all layers in init
        # Layer of Block 1
        self.Base  = tf.keras.layers.Conv2D(32, 3, strides=2, activation="relu")

        self.GAP   = tf.keras.layers.GlobalAveragePooling2D()
        self.OUT   = tf.keras.layers.Dense(10, activation='softmax')

    def call(self, input_tensor, training=False):
        x  = self.Base(input_tensor)
        x  = self.GAP(x)
        return self.OUT(x)

# train set / data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = np.expand_dims(x_train, axis=-1)
x_train = np.repeat(x_train, 3, axis=-1)
x_train = x_train.astype('float32') / 255
# train set / target
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)

# compile
print('Model Sub-Classing API')
sub_classing_model = Classifier((28,28,3))
sub_classing_model.compile(loss = tf.keras.losses.CategoricalCrossentropy(),
              metrics = tf.keras.metrics.CategoricalAccuracy(),
              optimizer = tf.keras.optimizers.Adam())
# fit
sub_classing_model.fit(x_train, y_train,
         batch_size=128, epochs=1);

# few checkpoints
sub_classing_model.save_weights('sub1.h5')
sub_classing_model.save_weights('sub2.h5')
sub_classing_model.save_weights('sub3.h5')

for i in sorted(glob('./sub*.h5')):
    sub_classing_model = CassavaClassifier((28,28,3))
    sub_classing_model.build((None, *(28,28,3)))
    sub_classing_model.load_weights(i)
    z = sub_classing_model.predict(x_train) # <------ Perhaps something makes it happen
    del sub_classing_model
    gc.collect()

@innat
I ran the code shared and face a different error, please refer to gist here. if possible please a colab gist with the issue reported.

@Saduf2019 Find it here.
However, As I told you earlier, it can't be reproduced in colab or kaggle, it's happening in the local machine, having the same version of packages. I just want to know, if this is a normal warning message, what makes a clean output log in those virtual environments?

@innat
Could you please check if you are facing the same issue in a virtual environment?
Alternatively, you can also try suppressing the warnings by running the below code before importing TensorFlow.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
Thanks!

source: gharchive/issue

repo: tensorflow/tensorflow

license_type: permissive

license_source: github-api


text:
experimental apis in iOS pod is missing.

Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template
System information

TensorFlow version (you are using): v2.4
Are you willing to contribute it (Yes/No): No

Describe the feature and the current behavior/state.
experimental apis  in iOS pod is missing.
Will this change the current api? How?
No
Who will benefit with this feature?
Any Other info.

@songww,
Can you please elaborate. Also, can you please specify a use case with respect to this request. Thanks!

Invoke TfLiteInterpreterResetVariableTensors .
the results of prediction for same inputs super difference within one interpreter.

It's still not clear what the issue is.

experimental apis in iOS pod is missing.

Invoke TfLiteInterpreterResetVariableTensors .

Are you saying that you'd like this API to be added to the iOS Cocoapods?

Are you saying that you'd like this API to be added to the iOS Cocoapods?
Yes.

Seems APIs in tensorflow/lite/c/c_api_experimental.h are not exported to to CocoaPods for now, not sure if we have plans to do so. Assigning @yyoon for further information.

@yyoon  Any plan for that?

Seems reasonable to add, given that the same feature has been available on Android side for a while. Will add this feature soon.

Seems reasonable to add, given that the same feature has been available on Android side for a while. Will add this feature soon.

Thanks.

Hi @songww. I believe this should be done as #49313 is merged. This will be included in the next stable release, and also should be available through nightly builds in the meantime. Please let us know if you still have issues.

authors: jdduke rmothukuru songww teijeong yyoon


text:
Minor instruction issue in functional_ops.py/scan

I had been using functional_ops.py/scan for implementing RNNs.
I've just checked the instruction, and think this is little bit wrong.
In master branch, from line 419 in functional_ops.py, it says
  Args:
    fn: The callable to be performed.  It accepts two arguments.  The first
      will have the same (possibly nested) structure as `elems`.  The second
      will have the same structure as `initializer` if one is provided,
      otherwise it will have the same structure as `elems`.  Its output
      must have the same structure as `initializer` if one is provided,
      otherwise it must have the same structure as `elems`.

.
But actually the first argument of fn should have the same structure as initializer,
and the second should have the same structure as elems.
As we can see this in few examples just below, from line 448,
  Examples:
    ```python
    elems = np.array([1, 2, 3, 4, 5, 6])
    sum = scan(lambda a, x: a + x, elems)
    # sum == [1, 3, 6, 10, 15, 21]
    ```
    ```python
    elems = np.array([1, 2, 3, 4, 5, 6])
    initializer = np.array(0)
    sum_one = scan(
        lambda a, x: x[0] - x[1] + a, (elems + 1, elems), initializer)
    # sum_one == [1, 2, 3, 4, 5, 6]
    ```
    ```python
    elems = np.array([1, 0, 0, 0, 0, 0])
    initializer = (np.array(0), np.array(1))
    fibonaccis = scan(lambda a, _: (a[1], a[0] + a[1]), elems, initializer)
    # fibonaccis == ([1, 1, 2, 3, 5, 8], [1, 2, 3, 5, 8, 13])
    ```

, the instruction is slightly wrong.

@yhg0112 I believe you're right, thanks for filing this!  Would you like to submit a pull request to fix this?
Adding @ebrevdo to keep him in the loop.

@yhg0112 May you mind I create a pull request for this? Or you plan to fix this?

Refer to
      packed_a = output_pack(a_flat)
      a_out = fn(packed_a, packed_elems) <--
      nest.assert_same_structure(

The comment about fn is incorrect.

Fyi look at tf.nn.dynamic_rnn also.

@haosdent i absolutely don't mind. please create a pull request for this.

@yhg0112 thank you, create a pr at https://github.com/tensorflow/tensorflow/pull/4870

I think this is closed, as https://github.com/tensorflow/tensorflow/pull/4870 has been committed.

authors: ebrevdo haosdent tatatodd wolffg yhg0112


text:
bug in rsqrt

---------------------------------------------------------------------------part 1-----------------------------------------------------------------
my code:
import tensorflow as tf
import os
import numpy as np
np.set_printoptions(threshold=np.inf)

def test():
    with tf.device("/device:CPU:0"):
    # with tf.device('/gpu:1'):
        a=tf.constant([1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38])
        b = tf.rsqrt(a)
    return b

if __name__=='__main__':
    with tf.Session() as sess:
        print(sess.run(test()))

expect the right result:
[9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18, 9.223371843921341e+18]
in the env: tensorflow== 1.14.0  python=3.6.8
get the wrong result:
[1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19
1.383168e+19 1.383168e+19 1.383168e+19 9.223372e+18]
in the env: tensorflow== 1.14.0  python=3.6.8
get the wrong result:
[1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19
1.383168e+19 1.383168e+19 1.383168e+19 9.223372e+18]
in the env: tensorflow== 1.14.0  python=2.7.18
get the wrong result:
[         inf          inf          inf          inf          inf
inf          inf          inf 9.223372e+18]
note:if I use the gpu device, also get the wrong result
----------------------------------------------------------------------part 2-----------------------------------------------------------------
my code2:
import tensorflow as tf
import os
import numpy as np
np.set_printoptions(threshold=np.inf)
import math

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def test():
    a=tf.constant([1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38])
    b = tf.math.rsqrt(a)
    print(b)
    return

if __name__=='__main__':
    test()

in the env: tensorflow== 2.4.0  python=3.8.5
get the wrong result:
[1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19
1.383168e+19 1.383168e+19 1.383168e+19 9.223372e+18]
in the same env tensorflow== 2.4.0  python=3.8.5 if I use GPU, I mean os.environ["CUDA_VISIBLE_DEVICES"] = "1"
get the right result:
[9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18
9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18]
----------------------------------------------------------------------part 3-----------------------------------------------------------------
note:
1、the small shape tend to get the right result
2、similarity bug also in tf.floor

@BlueSkyyyyyy ,
We see that the issue template has not been filled, could you please do so as it helps us analyse the issue.Thanks!

@BlueSkyyyyyy ,
We see that the issue template has not been filled, could you please do so as it helps us analyse the issue.Thanks!

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):no
OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Linux Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: cpu
TensorFlow installed from (source or binary):binary
TensorFlow version (use command below):2.4.0
Python version:3.8.5
Bazel version (if compiling from source):NA
GCC/Compiler version (if compiling from source):NA
CUDA/cuDNN version:NA
GPU model and memory:NA

Describe the current behavior
use tf.math.rsqrt to cal:
a=tf.constant([1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38])
b = tf.math.rsqrt(a)
get the wrong results:
[1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19
1.383168e+19 1.383168e+19 1.383168e+19 9.223372e+18]
Describe the expected behavior
[9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18
9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18]
Contributing

Do you want to contribute a PR? (yes/no):no
Briefly describe your candidate solution(if contributing):no

Standalone code to reproduce the issue
my code
import tensorflow as tf
import os
import numpy as np
np.set_printoptions(threshold=np.inf)
import math

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def test():
    a=tf.constant([1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38])
    b = tf.math.rsqrt(a)
    print(b)
    return

if __name__=='__main__':
    test()

Other info / logs Include any logs or source code that would be helpful to
no

@BlueSkyyyyyy ,
We see that the issue template has not been filled, could you please do so as it helps us analyse the issue.Thanks!

I use the template to describe the problem as above. not only tf 2.4 but also other version has the similarity problem( I have trytensorflow== 1.14.0+ python=3.6.8  and tensorflow== 1.14.0 python=2.7.18 ).

@Saduf2019 ,
I was able to reproduce the issue in tf v2.4,v2.5 and nightly.Please find the gist of it here.

@Saduf2019 ,@tensorflowbutler，
do you know why tensorflow has this bug and how to solve it

@BlueSkyyyyyy
Can you try setting the dtype of the tf.constant to tf. float 64 bit floating point tensor)
2)avoid using main, check if you can get better result by removing main.
If you really have to use main then try putting your code in py file in colab and try to execute using filename.py. [the loss of precision, due to the large number you have chosen causing the issue]

@Saduf2019
1）I remove main ,but have the same problem;
2)  if I use tf.float64, can get the right ans, but it is not a same question; float32 should support the precision.
as i use float32 in numpy or python,  can get the right ans:
code:
import numpy as np
np.set_printoptions(threshold=np.inf)
import math

a = [1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38,1.1754944e-38]

ans_py = []
for aa in a:
    ans_py.append(float(1.0/math.sqrt(float(aa))))
print("ans_py:{}".format(ans_py))

a_np = np.array(a,dtype="float32")
ans_np = 1.0/np.sqrt(a_np)
print("ans_np:{}".format(ans_np))

run get the ans:
ans_py:[9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18, 9.22337184392134e+18]
ans_np:[9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18
 9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18
 9.223372e+18 9.223372e+18 9.223372e+18 9.223372e+18]

so i think float32 is enough for this case .

@BlueSkyyyyyy
This is not a bug or performance issue, please move this to closed status and for any further queries open this at the tf discussion forum.

@Saduf2019
Why it is not a bug, I get obviously wrong result by tf .  Is there any knowledge or I have neglect lead to the wrong use ? thanks you r patient

I tested in colab both cpu and gpu implementation results match and I get
tf.Tensor(
[1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19
 1.383168e+19 1.383168e+19 1.383168e+19 9.223372e+18], shape=(9,), dtype=float32)

I wonder why you are seeing different results. You may try test with google colab.

@ymodak

tf.Tensor(
[1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19 1.383168e+19
1.383168e+19 1.383168e+19 1.383168e+19 9.223372e+18], shape=(9,), dtype=float32)
this result is not right, that is the key poit. 1.383168e+19 is wrong ,9.223372e+18 is right.

This is likely due to an Eigen approximation.  If EIGEN_FAST_MATH = 1 (the default), then Eigen uses the fast reciprocal sqrt approximation, which is much faster, but less accurate (particularly as you deviate away from zero).  See here.
If compiling TF from source, you can try explicitly disabling this (adding -DEIGEN_FAST_MATH=0 to the set of compile flags).

@cantonios
thank you so much

@cantonios thank you so much

If your issue is resolved, could you please close the issue. Thanks!

authors: BlueSkyyyyyy Saduf2019 cantonios sachinprasadhs tilakrayal ymodak


text:
Can't install on ubuntu 12.04.5 LTS

I am running into an error while trying to install tensor flow on Ubuntu.

$  sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
Downloading tensorflow-0.5.0-cp27-none-linux_x86_64.whl (10.9Mb): 10.9Mb downloaded
Running setup.py egg_info for package from https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
Traceback (most recent call last):
File "", line 14, in
IOError: [Errno 2] No such file or directory: '/tmp/pip-nulCWJ-build/setup.py'
Complete output from command python setup.py egg_info:
Traceback (most recent call last):
File "", line 14, in
IOError: [Errno 2] No such file or directory: '/tmp/pip-nulCWJ-build/setup.py'
Command python setup.py egg_info failed with error code 1

The issue was solved by updating to the latest version of pip - however I'm getting a new error.
ImportError: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.17 not found

Yes, latest pip solves the first part. But the libc.so is bigger problem. It is non trivial hacks to give the one app newer glibc than the system one.
You may try recompile it on your system. But it may take some effort just to compile bazel, you will not have java8, nor newer versions of numpy nor anything else... Is upgrading to at least 14.04 LTS realy not an option for you?

I am sadly also in a similar situation trying to deploy on a nation-wide GPU cluster which is really the only access to GPUs that my research group has.  We have a module system that can give us access to plenty of modern tooling, but I only have access to glibc 2.12.  Installing binary bazel releases fail since they depend on glibc 2.14 and I have also been unable to build bazel 0.1.0/0.1.1/HEAD from scratch due to compilation errors.  For now I have given up on making TensorFlow work on the cluster pending the issue I filed with bazel.

welp... time for 14.04 I guess

Upgrading ubuntu is definitely the best way to go. But if not...
I would not upgrade the system libc.so on the shared cluster. It should be backward compatible but there are regression here and there. You can still make it work if you want to. If you grep libc from newer package [http://packages.ubuntu.com/trusty/libc6] (click on amd64). You can extract that deb. Then you can use LD_PRELOAD the files from content directory. See something like [https://rafalcieslak.wordpress.com/2013/04/02/dynamic-linker-tricks-using-ld_preload-to-cheat-inject-features-and-investigate-programs/] or [http://stackoverflow.com/questions/5223971/question-about-overriding-c-standard-library-functions-and-how-to-link-everythin].
Libc has pretty much no dependencies but kernel. I believe tensorflow does not need anything fancy new there. Newer libc in custom directory and LD_PRELOAD trick should allow you to run the bazel binaries from download as well as the tensorflow pip (you may not need to recompile anymore). Good for shared cluster with old system.
BTW: Google / bazel philosophy is to rebuild everything from sources (that's why bazel download so many git repos). That also means there are very little external dependencies. But yes, it depends on libc.

I don't have sudo permission, so updating or installing GLIBC is not possible. I tried extracting libc6 from debian package and set the path in LD_PRELOAD but it's not using that instead it always point to /lib64/libc.so.6(ImportError: /lib64/libc.so.6: version `GLIBC_2.17' not found) but my LD_PRELOAD is defined as /home/hpc/pr63so/ga93yih2/lib/libc.so.6

Does anyone who solve this?
I use cent os 6.x and it's glibc version is under the 2.12.
(tensorflow)[dev-srv /home/user/tensorflow]# yum info glibc
Loaded plugins: fastestmirror, security
Determining fastest mirrors
Installed Packages
Name        : glibc
Arch        : i686
Version     : 2.12

I download glibc-2.17-78.el7.x86_64.rpm in the Internet and uncompress rpm to /opt/tensorflow/glibc diretory.
(tensorflow)[dev-srv /home/user/tensorflow]# ls /opt/tensorflow/glibc/lib64/
ld-2.17.so               libanl-2.17.so   libcidn.so.1      libm-2.17.so           libnss_compat.so.2  libnss_files-2.17.so   libnss_nis.so.2         libresolv-2.17.so    libthread_db.so.1
ld-linux-x86-64.so.2     libanl.so.1      libcrypt-2.17.so  libm.so.6              libnss_db-2.17.so   libnss_files.so.2      libnss_nisplus-2.17.so  libresolv.so.2       libutil-2.17.so
libBrokenLocale-2.17.so  libc-2.17.so     libcrypt.so.1     libnsl-2.17.so         libnss_db.so.2      libnss_hesiod-2.17.so  libnss_nisplus.so.2     librt-2.17.so        libutil.so.1
libBrokenLocale.so.1     libc.so.6        libdl-2.17.so     libnsl.so.1            libnss_dns-2.17.so  libnss_hesiod.so.2     libpthread-2.17.so      librt.so.1           rtkaio
libSegFault.so           libcidn-2.17.so  libdl.so.2        libnss_compat-2.17.so  libnss_dns.so.2     libnss_nis-2.17.so     libpthread.so.0         libthread_db-1.0.so

Finally, I ran convolutional.py with LD_PRELOAD or LD_LIBRARY_PATH option and those neither helpful. Here is an error message.
(tensorflow)[dev-srv /home/irteam/tensorflow]# LD_PRELOAD=/opt/tensorflow/glibc/lib64/libc.so.6 python ./lib/python2.7/site-packages/tensorflow/models/image/mnist/convolutional.py
python: relocation error: /opt/tensorflow/glibc/lib64/libc.so.6: symbol _dl_starting_up, version GLIBC_PRIVATE not defined in file ld-linux-x86-64.so.2 with link time reference

(tensorflow)[dev-srv /home/user/tensorflow]# LD_LIBRARY_PATH=/opt/tensorflow/glibc/lib64 python ./lib/python2.7/site-packages/tensorflow/models/image/mnist/convolutional.py
python: relocation error: /opt/tensorflow/glibc/lib64/libc.so.6: symbol _dl_starting_up, version GLIBC_PRIVATE not defined in file ld-linux-x86-64.so.2 with link time reference

Does anyone who really solve this issue?

I am also eagerly waiting for a solution

I use OS X to install tensorflow, it's succeed,but when I use python to write first program,I find some error, who can help me?
there is error message:

import tensorflow as tf
Traceback (most recent call last):
File "", line 1, in
File "/Library/Python/2.7/site-packages/tensorflow/init.py", line 4, in
from tensorflow.python import *
File "/Library/Python/2.7/site-packages/tensorflow/python/init.py", line 13, in
from tensorflow.core.framework.graph_pb2 import *
File "/Library/Python/2.7/site-packages/tensorflow/core/framework/graph_pb2.py", line 8, in
from google.protobuf import reflection as _reflection
File "/Library/Python/2.7/site-packages/google/protobuf/reflection.py", line 58, in
from google.protobuf.internal import python_message as message_impl
File "/Library/Python/2.7/site-packages/google/protobuf/internal/python_message.py", line 59, in
import six.moves.copyreg as copyreg
ImportError: No module named copyreg

In addition, even loading both libc.so.6 and ld-linux-x86-64.so.2 doesn't work.
(tensorflow)[dev-srv /home/user/tensorflow]# LD_PRELOAD="/opt/tensorflow/glibc/lib64/libc.so.6 /opt/tensorflow/glibc/lib64/ld-linux-x86-64.so.2" python ./lib/python2.7/site-packages/tensorflow/models/image/mnist/convolutional.py
Segmentation fault

I think we do require glibc >2.17 at the moment. Closing for now but if someone has a solution for earlier versions, would be great to have it here.  Thanks!

Anyone able to find a solution for it ?

Managed to get it to build from source.
Overview of how to do it.
Install Bazel - (but not the latest version, because it apparently fails to compile tensorflow according to a ticket I saw there)
I used Build label: 0.1.1
Update pip, install wheels and setuptools
Check out tensorflow's latest version, modify tensorflow/tensorflow.bzl file
def tf_extension_linkopts():

return []  # No extension link opts

return ["-lrt"]  # No extension link opts

Then build according to the instructions on the source build page:
Also, on the instructions page (http://tensorflow.org/get_started/os_setup.md#installing_from_sources) you will see these three commands, but you have to add —config=cuda, like so:
Create the pip package and install
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
The name of the .whl file will depend on your platform.
$ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

My problem lies with old GLIBC version(2.11.3), and I tried installing bazel for which it requires a GLIBC version 2.14 or greater.
Any other solution?
Note: I can't update GLIBC version, because I am using a cluster system and naturally am not given a admin privileges.

I have this problem just now！Here maybe help you ！！ My system is ubuntu 12.04
Reference Linking：http://stackoverflow.com/questions/33655731/error-while-importing-tensorflow-in-python2-7-in-ubuntu-12-04-glibc-2-17-not-f
$ sudo pip install --upgrade virtualenv
$ virtualenv --system-site-packages ~/tensorflow
$ cd ~/tensorflow
$ source bin/activate
$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
$ cd /tmp
$ wget http://launchpadlibrarian.net/137699828/libc6_2.17-0ubuntu5_amd64.deb
$ wget http://launchpadlibrarian.net/137699829/libc6-dev_2.17-0ubuntu5_amd64.deb
$ mkdir libc6_2.17
$ cd libc6_2.17
$ ar p libc6_2.17-0ubuntu5_amd64.deb data.tar.gz | tar zx
$ ar p libc6-dev_2.17-0ubuntu5_amd64.deb data.tar.gz | tar zx
$ cd -
$ LD_LIBRARY_PATH=/tmp/libc6_2.17/lib/x86_64-linux-gnu/ /tmp/libc6_2.17/lib/x86_64-linux-gnu/ld-2.17.so bin/python local/lib/python2.7/site-packages/tensorflow/models/image/mnist/convolutional.py

This may work for local pc but not for clusters or for those who don't have admin privileges.

You can install virtualenv locally:
pip install virtualenv --user. My problem is that the installation of tensorflow in virtualenv encounters maximum recursion depth exceeded:
        self.finalize_options()
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/setuptools/dist.py", line 257, in finalize_options
        ep.require(installer=self.fetch_build_egg)
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 2029, in require
        working_set.resolve(self.dist.requires(self.extras),env,installer))
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 580, in resolve
        dist = best[req.key] = env.best_match(req, self, installer)
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 825, in best_match
        return self.obtain(req, installer) # try and download/install
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 837, in obtain
        return installer(requirement)
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/setuptools/dist.py", line 272, in fetch_build_egg
        dist = self.__class__({'script_args':['easy_install']})
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/setuptools/dist.py", line 225, in __init__
        _Distribution.__init__(self,attrs)
      File "/usr/lib/python2.7/distutils/dist.py", line 287, in __init__
        self.finalize_options()
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/setuptools/dist.py", line 257, in finalize_options
        ep.require(installer=self.fetch_build_egg)
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 2029, in require
        working_set.resolve(self.dist.requires(self.extras),env,installer))
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 579, in resolve
        env = Environment(self.entries)
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 748, in __init__
        self.scan(search_path)
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 777, in scan
        for dist in find_distributions(item):
      File "/home/phong/.local/lib/python2.7/site-packages/distribute-0.6.28-py2.7.egg/pkg_resources.py", line 1702, in find_distributions
        importer = get_importer(path_item)
      File "/usr/lib/python2.7/pkgutil.py", line 394, in get_importer
        importer = ImpImporter(path_item)
    RuntimeError: maximum recursion depth exceeded

    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-E19HDw/numpy

I managed to have it running on a CentOS 6.7 without being admin, two solutions : http://stackoverflow.com/a/34897674/1990516
http://stackoverflow.com/a/34900471/1990516
:)

I was able to build bazel-0.4.1 and tensorflow-0.12.0rc0 from source, on CentOS 6.5, without having root privileges. And it seem to work OK with glibc 2.12, without any LD_LIBRARY_PATH or LD_PRELOAD tricks. Editing tensorflow/tensorflow.bzl is one of the options (but it might be not the only thing to do), but, personally, for me it was simpler to do the same thing in "gpu/CROSSTOOL.tpl#L59".

For those facing glib problem, switch keras config file to use Theano instead of Tensorflow. I did that and it solved my problem.

I am facing similar problems. I am trying to run TensorFlow at work, on my RHEL 6.8 box. IT will not let us use package managers, and we cannot upgrade the bundled python, or gcc.
I tried to get around this by installing Python 2.7, TensorFlow, and latest GCC locally on the box in /scratch
I update LD_LIBRARY_PATH to point to my own copy of GCC shared libraries. It seems that tensorflow does not care about LD_LIBRARY_PATH, and it is searching in /lib64/libc.so.6 for GLIBC_2.16

You need to update glibc or update Ubuntu to 14.04. It will work like charm.
On Apr 5, 2017 3:21 AM, "wilderfield" notifications@github.com wrote:

I am facing similar problems. I am trying to run TensorFlow at work, on my
RHEL 6.8 box. IT will not let us use package managers, and we cannot
upgrade the bundled python, or gcc.
I tried to get around this by installing Python 2.7, TensorFlow, and
latest GCC locally on the box in /scratch
I update LD_LIBRARY_PATH to point to my own copy of GCC shared libraries.
It seems that tensorflow does not care about LD_LIBRARY_PATH, and it is
searching in /lib64/libc.so.6 for GLIBC_2.16
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/tensorflow/tensorflow/issues/53#issuecomment-291642438,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AMRpkFCPPL_mPr7GvDYhuKxqapUY7ceaks5rsrtsgaJpZM4GfDu8
.

Hey, guys. Here are the solutions.
`mkdir ~/glibc
cd ~/glibc
wget http://ftp.gnu.org/gnu/glibc/glibc-2.23.tar.gz
tar zxvf glibc-2.23.tar.gz
cd glibc-2.23
mkdir build
cd build
../configure --prefix=/opt/glibc-2.23
make -j4
sudo make install
export LD_LIBRARY_PATH=/opt/glibc-2.23/lib`
https://github.com/mind/wheels/issues/7

conda install tensorflow works for me

conda install tensorflow works for me too

conda install tensorflow work for me too!!!

conda install tensorflow and life is easy

I've tried conda install tensorflow and I'm still getting the same issue. GLIBC 2.17 not found.

authors: MrXiaoWan TheMoakMan anjishnu blacknes cdancette chandu8542 concinntyTurtle empty16 evanthebouncy i3v initproc jendap ninjin qiaofeiw ttrouill vinodrajendran001 vodp vrv wilderfield


text:
why does tensorflow use multi threads to launch gpu kernels?

I noticed that when I launch kernel to the same GPU with multi threads, cudaLaunchKernel  api will get much slower.
But seems it's the case in tensorflow. why not use a single thread to communicate with GPU?
Here is my test，when thread_num is larger, cuda api cost will be huge.

`#include
#include
using namespace std;
bool running = true;
const int thread_num = 20;
const int launch_num = 25;
global void task_kernel(int x) {
int ans = 1024;
for(int i = 0; i < x; i++) {
if(ans > 0) {
ans *= (x + 2);
}
}
}
void task(int x) {
while(running) {
for(int i = 0; i < launch_num; i++) {
task_kernel<<<1, 1>>>(x);
}
this_thread::sleep_for(1ms);
}
}
void test() {
vector threads;
for(int i = 0; i < thread_num; i++) {
thread t(task, i);
threads.push_back(std::move(t));
}
this_thread::sleep_for(100ms);
running = false;
for(auto& t: threads) {
t.join();
}
}
int main() {
cudaFree(0);
test();
return 0;
}`

Hi @Saduf2019 ! Could you please look at this issue?

@sleepwalker2017
We see that you have not filled the issue template without the important information like tf version and gpu card used, its hard to help you.
May be you have to fine tune your cuda configurations for TF.
This does not seem like a bug or feature request as this repo is for them, can you open this in discussion forum for further queries as there is a large community to guide there.

discussion forum

OK, I'll go to the forum for some more discuss. thank you.

@sleepwalker2017
Can you move this to closed status


text:
Tensorboard - Black box for graphs after upgrading to TF 0.11

I am currently getting a black box where a graph should be when using Tensorboard. I was previously running TF 0.9 and upgraded last night to 0.11 via the pip link below. When I hover over the black box I do see the onHover messages displaying valid values, but I just can't see anything.
--The error received is below and the same thing shown in the screenshot above:

All candidate resources failed to load. Media load paused.  127.0.1.1:6006
Unexpected value  parsing viewBox attribute. d3.js:662:6
NS_ERROR_FAILURE:  tf-tensorboard.html-28.js:199
LineChart.prototype.setupTooltips/< http://127.0.1.1:6006/dist/tf-tensorboard.html-28.js:199:34
CallbackSet</CallbackSet.prototype.callCallbacks/< http://127.0.1.1:6006/plottable/plottable.js:643:21
Set</Set.prototype.forEach/callbackWrapper http://127.0.1.1:6006/plottable/plottable.js:257:77
forEach self-hosted
Set</Set.prototype.forEach http://127.0.1.1:6006/plottable/plottable.js:258:21
CallbackSet</CallbackSet.prototype.callCallbacks http://127.0.1.1:6006/plottable/plottable.js:642:17
Pointer</Pointer.prototype._handlePointerEvent http://127.0.1.1:6006/plottable/plottable.js:11190:21
Pointer/this._mouseMoveCallback http://127.0.1.1:6006/plottable/plottable.js:11166:65
CallbackSet</CallbackSet.prototype.callCallbacks/< http://127.0.1.1:6006/plottable/plottable.js:643:21
Set</Set.prototype.forEach/callbackWrapper http://127.0.1.1:6006/plottable/plottable.js:257:77
forEach self-hosted
Set</Set.prototype.forEach http://127.0.1.1:6006/plottable/plottable.js:258:21
CallbackSet</CallbackSet.prototype.callCallbacks http://127.0.1.1:6006/plottable/plottable.js:642:17
Mouse</Mouse.prototype._measureAndDispatch http://127.0.1.1:6006/plottable/plottable.js:10501:25
Mouse/processMoveCallback http://127.0.1.1:6006/plottable/plottable.js:10363:65

What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?
I have not found anyone having this as of yet when performing a search. I was previously running
Environment info
Operating System: Ubuntu 16.04
Installed version of CUDA and cuDNN:
(please attach the output of ls -l /path/to/cuda/lib/libcud*):
CUDA 8 and Cudnn 5
output of above ls:

total 64
drwxr-xr-x  3 root root 4096 Nov 20 21:59 bin
drwxr-xr-x  5 root root 4096 Nov 20 21:59 doc
drwxr-xr-x  5 root root 4096 Nov 20 21:59 extras
drwxr-xr-x  5 root root 4096 Nov 20 22:19 include
drwxr-xr-x  5 root root 4096 Nov 20 21:59 jre
drwxr-xr-x  3 root root 4096 Nov 20 22:19 lib64
drwxr-xr-x  8 root root 4096 Nov 20 21:59 libnsight
drwxr-xr-x  7 root root 4096 Nov 20 21:59 libnvvp
drwxr-xr-x  3 root root 4096 Nov 20 21:59 nvml
drwxr-xr-x  7 root root 4096 Nov 20 21:59 nvvm
drwxr-xr-x  2 root root 4096 Nov 20 21:59 pkgconfig
drwxr-xr-x 11 root root 4096 Nov 20 21:59 samples
drwxr-xr-x  3 root root 4096 Nov 20 21:59 share
drwxr-xr-x  2 root root 4096 Nov 20 21:59 src
drwxr-xr-x  2 root root 4096 Nov 20 21:59 tools
-rw-r--r--  1 root root   20 Nov 20 21:59 version.txt

If installed from binary pip package, provide:

A link to the pip package you installed:
Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Install from sources" below.
TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0-cp27-none-linux_x86_64.whl

The output from python -c "import tensorflow; print(tensorflow.__version__)".
0.11

I would love to know why this is happening still in FF should anyone have any answers, but I just found that this is working fine in Google Chrome.  I am going to close this since I do have a solution.

@xtr33me I've found the issue in Firefox. It will be fixed in the 0.12 release.

@danmane Thats awesome news! Thanks for the info


text:
tf.truncatediv not support float, but doc support.

Click to expand!
Issue Type
Bug
Source
source
Tensorflow Version
v2.9.1-132-g18960c44ad3 2.9.2
Custom Code
Yes
OS Platform and Distribution
colab
Mobile device
colab
Python version
3.8
Bazel version
No response
GCC/Compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current Behaviour?
The tf.truncatediv documentation shows that float is supported, but the actual test only supports integer types.
document: https://www.tensorflow.org/versions/r2.7/api_docs/python/tf/truncatediv

Standalone code to reproduce the issue
import tensorflow as tf
import numpy as np

a = np.array([1., 2., 3.])
x = tf.convert_to_tensor(a)
b = np.array([2.])
y = tf.convert_to_tensor(b)
tf.truncatediv(x, y)

Relevant log output
NotFoundError: Could not find device for node: {{node TruncateDiv}} = TruncateDiv[T=DT_DOUBLE]
All kernels registered for op TruncateDiv:
  device='GPU'; T in [DT_UINT64]
  device='GPU'; T in [DT_UINT32]
  device='GPU'; T in [DT_INT8]
  device='GPU'; T in [DT_INT64]
  device='GPU'; T in [DT_INT16]
  device='GPU'; T in [DT_UINT16]
  device='GPU'; T in [DT_UINT8]
  device='CPU'; T in [DT_INT64]
  device='CPU'; T in [DT_INT32]
  device='CPU'; T in [DT_INT16]
  device='CPU'; T in [DT_INT8]
  device='CPU'; T in [DT_UINT64]
  device='CPU'; T in [DT_UINT32]
  device='CPU'; T in [DT_UINT16]
  device='CPU'; T in [DT_UINT8]
 [Op:TruncateDiv]

</details>

@DavidFFFan,
The code mentioned above was able to execute without any issue/error in the tf-nightly version(2.12.0-dev20221226). Could you please find the gist of it here and confirm if it resolves the issue? Thank you!

@DavidFFFan, The code mentioned above was able to execute without any issue/error in the tf-nightly version(2.12.0-dev20221226). Could you please find the gist of it here and confirm if it resolves the issue? Thank you!
Thanks! tf-nightly does already solve this problem.

@DavidFFFan,
Glad the issue is resolved for you, please feel free to move this issue to closed status. Thank you!

authors: DavidFFFan tilakrayal


text:
How to fix invalid argument error while training a deep learning model for text classification

Click to expand!
Issue Type
Bug
Have you reproduced the bug with TF nightly?
Yes
Source
source
Tensorflow Version
2.10.1
Custom Code
Yes
OS Platform and Distribution
No response
Mobile device
No response
Python version
No response
Bazel version
No response
GCC/Compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current Behaviour?
A bug happened!
Standalone code to reproduce the issue
Epoch 1/30
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
Cell In[102], line 2
      1 # Entraîner le modèle sur l'ensemble d'entraînement
----> 2 model.fit(X_train, Y_train_categorical, batch_size=128, epochs=30, validation_split=0.1)
      4 # Évaluer les performances du modèle sur l'ensemble de test
      5 score = model.evaluate(X_test, Y_test_categorical, batch_size=12)

File ~\anaconda3\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     52 try:
     53   ctx.ensure_initialized()
---> 54   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     55                                       inputs, attrs, num_outputs)
     56 except core._NotOkStatusException as e:
     57   if name is not None:

InvalidArgumentError: Graph execution error:

Detected at node 'model_16/embedding_16/embedding_lookup' defined at (most recent call last):
    File "C:\Users\achra\anaconda3\lib\runpy.py", line 196, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Users\achra\anaconda3\lib\runpy.py", line 86, in _run_code
      exec(code, run_globals)
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel_launcher.py", line 17, in <module>
      app.launch_new_instance()
    File "C:\Users\achra\anaconda3\lib\site-packages\traitlets\config\application.py", line 992, in launch_instance
      app.start()
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 711, in start
      self.io_loop.start()
    File "C:\Users\achra\anaconda3\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
      self.asyncio_loop.run_forever()
    File "C:\Users\achra\anaconda3\lib\asyncio\base_events.py", line 603, in run_forever
      self._run_once()
    File "C:\Users\achra\anaconda3\lib\asyncio\base_events.py", line 1906, in _run_once
      handle._run()
    File "C:\Users\achra\anaconda3\lib\asyncio\events.py", line 80, in _run
      self._context.run(self._callback, *self._args)
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 510, in dispatch_queue
      await self.process_one()
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 499, in process_one
      await dispatch(*args)
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 406, in dispatch_shell
      await result
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 729, in execute_request
      reply_content = await reply_content
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 411, in do_execute
      res = shell.run_cell(
    File "C:\Users\achra\anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 531, in run_cell
      return super().run_cell(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2961, in run_cell
      result = self._run_cell(
    File "C:\Users\achra\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3016, in _run_cell
      result = runner(coro)
    File "C:\Users\achra\anaconda3\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
      coro.send(None)
    File "C:\Users\achra\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3221, in run_cell_async
      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
    File "C:\Users\achra\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3400, in run_ast_nodes
      if await self.run_code(code, result, async_=asy):
    File "C:\Users\achra\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "C:\Users\achra\AppData\Local\Temp\ipykernel_18948\1992395688.py", line 2, in <module>
      model.fit(X_train, Y_train_categorical, batch_size=128, epochs=30, validation_split=0.1)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\training.py", line 1564, in fit
      tmp_logs = self.train_function(iterator)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\training.py", line 1160, in train_function
      return step_function(self, iterator)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\training.py", line 1146, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\training.py", line 1135, in run_step
      outputs = model.train_step(data)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\training.py", line 993, in train_step
      y_pred = self(x, training=True)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\training.py", line 557, in __call__
      return super().__call__(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1097, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\functional.py", line 510, in call
      return self._run_internal_graph(inputs, training=training, mask=mask)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\functional.py", line 667, in _run_internal_graph
      outputs = node.layer(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\engine\base_layer.py", line 1097, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\achra\anaconda3\lib\site-packages\keras\layers\core\embedding.py", line 208, in call
      out = tf.nn.embedding_lookup(self.embeddings, inputs)
Node: 'model_16/embedding_16/embedding_lookup'
indices[48,995,25] = -1 is not in [0, 2230)
	 [[{{node model_16/embedding_16/embedding_lookup}}]] [Op:__inference_train_function_112178]

Relevant log output
No response

Hi @MrAchraf10 ,
Request you to fill the all details mentioned in template. Also I see there is no code snippet to debug the issue.
I see the error arises during model.fit, which indicates may be the inputs or labels you are feeding to the model might not be compatible which model was expecting.There are also other probable reasons. But without the code snippet we can't confirm the root cause. Request you to provide same for looking into this issue.
Thanks!


text:
keep special GPU kernels inside #if GOOGLE_CUDA... #endif

We don't need to compile these GPU kernels when compiling Tensorflow for CPU device only.

Can one of the admins verify this patch?

@tensorflow-jenkins: test this please

A mistake in constant_op.cc. I will fix it.

@tensorflow-jenkins: test this please

merged.

Thanks!

@martinwicke, there are two commits needed to merge.

I squashed them into one.

source: gharchive/pull-request

authors: cg31 martinwicke tensorflow-jenkins vrv


text:
Fix: slot and primary can be different shape

See issue #19457

@protoget

Can one of the admins verify this patch?

@tensorflowbutler CI build failed, it seems other reason. How to retrigger CI build?

@candyzone Could you please address Ubuntu Sanity errors? Thanks!

@candyzone Gentle ping to address Ubuntu Sanity errors? Thanks!

@allenlavoie

authors: candyzone gbaned mellanox-github


text:
Uniform indentation with other functions.

Uniform indentation with other functions.

Since the line fits within 80 columns this formatting is not needed. Hence the formatter did not change it. I believe we only split lines for arguments if all the arguments can't fit within 80 columns.


text:
While doing the assignment, I noticed that my train_dataset was getting trampled

My laptop loads the original data very slowly

Can one of the admins verify this patch?

authors: emmjaykay tensorflow-jenkins


text:
Cumsum

Implements cumsum operation

This change is

Thanks. Left a few comments - mostly how to do some transposing and rehsaping before you call the webgl shader. This will simplify the work a lot and allow summation across arbitrary axis.

Reviewed 5 of 8 files at r1.
Review status: 5 of 8 files reviewed at latest revision, all discussions resolved, some commit checks failed.

src/tensor.ts, line 420 at r1 (raw file):

  /**
   * Returns a `Tensor` that has expanded rank, by inserting a dimension

this jsdoc doesn't seem right. Is this taken from expandDim? Did you mean Compute the cumulative sum of the tensor x along axis.

src/tensor.ts, line 423 at r1 (raw file):

   * into the tensor's shape. See `expandDims` for details.
   *
   * @param axis The dimension index at which to insert shape of 1. Defaults to

the axis along which to sum.

src/kernels/webgl/cumsum_gpu.ts, line 49 at r1 (raw file):

        ${dtype} adjustableCoords = ${dtype}(${outputCoords});
        int finalCoord = int(${finalCoord});

remove whitespace

src/kernels/webgl/cumsum_gpu.ts, line 68 at r1 (raw file):

}

function getCoords(rank: number, name: string): string {

you can simplify this shader a bit if you do the following:

transpose to make axis be the inner-most axis
reshape to 2D where all other-most shapes are flattened
shader operates always on a 2d tensor and sums along it's inner-most axis
after shader runs, undo the reshape, and undo the transpose

src/ops/array_ops.ts, line 1147 at r1 (raw file):

   * x.cumsum().print();
   * ```
   *

add one more code snippet with 2d and sum along axis 0

src/ops/array_ops.ts, line 1151 at r1 (raw file):

   * @param axis The axis on which to perform the sum. Optional. Defaults to 0.
   * @param exclusive Whether to perform exclusive cumulative sum. Optional.
   *     Defaults to false.

Expand a bit on what exclusive means.

src/ops/array_ops.ts, line 1164 at r1 (raw file):

    return ENV.engine.runKernel(
               backend => backend.cumsum(x, axis, exclusive, reverse), {x},

this won't work for axis that are not inner most unless you transpose to make the axis be inner-most, do the computation and then transpose back again. See https://github.com/tensorflow/tfjs-core/blob/master/src/ops/reduction_ops.ts#L118 for how to transpose.

src/ops/array_ops_test.ts, line 2295 at r1 (raw file):

  });

  it('2D reverse exclusive', () => {

add test for 2d with axis=0

Comments from Reviewable

added the transpose so that it works on any axis.

Review status: 5 of 8 files reviewed at latest revision, 8 unresolved discussions, some commit checks failed.

src/tensor.ts, line 420 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
this jsdoc doesn't seem right. Is this taken from expandDim? Did you mean Compute the cumulative sum of the tensor x along axis.

Done.

src/tensor.ts, line 423 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
the axis along which to sum.

Done.

src/kernels/webgl/cumsum_gpu.ts, line 49 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
remove whitespace

Done.

src/kernels/webgl/cumsum_gpu.ts, line 68 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
you can simplify this shader a bit if you do the following:

transpose to make axis be the inner-most axis
reshape to 2D where all other-most shapes are flattened
shader operates always on a 2d tensor and sums along it's inner-most axis
after shader runs, undo the reshape, and undo the transpose

added transpose/undo transpose in array_ops so it applies for both cpu and gpu.

src/ops/array_ops.ts, line 1147 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
add one more code snippet with 2d and sum along axis 0

Done.

src/ops/array_ops.ts, line 1151 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
Expand a bit on what exclusive means.

done

src/ops/array_ops.ts, line 1164 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
this won't work for axis that are not inner most unless you transpose to make the axis be inner-most, do the computation and then transpose back again. See https://github.com/tensorflow/tfjs-core/blob/master/src/ops/reduction_ops.ts#L118 for how to transpose.

Done.

src/ops/array_ops_test.ts, line 2295 at r1 (raw file):
Previously, dsmilkov (Daniel Smilkov) wrote…
add test for 2d with axis=0

Done.

Comments from Reviewable

Reviewed 1 of 4 files at r3.
Review status: 2 of 8 files reviewed at latest revision, 8 unresolved discussions.

src/kernels/webgl/cumsum_gpu.ts, line 3 at r4 (raw file):

/**
 * @license
 * Copyright 2017 Google Inc. All Rights Reserved.

2018

src/ops/array_ops.ts, line 1188 at r4 (raw file):

    };
    let value =  ENV.engine.runKernel(
      backend => backend.cumsum(permutedX, axis, exclusive, reverse),

indent 2 more

src/ops/array_ops_test.ts, line 2349 at r4 (raw file):

  it('1D reverse', () => {
    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, false, true);

pull these false / true to variables for readability

src/ops/array_ops_test.ts, line 2394 at r4 (raw file):

  it('3D standard', () => {
    const res =tf.tensor3d([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).cumsum(2);

spacing here (btw if you use vscode this will auto format for you)

Comments from Reviewable

Review status: 2 of 8 files reviewed at latest revision, 12 unresolved discussions, all commit checks successful.

src/kernels/webgl/cumsum_gpu.ts, line 3 at r4 (raw file):
Previously, nsthorat (Nikhil Thorat) wrote…
2018

Done.

src/ops/array_ops.ts, line 1188 at r4 (raw file):
Previously, nsthorat (Nikhil Thorat) wrote…
indent 2 more

Done.

src/ops/array_ops_test.ts, line 2349 at r4 (raw file):
Previously, nsthorat (Nikhil Thorat) wrote…
pull these false / true to variables for readability

Done.

src/ops/array_ops_test.ts, line 2394 at r4 (raw file):
Previously, nsthorat (Nikhil Thorat) wrote…
spacing here (btw if you use vscode this will auto format for you)

Done.

Comments from Reviewable

authors: dsmilkov jameswex nsthorat

repo: tensorflow/tfjs-core

license_source: bigquery


text:
TT-Metal VCS Simulator Infra

See related gitlab issue: https://yyz-gitlab.local.tenstorrent.com/tenstorrent/tt-metal/-/issues/1

https://github.com/tenstorrent/tt-metal/pull/11101 merged

repo: tenstorrent/tt-metal


text:
Normalize GNUInstallDirs for external plugins

:notebook_with_decorative_cover: Description
Plugins built against an installed VAST expect relative GNUInstallDirs, which some package managers (looking at you, Nix) stupidly set to absolute paths. This patch externalizes our normalization we do for VAST normally and applies it to all dynamic plugins.
:memo: Checklist

[x] All user-facing changes have changelog entries.
[x] The changes are reflected on docs.tenzir.com/vast, if necessary.
[x] The PR description contains instructions for the reviewer, if necessary.

:dart: Review Instructions
Test it locally by setting -D CMAKE_INSTALL_PREFIX=${PWD}/build.install -D CMAKE_INSTALL_LIBDIR=${PWD}/build.install/lib or similar.

Tested with the static build locally.

authors: dominiklohmann tobim

repo: tenzir/vast


text:
[PARTIAL] Functional equational approach based on Pace Nielsen's proof

I have formalized everything except the case 1 check and constructing the final function based on extensions, but don't have time to finish it, so posting this so someone can maybe finish it.
It's designed to constructively create the counterexample and represents the free abelian group using DFinsupp; it would be nice to cut down on that setup code, but I'm not sure if there is a shorter way of doing it especially constructively.
The partial sets are E are defined as partial functions, again using DFinsupp going to an Option type; the "fup" def lifts them to Option -> Option functions that are none on none.
The construction follows the paper and there are some additional notes on why Case 1 holds, which may help in designing its proof.

I'm inclined to leave this PR unmerged for now until someone reclaims #506, and they can decide to what extent they will build off of this one.  (Though one argument in favor of merging now is that it will avoid merge conflicts and build issues later on.  So perhaps one can revisit this issue in a few days if nobody ends up reclaiming #506.)

I'm inclined to leave this PR unmerged for now until someone reclaims #506, and they can decide to what extent they will build off of this one. (Though one argument in favor of merging now is that it will avoid merge conflicts and build issues later on. So perhaps one can revisit this issue in a few days if nobody ends up reclaiming #506.)

In that case I will mark it as  a draft PR

No longer required since 1076 has been formalized by other means.

authors: Shreyas4991 lyphyser teorth


text:
Incorrect result for directories with dot entries

Versions

Windows 10 (18362)
Node.js 12.0.0

Problem
The tiny-glob package returns results without prefer the dot option.
Reproduce steps
directory/
  - .git
  - .editorconfig
  - .gitignore
  - package.json

const tg = require('tiny-glob');

(async () => {
	const entries = await tg('*');

	console.dir(entries, { colors: true });
})();

Actual behaviour
⚠ The .editorconfig file excluded as expected, but not .git and .gitignore.  The dot option with true as value returns .editorconfig as expected.
[
	'.git',
	'.gitignore',
	'package.json'
]

Expected behaviour
[
	'package.json'
]

I don't know why, but if I delete /g for isHidden RegExp, all works correctly.

Thank you @mrmlnc! I guess we can remove /g from the RegExp

authors: mrmlnc terkelg

repo: terkelg/tiny-glob

license: MIT


text:
Weird error while running cargo xtask install

I get an error while running cargo xtask install. After downloading all the crates, its throwing an error stating the following
error: Permission denied (os error 13) at path "/home/socks/weechat-discord/targetYbrnIW"
and the "target" thing at the end keeps randomizing every single time I run the xtask install command.
How do I fix this?

This is on the mk3 branch.

repo: terminal-discord/weechat-discord


text:
Show an overview of cost of running Atlantis in Fargate

Hi !
I'm curious to run Atlantis and I would like to have an idea what would approximately be the monthly cost of running it in Fargate using the Terraform Stack provided in this repo.
I assume it would depend on the number of containers that are running, but maybe we can have a global overview already ?
Thanks !
Thomas

Hi,
I have this setup in an account with no other ECS or ECR, so I can give you my costs.
Daily I see $0.30/day for ECS, and less than a penny a day for ECR storage of the image (I tweaked it a little, so I store it in ECR.  You might not need to), so monthly ~$9 for one task/container.  That is not including the EFS.  I am only using 1GB EFS, so at $0.043GB/mo, I should still be right about that $9.  Call it $10/mo to be safe.
This is with the default sizing:
`variable "ecs_task_cpu" {
description = "The number of cpu units used by the task"
type        = number
default     = 256
}
variable "ecs_task_memory" {
description = "The amount (in MiB) of memory used by the task"
type        = number
default     = 512
}`
(I've yet to get multiple containers to work with EFS storage, but that'll be a different issue.)

Hi,
The cost is indeed really affordable!
Thank you so much for this very detailed response @joerawr !


text:
Make default KMS alias optional or configurable

Is your request related to a problem? Please describe.
We are running into an AlreadyExistsException for the KMS alias in our labbing environment. This is because we have made some mistakes and are not able to remove the KMS keys that was previously created by this module. We are waiting for AWS support to have the KMS keys removed.
Describe the solution you'd like.
It would be nice if the module was able to have the default kms alias optional or configurable in some way

it takes 7 to 30 days for a KMS key to be deleted.
if you apply, destroy , apply, it will use the exact same name for the alias, and will fail.
ideally you use a name_prefix to avoid clashing

sadky this module opts for a static key name
https://github.com/terraform-aws-modules/terraform-aws-eks/blob/master/main.tf#L132-L136
a PR could improve that.

the downstream module supports both
https://github.com/terraform-aws-modules/terraform-aws-kms/blob/master/main.tf#L362

authors: FernandoMiguel kodakmoment

repo: terraform-aws-modules/terraform-aws-eks


text:
fix: Auto Mode custom tag policy should apply to cluster role, not node role

Description

Auto Mode custom tag policy should apply to cluster role, not node role

Motivation and Context

Currently this is incorrect per https://docs.aws.amazon.com/eks/latest/userguide/auto-learn-iam.html#tag-prop and therefore instances fail to provision when custom tags are applied to a node class

Breaking Changes

Claiming this as a no since it never worked as intended. Yes, the policy will be destroyed and permissions removed from the node but those permissions were not being used for anything - they need to be applied to the cluster role

How Has This Been Tested?

[ ] I have updated at least one of the examples/* to demonstrate and validate my change(s)
[ ] I have tested and validated these changes using one or more of the provided examples/* projects

[x] I have executed pre-commit run -a on my pull request

This PR is included in version 20.31.4 :tada:

authors: antonbabenko bryantbiggs


text:
feat: Change EKS default version to 1.16

PR o'clock
Description
Upgrading module to support EKS 1.16. Resolves issue https://github.com/terraform-aws-modules/terraform-aws-eks/issues/856.
Checklist

[X] Change added to CHANGELOG.md. All changes must be added and breaking changes and highlighted
[x] CI tests are passing
[X] README.md has been updated after any changes to variables and outputs. See https://github.com/terraform-aws-modules/terraform-aws-eks/#doc-generation

No changes are needed to the module to support 1.16. Users can use it by setting the cluster_version variable. You're changing the module's default version and the PR title should reflect that.

Changed.

Nice. Thanks @sc250024 for your contribution.

Nice. Thanks @sc250024 for your contribution.

Thank you!


text:
Create Cloudwatch log groups for Lambda@Edge in each available region

Is your request related to a new offering from AWS?
Not new
Is your request related to a problem? Please describe.
Hi, I found this module during searching for a solution to create Lambda@Edge log groups in each available/enabled region. Unfortunately, I don't see that this situation is handled.
In addition, here value '0' is possible but not mentioned. Would be nice to add validation.
Describe the solution you'd like.
To configure logs retention period, create Cloudwatch log groups for Lambda@Edge in each available region.
Describe alternatives you've considered.
Manual retention setting.
Additional context
Some links about that:
AWS:

https://forums.aws.amazon.com/thread.jspa?messageID=899555&tstart=0
https://aws.amazon.com/ru/blogs/networking-and-content-delivery/aggregating-lambdaedge-logs/

Terraform:

https://github.com/hashicorp/terraform/issues/16967
https://stackoverflow.com/questions/69885339/how-to-deploy-aws-resources-across-all-regions-via-terraform
https://github.com/hashicorp/terraform/issues/24476
https://discuss.hashicorp.com/t/provider-registry-registry-terraform-io-does-not-have-a-provider-named-registry-terraform-io-hashicorp-each/21695/4
https://stackoverflow.com/a/48667361/6701498

Hi @antonbabenko, thanks for the answer. I'm talking about the case when you want a log group(s) to be created by the module (as it has inputs for that), but in fact it will be created in one region. If end users accessed the service from other regions, log group will be automatically created in that regions, but there is no consistency in settings between created log groups by this module and other ones created automatically.

Actually, I think you should be able to create log groups outside of the module with the desired retention in each region and Lambda@Edge will be able to write into those and not reset retention.

Actually, I think you should be able to create log groups outside of the module with the desired retention in each region and Lambda@Edge will be able to write into those and not reset retention.

Yes, it's so, as for different AWS services like RDS, but input variable of this module promises creation of log group and creates it only in one region, that might be unexpected for end user.

authors: antonbabenko atsikham


text:
lambda_function_from_package module could not find the package already created by package_with_docker module.

Hi, I am using the package_with_docker module to build dependencies. The file was created but, the lambda_function_from_package module was not able to find the file.
module.package_with_docker.null_resource.archive[0] (local-exec): Created: builds/6e7d9546aee118b5bcdd519949958f7d726e4a2a57ec3e00d7d6a33b8b432f55.zip
module.package_with_docker.null_resource.archive[0]: Creation complete after 39s [id=8857212659621213118]

Error: Unable to load "builds/6e7d9546aee118b5bcdd519949958f7d726e4a2a57ec3e00d7d6a33b8b432f55.zip": open builds/6e7d9546aee118b5bcdd519949958f7d726e4a2a57ec3e00d7d6a33b8b432f55.zip: no such file or directory

  on .terraform/modules/lambda_function_from_package/terraform-aws-lambda-1.17.0/main.tf line 14, in resource "aws_lambda_function" "this":
  14: resource "aws_lambda_function" "this" {

module "package_with_docker" {
  source = "terraform-aws-modules/lambda/aws"

  create_function = false

  runtime = "python3.8"
    source_path = [
    "${path.module}/../src",
    {
      pip_requirements = "${path.module}/../src/requirements.txt"
      prefix_in_zip    = "azdir"
    }
    ]
  hash_extra = "something-unique-to-not-conflict-with-module.package_with_pip_requirements_in_docker"

  build_in_docker       = true
  docker_pip_cache      = false
  docker_with_ssh_agent = true
  //  docker_file           = "${path.module}/../src/docker/Dockerfile"
  docker_build_root = "${path.module}/../src/docker"
  docker_image      = "${random_pet.this.id}/build:python38"
}

module "lambda_function_from_package" {
  source = "terraform-aws-modules/lambda/aws"

  create_package         = false
  local_existing_package = module.package_with_docker.local_filename

  function_name = "${random_pet.this.id}-function-packaged"
  handler       = "index.on_trigger_event"
  runtime       = "python3.6"
  timeout       = "120"
  environment_variables  = {
..
  }

}```

Hi @jihed !
The issue you have experienced is related to the behavior of Terraform 0.12 where it didn't really know that at first, it should build the package and then use it. The workarounds for Terraform 0.12 include using -target, using null_data_source as a proxy (see this example), or reorganize code into several directories.
In Terraform 0.13 it is now possible to give a hint to Terraform using depends_on like this:
module "package_with_docker" {
  # ...
}

module "lambda_function_from_package" {
  # ...
  local_existing_package = module.package_with_docker.local_filename

  depends_on = [module.package_with_docker]
}

There is nothing we can do with this in the module, so I am going to close this issue.

authors: antonbabenko jihed


text:
fix(iam): add permission to create log group

Description
add iam CreateLogGroup permission
Motivation and Context
To be in line with the AWSLambdaBasicExecutionRole.

v1.35.0 has been just released.

v1.35.0 has been just released.

authors: antonbabenko svenlito


text:
Why does the module create private route tables when I only have database and non-private subnets?

module "vpc_sae1" {
  source    = "terraform-aws-modules/vpc/aws"
  version   = "3.11.0"

  name = "my-vpc"
  cidr = "10.XX.0.0/16"

  azs              = ["az-1a", "az-1b", "az-1c"]
  intra_subnets  = ["10.XX.XX.0/22", "10.XX.XX.0/22", "10.XX.XX.0/22"]
  public_subnets   = ["10.XX.XX.0/22", "10.XX.XX.0/22", "10.XX.XX.0/22"]
  database_subnets = ["10.XX.XX.0/22", "10.XX.XX.0/22", "10.XX.XX.0/22"]

  single_nat_gateway     = false
  one_nat_gateway_per_az = false
  enable_nat_gateway     = false

  manage_default_network_acl     = true
  public_dedicated_network_acl   = true
  intra_dedicated_network_acl  = true
  database_dedicated_network_acl = true

  manage_default_route_table         = true
  create_database_subnet_route_table = true

}

probably has to do with this line:
https://github.com/terraform-aws-modules/terraform-aws-vpc/blob/57ba0ef08063390636daedcf88f71443281c2b84/main.tf#L243
the count will always default local.nat_gateway_count, which defaults to the local.max_subnet_length.

Thanks!

authors: evairmarinho kkolberg-mission

repo: terraform-aws-modules/terraform-aws-vpc


text:
chore(deps): update ci dependencies

This PR contains the following updates:

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require
minor
v1.21.10 -> v1.23.4

terraform-ibm-modules/common-pipeline-assets
action
minor
v1.17.9 -> v1.18.1

Release Notes

terraform-ibm-modules/ibmcloud-terratest-wrapper (github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper)
v1.23.4
Compare Source
Bug Fixes

add unauthenticated clone if authentication fails (#653) (f575f65)

v1.23.3
Compare Source
Bug Fixes

deps: update github.com/ibm-cloud/bluemix-go digest to dfb1429 (#671) (fcd05c4)

v1.23.2
Compare Source
Bug Fixes

deps: update packages to latest (#669) (6eab475)

v1.23.1
Compare Source
Bug Fixes

deps: update gomod (#654) (59b80d3)

v1.23.0
Compare Source
Features

added json comparison functions (#593) (1781c81)

v1.22.0
Compare Source
Features

cbr cleanup (#658) (7721cbe)

terraform-ibm-modules/common-pipeline-assets (terraform-ibm-modules/common-pipeline-assets)
v1.18.1
Compare Source
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install dependencies before running pre-commit stage (#566) (97afd7d)

v1.18.0
Compare Source
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added the ability pass tfswitchDir (#565) (d492025)

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chore(deps): update module github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper to v1.10.8

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v1.10.8
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deps: update module github.com/gruntwork-io/terratest to v0.43.6 (#576) (93c8433)

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text:
chore(deps): update ci dependencies

This PR contains the following updates:

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common-dev-assets

digest
e511af4 -> 445c71f

github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
require
patch
v1.29.5 -> v1.29.6

Release Notes

terraform-ibm-modules/ibmcloud-terratest-wrapper (github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper)
v1.29.6
Compare Source
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deps: update gomod (#774) (fa0ee2f)

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chore(deps): update common-dev-assets digest to 497c14b

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text:
chore(deps): update module github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper to v1.3.1

This PR contains the following updates:

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github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
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terraform-ibm-modules/ibmcloud-terratest-wrapper
v1.3.1
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deps: update module github.com/go-git/go-git/v5 to v5.5.2 (#297) (e69b2bf)

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text:
chore(deps): update common-dev-assets digest to c72cc0b

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text:
chore(deps): update terraform terraform-ibm-modules/kms-all-inclusive/ibm to v4.16.10

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terraform-ibm-modules/kms-all-inclusive/ibm (source)
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4.16.9 -> 4.16.10

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terraform-ibm-modules/terraform-ibm-kms-all-inclusive (terraform-ibm-modules/kms-all-inclusive/ibm)
v4.16.10
Compare Source
Bug Fixes

deps: update terraform terraform-ibm-modules/cbr/ibm to v1.29.0 (#588) (0319e5f)

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text:
fix typo in ingress error message

Currently the actual output looks like
│ Cannont upate default ingress forcluster '28gjcif4b7fluce0uksce104492bjk0h': status is 400 ...

Expected:
Cannot update default ingress for cluster ...

cc @gdbranco

/ok-to-test

/lgtm

/retest

authors: gdbranco lihongan nirarg


text:
SW-5806 Add more details about the Accelerator in the Apply card

I know previously when Karthik was still here, we pushed back against inline links within copy and we made the decision to move in that direction for all future copy. I didn't realize how simple it actually is to include a link inline, this looks great! 🙌

authors: constanzauanini joshbuchea

repo: terraware/terraware-web


text:
Add vite proxy for server

Get /api to work

Done in #25


text:
t2 run/push: provide firmware node.js version to pre-compiled module resolution. Fixes gh-843

Signed-off-by: Rick Waldron waldron.rick@gmail.com

LGTM :+1:

authors: HipsterBrown rwaldron

license: mit


text:
crash-reporter: sanitize argv

Removes passwords from t2 wifi ... and t2 ap ... commands
Signed-off-by: Rick Waldron waldron.rick@gmail.com

LGTM

Looks good to me. :+1: Thanks for getting this out so quickly.

authors: HipsterBrown rwaldron tikurahul


text:
Playing StreamingSoundData panics in 0.8

After updating from 0.7.0 -> 0.8.1 I'm getting an error when trying to play StreamingSoundData.
Here's the stack trace, let me know if anything else might be helpful.
thread '<unnamed>' panicked at 'did not get expected frame after seeking decoder', /vendor/kira/src/sound/streaming/sound/decode_scheduler.rs:159:22
   0:     0x55f1096ae84a - std::backtrace_rs::backtrace::libunwind::trace::heb3166a6cb1fd02b
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/../../backtrace/src/backtrace/libunwind.rs:93:5
   1:     0x55f1096ae84a - std::backtrace_rs::backtrace::trace_unsynchronized::h34837841e045a1d4
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/../../backtrace/src/backtrace/mod.rs:66:5
   2:     0x55f1096ae84a - std::sys_common::backtrace::_print_fmt::h4348a4b63f328ee4
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys_common/backtrace.rs:65:5
   3:     0x55f1096ae84a - <std::sys_common::backtrace::_print::DisplayBacktrace as core::fmt::Display>::fmt::h1bad440d01eeeb29
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys_common/backtrace.rs:44:22
   4:     0x55f1096d386e - core::fmt::write::hb5a073c651a6cb64
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/fmt/mod.rs:1232:17
   5:     0x55f1096ab875 - std::io::Write::write_fmt::h97cc768331421fb2
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/io/mod.rs:1682:15
   6:     0x55f1096ae615 - std::sys_common::backtrace::_print::h23bd93d6897965f3
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys_common/backtrace.rs:47:5
   7:     0x55f1096ae615 - std::sys_common::backtrace::print::h4d41ae33ce7a824f
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys_common/backtrace.rs:34:9
   8:     0x55f1096affcf - std::panicking::default_hook::{{closure}}::h8ab48635107c713a
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:267:22
   9:     0x55f1096afd0b - std::panicking::default_hook::h6305ca3c910e41d2
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:286:9
  10:     0x55f1096b06d9 - std::panicking::rust_panic_with_hook::ha8dfad8ee636a9a3
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:688:13
  11:     0x55f1096b0479 - std::panicking::begin_panic_handler::{{closure}}::h4fcf608bbbd27b26
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:579:13
  12:     0x55f1096aecfc - std::sys_common::backtrace::__rust_end_short_backtrace::h86f63ffb2338c5c8
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys_common/backtrace.rs:137:18
  13:     0x55f1096b0182 - rust_begin_unwind
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:575:5
  14:     0x55f108d82353 - core::panicking::panic_fmt::ha12d43373dd0f588
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/panicking.rs:64:14
  15:     0x55f1096d15d1 - core::panicking::panic_display::h17cae662562d6a06
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/panicking.rs:147:5
  16:     0x55f1096d157b - core::panicking::panic_str::hfa275bfdfc298aa4
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/panicking.rs:131:5
  17:     0x55f108d82316 - core::option::expect_failed::h09e72beb5cdbcf2e
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/option.rs:1924:5
  18:     0x55f1092987cc - core::option::Option<T>::expect::h45a4a6c94b2112a8
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/option.rs:786:21
  19:     0x55f1092987cc - kira::sound::streaming::sound::decode_scheduler::DecodeScheduler<Error>::frame_at_index::h1ffee1aa2b581f79
                               at /vendor/kira/src/sound/streaming/sound/decode_scheduler.rs:159:7
  20:     0x55f109298fc5 - kira::sound::streaming::sound::decode_scheduler::DecodeScheduler<Error>::run::ha0b5650e36e80c0c
                               at /vendor/kira/src/sound/streaming/sound/decode_scheduler.rs:120:15
  21:     0x55f10929971f - kira::sound::streaming::sound::decode_scheduler::DecodeScheduler<Error>::start::{{closure}}::h8d76de61f9a6d530
                               at /vendor/kira/src/sound/streaming/sound/decode_scheduler.rs:85:10
  22:     0x55f109270e10 - std::sys_common::backtrace::__rust_begin_short_backtrace::ha54d63d75c2749dc
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys_common/backtrace.rs:121:18
  23:     0x55f10926cd40 - std::thread::Builder::spawn_unchecked_::{{closure}}::{{closure}}::h35a6626b5833273f
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/thread/mod.rs:558:17
  24:     0x55f109443294 - <core::panic::unwind_safe::AssertUnwindSafe<F> as core::ops::function::FnOnce<()>>::call_once::hfdabcee9c49c59f1
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/panic/unwind_safe.rs:271:9
  25:     0x55f109443110 - std::panicking::try::do_call::hedb9f28a17b1b15a
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:483:40
  26:     0x55f10944321b - __rust_try
  27:     0x55f109442f2c - std::panicking::try::hbb5be7c505a66fc5
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panicking.rs:447:19
  28:     0x55f1092d60b0 - std::panic::catch_unwind::h698223ec4512f974
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/panic.rs:140:14
  29:     0x55f10926c640 - std::thread::Builder::spawn_unchecked_::{{closure}}::h9231232d909805a1
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/thread/mod.rs:557:30
  30:     0x55f1092b83af - core::ops::function::FnOnce::call_once{{vtable.shim}}::hc087fc5fef99e45e
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/core/src/ops/function.rs:250:5
  31:     0x55f1096b4d13 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h9646bd6a135081c9
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/alloc/src/boxed.rs:1988:9
  32:     0x55f1096b4d13 - <alloc::boxed::Box<F,A> as core::ops::function::FnOnce<Args>>::call_once::h180d98f11b902552
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/alloc/src/boxed.rs:1988:9
  33:     0x55f1096b4d13 - std::sys::unix::thread::Thread::new::thread_start::h83489d4028ebb6e1
                               at /rustc/ef982929c0b653436a6ea6892a2a839fba7c8b57/library/std/src/sys/unix/thread.rs:108:17
  34:     0x7fd40599e609 - start_thread
                               at /build/glibc-SzIz7B/glibc-2.31/nptl/pthread_create.c:477:8
  35:     0x7fd40576c133 - clone
                               at /build/glibc-SzIz7B/glibc-2.31/misc/../sysdeps/unix/sysv/linux/x86_64/clone.S:95
  36:                0x0 - <unknown>

Can you post a small project to replicate the issue? Preferably with the audio in question?

While creating the example, I've noticed that it only panics for .ogg files, and not .mp3.
Here's the project: https://github.com/vdrn/kira_streaming_ogg_panics

I can reproduce the issue and am working on a fix.

I released a new version that makes your ogg work, but I found a couple more issues that I think are bugs in Symphonia, but they may not be. More to come on that.

authors: tesselode vdrn

repo: tesselode/kira