text:
CMake build without GRPC and Python bindings fails

System information

OS: Linux Ubuntu 14.04
Tensorflow: installed from source (master branch)
GCC 4.8.4
CMake 3.8.2
No GPU

Problem
The CMake build fails on Ubuntu when GRPC support is disabled.
Command
cmake \
    	-DCMAKE_INSTALL_PREFIX=../test/ \
    	-DCMAKE_BUILD_TYPE=Release \
        -Dtensorflow_BUILD_SHARED_LIB=ON \
    	-Dtensorflow_BUILD_ALL_KERNELS=ON \
    	-Dtensorflow_BUILD_CONTRIB_KERNELS=OFF \
    	-Dtensorflow_BUILD_CC_EXAMPLE=OFF \
    	-Dtensorflow_BUILD_PYTHON_BINDINGS=OFF \
    	-Dtensorflow_ENABLE_GRPC_SUPPORT=OFF \
    	-Dtensorflow_ENABLE_SSL_SUPPORT=OFF \
    	-Dtensorflow_BUILD_CC_TESTS=OFF \
    	-Dtensorflow_BUILD_PYTHON_TESTS=OFF \
        -Dtensorflow_ENABLE_GPU=OFF ..
make

Logs
[...]
make[2]: *** No rule to make target `../grpc', needed by `tensorflow/core/debug/debug_service.grpc.pb.cc'.
Stop.
make[1]: *** [CMakeFiles/tf_protos_cc.dir/all] Error 2

Actually it also fails if the python bindings are disabled with -Dtensorflow_BUILD_PYTHON_BINDINGS=OFF:
[...]
Scanning dependencies of target tf_c_python_api
[ 65%] Building CXX object CMakeFiles/tf_c_python_api.dir/tensorflow/tensorflow/c/python_api.cc.o
/tensorflow/tensorflow/c/python_api.cc:19:64: fatal error: tensorflow/python/framework/cpp_shape_inference.pb.h: No such file or directory
 #include "tensorflow/python/framework/ccp_shape_inference.pb.h"

Why exposing these flags in the CMakeLists.txt if GRPC and Python are anyway required to successfully build ?

This seems to fix the issue with the Python binding:
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index b47c32f..94dda24 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -127,6 +127,7 @@ file(GLOB_RECURSE tf_protos_cc_srcs RELATIVE ${tensorflow_source_dir}
     "${tensorflow_source_dir}/tensorflow/core/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/boosted_trees/proto/*.proto"
     "${tensorflow_source_dir}/tensorflow/contrib/tpu/proto/*.proto"
+    "${tensorflow_source_dir}/tensorflow/python/framework/*.proto"
 )

 RELATIVE_PROTOBUF_GENERATE_CPP(PROTO_SRCS PROTO_HDRS

Regarding the GRPC dependency, this helps:
diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index b47c32f..34ed324 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -177,13 +177,16 @@ RELATIVE_PROTOBUF_TEXT_GENERATE_CPP(PROTO_TEXT_SRCS PROTO_TEXT_HDRS
 if(WIN32)
   add_library(tf_protos_cc ${PROTO_SRCS} ${PROTO_HDRS})
 else()
-  file(GLOB_RECURSE tf_protos_grpc_cc_srcs RELATIVE ${tensorflow_source_dir}
-      "${tensorflow_source_dir}/tensorflow/core/debug/*.proto"
-  )
-  RELATIVE_PROTOBUF_GENERATE_GRPC_CPP(PROTO_GRPC_SRCS PROTO_GRPC_HDRS
-      ${tensorflow_source_dir} ${tf_protos_grpc_cc_srcs}
-  )
-  add_library(tf_protos_cc ${PROTO_GRPC_SRCS} ${PROTO_GRPC_HDRS} ${PROTO_SRCS} ${PROTO_HDRS})
+  if(tensorflow_ENABLE_GRPC_SUPPORT)
+    file(GLOB_RECURSE tf_protos_grpc_cc_srcs RELATIVE ${tensorflow_source_dir}
+        "${tensorflow_source_dir}/tensorflow/core/debug/*.proto"
+    )
+    RELATIVE_PROTOBUF_GENERATE_GRPC_CPP(PROTO_GRPC_SRCS PROTO_GRPC_HDRS
+        ${tensorflow_source_dir} ${tf_protos_grpc_cc_srcs}
+    )
+    add_library(tf_protos_cc ${PROTO_GRPC_SRCS} ${PROTO_GRPC_HDRS})
+  endif(tensorflow_ENABLE_GRPC_SUPPORT)
+  add_library(tf_protos_cc ${PROTO_SRCS} ${PROTO_HDRS})
 endif()

However some issues remain with debug_io_utils.h and debug_io_utils.cc, which require GRPC and are included by  debug_ops.h.

@mrry Can you take a look at this?
@Skydes Since you already have some fixes, please feel free to make a pull request!

We don't build with these options in our continuous integration, so this particular options seems to have rotted. Opening it for community contributions.

@Skydes
Is this still an issue?
Could you please update TensorFlow to the latest stable version v.2.6 and let us know if you are facing the same error. Thanks!

@tilakrayal I am not using TensorFlow anymore so I won't be able to help on this - closing this issue now.

authors: Skydes angersson mrry tilakrayal


text:
XLA makes incorrect calls into cuBLAS

XLA GPU will sometimes make what looks like illegal calls into cuBLAS.
 $ cat /tmp/bad-dot.hlo
HloModule Main

ENTRY Main {
  p0 = f32[6656,96] parameter(0)
  p1 = f32[6656,384] parameter(1)
  dot = f32[96,384] dot(p0, p1), lhs_contracting_dims={0}, rhs_contracting_dims={0}
  ROOT tr = f32[384,96] transpose(dot), dimensions={1,0}
}

 $ bazel run -c opt tensorflow/compiler/xla/tools:replay_computation_gpu -- /tmp/bad-dot.hlo --use_fake_data

...
2019-02-14 09:17:41.060396: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened CUDA library libcudart.so.9.0
2019-02-14 09:17:41.062661: E tensorflow/compiler/xla/tools/replay_computation.cc:389] iteration complete.
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ** On entry to GEMM_EX  parameter number 9 had an illegal value
 ...

And to add a little more context: my current guess is that maybe the lda and ldb parameters are wrong during autotuning if the output has a {0, 1} layout.

@jlebar Could you please take a look at this issue? Thanks!

ltrace gives
[pid 35759] cublasGemmEx@libcublas.so.10.0(0x559288d7aae0, 0, 1, 384, 96, 6656, 1.000000, 0x7f1b2c800000, 0, 96, 0x7f1b2cc00000, 0, 384, 0.000000, 0x7f1b2c600000, 0, 384, 0, -1) = 7

so transa is 0/n, this means that lda (parameter 9) should be at least m. m in this case is 384, and lda is 96.

source: gharchive/issue

authors: hawkinsp jvishnuvardhan ngimel sanjoy

repo: tensorflow/tensorflow

license_type: permissive

license_source: github-api


text:
googletest.h used in open source project

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

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04.1 LTS
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): source
TensorFlow version: commit 6583b83dc9393118c60a9c11f15453c650fab89f
Python version: 2.7.15rc1
Installed using virtualenv? pip? conda?: sources
Bazel version (if compiling from source): 0.20.0
GCC/Compiler version (if compiling from source): gcc (Ubuntu 7.3.0-27ubuntu1~18.04) 7.3.0
CUDA/cuDNN version: -
GPU model and memory: -

Describe the problem
During compilation I get an error:
ERROR: /local/people/kamil_herba/tensorflow-x86/tensorflow/lite/testing/kernel_test/BUILD:81:1: Couldn't build file tensorflow/lite/testing/kernel_test/_objs/input_generator_test/input_generator_test.o: C++ comp
ilation of rule '//tensorflow/lite/testing/kernel_test:input_generator_test' failed (Exit 1)
tensorflow/lite/testing/kernel_test/input_generator_test.cc:21:44: fatal error: testing/base/public/googletest.h: No such file or directory
compilation terminated.

I guess that googletest.h is some internal Google header file which shouldn't be use in open source projects.
Provide the exact sequence of commands / steps that you executed before running into the problem
bazel test tensorflow/lite/testing/kernel_test:input_generator_test
Any other info / logs
@liyunlu0618 could you take a look on that?
Other similar problems from the same commit

tensorflow/lite/testing/kernel_test/generate_diff_report.cc:

tensorflow/lite/testing/kernel_test/generate_diff_report.cc:22:3: error: 'string' was not declared in this scope
   string base, test, output;

tensorflow/lite/testing/kernel_test/util_test.cc

testing/base/public/googletest.h: No such file or directory

tensorflow/lite/testing/kernel_test/diff_analyzer_test.cc:
googletest.h is not used but FLAGS_test_tmpdir which probably comes from this header is used so it should be removed as well.

I have met the same problem when running the test for tensorflow/lite/testing/kernel_test/util_test.cc.

Google Test is a publicly released open source library that can be used by any other open source project. I see it is included as depenency in the util_test.cc target: https://github.com/tensorflow/tensorflow/blob/656e7a55387fac2df1b786013adbde79cdc95fd5/tensorflow/lite/testing/kernel_test/BUILD#L33-L49
Can you recompile from scratch and attach the log? Most likely the download of googletest archive didn't succeed and you missed that in the rest of the compilation log.

@mihaimaruseac I think the error is caused by #include "testing/base/public/googletest.h". The error message is as below:
ERROR: /home/abc/tensorflow/tensorflow/lite/testing/kernel_test/BUILD:33:1: C++ compilation of rule '//tensorflow/lite/testing/kernel_test:util_test' failed (Exit 1)
tensorflow/lite/testing/kernel_test/util_test.cc:21:10: fatal error: testing/base/public/googletest.h: No such file or directory
 #include "testing/base/public/googletest.h"
          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
Target //tensorflow/lite/testing/kernel_test:util_test failed to build

This PR #33123 is submitted to fix it. Could you please take a look?

Confirmed that I cannot compile tensorflow/lite/testing/kernel_test:input_generator_test although I can compile other targets using Google test. Assigning to lite/ code owners for review.

Hi @herbakamil, Looks like this issue has been fixed.
Triage Note*
As this issue has been fixed by @nairb774 via (https://github.com/tensorflow/tensorflow/commit/1c88fff548acb048f2fe64fd2564e885759def12, https://github.com/tensorflow/tensorflow/commit/c9c0e70d0b1d6d4fd618c5ebdebd859dbe59848f, https://github.com/tensorflow/tensorflow/commit/db5816707ab0fc4ae6d3959a313d67dd871a8d9d)
Please take a look at this comment. Kindly move this to closed status, if you think its resolved. Thanks!

authors: feihugis gadagashwini herbakamil mihaimaruseac


text:
[TF 2.0 alpha] Fix "Load images with tf.data" to run on window

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

TensorFlow version: Tensorflow 2.0 alpha
Doc Link: Tutorial Load images with tf.data

Describe the documentation issue
Tutorial Load images with tf.data will have an error if it runs on windows because of the backslash of the path in this cell.
import IPython.display as display

def caption_image(image_path):
    image_rel = pathlib.Path(image_path).relative_to(data_root)
    return "Image (CC BY 2.0) " + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1])

So I fixed it by replacing the backslash with the slash.
import IPython.display as display
def caption_image(image_path):
    image_rel = pathlib.Path(image_path).relative_to(data_root)
    image_rel = str(image_rel)
    image_rel=image_rel.replace('\\','/')
    str_return = "Image (CC BY 2.0) " + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1])
    return str_return

We welcome contributions by users. Will you be able to update submit a PR (use the doc style guide) to fix the doc Issue?

Pathlib.Path should create either a WindowsPath or a PosixPath based on your OS (takes care of slashes and backslashes under the hood).
Can you share some information about your setup, like what python version you are running etc...

Sure. I'm using Anaconda, python 3.6.8 on win10. I run the code on Jupyter notebook 5.7.4.
Pathlib takes care of the path but those picture's paths in "LICENSE.txt" are using the backslash.
Ex:
import os
attributions = (data_root/"LICENSE.txt").open(encoding='utf-8').readlines()[4:]
attributions = [line.split(' CC-BY') for line in attributions]
attributions = dict(attributions)

The dict attributions has the key daisy/7568630428_8cf0fc16ff_n.jpg
So when we use  str_return = "Image (CC BY 2.0) " + ' - '.join(attributions[str(image_rel)].split(' - ')[:-1]) to extract the author name of the picture by the key it will get error because the image_path is still the WindowsPath daisy\7568630428_8cf0fc16ff_n.jpg after we converted it to string.

@Ayush517 Can you please take a look at this issue? Thanks!

authors: MostafaGazar shaolinkhoa ymodak


text:
tensorflow-gpu installation failing

Please find the error below.

import tensorflow as tf
Traceback (most recent call last):
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "", line 1, in
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow_init_.py", line 24, in
from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python_init_.py", line 49, in
from tensorflow.python import pywrap_tensorflow
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 74, in
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 58, in
from tensorflow.python.pywrap_tensorflow_internal import *
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 28, in
_pywrap_tensorflow_internal = swig_import_helper()
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\imp.py", line 243, in load_module
return load_dynamic(name, filename, file)
File "C:\Users\priya\Anaconda3\envs\tensorflow\lib\imp.py", line 343, in load_dynamic
return _load(spec)
ImportError: DLL load failed: The specified module could not be found.
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors
for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

@priyak1917  Hey! sorry to hear this, can you tell me the version of tensorflow you are installing plus the version of CUDA and CUDNN you have installed in your system!

Hi Aman,
I'm a beginner and tried to install Tensorflow. I understood the problem, i
don't have NVIDIA Graphics card in my System.
I want to uninstall the Tensowflow-GPU and continue to use Tensorflow-Cpu,
is it possible? Can you please guide?
Regards
On Wed, Apr 3, 2019 at 1:57 AM Aman Patel notifications@github.com wrote:

@priyak1917 https://github.com/priyak1917 Hey! sorry to hear this, can
you tell me the version of tensorflow you are installing plus the version
of CUDA and CUDNN you have installed in your system!
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tensorflow/tensorflow/issues/27434#issuecomment-479183550,
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National Institute of Technology, Warangal
Warangal-506 021
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Contact no: +919886032005

@priyak1917 yeah sure I can help you but it will be much easier if you tell me that how you installed tensorflow-gpu??
through anaconda distribution or through pip install manager??

https://www.youtube.com/watch?v=SNdQqYpfCV4
I followed exactly above mentioned Video procedure.
Regards
On Wed, Apr 3, 2019 at 6:32 PM Aman Patel notifications@github.com wrote:

@priyak1917 https://github.com/priyak1917 yeah sure I can help you but
it will be much easier if you tell me that how you installed
tensorflow-gpu??
through anaconda distribution or through pip install manager??
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tensorflow/tensorflow/issues/27434#issuecomment-479477417,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AayicnAi-YaMqQ2-0vppZT61Xt1VXxDVks5vdKZOgaJpZM4cY0Kv
.

--
Ms.Priyanka Kolli
BTech-Dept of BioTechnology
National Institute of Technology, Warangal
Warangal-506 021
Andhra Pradesh
India.
Contact no: +919886032005

@priyak1917 just activate your virtual environment and type conda remove tensorflow-gpu this will remove your gpu version of tensorflow!
Do this and let me know if it works!

Hi aman,
I had activated the tensorflow and gave the command "conda remove
tensorflow-gpu".
[image: image.png]
It actually got failed.
[image: image.png]
Can you tell me how to uninstall entire tensorflow? I will re install only
CPU version?
On Wed, Apr 3, 2019 at 11:44 PM Aman Patel notifications@github.com wrote:

@priyak1917 https://github.com/priyak1917 just activate your virtual
environment and type conda remove tensorflow-gpu this will remove your gpu
version of tensorflow!
Do this and let me know if it works!
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/tensorflow/tensorflow/issues/27434#issuecomment-479598789,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AayicrbrsHz6ycSfdNQ8idjwA91piyx5ks5vdO91gaJpZM4cY0Kv
.

--
Ms.Priyanka Kolli
BTech-Dept of BioTechnology
National Institute of Technology, Warangal
Warangal-506 021
Andhra Pradesh
India.
Contact no: +919886032005

@priyak1917 If you have installed in Virtual environment, you can deactivate that env and create another virtual env and install tensorflow. Installing tf_cpu is very easy. Just open a terminal and type "pip install tensorflow". Thanks!

@vishu
Thank you so much I will try and let you know.
Sent from my iPhone

On 05-Apr-2019, at 2:50 AM, Vishnuvardhan Janapati notifications@github.com wrote:
@priyak1917 If you have installed in Virtual environment, you can deactivate that env and create another virtual env and install tensorflow. Installing tf_cpu is very easy. Just open a terminal and type "pip install tensorflow". Thanks!
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!


text:
tf.Dataset.map() only processes one example in my dataset

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
OS Platform and Distribution: Linux Ubuntu 16.04
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 1.12.2
Python version: 3.5
CUDA/cuDNN version: V8.0.61
GPU model and memory: Tesla P100-PCIE, 12193MiB

Describe the current behavior
I have a dataset containing 592 examples, but tf.Dataset.map only processes one of those, as evidenced by a global counter, which I increment in the function given to map(). Why is it not processing all examples in the dataset? I run in eager execution, but code inside .map() is not executed eagerly.
Describe the expected behavior
All 592 examples in my dataset should be processed (with the counter being 592 afterwards).
Code to reproduce the issue
My dataset: https://we.tl/t-rZYNJ6I9oU
from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
import sys
import utilities

counter = 0

def decode_png_mask(image_buffer):
    """
    Takes string of bytes encoding a PNG and produces a tensor image
    """
    image = tf.squeeze(tf.image.decode_image(image_buffer, channels=1), axis=2)
    image.set_shape([None, None])
    image = tf.greater(image, 0)
    image = tf.cast(image, dtype=tf.uint8)
    return image

def masks_to_onehots(tag_masks, tag_class_indices, num_classes):
    def onehotify(pixel_tag_masks):
        tag_mask_sizes_suppressed = tf.where(tf.not_equal(tag_mask_sizes, 0), tag_mask_sizes, tag_mask_sizes + 9999999)
        smallest_mask_index = tf.argmin(tag_mask_sizes_suppressed)
        onehot = tf.one_hot(smallest_mask_index
                            , depth=num_classes, dtype=tf.uint8)
        return onehot
    tag_mask_sizes = tf.reduce_sum(tag_masks, axis=[1, 2])
    image_masks = tf.transpose(tag_masks, perm=[1, 2, 0])
    onehots = tf.map_fn(lambda x: tf.map_fn(onehotify, x), image_masks)
    return onehots

def parse_example(example_proto, width, height, num_classes):
    features = {
        'image/encoded': tf.FixedLenFeature((), tf.string),
        'image/height': tf.FixedLenFeature((), tf.int64),
        'image/width': tf.FixedLenFeature((), tf.int64),
        'image/filename': tf.FixedLenFeature((), tf.string),
        'image/object/bbox/xmin': tf.VarLenFeature(tf.float32),
        'image/object/bbox/xmax': tf.VarLenFeature(tf.float32),
        'image/object/bbox/ymin': tf.VarLenFeature(tf.float32),
        'image/object/bbox/ymax': tf.VarLenFeature(tf.float32),
        'image/object/class/label': tf.VarLenFeature(tf.int64),
        'image/object/class/text': tf.VarLenFeature(tf.string),
        'image/object/mask': tf.VarLenFeature(tf.string),
        'image/depth': tf.FixedLenFeature((), tf.string)
    }

    global counter
    counter = counter + 1

    parsed_example = tf.parse_single_example(example_proto, features)

    # Decode image
    image = tf.image.decode_jpeg(parsed_example['image/encoded'])
    parsed_example['image/encoded'] = image

    tag_masks = tf.sparse.to_dense(parsed_example['image/object/mask'], default_value="")
    tag_masks = tf.map_fn(decode_png_mask, tag_masks, dtype=tf.uint8)
    tag_masks = tf.reshape(tag_masks, shape=tf.stack([-1, height, width]), name='tag_masks')

    # All segmentation now have their mask in mask, their labelmap index in classes_indices and their tagname in classes_text
    tag_class_indices = tf.sparse.to_dense(parsed_example['image/object/class/label'])
    tag_class_names = tf.sparse.to_dense(parsed_example['image/object/class/text'], default_value="")
    onehots = masks_to_onehots(tag_masks, tag_class_indices, num_classes)
    parsed_example['image/labels'] = onehots

    return parsed_example

tf.enable_eager_execution()

NUM_CLASSES = 21

tfrecord_train = "/path/to/tf.record"
dataset_train = tf.data.TFRecordDataset(tfrecord_train)

# Read image widht/height from the TFRecord file
iterator = dataset_train.make_one_shot_iterator()
next_element = iterator.get_next()
parsed_element = np.fromstring(next_element.numpy(), dtype=np.uint8)
example = tf.train.Example.FromString(parsed_element)
height = example.features.feature['image/height'].int64_list.value[0]
width = example.features.feature['image/width'].int64_list.value[0]

dataset_train = dataset_train.map(lambda x: parse_example(x, width, height, NUM_CLASSES))
print(counter)

@EmielBoss Ran the code provided here, I got the output as 1

@EmielBoss Could you share the data as the link expired. Also, try to check it with Latest TF1.14.0. Thanks!

Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!

authors: EmielBoss jvishnuvardhan muddham


text:
undefined reference to `tensorflow::str_util::EndsWith

On Kubuntu 18.08
TensorFlow 1.13 installed from source.
Python 3.6
Bazel 19.2
GCC 7.4.0
In the past, i have a tested program in ubuntu 16.04 and it run fine. I make the migration to ubuntu 18.04, recompile tensorflow for c++ and obtain the libraries libtensorflow_cc.so and libtensorflow_framework.so.
When i tried to compile my program using "cmake .. && make"  see the following messages:
tensorflowlabelimageclassification.cpp:(.text+0x2bff): undefined reference to tensorflow::str_util::EndsWith(std::basic_string_view<char, std::char_traits<char> >, std::basic_string_view<char, std::char_traits<char> >)' tensorflowlabelimageclassification.cpp:(.text+0x2d3c): undefined reference to tensorflow::str_util::EndsWith(std::basic_string_view<char, std::char_traits >, std::basic_string_view<char, std::char_traits >)'
tensorflowlabelimageclassification.cpp:(.text+0x2ee3): undefined reference to `tensorflow::str_util::EndsWith(std::basic_string_view<char, std::char_traits >, std::basic_string_view<char, std::char_traits >)'
Any help?
Thanks.
Dibet
Describe the problem
Provide the exact sequence of commands / steps that you executed before running into the problem
Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

@dibet, The TensorFlow team does not officially support cmake, sorry. Please try out with Bazel.

Hi, i just write to inform how i resolve my problem.
In the past (ubuntu 16.04 and gcc 5) i was compiling using  -std=c++17.
Now, on ubuntu 18.04 and gcc 7 i need to use  -std=c++14
I don't know the reason but i fix it in that way.
Thanks.

Thanks for sharing your resolution. Closing since its resolved. Thanks!

authors: dibet gadagashwini ymodak


text:
tf-lite version info

I have preinstalled TensorFlow - lite on my system, is it possible to get version information of it programmatically? If so - how?
Thanks.

TF Lite comes with TensorFlow prebuilt binary. So it will be the same version as your installed TF.
From python interpreter you may try;
import tensorflow as tf
tf.__version__

Thanks. How about in C++ case?
Dne pá 15. 11. 2019 19:25 uživatel Yasir Modak notifications@github.com
napsal:

TF Lite comes with TensorFlow prebuilt binary. So it will be the same
version as your installed TF.
From python interpreter you may try;
import tensorflow as tf
tf.version
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The version will be same and you may use the above snippet to get tf version. Thanks!
Closing this issue now, feel free to reopen if have any questions. Thanks!


text:
cannot import name 'resnet '

I installed tensorflow using the following command
conda install tensorflow-gpu
conda install keras-gpu
then
import tensorflow
I get the following error:

import tensorflow
Traceback (most recent call last):
File "", line 1, in
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow/init.py", line 98, in
from tensorflow_core import *
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow_core/init.py", line 40, in
from tensorflow.python.tools import module_util as _module_util
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow/init.py", line 50, in getattr
module = self._load()
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow/init.py", line 44, in _load
module = _importlib.import_module(self.name)
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/importlib/init.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow_core/python/init.py", line 83, in
from tensorflow.python import keras
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow_core/python/keras/init.py", line 26, in
from tensorflow.python.keras import activations
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow_core/python/keras/init.py", line 27, in
from tensorflow.python.keras import applications
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow_core/python/keras/applications/init.py", line 64, in
from tensorflow.python.keras.applications.resnet import ResNet50
File "/home/nnir712/anaconda3/envs/tfenv/lib/python3.6/site-packages/tensorflow_core/python/keras/applications/resnet.py", line 22, in
from keras_applications import resnet
ImportError: cannot import name 'resnet'

System information
-ubuntu18.04
anaconda env:
Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main    defaults
_tflow_select             2.1.0                       gpu    defaults
absl-py                   0.8.1                    py36_0    defaults
astor                     0.8.0                    py36_0    defaults
blas                      1.0                         mkl    defaults
c-ares                    1.15.0            h7b6447c_1001    defaults
ca-certificates           2020.1.1                      0    defaults
certifi                   2019.11.28               py36_0    defaults
cudatoolkit               10.0.130                      0    defaults
cudnn                     7.6.5                cuda10.0_0    defaults
cupti                     10.0.130                      0    defaults
gast                      0.2.2                    py36_0    defaults
google-pasta              0.1.8                      py_0    defaults
grpcio                    1.16.1           py36hf8bcb03_1    defaults
h5py                      2.10.0           py36h7918eee_0    defaults
hdf5                      1.10.4               hb1b8bf9_0    defaults
intel-openmp              2019.4                      243    defaults
keras-applications        1.0.8                      py_0    defaults
keras-base                2.2.4                    py36_0    defaults
keras-gpu                 2.2.4                         0    defaults
keras-preprocessing       1.1.0                      py_1    defaults
ld_impl_linux-64          2.33.1               h53a641e_7    defaults
libedit                   3.1.20181209         hc058e9b_0    defaults
libffi                    3.2.1                hd88cf55_4    defaults
libgcc-ng                 9.1.0                hdf63c60_0    defaults
libgfortran-ng            7.3.0                hdf63c60_0    defaults
libprotobuf               3.11.2               hd408876_0    defaults
libstdcxx-ng              9.1.0                hdf63c60_0    defaults
markdown                  3.1.1                    py36_0    defaults
mkl                       2019.4                      243    defaults
mkl-service               2.3.0            py36he904b0f_0    defaults
mkl_fft                   1.0.15           py36ha843d7b_0    defaults
mkl_random                1.1.0            py36hd6b4f25_0    defaults
ncurses                   6.1                  he6710b0_1    defaults
numpy                     1.18.1           py36h4f9e942_0    defaults
numpy-base                1.18.1           py36hde5b4d6_1    defaults
openssl                   1.1.1d               h7b6447c_3    defaults
opt_einsum                3.1.0                      py_0    defaults
pip                       20.0.2                   py36_1    defaults
protobuf                  3.11.2           py36he6710b0_0    defaults
python                    3.6.10               h0371630_0    defaults
pyyaml                    5.2              py36h7b6447c_0    defaults
readline                  7.0                  h7b6447c_5    defaults
scipy                     1.3.2            py36h7c811a0_0    defaults
setuptools                45.1.0                   py36_0    defaults
six                       1.14.0                   py36_0    defaults
sqlite                    3.30.1               h7b6447c_0    defaults
tensorboard               2.0.0              pyhb38c66f_1    defaults
tensorflow                2.0.0           gpu_py36h6b29c10_0    defaults
tensorflow-base           2.0.0           gpu_py36h0ec5d1f_0    defaults
tensorflow-estimator      2.0.0              pyh2649769_0    defaults
tensorflow-gpu            2.0.0                h0d30ee6_0    defaults
termcolor                 1.1.0                    py36_1    defaults
tk                        8.6.8                hbc83047_0    defaults
werkzeug                  0.16.1                     py_0    defaults
wheel                     0.34.1                   py36_0    defaults
wrapt                     1.11.2           py36h7b6447c_0    defaults
xz                        5.2.4                h14c3975_4    defaults
yaml                      0.1.7                had09818_2    defaults
zlib                      1.2.11               h7b6447c_3    defaults
(tfenv) nnir712@nnir712-Lenovo:~/temp$ python
Python 3.6.10 |Anaconda, Inc.| (default, Jan  7 2020, 21:14:29)

@zihaozhang9,
I was able to run the above code without any issues. Please find the Gist here. Thanks!

@zihaozhang9,
I was able to run the above code without any issues. Please find the Gist here. Thanks!
Thank you very much for your continued attention!
The day before yesterday, I tried many versions of graphics driver, CUDA, cudnn and tensorflow.
Then I discovered a magical phenomenon, when only 10 pictures are loaded in the graphics card, it can be trained normally. When loading 100 pictures, the video memory uses about 3G, and then reports an error.
From this I suspect that it is not the version that is wrong, it should be the graphics card hardware failure.I will contact nvidia's technical engineer to repair the graphics card.

@zihaozhang9,
Thank you for the update. Please feel free to close the issue if it is resolved. Thanks!

It is a bit late but I've got the same problem and it is not implemented in your version
https://github.com/tensorflow/tensorflow/issues/29040

authors: MedAliMimouni amahendrakar zihaozhang9


text:
Entity <function pfor..f at 0x644d84950> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS 10.14.6 (18G2022)
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: no
TensorFlow installed from (source or binary): binary, from Anaconda
TensorFlow version (use command below): unknown 1.14.0
Python version: 3.7.4
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version: cudatoolkit-9.0
GPU model and memory: not used
Exact command to reproduce: run the attached script get_partial_derivatives.py.

Describe the problem
I ran into a bug when I run the following small code:
import tensorflow as tf
tf.enable_v2_behavior()

input = tf.Variable([[1.0, 2.0], [3.0, 4.0]])
w = tf.Variable([[1.0, 7.0], [3.0, 5.0]])

with tf.GradientTape(persistent=True) as t:
    f = tf.matmul(input, tf.transpose(w))

print("f:")
print(f)

print("Derivative:")
print(t.jacobian(f, input))

Source code / logs
Log of the error:
WARNING:tensorflow:Entity <function pfor.<locals>.f at 0x644d84950> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <function pfor.<locals>.f at 0x644d84950>: AssertionError: Bad argument number for Name: 3, expecting 4

The full log (with AUTOGRAPH_VERBOSITY=10) is attached.
get_partial_derivatives.py.txt
logfile.txt

@dmitry-kabanov I have executed the code provided by you [on 1.14 as mentioned in by you] and do not find any warning/issue faced by you, please find the gist here for the same.
if you still face an issues please share a gist of the issue.

Well, I do not know how to explain it. I can see this bug but I have Tensorflow provided by Anaconda distribution.
If it is irreproducible, then should I close it?

@dmitry-kabanov could you please install tensorflow using pip and try on virtual environment, let us know if that helps resolve the issue.

@Saduf2019 I installed Python 3.7 via conda in a conda environment and install tensorflow and all dependencies via pip. I still get the same issue. Please see the attached logfile.
My version of Python is 3.7.6 h359304d_2.
logfile.txt

@dmitry-kabanov
Can you please refer to link where workaround is working by installing gast ( pip install gast==0.2.2).
let us know if that helps.

Somehow I replied three days ago but my last comment is not here.
I confirm that the workaround works. Installing gast 0.2.2 removed gast 0.3.3 and then it worked correctly.
~/dev/2020-02-17-tensorflow-jacobian-bug via 🅒 /Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env
➜ pip install "gast==0.2.2"
Processing /Users/dima/Library/Caches/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd/gast-0.2.2-cp37-none-any.whl
Installing collected packages: gast
  Attempting uninstall: gast
    Found existing installation: gast 0.3.3
    Uninstalling gast-0.3.3:
      Successfully uninstalled gast-0.3.3
Successfully installed gast-0.2.2

~/dev/2020-02-17-tensorflow-jacobian-bug via 🅒 /Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env took 2s
➜ python get_partial_derivatives.py
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/dima/dev/2020-02-17-tensorflow-jacobian-bug/conda-env/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From get_partial_derivatives.py:2: The name tf.enable_v2_behavior is deprecated. Please use tf.compat.v1.enable_v2_behavior instead.

2020-02-18 12:33:29.302126: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
f:
tf.Tensor(
[[15. 13.]
 [31. 29.]], shape=(2, 2), dtype=float32)
Derivative:
tf.Tensor(
[[ 4. 12.]
 [ 4. 12.]], shape=(2, 2), dtype=float32)
Jacobian:
tf.Tensor(
[[[[1. 7.]
   [0. 0.]]

  [[3. 5.]
   [0. 0.]]]

 [[[0. 0.]
   [1. 7.]]

  [[0. 0.]
   [3. 5.]]]], shape=(2, 2, 2, 2), dtype=float32)

@dmitry-kabanov Moving this issue to resolved status and as the issue is resolved.


text:
Deep Training with Tensorflow

Hello,
i'm trying to make an experiment about performance with Keras and Tensorflow. I'm interested in training some parts of the networks. In my case, my objective is training some connections (neurons) of certains layers. Is it possible to do that experiment? I know that re-training layers is possible with trainable=True, but I would like to do it in a deeper way, going to the connections (fully connected phase) and filters (convolutional phase)
Thanks,
Javier

@javipa1995 Can you please describe your use case or the reason behind this specific experiment? Thanks!

We probably needs more context than this, for example, do you have existing example (written in TF), or the problems to solve?

@javipa1995 I think you can monitor filters and see how they get updated during training. There are couple of resources like this and this. You can find many more when you search.
TF website has lot of resources for performance. You can check this resource to profile the model performance. In future, post this kind of questions in Stackoverflow as there is a huge community helping each other.
GitHub is mainly for bug and performance related issues. Thanks!

Closing this as this was resolved with the above comment. Please feel free to reopen if I am mistaken. Thanks!

Hi,
While we lack context (and responses), I do not think that this is actually solved. If I understand well, the question is to know whether there is a mechanism to selectively set some of the weights within a layer trainable, while the other weights would not change. In other words, the question is to know whether there is a straightforward way to have a more granular approach of the layer.trainable API.
As far as I know, there is no such mechanism, but it could be achieved by selectively applying no-gradient ops, which would probably be somewhat tedious... @jvishnuvardhan - or anyone else on the TF team - would you have some additional thoughts, insights or knowledge as to that?
Thank you.

authors: javipa1995 jvishnuvardhan pandrey-fr tanzhenyu


text:
有关tensorflow estimator   About tensorflow estimator

I am using the estimator model to process some financial data. The API is tf.estimator.DNNLinearCombinedRegressor However, the document does not provide a detailed description of the use of this API. I would like to ask: 1. If I set 960 lines of batch data for input and use ten batches for prediction. Do different batches affect the prediction results? For example, will batch 1 affect the calculation of batch 2? If you still can't understand my question, let me give another example. Suppose that the correct results can be obtained by inputting batch 1 and batch 2. But now, due to data error, all data of batch 1 become 0, while batch 2 has not changed. In this case, can you still get correct results by inputting batch 1 and batch 2? 2. If I set 960 rows of data for a batch, does the data in the first row affect the data calculation in the second row? This problem is similar to the first one, except that different batches are changed into different lines. Please answer my question, thank you!

@anavanab99
Please, fill issue template..
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!

URL(s) with the issue:
https://tensorflow.google.cn/versions/r1.15/api_docs/python/tf/estimator/DNNLinearCombinedRegressor
Please provide a link to the documentation entry, for example:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/MyMethod
Description of issue (what needs changing):
?
I don't think it is necessary to provide relevant code for this problem. It is just a supplement to the API description. If you need to provide relevant code, you can refer to your own API documentation https://tensorflow.google.cn/versions/r1.15/api_docs/python/tf/estimator/DNNLinearCombinedRegressor
Is the link to the source code correct?
Are all parameters defined and formatted correctly?
Are return values defined?
Are the errors defined? For example,
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/feature_column/categorical_column_with_vocabulary_file#raises
Is there a usage example?
See the API guide: https://www.tensorflow.org/community/contribute/docs_ref
on how to write testable usage examples.
Request visuals, if applicable
Are there currently visuals? If not, will it clarify the content?
Submit a pull request?
Are you planning to also submit a pull request to fix the issue? See the docs
contributor guide: https://www.tensorflow.org/community/contribute/docs,
docs API guide: https://www.tensorflow.org/community/contribute/docs_ref and the
docs style guide: https://www.tensorflow.org/community/contribute/docs_style

This is not Build/Installation or Bug/Performance issue. Please post this kind of support questions at Stackoverflow. There is a big community to support and learn from your questions. GitHub is mainly for addressing bugs in installation and performance. Thanks!

authors: anavanab99 jvishnuvardhan ravikyram


text:
The chained == operator throws error when converting to TFLite from tf.function.

1. System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04 (Google Colab)
TensorFlow installation (pip package or built from source):  pip package (Google Colab)
TensorFlow library (version, if pip package or github SHA, if built from source): 2.4.1 (Google Colab)

2. Code
Option B: Paste your code here:
Consider the scenario, in which one would like to zero-out (or filter) a tensor, based on multiple equality checks:
import tensorflow as tf

SHAPE = (10, )

@tf.function(
    input_signature=[tf.TensorSpec(shape=SHAPE, dtype=tf.int32)]
)
def my_function(inputs):

    # Let's say that we want to zero-out all values BUT 1 and 2 from our input tensor.

    filtered_inputs = tf.where(
        (inputs == 1) | (inputs == 2),
        inputs,
        0
    )
    return filtered_inputs

Let us call this function on a mock input:
mock_inputs = tf.random.uniform(shape=SHAPE, minval=1, maxval=5, dtype=tf.int32)
my_function(mock_inputs)

It works (so far). Now let us convert this to Tensorflow Lite as per official documentation
concrete_func = my_function.get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()   # ERROR.

The above will fail with rather long error message that ends with
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Equal {device = "", incompatible_shape_error = false}

Now, there is a workaround: one can use the tf.math.equal instead of the == operator:
@tf.function(
    input_signature=[tf.TensorSpec(shape=SHAPE, dtype=tf.int32)]
)
def my_workaround_function(inputs):

    # Use Tensorflow ops instead of Python ops

    filtered_inputs = tf.where(
        (tf.math.equal(inputs, 1)) | (tf.math.equal(inputs, 2)),
        inputs,
        0
    )
    return filtered_inputs

And the above can be safely converted to TFLite:
concrete_func = my_workaround_function.get_concrete_function()
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
tflite_model = converter.convert()  # OK.

I am opening the issue as I am unsure if this is a intended or known behaviour. For example, some people might be stuck on this situation, without figuring out the workaround. Also: as per the Tensorflow documentation, the tf.function is intended to work with simple Python ops.
If this is somewhat intended, let me know and I will close the issue (though I would greatly appreciate some explanation if possible).
Best regards,
Sebastian
5. (optional) Any other info / logs
Here is a full error message:
Exception: /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1750:0: error: 'tf.Equal' op is neither a custom op nor a flex op
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201:0: note: called from
<ipython-input-54-3ccb02792638>:14:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:973:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:634:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:990:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3206:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3361:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2969:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:726:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1750:0: error: 'tf.Equal' op is neither a custom op nor a flex op
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201:0: note: called from
<ipython-input-54-3ccb02792638>:14:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:973:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:634:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:990:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3206:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3361:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2969:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:726:0: note: called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Equal {device = "", incompatible_shape_error = false}

During handling of the above exception, another exception occurred:

ConverterError                            Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/convert.py in toco_convert_protos(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)
    214       return model_str
    215     except Exception as e:
--> 216       raise ConverterError(str(e))
    217
    218   if distutils.spawn.find_executable(_toco_from_proto_bin) is None:

ConverterError: /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1750:0: error: 'tf.Equal' op is neither a custom op nor a flex op
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201:0: note: called from
<ipython-input-54-3ccb02792638>:14:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:973:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:634:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:990:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3206:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3361:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2969:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:726:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1750:0: error: 'tf.Equal' op is neither a custom op nor a flex op
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201:0: note: called from
<ipython-input-54-3ccb02792638>:14:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:973:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:634:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:990:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3206:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:3361:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py:2969:0: note: called from
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:726:0: note: called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Equal {device = "", incompatible_shape_error = false}

@sebastian-sz You can enable tf.Equal operator by using select TF option. https://www.tensorflow.org/lite/guide/ops_select

Could you try the above two options with tf-nightly? I confirmed that both works well with tf-nightly.

It does work both with tf-nightly and with tf.lite.OpsSet.SELECT_TF_OPS. Thank you! I'm closing the issue.

authors: abattery sebastian-sz


text:
Fails to run the model in an Android app

Hi, I've created a model using keras. The model works fine in google-colab but when I attach it to my app in Android Studio I get the following comment:
java.lang.AssertionError: Error occurred when initializing ImageClassifier: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOptions metadata to preprocess input images.
My app works fine with a different model so I believe the problem is in the model (in the metadata to my understanding).
This is the metadata code:
Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = "Pedastrian Lights classifier"
model_meta.description = ("checks what color is the light if exists in frame")
model_meta.version = "v1"
model_meta.author = "Neta Kletshevsky"
Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
Creates output info.
output_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"Input image to be classified.".format(160, 160))
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = [127.5]
input_normalization.options.std = [127.5]
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [255]
input_stats.min = [0]
input_meta.stats = input_stats
Creates output info.
output_meta = _metadata_fb.TensorMetadataT()
output_meta.name = "probability"
output_meta.description = "Probabilities of the 1001 labels respectively."
output_meta.content = _metadata_fb.ContentT()
output_meta.content.content_properties = _metadata_fb.FeaturePropertiesT()
output_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_stats = _metadata_fb.StatsT()
output_stats.max = [1.0]
output_stats.min = [0.0]
output_meta.stats = output_stats
#label_file = _metadata_fb.AssociatedFileT()
#label_file.name = "labels"#label_file.description = "Labels for objects that the model can recognize."
#label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
#output_meta.associatedFiles = [label_file]
Creates subgraph info.
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [output_meta]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(
model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
metadata_buf = b.Output()
populator = _metadata.MetadataPopulator.with_model_file("converted_model.tflite")
populator.load_metadata_buffer(metadata_buf)
#populator.load_associated_files('/content/drive/MyDrive/android/assets')
populator.populate()
What NormalizationOptions do I need to add and how?

@lu-wang-g could you take a look at this? Thanks

I would highly appreciate if someone can help!

You should be able to pack the normalization params with the following lines of code:
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = [127.5]
input_normalization.options.std = [127.5]

Not sure why it doesn't work for you.
Please try the MetadataWriter Library to populate the metadata:

Install the TFLite Support nightly Pypi package:

pip install tflite_support_nightly

Write metadata to the model using the following script:

from tflite_support.metadata_writers import image_classifier
from tflite_support.metadata_writers import writer_utils
from tflite_support import metadata

ImageClassifierWriter = image_classifier.MetadataWriter
_MODEL_PATH = "mobilenet_v1_1_default_1.tflite"
_LABEL_FILE = "labelmap.txt"
_SAVE_TO_PATH = "mobilenet_v1_1_default_1_metadata.tflite"
_MEAN = 127.5
_STD = 127.5

writer = ObjectDetectorWriter.create_for_inference(
    writer_utils.load_file(_MODEL_PATH), [_MEAN], [_STD], [_LABEL_FILE])
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)

# Verify the populated metadata and associated files.
displayer = metadata.MetadataDisplayer.with_model_file(_SAVE_TO_PATH)
print("Metadata populated:")
print(displayer.get_metadata_json())
print("Associated file(s) populated:")
print(displayer.get_packed_associated_file_list())

Using mean=127.5 and std=127.5 will normalize your image from [0, 255] to [-1, 1]. Some models may normalize it to [0, 1], so mean=0, std=255. Make sure the values are set properly for your case.

Thank you very much, it's working now. Very appreciate your help!

How to find out what normalization values I need? I just used 127.5/127.5 because it's the default and I just...threw images into training with a mostly default pipeline.config, yet I'm getting this error.

authors: Regenhardt abattery lu-wang-g neta111111


text:
Error in tf.keras.metrics.AUC

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): No
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS Catalina
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: No
TensorFlow installed from (source or binary): No
TensorFlow version (use command below): 2.4.1
Python version: 3.6.6
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with:

TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior
tf.keras.metrics.AUC(from_logits=True) and tf.keras.metrics.AUC(from_logits=False) outputs TypeError: __init__() got an unexpected keyword argument 'from_logits'
Describe the expected behavior
Contributing - Do you
want to contribute a PR? (yes/no): - Briefly describe your candidate solution
(if contributing):
Standalone code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
Reproducible error: https://colab.research.google.com/drive/1-ZMVqG7t54a2QO-Vh2zKDdyT2gKYB47u?usp=sharing
Other info / logs Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached.
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: __init__() got an unexpected keyword argument 'from_logits'

Cause It is available from TF 2.5.0
Please upgrade your TF version

p.s. in Colab pip install --upgrade tensorflow

This is also mentioned in TF 2.5 release notes. See https://github.com/tensorflow/tensorflow/releases

Thanks!

authors: bhack rohanshingade ymodak


text:
Error when Saving model with data augmentation layer on Tensorflow 2.7

I am getting an error when trying to save a model with data augmentation layers in last tensorflow version (2.7.0).
Here is the code of data augmentation:

input_shape_rgb = (img_height, img_width, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [
    layers.RandomFlip("horizontal"),
    layers.RandomFlip("vertical"),
    layers.RandomRotation(0.5),
    layers.RandomZoom(0.5),
    layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)

Now I build my model like this:

 input_shape = (img_height, img_width, 3)

model = Sequential([
layers.Input(input_shape),
data_augmentation_rgb,
layers.Rescaling((1./255)),

layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1,
   data_format='channels_last'),
layers.MaxPooling2D(),
layers.BatchNormalization(),

layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
layers.MaxPooling2D(),
layers.BatchNormalization(),

layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
layers.MaxPooling2D(),
layers.BatchNormalization(),

layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
layers.MaxPooling2D(),
layers.BatchNormalization(),

layers.Flatten(),
layers.Dense(128, activation='relu'), # best 1
layers.Dropout(0.1),
layers.Dense(128, activation='relu'), # best 1
layers.Dropout(0.1),
layers.Dense(64, activation='relu'), # best 1
layers.Dropout(0.1),
layers.Dense(num_classes, activation = 'softmax')
 ])

 model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=metrics)
 model.summary()

Then after the training is done I just make:

 model.save("./")

And I'm getting this error:

  ---------------------------------------------------------------------------
  KeyError                                  Traceback (most recent call last)
  <ipython-input-84-87d3f09f8bee> in <module>()
  ----> 1 model.save("./")

  /usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in
   error_handler(*args, **kwargs)
   65     except Exception as e:  # pylint: disable=broad-except
   66       filtered_tb = _process_traceback_frames(e.__traceback__)
   ---> 67       raise e.with_traceback(filtered_tb) from None
   68     finally:
   69       del filtered_tb

   /usr/local/lib/python3.7/dist-
   packages/tensorflow/python/saved_model/function_serialization.py in
   serialize_concrete_function(concrete_function, node_ids, coder)
   66   except KeyError:
   67     raise KeyError(
   ---> 68         f"Failed to add concrete function '{concrete_function.name}' to
   object-"
   69         f"based SavedModel as it captures tensor {capture!r} which is
   unsupported"
   70         " or not reachable from root. "

   KeyError: "Failed to add concrete function
   'b'__inference_sequential_46_layer_call_fn_662953'' to object-based SavedModel as it
   captures tensor <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>> which
   is unsupported or not reachable from root. One reason could be that a stateful
   object or a variable that the function depends on is not assigned to an attribute of
   the serialized trackable object (see SaveTest.test_captures_unreachable_variable)."

I inspected the reason of getting this error by changing the architecture of my model and I just found that the reason came from the data_augmentation layer since the RandomFlip and RandomRotation and others are changed from layers.experimental.prepocessing.RandomFlip to layers.RandomFlip, but still the error appears.

@moumed ,
In order to expedite the trouble-shooting process, could you please provide the complete code and dataset to reproduce the issue reported here.

I am also having the same issue with data augmentation layers. Interestingly, no error raised after removing data_augmentation from model.
data_augmentation = tf.keras.Sequential(
    [
        tf.keras.layers.RandomFlip(),
        tf.keras.layers.RandomContrast(.1),
        tf.keras.layers.RandomRotation(.5, fill_mode='nearest'),
    ]
)

inputs = tf.keras.Input(shape=(IMG_SIZE, IMG_SIZE, channels))
base_model = tf.keras.applications.ResNet50V2(
    input_shape=(IMG_SIZE, IMG_SIZE, channels),
    include_top=False,
)
x = data_augmentation(inputs)
x = tf.keras.applications.resnet_v2.preprocess_input(x)
x = base_model(x, training=False)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dropout(dropout)(x)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

As I encountered the same issue as @moumed without his complete code and dataset, I believe the issue is not specific to any dataset. Here's my callbacks.
callbacks = [
    tf.keras.callbacks.TensorBoard(
        log_dir=tb_dir,
        histogram_freq=5,
        write_graph=True,
        write_images=True,
    ),
    tf.keras.callbacks.ModelCheckpoint(
        model_dir,
        monitor='val_accuracy',
        save_best_only=True,
    ),
]
model.fit(train_ds, epochs=100, callbacks=callbacks, validation_data=val_ds)

@moumed , In order to expedite the trouble-shooting process, could you please provide the complete code and dataset to reproduce the issue reported here.

like @ngchanway said the problem is not specific to any dataset, just creating and compiling the model with data_augmentation_rgb  and then saving the model raises the error, and when removing the data data_augmentation_rgb  the error disappears.
I tried to test the provided colab in tensorflow core documentation with the provided tf_flowersdataset: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/data_augmentation.ipynb
the error appears when saving model with  the option 1 of Making the preprocessing layers part of the model.

Posted a workaround here.

@moumed ,
Please post this issue on keras-team/keras repo.
To know more refer to:
https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999

Posted a workaround here.

i am getting this error when trying to save with h5 format:

NotImplementedError:
Layer RandomColorDistortion has arguments ['self', 'contrast_range', 'brightness_delta', 'gamma_correction', 'contrast_fact', 'crop_fact', 'variablity']
in __init__ and therefore must override get_config().

@moumed you obviously have to override the config of your custom layer and add the parameters you are using. It has nothing to the with the original issue that you posted.

@moumed you obviously have to override the config of your custom layer and add the parameters you are using. It has nothing to the with the original issue that you posted.

Here is the error when trying to load the model:

ValueError: Unknown layer: RandomColorDistortion. Please ensure this object is passed to the custom_objects argument.

@moumed
Please feel free to close this issue here as it has tracked in Keras repo.Thanks

this issue is moved to keras repo. Here is the link: https://github.com/keras-team/keras/issues/15699

authors: Yoan-D moumed ngchanway tilakrayal


text:
[TF:TRT] Tensorflow Docker 2.7.0-gpu has incompatible TRT version 8.0.0

System information

OS Platform and Distribution: Linux Ubuntu 20.04
TensorFlow installed from: binary
TensorFlow version: 2.7.0
Python version: 3.8
Installed using virtualenv? pip? conda?: pip
CUDA/cuDNN version: 11.2
GPU model and memory: NVIDIA T4, 16GB

Steps to reproduce the issue
Setup tensorflow/tensorflow:2.7.0-gpu container
$ sudo docker run -it --name tf270 --gpus all tensorflow/tensorflow:2.7.0-gpu

Test TF/TRT
$ apt list --installed | grep infer

libnvinfer-plugin8/unknown,now 8.0.0-1+cuda11.0 amd64 [installed,upgradable to: 8.2.0-1+cuda11.4]
libnvinfer8/unknown,now 8.0.0-1+cuda11.0 amd64 [installed,upgradable to: 8.2.0-1+cuda11.4]

$ python3
import tensorflow as tf
import tensorflow.compiler as tf_cc
tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version()

(7, 2, 2)

As you can see the installed TRT version is 8.0.0 but Tensorflow was built and linked with TRT 7.2.2.
@DEKHTIARJonathan  @bixia1 @sanjoy

Hi @Saduf2019 ! Could you look at this issue?

@apivovarov
This is already tracked in your issue link.
Can we have this in one issue so we do not duplicate it.

Issue 53529 says "TRT Converter not working". My (this) issue is more general - TF/TRT is not working in TF 2.7.0 Docker image in General.
Question. Where is Docker file for TF 2.7.0 Docker image? I can open PR to fix it.

@apivovarov
Please feel free to open a PR for the fix.

@Saduf2019 Do you know which Dockerfile is used to build TF 2.7.0 Docker image?

@Saduf2019 Do you know which Dockerfile is used to build TF 2.7.0 Docker image?

Hi @apivovarov ,
Please find the source for files that used to build docker images. As Tf2.7 version is outdated most likely the issue was resolved with latest stable releases.Please try the Docker Image of any latest release(>2.10v) and let us know if you still observed this version mismatch error please come back and confirm us.
Thank you!

For TF2.12v the linked TF:TRT version is 8.4.3 which is mentioned at source here from docker files.
The same is also tested and outputs TF:TRT version as 8.4.3 for both TF2.12v and tf-nightly versions as well. Please refer to attached gist also. The same applicable to docker images also.

Closing as stale. Please reopen if you'd like to work on this further. Thanks.

authors: Saduf2019 SuryanarayanaY apivovarov mohantym


text:
Tensorflow Profiler does not work on WSL2: Failed to load libcupti (is it installed and accessible?)

Issue type
Bug
Have you reproduced the bug with TensorFlow Nightly?
No
Source
binary
TensorFlow version
v2.12.0-rc1-12-g0db597d0d75 2.12.0
Custom code
Yes
OS platform and distribution
Windows 10 WSL Ubuntu
Mobile device
Ubuntu 22.04
Python version
3.9
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
11.8/8.6
GPU model and memory
No response
Current behavior?
After following exactly the steps mentioned in https://www.tensorflow.org/install/pip for installing Tensorflow on WSL2, and installing the latest version of the profiler plugin, the Tensorboard profiler does not seem to work.
This is with a fresh WSL2 install, miniconda install, etc.

Failed to load libcupti (is it installed and accessible?)

No step marker observed and hence the step time is unknown. This may happen if (1) training steps are not instrumented (e.g., if you are not using Keras) or (2) the profiling duration is shorter than the step time. For (1), you need to add step instrumentation; for (2), you may try to profile longer.

The problem does not seem to be that lubcupti fails to load (despite what is indicated by Tensorboard); libcupti seems to be found just fine, but there may be some problems - See the attached log output for possible clues as to what's happening.
Standalone code to reproduce the issue
# The below code is copied directly from https://github.com/keras-team/keras-io/blob/master/examples/vision/mnist_convnet.py - with the single addition of adding Tensorboard profiling.

import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

"""
## Prepare the data
"""

# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)

# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

"""
## Build the model
"""

model = keras.Sequential(
    [
        keras.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax"),
    ]
)

model.summary()

"""
## Train the model
"""

batch_size = 128
epochs = 15

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1, callbacks=[
    keras.callbacks.TensorBoard(profile_batch=[20, 30])
])

"""
## Evaluate the trained model
"""

score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

Relevant log output
17/422 [>.............................] - ETA: 2s - loss: 2.0755 - accuracy: 0.38602023-07-07 15:45:09.034256: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2023-07-07 15:45:09.034284: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2023-07-07 15:45:09.034300: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-07-07 15:45:09.034304: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:186] cuptiSubscribe: ignored due to a previous error.
2023-07-07 15:45:09.034307: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-07-07 15:45:09.034309: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1730] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error
 25/422 [>.............................] - ETA: 2s - loss: 1.8839 - accuracy: 0.46882023-07-07 15:45:09.105818: I tensorflow/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.
2023-07-07 15:45:09.105940: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2023-07-07 15:45:09.105943: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-07-07 15:45:09.105945: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1822] function cupti_interface_->Finalize()failed with error
2023-07-07 15:45:09.107652: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-07-07 15:45:09.107660: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-07-07 15:45:09.107663: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_collector.cc:541]  GpuTracer has collected 0 callback api events and 0 activity events.
2023-07-07 15:45:09.107809: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.

I am facing the same problem.  Were you able to sort this out?

I have the same problem on TF 2.14. Same config, Ubuntu 22.04 WSL2 on Windows 10.

I am seeing the same problem. Have you found a solution?

Unfortunately no

Same here. Any suggestions would be appreciated.

authors: BeteixZ JustASquid brenerrr giulatona valentinkapitany


text:
OneRemoteGPUStrategy

Issue type
Feature Request
Have you reproduced the bug with TensorFlow Nightly?
No
Source
binary
TensorFlow version
2.8
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 behavior?
I have been able to run a ParameterServer with only one external worker and an external parameter server in a remote machine. Unfortunately, the communication overhead is very big compared to a local run, for trainings with keras model.fit
I wonder if someone has developed a simpler version of PSS, that executes multiple steps before returning, or that is able to cache the variables to avoid overhead.
Standalone code to reproduce the issue
#ideally I want to run the worker similarly to PSS, this is, just setting the TCONFIG and running #Server(), and in the main or coordinator side create the code:

with my_one_remote_machine_strategy:
    model.compile(...)

model.fit()

Relevant log output
No response

@arivero ,
Ideally ParameterServerStrategy is designed for multi worker training to enable data parallelism which can scale up a machine learning model on multiple machines. With single worker you can't get performance because of communication overhead.
For single device training (on same machine), you can use tf.distribute.MirroredStrategy or tf.distribute.OneDeviceStrategy. For distribution training across multiple workers you can use
 tf.distribute.MultiWorkerMirroredStrategy .
All the above mentioned are synchronous training strategies and ParameterServerStrategy is asynchronous which is also under experimental stage only for now.

@SuryanarayanaY
The great point about PSS is that it uses tf.distribute.Server in a way such that it does not require to create, install and execute a separate python script in the remote worker to describe and run the model.
Consider for instance a scenario where the user is running an analysis in a Jupyter notebook in a gpu-less machine, and some other machine offers their GPUS via tf.distribute.Server. Currently a ParameterServerStrategy can access such GPUs to use them as workers, but no other Strategy can.
That should be the role of OneRemoteGPUStrategy, it would have the easy use of PSS and the efficiency of DefaultStrategy.
Of course the alternative could be some tweak of PSS parameter update for the special case of only a parameter server and one single worker, or perhaps to force that if zero workers are found, the parameter server could do the calculation. But on the other hand, a specific OneRemoteGPUStrategy could be the base for future multiworker strategies using tf.distribute.Server in the same way that PSS does.

@arivero ,
Rightnow ParameterServerStrategy is an multi-worker strategy. I am not sure whether this can be tweaked to make it suitable for single worker and also not sure of the drawbacks and how it affects the performance.Escalating the issue to the concern SME to hear from them.
Please note that feature requests can be considered based on its impact or how large the community asking for it etc.

Thanks. To argue for the feature:

the equivalent pathway of launching a RemoteWorker strategy does not use the distribute.Server.
going tf1.0 and allocating manually the graph in the remote GPU is not covered anymore in any tutorial, and it could be against the current development plans.
the case use of Jupyter in a gpu-less machine is very frequent, but not unique. Training in datalakes without GPU is also a common thing.
the final method could benefit not only to mid-large users with mixed datacenters but also small users with access to one or two GPUs in gaming machines, where a full linux deploy is not being considering but a small script with distribute.Server could be.

Hi,
Thank you for opening this issue. Since this issue has been open for a long time, the code/debug information for this issue may not be relevant with the current state of the code base.
The Tensorflow team is constantly improving the framework by fixing bugs and adding new features. We suggest you try the latest TensorFlow version with the latest compatible hardware configuration which could potentially resolve the issue. If you are still facing the issue, please create a new GitHub issue with your latest findings, with all the debugging information which could help us investigate.
Please follow the release notes to stay up to date with the latest developments which are happening in the Tensorflow space.

authors: SuryanarayanaY arivero tilakrayal


text:
Traceback (most recent call last)

Issue type
Bug
Have you reproduced the bug with TensorFlow Nightly?
Yes
Source
source
TensorFlow version
tf 2.17.0
Custom code
Yes
OS platform and distribution
Windows 10
Mobile device
No response
Python version
3.9
Bazel version
No response
GCC/compiler version
No response
CUDA/cuDNN version
No response
GPU model and memory
No response
Current behavior?
I expect it to run without any problem
Standalone code to reproduce the issue
Traceback (most recent call last):
  File "C:\Users\Rolanddev\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization
routine failed.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "c:\Users\Rolanddev\Desktop\One.py", line 24, in <module>
    from tensorflow.keras.models import Sequential
  File "C:\Users\Rolanddev\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\__init__.py", line 38,
in <module>
    from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
  File "C:\Users\Rolanddev\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 85, in <module>
    raise ImportError(
ImportError: Traceback (most recent call last):
  File "C:\Users\Rolanddev\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 70, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization
routine failed.

Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/errors for some common causes and solutions.
If you need help, create an issue at https://github.com/tensorflow/tensorflow/issues and include the entire stack trace above this error message.

Relevant log output
No response

Hi @rolandweb3 ,
There are at least 3 possible scenarios:
You need to install the MSVC 2019 redistributable
Your CPU does not support AVX2 instructions
Your CPU/Python is on 32 bits
There is a library that is in a different location/not installed on your system that cannot be loaded.
Also in order to expedite the trouble-shooting process, could you please provide the following information
OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):
Installed using virtualenv? pip? conda?:
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory:
and the exact sequence of commands / steps that you executed before running into the problem.
https://github.com/tensorflow/tensorflow/issues/61887
Thank you!


text:
Unable to Install TensorFlow on Raspberry Pi Zero W (ARMv6)

Issue type
Build/Install
Have you reproduced the bug with TensorFlow 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 behavior?
I'm trying to install TensorFlow on a Raspberry Pi Zero W with ARMv6 architecture with 32-bit bookworm. However, the installation fails, and I cannot find a version of TensorFlow compatible with ARMv6 architecture. I would appreciate guidance or an official release for ARMv6 support.
raspberrypiwh@raspberrypi:~ $ uname -m
armv6l
raspberrypiwh@raspberrypi:~ $ cat /etc/os-release
PRETTY_NAME="Raspbian GNU/Linux 12 (bookworm)"
NAME="Raspbian GNU/Linux"
VERSION_ID="12"
VERSION="12 (bookworm)"
VERSION_CODENAME=bookworm
ID=raspbian
ID_LIKE=debian
HOME_URL="http://www.raspbian.org/"
SUPPORT_URL="http://www.raspbian.org/RaspbianForums"
BUG_REPORT_URL="http://www.raspbian.org/RaspbianBugs"
Standalone code to reproduce the issue
I'm trying to install TensorFlow on a Raspberry Pi Zero W with ARMv6 architecture with 32-bit bookworm. However, the installation fails, and I cannot find a version of TensorFlow compatible with ARMv6 architecture. I would appreciate guidance or an official release for ARMv6 support.

raspberrypiwh@raspberrypi:~ $ uname -m
armv6l
raspberrypiwh@raspberrypi:~ $ cat /etc/os-release
PRETTY_NAME="Raspbian GNU/Linux 12 (bookworm)"
NAME="Raspbian GNU/Linux"
VERSION_ID="12"
VERSION="12 (bookworm)"
VERSION_CODENAME=bookworm
ID=raspbian
ID_LIKE=debian
HOME_URL="http://www.raspbian.org/"
SUPPORT_URL="http://www.raspbian.org/RaspbianForums"
BUG_REPORT_URL="http://www.raspbian.org/RaspbianBugs"

Relevant log output
No response

Hi, @chaudhariraj
I apologize for the delayed response, I see community-built TensorFlow binaries which provides an official wheel for Python3.5 on Raspberry Pi 1-3 and Pi Zero. if you're looking for an official wheel for Python3.5 on Raspberry Pi Zero please download from it here and install it on your Raspberry Pi Zero W (ARMv6)
You can also build from source for the Raspberry Pi Zero (ARMv6) and We recommend cross-compiling the TensorFlow Raspbian package. Cross-compilation is using a different platform to build the package than deploy to. Instead of using the Raspberry Pi's limited RAM and comparatively slow processor, it's easier to build TensorFlow on a more powerful host machine running Linux, macOS, or Windows. Please refer this documentation.
If I have missed something here please let me know.
Thank you for your cooperation and patience.

Hi, @gaikwadrahul8
I recently purchased a Raspberry Pi 4 Model B (2018), installed a 64-bit OS (Bookworm, aarch64 architecture), and the default Python version is 3.11. I installed TensorFlow version 2.17 in a virtual environment (venv). However, I'm encountering issues accessing the Pi Camera using the picamera2 library. The error states that the libcamera module cannot be found, even though I have installed picamera2 and its dependencies, including libcamera-apps.
When I attempt to install TensorFlow directly in the root environment (outside of the virtual environment), I encounter errors. How can I resolve these issues to successfully access the Pi Camera while using TensorFlow and also how i install tensor flow specific version like 2.10 in root or is possible to install tensor flow with apt command?
Error:
raspberrypi4b@raspberrypi:~ $ pip install tensorflow-2.18.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
error: externally-managed-environment
× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
python3-xyz, where xyz is the package you are trying to
install.
If you wish to install a non-Debian-packaged Python package,
create a virtual environment using python3 -m venv path/to/venv.
Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
sure you have python3-full installed.

For more information visit http://rptl.io/venv

note: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.
hint: See PEP 668 for the detailed specification.
OS Details:
raspberrypi4b@raspberrypi:~ $ uname -m
aarch64
raspberrypi4b@raspberrypi:~ $ uname -r
6.6.62+rpt-rpi-v8
raspberrypi4b@raspberrypi:~ $  cat /etc/os-release
PRETTY_NAME="Debian GNU/Linux 12 (bookworm)"
NAME="Debian GNU/Linux"
VERSION_ID="12"
VERSION="12 (bookworm)"
VERSION_CODENAME=bookworm
ID=debian
HOME_URL="https://www.debian.org/"
SUPPORT_URL="https://www.debian.org/support"
BUG_REPORT_URL="https://bugs.debian.org/"
raspberrypi4b@raspberrypi:~ $
Wheel: link - https://www.tensorflow.org/install/pip
https://storage.googleapis.com/tensorflow/versions/2.18.0/tensorflow-2.18.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Hi, @chaudhariraj
I apologize for the delayed response, could you please follow the below instructions before installing the TensorFlow and see is it resolving your issue or not ?
1. sudo apt install python3-venv
2. python3 -m venv ~/tf
3. source ~/tf/bin/activate

After trying above instructions if issue still persists please let us know with updated error log to investigate this issue further from our end.
Thank you for your cooperation and patience.

Hi, @chaudhariraj
To confirm, have you got chance to try above provided instructions ? If so please let us know is it working as expected or not ?
Thank you for your cooperation and patience.


text:
BugFix for Losses with reduction None.

This is bug fix for issue #27190

@fchollet , can you please look into this PR and provide your feedback.
Regards
Amit

@amitsrivastava78 Thank you for the PR! The loss functions actually do not perform any reduction by default. The expectation is that the input passed to the functions will be atleast 2D. These functions return one loss value per sample as output.
Eg if y_true and y_pred have the shape (3, 3), return value will be of shape (3,). => we get one loss value per sample
if y_true and y_pred have the shape (3, 2, 4), return value will be of shape (3, 2) => we get one loss value per sample, per timestep etc.
Sorry that the functions do not have any documentation about this currently. We are working on adding that, please feel free to contribute to the documentation if you are interested.

@pavithrasv , thanks for the response i am closing this PR.Will wait for the documentation update.
Regards
Amit

source: gharchive/pull-request


text:
Increase the clarity of reshuffle_each_iteration documentation

When reshuffle_each_iteration is None, private instance variable _reshuffle_each_iteration gets set to True, which is I think what the original docstring is alluding to.
The original docstring says that the value defaults to True which is slightly confusing as the default is a None.  I've made changes to the docstrings to attempt to increase its clarity.

It is common practice for None value to indicate that the default behavior should be used

👍 closing this.


text:
[MLIR/XLA] fix wrong tilingSize dimension in lhlo-fuse-linalg pass

The default action of lhlo-fuse-linalg pass is to tile all loops which is equal to getNumLoops() instead of getNumInputsAndOutputs(). The number of inputs and outputs has nothing to do with the dimension number.
As shown in the added test, for generic ops having 4-dimension operands , original behavior is to tile the first three loops which is equal to getNumInputsAndOutputs() (we have two inputs and one output), and new behavior is to tile all the four loops.

Could you expand the PR description to say why it was wrong and what would have happened vs what should have happened? Thanks

@jpienaar
According to my understanding, the default action of lhlo-to-linalg pass is to tile all loops which is equal to genericOp.getNumLoops() instead of getNumInputsAndOutputs(). The number of inputs and outputs has nothing to do with the dimension number.
As shown in the added test, for generic ops having 4-dimension operands , original behavior is to tile the first three loops which is equal to  getNumInputsAndOutputs() (we have two inputs and one output),  and new behavior is to tile all the four loops.

Could you add that to the PR description so that the commit would be easy to follow in git logs for future folks?

Done! Thanks!

authors: jpienaar wyzero


text:
Update the version of re2 to support building with g++ 6.2.

Fixes the build on Ubuntu 16.10

Jenkins, test this please

Jenkins, test this please.

authors: drpngx jhseu


text:
Upgrade to tfx 1.7 in CI

Upgrade TFX 1.6 for CI.

[ ] Tests pass
[ ] Appropriate changes to README are included in PR

@hanneshapke that seems to have fixed it

repo: tensorflow/tfx-addons

license_source: bigquery


text:
Use tilized operators for nanoGPT model

Enhance inference speed in the nanoGPT model by configuring all operations to produce tensors in TILE layout.
Use the tt_lib.tensor.tilize_with_val_padding when necessary

Reference: https://github.com/tenstorrent-metal/tt-metal/tree/a3740def58c3b8672b7e3279261506ae70b97810/models/demos/resnet/tt/metalResnetBlock50.py

If this is ready for review lets make a PR.

Waiting for #4221 to be merged

authors: Sudharsan-V boris-drazic saichandax

repo: tenstorrent-metal/tt-metal


text:
SIGTERM sometimes hangs when running VAST within docker

After some debugging with @lava, we identified that this was again caused by SIGTERM not being propagated to the child process.
:memo: Checklist

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

:dart: Review Instructions
Also increased overall restart timeout to factor in shutdown time.

https://github.com/tenzir/vast/actions/runs/2650960278

repo: tenzir/vast


text:
TREE: Refute 1447 -> 1429, 4269

There is an explicit construction given in https://leanprover.zulipchat.com/#narrow/channel/458659-Equational/topic/713.2C.201289.2C.201447/near/482236139 which would work without any greedy construction.  Alternatively, one can try to formalize the ATP-powered greedy construction, discussed at https://leanprover.zulipchat.com/#narrow/channel/458659-Equational/topic/713.2C.201289.2C.201447/near/481629774
This would go in the ManuallyProved folder. When done, update ManuallyProved.lean and Conjectures.lean.

claim

propose PR  #892

authors: goens teorth


text:
Possible licence issue with Xceed.Wpf.Toolkit

Because you are using a version > 3.8 of the Xceed.Wpf.Toolkit there could be an issue with the licence.
See this issue where the maintainer does not deny the issue: xceedsoftware/wpftoolkit#1598
At least your distributed version 2.0.38 contains Xceed.Wpf.Toolkit.dll in version 4.0.0.0
Do you have other/additional information on this topic?

Oops - I was not aware of these requirements. I have removed all binary releases and will soon replace the color picker control that was used. I am sorry about this mistake.

Dependency has now been removed.

authors: FroggieFrog terjeio

repo: terjeio/ioSender