text:
Have install future module, but still can not find builtins when Building from source

System information

OS Platform and Distribution (Linux Ubuntu 16.04, kerenl: Linux dell 4.15.0-54-generic):
Mobile device (not)
TensorFlow installed from: source
TensorFlow version: master branch, git clone https://github.com/tensorflow/tensorflow.git
Python version: 2.7.12
Installed using virtualenv? pip? conda?:
Bazel version: 0.29.1
GCC/Compiler version:  5.4.0
CUDA/cuDNN version:  CUDA 9.0, cuDNN 7.3.1
GPU model and memory: GeForce GT 730

Configure info

jxl@dell:~/third_softwares/tensorflow$ ./configure
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.29.1 installed.
Please specify the location of python. [Default is /usr/bin/python]:

Found possible Python library paths:
  /opt/ros/kinetic/lib/python2.7/dist-packages
  /usr/lib/python2.7/dist-packages
  /usr/local/lib/python2.7/dist-packages
  /home/jxl/kalibr_ws/devel/lib/python2.7/dist-packages
  /home/jxl/third_softwares/Autoware/ros/install/rslidar_pointcloud/lib/python2.7/dist-packages
Please input the desired Python library path to use.  Default is [/opt/ros/kinetic/lib/python2.7/dist-packages]

Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.

Found CUDA 9.0 in:
    /usr/local/cuda/lib64
    /usr/local/cuda/include
Found cuDNN 7 in:
    /usr/lib/x86_64-linux-gnu
    /usr/include

Please specify a list of comma-separated CUDA compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 [Default is: 3.5]:

Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]:

Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
	--config=mkl         	# Build with MKL support.
	--config=monolithic  	# Config for mostly static monolithic build.
	--config=ngraph      	# Build with Intel nGraph support.
	--config=numa        	# Build with NUMA support.
	--config=dynamic_kernels	# (Experimental) Build kernels into separate shared objects.
	--config=v2          	# Build TensorFlow 2.x instead of 1.x.
Preconfigured Bazel build configs to DISABLE default on features:
	--config=noaws       	# Disable AWS S3 filesystem support.
	--config=nogcp       	# Disable GCP support.
	--config=nohdfs      	# Disable HDFS support.
	--config=nonccl      	# Disable NVIDIA NCCL support.
Configuration finished
jxl@dell:~/third_softwares/tensorflow$

Describe the problem

jxl@dell:~/third_softwares/tensorflow$ sudo pip  install future
[sudo] jxl 的密码：
/usr/local/lib/python2.7/dist-packages/pip/_vendor/requests/__init__.py:83: RequestsDependencyWarning: Old version of cryptography ([1, 2, 3]) may cause slowdown.
  warnings.warn(warning, RequestsDependencyWarning)
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support
Requirement already satisfied: future in /home/jxl/.local/lib/python2.7/site-packages (0.18.2)
jxl@dell:~/third_softwares/tensorflow$

jxl@dell:~/third_softwares/tensorflow$ bazel build --config=opt --config=cuda  --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"   //tensorflow/tools/pip_package:build_pip_package
Starting local Bazel server and connecting to it...
WARNING: The following configs were expanded more than once: [cuda, using_cuda]. For repeatable flags, repeats are counted twice and may lead to unexpected behavior.
INFO: Options provided by the client:
  Inherited 'common' options: --isatty=1 --terminal_columns=231
INFO: Reading rc options for 'build' from /home/jxl/third_softwares/tensorflow/.bazelrc:
  'build' options: --apple_platform_type=macos --define framework_shared_object=true --define open_source_build=true --java_toolchain=//third_party/toolchains/java:tf_java_toolchain --host_java_toolchain=//third_party/toolchains/java:tf_java_toolchain --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone --strategy=Genrule=standalone -c opt --cxxopt=-std=c++14 --host_cxxopt=-std=c++14 --announce_rc --define=grpc_no_ares=true --incompatible_remove_legacy_whole_archive --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --config=v2
INFO: Reading rc options for 'build' from /home/jxl/third_softwares/tensorflow/.tf_configure.bazelrc:
  'build' options: --host_force_python=PY2 --action_env PYTHON_BIN_PATH=/usr/bin/python --action_env PYTHON_LIB_PATH=/opt/ros/kinetic/lib/python2.7/dist-packages --python_path=/usr/bin/python --action_env PYTHONPATH=/home/jxl/kalibr_ws/devel/lib/python2.7/dist-packages:/opt/ros/kinetic/lib/python2.7/dist-packages:/home/jxl/third_softwares/Autoware/ros/install/rslidar_pointcloud/lib/python2.7/dist-packages --action_env CUDA_TOOLKIT_PATH=/usr/local/cuda --action_env TF_CUDA_COMPUTE_CAPABILITIES=3.5 --action_env LD_LIBRARY_PATH=/home/jxl/kalibr_ws/devel/lib:/opt/ros/kinetic/lib:/opt/ros/kinetic/lib/x86_64-linux-gnu:/home/jxl/third_softwares/ssdcaffe/distribute/lib:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64 --action_env GCC_HOST_COMPILER_PATH=/usr/bin/gcc-5 --config=cuda --action_env TF_CONFIGURE_IOS=0
INFO: Found applicable config definition build:v2 in file /home/jxl/third_softwares/tensorflow/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:cuda in file /home/jxl/third_softwares/tensorflow/.bazelrc: --config=using_cuda --define=using_cuda_nvcc=true
INFO: Found applicable config definition build:using_cuda in file /home/jxl/third_softwares/tensorflow/.bazelrc: --define=using_cuda=true --action_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain
INFO: Found applicable config definition build:opt in file /home/jxl/third_softwares/tensorflow/.tf_configure.bazelrc: --copt=-march=native --copt=-Wno-sign-compare --host_copt=-march=native --define with_default_optimizations=true
INFO: Found applicable config definition build:cuda in file /home/jxl/third_softwares/tensorflow/.bazelrc: --config=using_cuda --define=using_cuda_nvcc=true
INFO: Found applicable config definition build:using_cuda in file /home/jxl/third_softwares/tensorflow/.bazelrc: --define=using_cuda=true --action_env TF_NEED_CUDA=1 --crosstool_top=@local_config_cuda//crosstool:toolchain
DEBUG: Rule 'io_bazel_rules_docker' indicated that a canonical reproducible form can be obtained by modifying arguments shallow_since = "1556410077 -0400"
DEBUG: Call stack for the definition of repository 'io_bazel_rules_docker' which is a git_repository (rule definition at /home/jxl/.cache/bazel/_bazel_jxl/630c8d4015ad0c22430bd92a914b2e86/external/bazel_tools/tools/build_defs/repo/git.bzl:195:18):
 - /home/jxl/.cache/bazel/_bazel_jxl/630c8d4015ad0c22430bd92a914b2e86/external/bazel_toolchains/repositories/repositories.bzl:37:9
 - /home/jxl/third_softwares/tensorflow/WORKSPACE:37:1
DEBUG: /home/jxl/.cache/bazel/_bazel_jxl/630c8d4015ad0c22430bd92a914b2e86/external/bazel_tools/tools/cpp/lib_cc_configure.bzl:118:5:
Auto-Configuration Warning: 'TMP' environment variable is not set, using 'C:\Windows\Temp' as default
Traceback (most recent call last):
  File "/home/jxl/.cache/bazel/_bazel_jxl/630c8d4015ad0c22430bd92a914b2e86/external/org_tensorflow/tensorflow/tools/git/gen_git_source.py", line 32, in <module>
    from builtins import bytes  # pylint: disable=redefined-builtin
ImportError: No module named builtins
INFO: Call stack for the definition of repository 'local_config_git' which is a git_configure (rule definition at /home/jxl/third_softwares/tensorflow/third_party/git/git_configure.bzl:66:17):
 - /home/jxl/third_softwares/tensorflow/tensorflow/workspace.bzl:74:5
 - /home/jxl/third_softwares/tensorflow/WORKSPACE:19:1
ERROR: An error occurred during the fetch of repository 'local_config_git':
   Traceback (most recent call last):
	File "/home/jxl/third_softwares/tensorflow/third_party/git/git_configure.bzl", line 64
		_fail(result.stderr)
	File "/home/jxl/third_softwares/tensorflow/third_party/git/git_configure.bzl", line 14, in _fail
		fail(("%sGit Configuration Error:%s %...)))
Git Configuration Error: Traceback (most recent call last):
  File "/home/jxl/.cache/bazel/_bazel_jxl/630c8d4015ad0c22430bd92a914b2e86/external/org_tensorflow/tensorflow/tools/git/gen_git_source.py", line 32, in <module>
    from builtins import bytes  # pylint: disable=redefined-builtin
ImportError: No module named builtins

Uninstall pip installed with get-pip.py, install pip with apt.
And then remove ~/.local/lib/python2.7/site-packages/  dir.
Then pip install future module.


text:
Scope Reusability in tf.nn.dynamic_rnn

I am getting this error when I try to run the code for RNN:
ValueError: Variable RNN/MultiRNNCell/Cell0/BasicLSTMCell/Linear/Matrix does not exist, disallowed. Did you mean to set reuse=None in VarScope?
Here is a snippet of my code for RNN:
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self._num_hidden, forget_bias=0.0)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * self._num_layers)
self._initial_state = cell.zero_state(batch_size, tf.float32)
inputs=[tf.reshape(data_,[batch_size,num_cat]) for data_ in tf.split(0,max_length,tf.transpose(data,[1,0,2]))]
output,_=tf.nn.dynamic_rnn(cell,inputs,initial_state=self._initial_state,sequence_length=self.length)
The error seems to be in line:
output,_=tf.nn.dynamic_rnn(cell,inputs,initial_state=self._initial_state,sequence_length=self.length)
My input is a tensor with dimensions : batch_size x max_ seq_length x feature_vector_size
I am reshaping it to a list of dimensions : batch_size x feature_vector_size
I am assuming that the reusability of scope is handled internally by tf.nn.dynamic_rnn function. Any idea why this would happen? What could be the solution?

Can you paste a complete snippet program the reliably reproduces?  Might be some outside scope that wraps the lines you pasted.

I actually have this exact same issue, but with different code.  I have posted the primary question here: https://stackoverflow.com/questions/38676668/rnn-cell-naming-issue-in-tensorflow

I can also provide somebody the specifics to my notebook but I need you to send me a message or something, I don't want to push my password out too widely :)

@drcrook1, we don't want to take your password. Could you extract the code from the notebook that could be run on standalone python?
@sunilraiyani could you attach the full program that reproducies the issue?
Without a self-contained reproducible test case, we will need to close the issue.

Automatically closing due to lack of recent activity. Please reopen when further information becomes available.

source: gharchive/issue

authors: aselle concretevitamin drcrook1 sunilraiyani

repo: tensorflow/tensorflow

license_type: permissive

license_source: github-api


text:
ModuleNotFoundError tensorflow.python' is not a package

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

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 Home
TensorFlow version (use command below): 2.0
Python version: 3.6.7 and 3.7.4

Describe the current behavior
When trying to import Tensorflow using this line:
import tensorflow as tf
I get this error:

Traceback (most recent call last):
File "singlestock/code.py", line 1, in
import tensorflow as tf
File "D:\documenten\programs\Python\3.6.7\lib\site-packages\tensorflow_init_.py", line 98, in
from tensorflow_core import *
File "D:\documenten\programs\Python\3.6.7\lib\site-packages\tensorflow_core_init_.py", line 40, in
from tensorflow.python.tools import module_util as _module_util
ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package

Describe the expected behavior
I shouldn't get this error. I should just be able to import tensorflow and then use it.
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
This is the code which doesn't work for me:
I've tried this in python 3.7 and 3.6.
import tensorflow as tf

tensor = tf.convert_to_tensor([[1, 2, 3, 4], [2, 3, 4, 5]], tf.int32)
print("hello")

I don't think the issue is in the code though, but I have no idea what the problem could be.
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.
error:

Traceback (most recent call last):
File "singlestock/code.py", line 1, in
import tensorflow as tf
File "D:\documenten\programs\Python\3.6.7\lib\site-packages\tensorflow_init_.py", line 98, in
from tensorflow_core import *
File "D:\documenten\programs\Python\3.6.7\lib\site-packages\tensorflow_core_init_.py", line 40, in
from tensorflow.python.tools import module_util as _module_util
ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package

code:
import tensorflow as tf

tensor = tf.convert_to_tensor([[1, 2, 3, 4], [2, 3, 4, 5]], tf.int32)
print("hello")

@R0b4 ,
Please try reinstalling Tensorflow by
Uninstall tensorflow:
pip uninstall tensorflow
Then reinstall it:
pip install tensorflow
Also refer this link.
Thanks!

I already tried this. I also tried installing it in different versions of python (3.7.4, 3.6.7, 3.6.6). This didn't work.

I just found something out. Tensorflow apparently does work when importing it in the console like this:
PS C:\Users\user> py
Python 3.6.2 (v3.6.2:5fd33b5, Jul  8 2017, 04:57:36) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.constant([1, 2, 3, 4]))
2019-12-02 14:39:53.575123: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
tf.Tensor([1 2 3 4], shape=(4,), dtype=int32)
>>>

But it doesn't work when executing a file like this:
py folder/code.py

@R0b4 ,
can you please make sure that both python and tensorflow are installed in the same path where you are trying to execute py folder/code.py ?Thanks!

@R0b4,
I had the same problem when I unimaginatively named file for tutorial I was following csv.py.
It broke the execution of example.py tutorial, that was in the same directory.
After renaming csv.py problem resolved.

Apparently alexander was right. The problem was the name, so this begs the question why can't i name my script "code.py"?

@R0b4 Both csv.py and code.py are modules from python standard library. Placing modules with the same names in current working directory messes tensorflow importing.

@R0b4 ,
Is the solution provided by @alexander-travov  resolves the issue ?Please close the issue if it is works fine.Thanks!

faced the same problem, changed filename from code.py ... Solved the problem!

@R0b4 Both csv.py and code.py clash with modules from python standard library.
Placing them in the working directory messes tensorflow import.

巨感谢，目录有个csv.py改名后，我又可以愉快的玩耍了

I can confirm issue with import statements. Just installed tensorflow on the clean system using pip (Python 3.7.0), this installs tensorflow 2.0.
When I try to execute my code I get
ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package

Looking in the site-packages, it seems directory layout in tensorflow 2.0 was changed and now there is no tensorflow.python but there is tensorflow_core.python. And this is the reason for this import error.

There is supposed to be mapping between tensorflow.python and tensorflow_core.python to make thie layout change transparent.
I implemented that, so I'll try to see why it doesn't work, if I can reproduce.

I have the same problem and by adding some prints and breakpoints observe that _LazyLoader for tensorflow.python kicks in and starts executing tensorflow_core/python/__init__.py it gets to the following line:
from tensorflow.python import keras

Which throws AttributeError("type object 'scipy.interpolate.interpnd.array' has no attribute '__reduce_cython__'") But this exception is not preserved or logged and instead we see ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package.
I see two problems here:

_LazyLoader does not handle import errors properly. They need to be delivered to the CLI. We need to see original error.
Something wrong with scipy. May be a version mismatch.

In my case the problem was solved by sudo apt install --reinstall python*-decorator. It was induced by a corrupted scipy. But this does not cancel the fact that tensorflow does not report an underlying error properly.

I do not think that the problem has been resolved. The error reporting is still broken and investigation of a simple problem has cost me hours of my life.

Reopened to have TF work on better error messages. But the root cause is not a TF failure.

You may have different paths for your python package. One from AppData/Locals and the other is from AppData/Roaming. Removing the python package in AppData/Roaming solves my problem.

@R0b4 Both csv.py and code.py clash with modules from python standard library.
Placing them in the working directory messes tensorflow import.

Thanks a bunch! Though in my case, it was Google Colab that allowed me to download "csv.py" from their example library and thus cause this unnecessary mess.

I had the same issue but soon resolved it.
I have realized that the problem existed because I have a file named imp.py in the same directory where I have my main.py (where I have imported keras module).
I have renamed my imp.py to impxyz.py and the problem disappeared.

Definitely, the error is due to the usage of a file name that is already used in tensorflow. Just change your file names.

Hey all,
I just had a similar issue due to completely different causes - mainly versioning and pip binary aliases - so I figured I'll share what worked for me :
python3 --version
python3 -m pip install --upgrade pip
python3 -m pip install tensorflow

On my system python3 links to version 3.6.9, just be sure to have a compatible one.
Cheers :+1:

Still having this issue. None of my files are named something generic that could conflict (unless lr_finder.py??).

@bhavints I prefer change all your files name and see if you get some result.

As mentioned above, renaming my files worked for me. I had a file named 'cmd.py' that caused the issue.

@R0b4,
I had the same problem when I unimaginatively named file for the tutorial I was following csv.py.
It broke the execution of another tutorial example.py in the same directory with described symptomes.
After renaming csv.py problem resolved.

Yes! Thanks. renaming the file solved the problem

authors: AshishGusain17 Black-Behemoth MesterRudi R0b4 Usama-Zafar adele-php alexander-travov alexbruy bharathsudharsan bhavints hregua kibergus mihaimaruseac oanush rahulsdhar tcoppex


text:
GPU delegate gives different result from CPU

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 18.04
Mobile device if the issue happens on mobile device: Pixel 3 XL
TensorFlow installed from (source or binary): source
TensorFlow version (use command below): r2.2.0 and master
Python version: 3.6
Bazel version (if compiling from source): 3.0.0
GCC/Compiler version (if compiling from source): default
CUDA/cuDNN version: 10.0
GPU model and memory:

Describe the current behavior
GPU delegate gives very different result from CPU.
I was able to hard-code the source (in tensorflow/lite/delegates/gpu/common/model_builder.cc) to allow some operations to be delegated to GPU. Out of 270+ operations:

Delegating just one Conv_2d will produce very similar result as the one by CPU only.
Delegating a few more operations seem to produce bigger difference.
Delegating just the first MUL operation will produce very different result.

Describe the expected behavior
result should be close
Standalone code to reproduce the issue
I personally hijacked tflite tools/benchmark code and give an sample image as deterministic input, instead of random input.
I would love to provide my code change if it helps.
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.
The tflite model was converted from InsightFace/ArcFace MXNet model
https://github.com/deepinsight/insightface/wiki/Model-Zoo (3.2 model)
link to download the tflite
https://drive.google.com/file/d/1pJX2I8btskVy-QHiF-mcUF7HF6ZFaQuf/view?usp=sharing
Also attached the graph of above model:
visualized_official_arcface_no_sub.zip

@srjoglekar246 @impjdi Thanks a lot for looking into this! The inference_diff tool is super for debugging the issue. Here is what I found so far:

Following @srjoglekar246's lead, I tried the inference_diff and got slightly different errors on my Pixel 3XL. And I used tensorflow master branch and r2.2 branch and both produced different results. So my first question is: which branch I should use for Android deployment? I notice most users are using org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly arr, but there is also org.tensorflow:tensorflow-lite-gpu:2.2.0 arr. I tried both in my app, the results are very different on GPU delegation but same on CPU.

I also tried to allow GPU delegation to each single operation. Surprisingly, the first MUL operation already produced some error. I trimmed out all the operations but the first MUL operation and convert this tiny model to TFLite. Running on inference_diff and I got same error already, using  gpu_precision_loss_allowed=true or false.

output_errors {       output_errors {       max_value: 0.0021719106       min_value: 0.00214503519       avg_value: 0.0021564289927482605       std_deviation: 1.0854229e-05     }

Furthermore, I noticed that the inference_diff tool uses random inputs of [-1, 1] while my original input is [-127, 127]. If I hardcoded the inputs to all -127, I got even much bigger error with GPU delegation. Here is one line of code change in  [https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/lite/tools/evaluation/stages/inference_profiler_stage.cc#L51]

    data->push_back(static_cast<T>(-127.0f));     //data->push_back(static_cast<T>(rand_float));
Here is the error I got:

``

Sorry I was accidently closed this issue but reopened it again.

@ShiyongL for the AAR, it depends on whether you would prefer to use the TF nightly branch or the stabler TF 2.2 master branch for your environment. TF2.2 will probably be safer, but not contain any recent patches you might need. If the results are very different, can you explain how? Are the results with the 2.2 branch more correct? Then the issue might have been introduced recently.
About the 1-op model diff: In most delegates, there will be some diff v/s CPU, mainly because of the accumulators/optimizations used. For example, even with MobileNet v1 (where the GPU gives comparable top-k accuracy), there is a diff of ~2e-5 for the output tensor.
Do you have a way to check the task-oriented 'accuracy' of the model? (for example, how well your app recognises objects for an object detection model). That might help us see how bad the actual performance is (maybe with/without the gpu_precision_loss param)

@ShiyongL for the AAR, it depends on whether you would prefer to use the TF nightly branch or the stabler TF 2.2 master branch for your environment. TF2.2 will probably be safer, but not contain any recent patches you might need. If the results are very different, can you explain how? Are the results with the 2.2 branch more correct? Then the issue might have been introduced recently.
About the 1-op model diff: In most delegates, there will be some diff v/s CPU, mainly because of the accumulators/optimizations used. For example, even with MobileNet v1 (where the GPU gives comparable top-k accuracy), there is a diff of ~2e-5 for the output tensor.
Do you have a way to check the task-oriented 'accuracy' of the model? (for example, how well your app recognises objects for an object detection model). That might help us see how bad the actual performance is (maybe with/without the gpu_precision_loss param)

@srjoglekar246 for the 1-op model, TF2.2 and TF nightly branch(I assume it is the Master branch) produced slightly results:
TF nightly:
OutputDiff[0]: avg_error=0.00215609, std_dev=1.08443e-05, gpu_precision_loss = false
OutputDiff[0]: avg_error=0.00215643, std_dev=1.08542e-05, gpu_precision_loss = true
TF 2.2 (exactly same results for gpu_precision_loss = true or false ):
output_errors { output_errors { max_value: 0.0021719106 min_value: 0.00214503519 avg_value: 0.0021564289927482605 std_deviation: 1.0854229e-05 }

The diff is 2e-3, which much larger than ~2e-5 by MobileNet v1.
When the input is 100x larger (change input from -1, 1 to -127, 127), the error is 100x larger. If you could reproduce the error using the single op model, it should help @impjdi for further debugging.

To answer your second question, I am using the official ArcFace model from InsigntFace https://github.com/deepinsight/insightface to recognize faces. The input is (112x112x3) image, output is a 512 tensor.

With the first MUL operation, I was getting all NAN at my output.
After removing the MUL operations (which I do the MUL to input first then passing the result to CNN model), I was getting more reasonable results but still quite different (for a sample image):
Outputs from CPU: [0.7939907, 0.5848467, -0.94,       -0.99,           ..., 0.3934663, 0.93600059]
Outputs from GPU: [0.7978515, 0.3818359, -0.96875, -0.9086914, ..., 0.1318359, 0.77441406] (with gpu_precision_loss_allowed=false)  TF2.2 and TF nightly produced exactly same results here (CPU or GPU.)
I would be very happy if the error is ~e-2.

The output differences between TF2.2 & nightly seem too tiny to show any change in behavior between the two (~1e-7). So we can assume that nothing has been broken since TF2.2
About the MUL op:

The MUL op provided by @ShiyongL has an input tensor being multiplied by a scalar value 0.007812. The avg diff w/ CPU does rise as the input sampling range is increased from {-1, 1} to {-100, 100} or {-1000, 1000} (proportionately). However, the std deviation of the diff values  remains ~5-e6, which indicates that the problem might just be accumulation errors that increase as the input values grow. I audited the OpenCL kernel on the GPU delegate, and it seems a straight-forward multiplication.

Even when I run inference_diff on the arcface model by rejecting all MUL ops (leading to delegation of only 5 ops somewhere in the middle of the graph), I still see an avg error of ~0.02. So MUL might not be the only issue.

A couple of clarifying questions from my side:

Is -127, 127 the expected input range for the model?

I am not sure what the 512-element output tensor represents, so looking at raw diffs might be misleading. Have you tried plugging in the model into an application that post-processes the output as needed? And does the precision with GPU fall a lot even after post-processing? A simple example I know is the SSD MobileNet, which has >1 raw diffs (CPU vs the DSP delegate) when we use inference_diff directly, but the COCO metrics remain good enough.

I agree with @srjoglekar246 that MUL is not the only issue. Since it is the first operation, it is easier to trim out the rest and focusing on this particular operation. Like @srjoglekar246 said, multiplication should be a straightforward operation, the difference should not be that big between CPU and GPU. It is super interesting to see the reason.
Re 1: -127, 127 was the expected input. Multiply by 0.007812 will normalize the input to -1, 1, which might be a better input for numerical stability reason. As I have shown the a previous comment, the difference between CPU and GPU is still quite large.
Re2: the 512 output tensor is a 512 vector. ArcFace would inference two images and get 2 512 vectors. Then sure the distance between the two vectors. If the two vectors are similar, the two images are considered to be matched. I am not sure what you meant by >1 raw diffs for SSD MobileNet, which has multiple outputs like class, score and bboxes. I agree we should not compare just the raw values but the error ratio should be relatively low, like less than 1% due to floating point precision.
I reported this issue when I got NAN as the output. Now we figured out the MUL is causing a big issue. After removing the MUL operation, I could get some more reasonable results, though the error is still not small and I am not totally convinced that this is caused by numerical stability. I will take the results anyhow and run the evaluation tomorrow. Will report back.
Thanks a lot!

@srjoglekar246 The errors between CPU and GPU are so large that it deteriorates the evaluation performance, when the inference results are used to determine whether one face image is similar to another.
It would be greatly appreciated if you or other engineers could look into it!

@ShiyongL , I have asked the GPU folks internally to take a look at this model/issue. Will keep you posted on the details :-).
On that note, did you try any other delegates for this? (for example, XNNPack?)

@ShiyongL , I have asked the GPU folks internally to take a look at this model/issue. Will keep you posted on the details :-).
On that note, did you try any other delegates for this? (for example, XNNPack?)

Thanks a lot @srjoglekar246 !
I haven't tried XNNPack. Will check it out.

@ShiyongL we just fixed this on our end (will probably take a day or so to reflect in nightly). There were a couple of small issues with some GPU graph optimizations & model parsing which are now fixed.
The error for input in [-127, 127] is now ~0.02 with precision loss, and ~1e-6 with precision loss disabled (the precision loss issues are likely due to numerical instability w/ FP16). Could you do check the inference results again and close the issue if things work now?

Perfect!
I are using org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly ARR. Do you know how long does it take for the fix to be available to the ARR, for example at JCenter?
On the other hand, do you mind sharing a link to a PR so that I could make sure to have the fixed code?
Thank you very much!

@ShiyongL it should reflect within 24 hours or so. (This is the commit after which the model should work fine).

Managed to validate the fix. Thank you very much!


text:
TypeError: '>' not supported between instances of 'NoneType' and 'float'

System Information

Running in Google Collab
Python 3.7
TensorFlow Version: 2.1.0
Keras Version: 2.3.0
Notebook is set to run on GPU

When I attempted to run the code above I got this error message:

Any thoughts on how to solve this?

@AndrewTheReese
Please share simple stand alone code such that we can replicate the issue reported or a colab gist with the error, the snapshots shared do not help is searching the issue.


text:
import tensorflow get a bug

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):

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): ubuntu 18.04

Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:

TensorFlow installed from (source or binary):pip install tensorflow-gpu 2.5.0

TensorFlow version (use command below):

Python version:

Bazel version (if compiling from source):

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version:cuda 11.4

cudnn :libcudnn8_8.2.1.32-1+cuda11.3_amd64.deb
libcudnn8-dev_8.2.1.32-1+cuda11.3_amd64.deb
libcudnn8-samples_8.2.1.32-1+cuda11.3_amd64.deb

GPU model and memory:nvidia 2080s 8g

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
When I upgraded tensorflow in the virtual environment anaconda, there was a bug that couldn't run. I don't know what the problem is. I have reinstalled the driver and CUDA cudnn, but it still doesn't work. There should be no mistake in installing the three files of cudnn. It seems that it reads tensorflow directly from the virtual environment rather than the absolute path. I don't know what the problem is. It should be a tensorflow bug. In addition, my soft link settings are as follows:
export PATH=$PATH:/usr/local/cuda-11.4/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.4/lib64
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.4/extras/CUPTI/lib64
2021-07-26 19:11:29.807559: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):
File "/home/zy/Project_save/stock_hht_pred/code/online_prediction_market.py", line 8, in
import tensorflow as tf
File "/home/zy/.local/lib/python3.6/site-packages/tensorflow/init.py", line 444, in
_ll.load_library(_main_dir)
File "/home/zy/.local/lib/python3.6/site-packages/tensorflow/python/framework/load_library.py", line 154, in load_library
py_tf.TF_LoadLibrary(lib)
tensorflow.python.framework.errors_impl.NotFoundError: /home/zy/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow/core/kernels/libtfkernel_sobol_op.so: undefined symbol: _ZN10tensorflow8OpKernel11TraceStringEPNS_15OpKernelContextEb
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.
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.

@anavanab99 ,
Every TensorFlow release is compatible with a certain version, for more information please take a look at the tested build configurations.In this case, can you please try installing TensorFlow v2.5 with CUDA 11.2 and cuDNN 8.1 and check if you are facing the same error. Thanks!


text:
Function _implements is missing when create a new object

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):debian10
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary):binary
TensorFlow version (use command below):2.7.0
Python version:3.9.0
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
_implements is missing in new objects.
Describe the expected behavior
_implements exists.
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.
import tensorflow as tf

experimental_implements = " ".join(['name: "addons:MaxUnpooling2D"'])
@tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.float32), tf.TensorSpec(shape=[], dtype=tf.float32)], experimental_implements = experimental_implements)
def test(a, b):
    return a+b

class Test(tf.Module):
    @tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.float32), tf.TensorSpec(shape=[], dtype=tf.float32)], experimental_implements = experimental_implements)
    def test(self, a, b):
        return a+b

model = Test()
model2 = Test()
model.test = test
assert model.test._implements == test._implements
assert model2.test._implements == Test.test._implements

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.

Hi @Saduf2019! Could you please look into this issue? It is replicating in TF 2.6, 2.7 and Nightly .

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.


text:
[MacOS] TensorFlow Metal Plugin Symbol Not Found Error with TensorFlow 2.18.0

Issue type
Bug
Have you reproduced the bug with TensorFlow Nightly?
No
Source
binary
TensorFlow version
2.18.0
Custom code
No
OS platform and distribution
macOS 15.1.1
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?
When trying to import TensorFlow with Metal plugin support on macOS using a virtual environment, an error occurs while loading the Metal plugin. Specifically, TensorFlow fails to locate a required symbol in the shared library libmetal_plugin.dylib. The error indicates a mismatch between the TensorFlow core library and the Metal plugin.
Standalone code to reproduce the issue
mkdir test_tf_latest
/opt/homebrew/bin/python3.9 -m venv .venv
source .venv/bin/activate
pip install tensorflow tensorflow-metal

python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__); print('Num GPUs Available:', len(tf.config.list_physical_devices('GPU')))"

Relevant log output
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/.../.../test_tf_latest/.venv/lib/python3.9/site-packages/tensorflow/__init__.py", line 437, in <module>
    _ll.load_library(_plugin_dir)
  File "/.../.../test_tf_latest/.venv/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library
    py_tf.TF_LoadLibrary(lib)
tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/.../.../test_tf_latest/.venv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN3tsl8internal10LogMessageC1EPKcii
  Referenced from: <D2EF42E3-3A7F-39DD-9982-FB6BCDC2853C> /.../.../test_tf_latest/.venv/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib
  Expected in:     <2A053E7E-6DBA-37C2-A28E-1D52A5836870> /.../.../test_tf_latest/.venv/lib/python3.9/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so

Hi @yuchen001 ,
Apologies for the delay, and thank you for raising your concern. Instead of using the command pip install tensorflow tensorflow-metal , please use the specific compatible version as documented: pip install tensorflow==2.14.0 tensorflow-metal. Additionally, the default NumPy version being installed is 2.02, but the compatible version is 1.24.3. You can install it with:pip install numpy==1.24.3.
I have tested this setup, and it is detecting everything as we expected.
(myenv) maayara-macbookpro:myenv maayara$ python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__); print('Num GPUs Available:', len(tf.config.list_physical_devices('GPU')))"
TensorFlow version: 2.14.0
Num GPUs Available: 1
(myenv) maayara-macbookpro:myenv maayara$

Thank you!

I have the same issue.
Have to use the previous version of tensorflow.
Fixed by installing tensorflow==2.17.0 and tensorflow-metal==1.1.0

This finally fixed my set up thankyou!
Here's the versions which now can run on a Mac M1 Silicon, and successfully reproduce the Image Classification Notebook here: https://www.tensorflow.org/tutorials/images/classification
Python 3.11.9
keras==2.14.0
numpy==1.24.3
tensorflow==2.14.0
tensorflow-metal==1.1.0
The final piece was numpy version, I cant see this requirement documented anywhere

Hi @yuchen001 , Apologies for the delay, and thank you for raising your concern. Instead of using the command pip install tensorflow tensorflow-metal , please use the specific compatible version as documented: pip install tensorflow==2.14.0 tensorflow-metal. Additionally, the default NumPy version being installed is 2.02, but the compatible version is 1.24.3. You can install it with:pip install numpy==1.24.3. I have tested this setup, and it is detecting everything as we expected.
(myenv) maayara-macbookpro:myenv maayara$ python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__); print('Num GPUs Available:', len(tf.config.list_physical_devices('GPU')))"
TensorFlow version: 2.14.0
Num GPUs Available: 1
(myenv) maayara-macbookpro:myenv maayara$

Thank you!


text:
scan in theano and tensorflow

There is a function written with theano:
`import numpy as np
import theano as theano
import theano.tensor as T

def forward_prop_step(x_t, s_t1_prev, s_t2_prev):
            # This is how we calculated the hidden state in a simple RNN. No longer!
            # s_t = T.tanh(U[:,x_t] + W.dot(s_t1_prev))

            # Word embedding layer
            x_e = E[:,x_t]

            # GRU Layer 1
            z_t1 = T.nnet.hard_sigmoid(U[0].dot(x_e) + W[0].dot(s_t1_prev) + b[0])
            r_t1 = T.nnet.hard_sigmoid(U[1].dot(x_e) + W[1].dot(s_t1_prev) + b[1])
            c_t1 = T.tanh(U[2].dot(x_e) + W[2].dot(s_t1_prev * r_t1) + b[2])
            s_t1 = (T.ones_like(z_t1) - z_t1) * c_t1 + z_t1 * s_t1_prev

            # GRU Layer 2
            z_t2 = T.nnet.hard_sigmoid(U[3].dot(s_t1) + W[3].dot(s_t2_prev) + b[3])
            r_t2 = T.nnet.hard_sigmoid(U[4].dot(s_t1) + W[4].dot(s_t2_prev) + b[4])
            c_t2 = T.tanh(U[5].dot(s_t1) + W[5].dot(s_t2_prev * r_t2) + b[5])
            s_t2 = (T.ones_like(z_t2) - z_t2) * c_t2 + z_t2 * s_t2_prev

            # Final output calculation
            # Theano's softmax returns a matrix with one row, we only need the row
            o_t = T.nnet.softmax(V.dot(s_t2) + c)[0]

return [o_t, s_t1, s_t2]`

I have tried to rewrite it with tensorflow:
`import numpy as np, tensorflow as tf, operator

def forward_prop_step(x_t, s_t1_prev, s_t2_prev):
                        # This is how we calculated the hidden state in a simple RNN. No longer!
                        # s_t = T.tanh(U[:,x_t] + W.dot(s_t1_prev))

                        # Word embedding layer
                        x_e = E[:,x_t]

                        # GRU Layer 1
                        z_t1 = tf.nn.sigmoid(tf.reduce_sum(tf.multiply(U[0], x_e)) + tf.reduce_sum(tf.multiply(W[0], s_t1_prev)) + b[0])
                        r_t1 = tf.nn.sigmoid(tf.reduce_sum(tf.multiply(U[1], x_e)) + tf.reduce_sum(tf.multiply(W[1], s_t1_prev)) + b[1])
                        c_t1 = tf.nn.tanh(tf.reduce_sum(tf.multiply(U[2], x_e)) + tf.reduce_sum(tf.multiply(W[2], (s_t1_prev * r_t1))) + b[2])
                        s_t1 = (tf.ones_like(z_t1) - z_t1) * c_t1 + z_t1 * s_t1_prev

                        # GRU Layer 2
                        z_t2 = tf.nn.sigmoid(tf.reduce_sum(tf.multiply(U[3], s_t1)) + tf.reduce_sum(tf.multiply(W[3], s_t2_prev)) + b[3])
                        r_t2 = tf.nn.sigmoid(tf.reduce_sum(tf.multiply(U[4], s_t1)) + tf.reduce_sum(tf.multiply(W[4], s_t2_prev)) + b[4])
                        c_t2 = tf.nn.tanh(tf.reduce_sum(tf.multiply(U[5], s_t1)) + tf.reduce_sum(tf.multiply(W[5], (s_t2_prev * r_t2))) + b[5])
                        s_t2 = (tf.ones_like(z_t2) - z_t2) * c_t2 + z_t2 * s_t2_prev

                        # Final output calculation
                        # Tensorflow's softmax returns a matrix with one row, we only need the row
                        o_t = tf.nn.softmax(tf.reduce_sum(tf.multiply(V, s_t2)) + c)[0]

                        return [o_t, s_t1, s_t2]`

In theano, scan function is called to perform "forward_prep_step" function in a loop:
`[o, s, s2], updates = theano.scan(
            forward_prop_step,
            sequences=x,
            truncate_gradient=self.bptt_truncate,
            outputs_info=[None,
                          dict(initial=T.zeros(self.hidden_dim)),
dict(initial=T.zeros(self.hidden_dim))])`

There is a scan function in tensorflow as well, but they don't get the same parameters. How could be the transformation of scan function above, into tensorflow scan function?

This question is better asked on  StackOverflow since it is not a  bug or feature request. There is also a larger community that reads questions there. Thanks!

authors: aselle saitarslanboun


text:
Several links error in README.md.

System information

OS: Windows 10 Enterprise x64:
Browser: Chrome 58 x64:
Location: China:

Issue
The Installing TensorFlow link from README.md fails with error:
Error: Not Found

The requested URL /versions/r0.12/install/index.html was not found on this server.

The Community link fails with error:
Error: Not Found

The requested URL /versions/r0.12/community/index.html was not found on this server.

This question is better asked on  StackOverflow since it is not a  bug or feature request. There is also a larger community that reads questions there. Please also include more information on what commands you ran to produce the output. Thanks!

@skye Thanks for reply. I know it's not a product bug, but it's a doc bug. The README.md doc has several invalid site links, i don't think this should be asked in StackOverflow.

Ah sorry, didn't read the issue closely enough!

Hm, the "Installing Tensorflow" link at https://github.com/tensorflow/tensorflow goes to https://www.tensorflow.org/install/, and the "Community" link goes to https://www.tensorflow.org/community/. Am I missing something?

Yes, you are right. But the two links redirect to invalid sites with a "404 Not Found" result.
https://www.tensorflow.org/install/ redirects to
https://www.tensorflow.org/versions/r0.12/install/index.html
and https://www.tensorflow.org/community/ redirects to https://www.tensorflow.org/versions/r0.12/community/index.html
btw: I'm in China, hope this helps.

@dr4b does this ring any bells for you? (See the above comment.) Those links don't redirect for me, but I'm not sure if being in China should affect this.

It seems like you might be accessing an old version of the site somehow. I'll ping our infrastructure team to see if they can look into this. In the meantime, if you have access to the github, you can look at the docs source directly:
Installation page: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/install/index.md
Community page: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/community/index.md
All the non-API pages should be in that docs_src directory, and the documentation for individual API methods, classes, etc. is generated from the docstrings in the source files. Let me know if you need help finding something.

@skye Thanks.

authors: skye spartucus


text:
Fixes issue when linking of rule '//tensorflow/contrib/lite/toco:toco…

Fixes issue when linking of rule '//tensorflow/contrib/lite/toco:toco' fails because LD_LIBRARY_PATH is not configured. The workaround is to add --action_env="LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" to bazel build as described here https://stackoverflow.com/questions/47080760/tensorflow-fails-to-compile/47295278#47295278.
Related to issue: https://github.com/tensorflow/tensorflow/issues/15142#issuecomment-352562394

The issue happens when using CUDA when running ./configure

Hi, is this fix planned to be merged in to r1.5?

As we are very close to 1.6 final, we probably wont backport this fix into 1.5.

still broken in both master and tf 1.8

source: gharchive/pull-request

authors: JackHunt frankgh gunan petacube


text:
Revert "Keras grouped convolutions rollback"

This PR resubmits the changes from #36773 which were rolled back in dd2ea875d92eeb83e81b1cb92e29e61d488e98b2.
I couldn't reproduce the failure mentioned in https://github.com/tensorflow/tensorflow/pull/36773#issuecomment-627079900 locally with or without XLA, so I am resubmitting the changes to see if CI is happy now.
@tanzhenyu could you take a look?

@tanzhenyu Any chance you could approve this so CI can run? Since this is a git revert of the commit that rolled back the merge of #36773, there should be no need for an indepth review again.

I rebased the PR and resolved the merge conflicts. @gbaned is there anything that still blocks this?

I'm not sure why it didn't show up in external CI. But here's what we got:
  File "/tensorflow/python/keras/layers/convolutional_test.py", line 296, in test_conv3d
    self._run_test(kwargs, expected_output_shape)
  File "/tensorflow/python/keras/layers/convolutional_test.py", line 265, in _run_test
    expected_output_shape=expected_output_shape)
  File "/tensorflow/python/framework/test_util.py", line 1717, in decorated
    result = f(self, *args, **kwargs)
  File "/tensorflow/python/keras/testing_utils.py", line 227, in layer_test
    model.train_on_batch(input_data, actual_output)
  File "/tensorflow/python/keras/engine/training.py", line 1476, in train_on_batch
    logs = train_function(iterator)
  File "/tensorflow/python/eager/def_function.py", line 766, in __call__
    result = self._call(*args, **kwds)
  File "/tensorflow/python/eager/def_function.py", line 826, in _call
    return self._stateless_fn(*args, **kwds)
  File "/tensorflow/python/eager/function.py", line 2812, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "/tensorflow/python/eager/function.py", line 1838, in _filtered_call
    cancellation_manager=cancellation_manager)
  File "/tensorflow/python/eager/function.py", line 1915, in _call_flat
    ctx, args, cancellation_manager=cancellation_manager))
  File "/tensorflow/python/eager/function.py", line 549, in call
    ctx=ctx)
  File "/tensorflow/python/eager/execute.py", line 60, in quick_execute
    inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.UnimplementedError:  Hit a case for convolution that is not implemented on GPU.
	 [[{{node cluster_131_1/xla_compile}}]] [Op:__inference_train_function_7135]

@tanzhenyu Thanks for taking another look, very much appreciated.
After some digging I was able to run the tests with XLA on a cloud VM and could reproduce the failure locally. It looks like currently train_on_batch fails for 3D grouped convolutions when XLA is enabled.
Since the underlying failure is not related to this PR and would also appear when using grouped convolutions with tf.nn.conv3d on master, I disbled the relevant part of the test in 215161ff7a7cd136feac27686561bcedbdaef493. Please let me know if that works for now.

Thanks for approving, looks like CI is also happy this time 💚

Thanks for approving, looks like CI is also happy this time 💚

Great, thanks for fixing!

authors: lgeiger tanzhenyu


text:
Remove std::function in ternary backward structure

Remove std::function to get rid of performance hit with dispatching at runtime and implement struct templates

Merged to main #10169

repo: tenstorrent/tt-metal


text:
ttnn.pad does not when support output_shape[-1] is odd

We convert aten.constant_pad_nd.default to ttnn.pad and it currently does not support cases wheb output_shape[-1] is odd
or it will TT_THROW pad_op.cpp:34: this->output_tensor_shape[3] % 2 == 0  RM padding requires output X dim to be a multiple of 2
These input varation are appeared in models and is related to this constraint
["Tensor<[1, 3, 224, 224]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 96, 112, 112]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 144, 56, 56]> self = ?", "List[int] pad = [1, 2, 1, 2]", "number value = 0.0"],
["Tensor<[1, 240, 28, 28]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 672, 14, 14]> self = ?", "List[int] pad = [1, 2, 1, 2]", "number value = 0.0"],
["Tensor<[1, 3, 240, 240]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 96, 120, 120]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 144, 60, 60]> self = ?", "List[int] pad = [1, 2, 1, 2]", "number value = 0.0"],
["Tensor<[1, 240, 30, 30]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 672, 15, 15]> self = ?", "List[int] pad = [2, 2, 2, 2]", "number value = 0.0"],
["Tensor<[1, 3, 260, 260]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 96, 130, 130]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 144, 65, 65]> self = ?", "List[int] pad = [2, 2, 2, 2]", "number value = 0.0"],
["Tensor<[1, 288, 33, 33]> self = ?", "List[int] pad = [1, 1, 1, 1]", "number value = 0.0"],
["Tensor<[1, 720, 17, 17]> self = ?", "List[int] pad = [2, 2, 2, 2]", "number value = 0.0"],
["Tensor<[1, 3, 300, 300]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 144, 150, 150]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 192, 75, 75]> self = ?", "List[int] pad = [2, 2, 2, 2]", "number value = 0.0"],
["Tensor<[1, 288, 38, 38]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 816, 19, 19]> self = ?", "List[int] pad = [2, 2, 2, 2]", "number value = 0.0"],
["Tensor<[1, 3, 380, 380]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 144, 190, 190]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 192, 95, 95]> self = ?", "List[int] pad = [2, 2, 2, 2]", "number value = 0.0"],
["Tensor<[1, 336, 48, 48]> self = ?", "List[int] pad = [0, 1, 0, 1]", "number value = 0.0"],
["Tensor<[1, 960, 24, 24]> self = ?", "List[int] pad = [1, 2, 1, 2]", "number value = 0.0"],

I think I have a fix for this that should be landing soon with the fixes for #12896 and #15511. I'll give you a heads up when it's merged.

I didn't manage to land a fix for this in my PR, but we'll be tackling this as an onboarding exercise for a new teammate in the New Year :)

authors: jaykru-tt swimdi


text:
14517: making all-gather and barrier support tiny-tiles

Ticket
https://github.com/tenstorrent/tt-metal/issues/14517
Problem description
Adding the ability for all-gather and barrier to support the following tiling configurations:
(height, width) =(32,32), (16,32), (8,32), (4,32), (2,32), and (1,32)
For all shapes already supported by all-gather
What's changed
Describe the approach used to solve the problem.
Summarize the changes made and its impact.
API changes to give the functionalities needed for Tiny Tiles, in particular with multi-device tensor manipulation
CCL common changes
Added 7 post-commit tests to verify the various configurations in all-gather. barrier tests put in t3000 frequent as it did not require any major changes to support tensors with tiny tile
Modifications to the host and kernel code of the all gather
Checklist

[x] Post commit CI passes https://github.com/tenstorrent/tt-metal/actions/runs/11730896322
[ ] Blackhole Post commit (if applicable)
[ ] Model regression CI testing passes (if applicable) T3000 frequent test : https://github.com/tenstorrent/tt-metal/actions/runs/11730904711 passed everywhere except the LLama 2 tests, but those are failing for all jobs, seem to be an issue in main or in the testing pipelines.
[ ] Device performance regression CI testing passes (if applicable)
[X] New/Existing tests provide coverage for changes

Overall looks good! Approving ahead of time but am requesting 2 sets of changes:

(minor) improve error messaging
Change macro defines to compile time args

Please also run the following pipelines with your changes: t3000_frequently, t3000 nightly, t3000 model perf (these three can be run in a "choose your own pipeline": https://github.com/tenstorrent/tt-metal/actions/workflows/pipeline-select-t3k.yaml) TG nightly.
Additionally, please run @Aswinmcw 's recently added perf script (#14651) to ensure no perf degradations. Although I don't suspect any, I am recommending this as a precaution since some CB sizing stuff was modified (though visually looks functionally identical).
Thanks!

With the exception of the llama tests that are still failing even on main, all the required tests succeeded


text:
#9323: Introduce template for new ttnn pull requests

Related Issues
#9323
Description
This introduces a template for future pull-requests opened under ttnn. These changes are introduced to start outlining the expectations from a developer opening up a pull-request. The goal here is to create a consistent interface for opening up a pull-request and enable reviewers to better understand the changes being merged in at a high-level.
Checklist

[x] Post commit CI passes - N/A
[x] Related model testing - N/A
[x] New tests added - N/A
[x] Change is made to ttnn and not tt_lib :)

Looping in @TT-BrianLiu as well
How often do we see changes in tt-lib? Should we start enforcing static-check level gating to ensure it doesn't change?

authors: cfjchu tt-rkim


text:
FRONTEND: broken link "smallest magma"

We have a broken link "The smallest magma obeying a given equation (up to N=5), if it exists" on the homepage.

claim

propose PR #474

authors: madvorak teorth


text:
Export equations

Exports the equations defined in Equations.lean and AllEquations.lean to a JSON file data/magma_equations.json.
Depends on PR #427.

I was also considering adding the generated JSON file to the PR, but I haven't done that yet since it's a sizeable file.

Thank you @0art0! Please make sure the project builds correctly by locally running scripts/run_before_push.sh.

Thank you @0art0! Please make sure the project builds correctly by locally running scripts/run_before_push.sh.

awaiting-review

I'm inclined to merge both PRs (#427 and #429) because they look fine to me (even if the branch of #429 seem to have been created from the branch of #427 which isn't considered best practice), but I may be missing something.
See discussion here https://leanprover.zulipchat.com/#narrow/stream/458659-Equational/topic/Do.20we.20need.20to.20build.20EquationalSearch.20files.3F/near/475682423

Thanks again @0art0


text:
Expo Web Build Error: Crypto?

I have started a new minimalist build with expo. I've added react-native-web-maps and react-native-maps and the web simulator works fine, the ios simulator works fine, but when I runnpx expo build:webI get the following error.
Failed to compile.

Module not found: Error: Can't resolve 'crypto' in '/Users/Alex/apps/liveWindApp/node_modules/expo-modules-core/build/uuid'
BREAKING CHANGE: webpack < 5 used to include polyfills for node.js core modules by default.
This is no longer the case. Verify if you need this module and configure a polyfill for it.

If you want to include a polyfill, you need to:
	- add a fallback 'resolve.fallback: { "crypto": require.resolve("crypto-browserify") }'
	- install 'crypto-browserify'
If you don't want to include a polyfill, you can use an empty module like this:
	resolve.fallback: { "crypto": false }

If I remove the react-native-web-maps import from my code the build works fine. I don't use crypto at all and have not added it to my project.
Package.json below
{
  "name": "livewindapp",
  "version": "1.0.0",
  "main": "node_modules/expo/AppEntry.js",
  "scripts": {
    "start": "expo start",
    "android": "expo start --android",
    "ios": "expo start --ios",
    "web": "expo start --web"
  },
  "dependencies": {
    "@expo/metro-runtime": "~3.1.3",
    "@teovilla/react-native-web-maps": "^0.9.4",
    "expo": "~50.0.14",
    "expo-status-bar": "~1.11.1",
    "react": "18.2.0",
    "react-dom": "18.2.0",
    "react-native": "0.73.6",
    "react-native-maps": "1.10.0",
    "react-native-web": "~0.19.6"
  },
  "devDependencies": {
    "@babel/core": "^7.20.0"
  },
  "private": true
}

I was able to remove the error by updating the create-expo-app package and starting a new project following all the same steps so the issue was probably in a discrepancy in the project setup

repo: teovillanueva/react-native-web-maps

license: MIT


text:
Release 5.4.1

please review #76

Leave a comment


text:
gulp-uglify压缩匿名函数js无效

gulpfile.js

a.js

b.js

压缩后的a.js

压缩后的b.js

为什么b.js没有内容？？？？？

b.js defines a FunctionStatement, but neither executes it nor assigns it to
a variable. Therefore, it's dead code, and removed by UglifyJS.
On Mon, Jun 17, 2019, 2:37 AM 前端小菜鸟 notifications@github.com wrote:

gulpfile.js
[image: gulpfile]
https://user-images.githubusercontent.com/20697436/59594293-28c08080-9126-11e9-9eda-d05f083d50d3.png
a.js
[image: a]
https://user-images.githubusercontent.com/20697436/59594469-76d58400-9126-11e9-957c-e69a3caa52c1.png
b.js
[image: b]
https://user-images.githubusercontent.com/20697436/59594478-7b01a180-9126-11e9-9ae5-0e9a181cc710.png
压缩后的a.js
[image: dist_a]
https://user-images.githubusercontent.com/20697436/59594487-7fc65580-9126-11e9-942a-53619bc09868.png
压缩后的b.js
[image: dist_b]
https://user-images.githubusercontent.com/20697436/59594504-88b72700-9126-11e9-8ad6-38241fcfbba7.png
为什么b.js没有内容？？？？？
—
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Reply to this email directly, view it on GitHub
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or mute the thread
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.

b.js定义了一个FunctionStatement，但既不执行它也不将它赋值给变量。因此，它是死代码，并由UglifyJS删除。
...
On Mon, Jun 17, 2019, 2:37 AM 前端小菜鸟 @.***> wrote: gulpfile.js [image: gulpfile] https://user-images.githubusercontent.com/20697436/59594293-28c08080-9126-11e9-9eda-d05f083d50d3.png a.js [image: a] https://user-images.githubusercontent.com/20697436/59594469-76d58400-9126-11e9-957c-e69a3caa52c1.png b.js [image: b] https://user-images.githubusercontent.com/20697436/59594478-7b01a180-9126-11e9-9ae5-0e9a181cc710.png 压缩后的a.js [image: dist_a] https://user-images.githubusercontent.com/20697436/59594487-7fc65580-9126-11e9-942a-53619bc09868.png 压缩后的b.js [image: dist_b] https://user-images.githubusercontent.com/20697436/59594504-88b72700-9126-11e9-8ad6-38241fcfbba7.png 为什么b.js没有内容？？？？？ — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#353?email_source=notifications&email_token=AACCYZNLNEIN5XSN7JWQTOTP25LNVA5CNFSM4HYU33O2YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4GZ2Y4ZQ>, or mute the thread https://github.com/notifications/unsubscribe-auth/AACCYZNJU5A25RPBEXEFWPLP25LNVANCNFSM4HYU33OQ .

额 能配置不被UglifyJS删除吗

repo: terinjokes/gulp-uglify

license: mit

license_source: bigquery


text:
ESLint: fix minor stylistic inconsistencies

Hi @marijnh - I remember you were previously not very fond of this idea, but as we get more contributions, I thought it might make sense to come up with a config that reflects style of 90% of the codebase and helps to catch minor mistakes more easily.
So I decided to give it a try via PR process.
Resulting diffs are quite minimal, but let me know if you find something unreasonable.
It doesn't cost pretty much anything to us, and as a bonus, ESLint is using Acorn too, but if you still don't like the idea at all, I can close the PR.

Were any of these inconsistencies something that actually worried you? I really don't care a lot about the quote type used or whether there are spaces inside curly braces. As for catching mistakes, would you be happy if we integrate blint instead of ESLint? That'll also help with catching mistakes, is much smaller and faster, and doesn't complain about stylistic trivialities.

@marijnh Different indentation in different switch statements or accidental double spaces and unnecessary character escapes are just slightly distracting when grasping through source code. As for quotes or var - it just looks better when everything is in one style IMO, and I disabled any trivialities that aren't important for this codebase.
Also, I added eslint-plugin-import as it helps to catch incorrect imports/exports which is particularly useful to check when doing refactorings (one of which I was willing to attempt).

As for size -  this is development dependency which is installed just once when starting to work on project, I don't see it as something critical as long as it doesn't affect consumers.

I added eslint-plugin-import

Oh, cool, I hadn't seen that yet. That is indeed a tangible improvement (I haven't gotten around to adding that feature to blint yet). Merged.
It runs 9 times slower than blint, but I guess still within acceptable bounds. Were you planning to set things up so that this runs on npm test, and the CI will automatically complain for non-linting pull requests? (I think we might as well.)

and the CI will automatically complain for non-linting pull requests? (I think we might as well.)

Yeah I can do that.

authors: RReverser marijnh

repo: ternjs/acorn


text:
feat!: Remove NONE validation method and set default to null

Identical to #135 but a major release.
Closes #139

This PR is included in version 5.0.0 :tada:

repo: terraform-aws-modules/terraform-aws-acm


text:
feat: Enable encrypt function on EFS

Description

As title
Motivation and Context

Enable encrypt function on EFS
Breaking Changes

No
How Has This Been Tested?

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

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

@titanlien this looks good - please update with master when you get a chance and we can merge

Hi @cytopia I believe you can fetch this upstream and update our PR again.

@bryantbiggs updated

once you update with master we can get this merged, thank you!

This PR is included in version 3.21.0 :tada:

authors: antonbabenko bryantbiggs snovikov titanlien


text:
Module assigning incorrect security groups

Description
When provisioning a fresh EKS cluster w module v20.24, the security groups that are created and assigned to the cluster are incorrect.  TF assigns the "cluster" sg as the "node" sg, and the node sg isn't assigned.  The cluster sg that gets assigned is a different sg that appears to be using default settings.
I have named the sg's according to their role ("EKS node security group") and output the results for both sg's.
After creation TF returns:
cluster-security-group = "sg-03f2460cef7842784"
node-security-group = "sg-0dc39183da4477e98"

However when I look in the console, I see "sg-03f2460cef7842784" is the node sg, "sg-0dc39183da4477e98" is created and named correctly as the node security group but isn't assigned to anything, and the cluster sg displayed in the console is "sg-0d6cda72afcca7336" and named "eks-cluster-sg-nameofmycluster-196899026"

[ * ] ✋ I have searched the open/closed issues and my issue is not listed.

Versions

Module version [Required]: 20.24

Terraform version:
Terraform v1.9.5
on darwin_arm64

Provider version(s):

provider registry.terraform.io/hashicorp/aws v5.64.0
provider registry.terraform.io/hashicorp/cloudinit v2.3.4
provider registry.terraform.io/hashicorp/null v3.2.2
provider registry.terraform.io/hashicorp/time v0.12.0
provider registry.terraform.io/hashicorp/tls v4.0.5

Reproduction Code [Required]
module "eks" {
  source  = "terraform-aws-modules/eks/aws"
  version = "~> 20.24"

  cluster_name        = local.platform_name
  cluster_version     = var.kubernetes_ver
  authentication_mode = "API"

  vpc_id = data.aws_vpc.network.id
  subnet_ids = flatten([
    data.aws_subnets.private.ids,
    data.aws_subnets.public.ids
  ])
  cluster_endpoint_public_access = true
  cluster_security_group_name    = "EKS Cluster Security Group"
  node_security_group_name       = "EKS Node Security Group"

  enable_cluster_creator_admin_permissions = true

  cloudwatch_log_group_retention_in_days = 90
  cluster_enabled_log_types = [
    "api",
    "audit",
    "authenticator",
    "controllerManager",
    "scheduler"
  ]

  fargate_profiles = {
    # kube-system Fargate profile must be set here so coreDNS comes up correctly.
    kube-system = {
      name       = "kube-system"
      selectors  = [{ namespace = "kube-system" }]
      subnet_ids = data.aws_subnets.private.ids
      tags       = { "utexas:application" = "kube-system" }
    }
  }
  fargate_profile_defaults = {
    iam_role_additional_policies = {
      additional = aws_iam_policy.fargate_logging.arn
    }
  }
}

output "cluster-security-group" {
  value = module.eks.cluster_security_group_id
}

output "node-security-group" {
  value = module.eks.node_security_group_id
}

(eks_tf.zip)
Steps to reproduce the behavior:

provision a new EKS cluster w module v20.24
(optional) specify names for cluster_security_group_name and node_security_group_name and add them to outputs

output "cluster-security-group" {
  value = module.eks.cluster_security_group_id
}

output "node-security-group" {
  value = module.eks.node_security_group_id
}

Go into AWS console and inspect what SGs actually got assigned.  Observe Cluster sg is default, node sg is actually the cluster sg, and the actual node sg is created correctly (with expected rules) and not assigned.

Expected behavior
Module should correctly provision and assign sg's
Actual behavior

Default Cluster SG is created with default settings and assigned to cluster
The specified Cluster SG is created and assigned as the node SG
The specified Node SG is created and not assigned to cluster at all.

Terminal Output Screenshot(s)
TF output (which shows the correct sg id's)

Console output which shows cluster sg as the node sg, and a new sg as the cluster sg.

I'll also add that for the Node SG that gets created, the SG it allows traffic from is the correct Cluster SG, not the SG that actually gets created and assigned as the Cluster SG.

this is just how EKS Fargate operates - you don't have the option to change/specify the security groups used by Fargate profiles; the cluster created security group is automatically used
Ref: https://github.com/terraform-aws-modules/terraform-aws-eks/blob/c60b70fbc80606eb4ed8cf47063ac6ed0d8dd435/tests/fargate-profile/main.tf#L47-L49

Ok I apologize I must be confused then.  The groups that get created don't have the needed ports open.  The node group that gets created (but not assigned) has all the "recommended rules" I need for the control plane to communicate with pods (like 10250).
How do I "node_security_group_enable_recommended_rules" on this SG?

Ok disregard this.  I had one of my open tickets resolved by AWS and the issue I was experiencing was actually tied to that (Port 10250 cant be used w Fargate).  I think I just confused myself here.  Thanks for the assistance and all the work you put into this module!

authors: andrewbcoyle bryantbiggs

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


text:
Support for pyproject.toml dependencies (PEP518)

Is your request related to a new offering from AWS?
Is this functionality available in the AWS provider for Terraform? See CHANGELOG.md, too.

No 🛑: please wait to file a request until the functionality is available in the AWS provider

Is your request related to a problem? Please describe.
I am used to set up a python project with the pyproject.toml format which was introduced by PEP518. As far as i understood, this should be a common interface to bundle a python project, maybe deprecating the known setup.py solution in a near future. With the introduction of this file, a requirements.txt file which is commonly used for managing a projects dependencies is no longer required, as the pyproject.toml capture requirements and optional dependencies.
The current package solution package.py is - from what i understand - only proven to work with pip dependencies managed by a requirements.txt.
Describe the solution you'd like.
Basically, i'd like to have support to create packages based on a pyproject.toml file.
Describe alternatives you've considered.
Replace my common approach with the convention of using a requirements.txt file.
Additional context
I am fully aware that this is a custom solution for this module and you may not want to support every possible variante of packaging code. My rational to create this request is that a pyproject.toml file seems to be the proper way to manage a python project, while the requirements.txt is rather a convention not backed up by a PEP. So i'd love to see a project adapting to that PEP.

Please note that it might not be required to write any code for this but write down an example at least.


text:
Terraform 2.x Patch:

Updated Terraform script for Terraform2.x release.
Please put this on separate branch, so we can serve both 1.x and 2.x release.

The examples are up-to-date to use Terraform 0.12 (as well as newer versions of the VM modules) after the merge of #17. I think we can close this one out.

authors: ajay-bangar zefdelgadillo


text:
chore(deps): update ci dependencies

This PR contains the following updates:

Package
Type
Update
Change

common-dev-assets

digest
2365130 -> 1d43759

github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
require
patch
v1.35.2 -> v1.35.3

go (source)
toolchain
patch
1.22.4 -> 1.22.5

Release Notes

terraform-ibm-modules/ibmcloud-terratest-wrapper (github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper)
v1.35.3
Compare Source
Bug Fixes

do not destroy the project on failure so schematic workspace logs can remain for debug (#837) (72f3c8e)

Configuration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

[ ] If you want to rebase/retry this PR, check this box

This PR has been generated by Renovate Bot.

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

/run pipeline

:tada: This issue has been resolved in version 1.3.2 :tada:
The release is available on:

GitHub release
v1.3.2

Your semantic-release bot :package::rocket:

repo: terraform-ibm-modules/mock-module


text:
chore(deps): update ci dependencies

This PR contains the following updates:

Package
Type
Update
Change

github.com/terraform-ibm-modules/ibmcloud-terratest-wrapper
require
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text:
feat: updated fscloud module to take location for serRef zone

Description
Added the ability to create zones with optional location which will support restriction per location.
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[ ] Patch release (x.x.X)
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User will now have the option to provide optional location for each serviceRef when creating zones.

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There will be the update in place if we do upgrade test for FSCloud example
2024/07/10 10:05:38 Terraform plan | Terraform used the selected providers to generate the following execution
 2024/07/10 10:05:38 Terraform plan | plan. Resource actions are indicated with the following symbols:
 2024/07/10 10:05:38 Terraform plan |   ~ update in-place
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan | Terraform will perform the following actions:
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |   # module.cbr_account_level.module.cbr_rule["schematics"].ibm_cbr_rule.cbr_rule will be updated in-place
 2024/07/10 10:05:38 Terraform plan |   ~ resource "ibm_cbr_rule" "cbr_rule" {
 2024/07/10 10:05:38 Terraform plan |         id                  = "84c1a3816c39a26208ae3d634178f244"
 2024/07/10 10:05:38 Terraform plan |         # (9 unchanged attributes hidden)
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |       - contexts {
 2024/07/10 10:05:38 Terraform plan |           - attributes {
 2024/07/10 10:05:38 Terraform plan |               - name  = "endpointType" -> null
 2024/07/10 10:05:38 Terraform plan |               - value = "public" -> null
 2024/07/10 10:05:38 Terraform plan |             }
 2024/07/10 10:05:38 Terraform plan |           - attributes {
 2024/07/10 10:05:38 Terraform plan |               - name  = "networkZoneId" -> null
 2024/07/10 10:05:38 Terraform plan |               - value = "84c1a3816c39a26208ae3d63417892f4" -> null
 2024/07/10 10:05:38 Terraform plan |             }
 2024/07/10 10:05:38 Terraform plan |         }
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |         # (2 unchanged blocks hidden)
 2024/07/10 10:05:38 Terraform plan |     }
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |   # module.cbr_account_level.module.cbr_zone["cloud-object-storage"].ibm_cbr_zone.cbr_zone will be updated in-place
 2024/07/10 10:05:38 Terraform plan |   ~ resource "ibm_cbr_zone" "cbr_zone" {
 2024/07/10 10:05:38 Terraform plan |         id                  = "32859474d611c291f25878c03979bfe6"
 2024/07/10 10:05:38 Terraform plan |       ~ name                = "fs-upg-cloud-object-storage-service-zone" -> "COS-zone-example-of-customized-zone-name"
 2024/07/10 10:05:38 Terraform plan |         # (11 unchanged attributes hidden)
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |         # (1 unchanged block hidden)
 2024/07/10 10:05:38 Terraform plan |     }
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |   # module.cbr_account_level.module.cbr_zone["codeengine"].ibm_cbr_zone.cbr_zone will be updated in-place
 2024/07/10 10:05:38 Terraform plan |   ~ resource "ibm_cbr_zone" "cbr_zone" {
 2024/07/10 10:05:38 Terraform plan |         id                  = "32859474d611c291f25878c03979c81b"
 2024/07/10 10:05:38 Terraform plan |         name                = "codeengine-zone-example-of-customized-zone-name"
 2024/07/10 10:05:38 Terraform plan |         # (11 unchanged attributes hidden)
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |       ~ addresses {
 2024/07/10 10:05:38 Terraform plan |             # (1 unchanged attribute hidden)
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |           ~ ref {
 2024/07/10 10:05:38 Terraform plan |               + location     = "au"
 2024/07/10 10:05:38 Terraform plan |                 # (2 unchanged attributes hidden)
 2024/07/10 10:05:38 Terraform plan |             }
 2024/07/10 10:05:38 Terraform plan |         }
 2024/07/10 10:05:38 Terraform plan |       + addresses {
 2024/07/10 10:05:38 Terraform plan |           + type = "serviceRef"
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |           + ref {
 2024/07/10 10:05:38 Terraform plan |               + account_id   = "52a1d940412a4e67b109175bd1fb6513"
 2024/07/10 10:05:38 Terraform plan |               + location     = "syd"
 2024/07/10 10:05:38 Terraform plan |               + service_name = "codeengine"
 2024/07/10 10:05:38 Terraform plan |             }
 2024/07/10 10:05:38 Terraform plan |         }
 2024/07/10 10:05:38 Terraform plan |     }
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |   # module.cbr_account_level.module.cbr_zone["server-protect"].ibm_cbr_zone.cbr_zone will be updated in-place
 2024/07/10 10:05:38 Terraform plan |   ~ resource "ibm_cbr_zone" "cbr_zone" {
 2024/07/10 10:05:38 Terraform plan |         id                  = "32859474d611c291f25878c03979e417"
 2024/07/10 10:05:38 Terraform plan |         name                = "fs-upg-server-protect-service-zone"
 2024/07/10 10:05:38 Terraform plan |         # (11 unchanged attributes hidden)
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |       ~ addresses {
 2024/07/10 10:05:38 Terraform plan |             # (1 unchanged attribute hidden)
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan |           ~ ref {
 2024/07/10 10:05:38 Terraform plan |               + location     = "au"
 2024/07/10 10:05:38 Terraform plan |                 # (2 unchanged attributes hidden)
 2024/07/10 10:05:38 Terraform plan |             }
 2024/07/10 10:05:38 Terraform plan |         }
 2024/07/10 10:05:38 Terraform plan |     }
 2024/07/10 10:05:38 Terraform plan |
 2024/07/10 10:05:38 Terraform plan | Plan: 0 to add, 4 to change, 0 to destroy.
 2024/07/10 10:05:38 Command finished successfully.

/run pipeline

Performed an upgrade test manually in Schematics for FSCloud example,  plan shows there is an update in-place as the FSCloud example is modified now to add locations for few serviceRefs to create zones.
2024/07/10 17:41:15 Terraform plan | Terraform used the selected providers to generate the following execution
 2024/07/10 17:41:15 Terraform plan | plan. Resource actions are indicated with the following symbols:
 2024/07/10 17:41:15 Terraform plan |   ~ update in-place
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan | Terraform will perform the following actions:
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |   # module.cbr_account_level.module.cbr_zone["cloud-object-storage"].ibm_cbr_zone.cbr_zone will be updated in-place
 2024/07/10 17:41:15 Terraform plan |   ~ resource "ibm_cbr_zone" "cbr_zone" {
 2024/07/10 17:41:15 Terraform plan |         id                  = "b0155b52af3e337e073a27346b6f4e89"
 2024/07/10 17:41:15 Terraform plan |       ~ name                = "cbr-cloud-object-storage-service-zone" -> "COS-zone-example-of-customized-zone-name"
 2024/07/10 17:41:15 Terraform plan |         # (11 unchanged attributes hidden)
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |         # (1 unchanged block hidden)
 2024/07/10 17:41:15 Terraform plan |     }
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |   # module.cbr_account_level.module.cbr_zone["codeengine"].ibm_cbr_zone.cbr_zone will be updated in-place
 2024/07/10 17:41:15 Terraform plan |   ~ resource "ibm_cbr_zone" "cbr_zone" {
 2024/07/10 17:41:15 Terraform plan |         id                  = "e722996aec9838b5f470169fd479d9de"
 2024/07/10 17:41:15 Terraform plan |         name                = "codeengine-zone-example-of-customized-zone-name"
 2024/07/10 17:41:15 Terraform plan |         # (11 unchanged attributes hidden)
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |       ~ addresses {
 2024/07/10 17:41:15 Terraform plan |             # (1 unchanged attribute hidden)
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |           ~ ref {
 2024/07/10 17:41:15 Terraform plan |               + location     = "au"
 2024/07/10 17:41:15 Terraform plan |                 # (2 unchanged attributes hidden)
 2024/07/10 17:41:15 Terraform plan |             }
 2024/07/10 17:41:15 Terraform plan |         }
 2024/07/10 17:41:15 Terraform plan |       + addresses {
 2024/07/10 17:41:15 Terraform plan |           + type = "serviceRef"
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |           + ref {
 2024/07/10 17:41:15 Terraform plan |               + account_id   = "52a1d940412a4e67b109175bd1fb6513"
 2024/07/10 17:41:15 Terraform plan |               + location     = "syd"
 2024/07/10 17:41:15 Terraform plan |               + service_name = "codeengine"
 2024/07/10 17:41:15 Terraform plan |             }
 2024/07/10 17:41:15 Terraform plan |         }
 2024/07/10 17:41:15 Terraform plan |     }
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |   # module.cbr_account_level.module.cbr_zone["server-protect"].ibm_cbr_zone.cbr_zone will be updated in-place
 2024/07/10 17:41:15 Terraform plan |   ~ resource "ibm_cbr_zone" "cbr_zone" {
 2024/07/10 17:41:15 Terraform plan |         id                  = "21569b00563e4c4356845bc55279220e"
 2024/07/10 17:41:15 Terraform plan |         name                = "cbr-server-protect-service-zone"
 2024/07/10 17:41:15 Terraform plan |         # (11 unchanged attributes hidden)
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |       ~ addresses {
 2024/07/10 17:41:15 Terraform plan |             # (1 unchanged attribute hidden)
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan |           ~ ref {
 2024/07/10 17:41:15 Terraform plan |               + location     = "au"
 2024/07/10 17:41:15 Terraform plan |                 # (2 unchanged attributes hidden)
 2024/07/10 17:41:15 Terraform plan |             }
 2024/07/10 17:41:15 Terraform plan |         }
 2024/07/10 17:41:15 Terraform plan |     }
 2024/07/10 17:41:15 Terraform plan |
 2024/07/10 17:41:15 Terraform plan | Plan: 0 to add, 3 to change, 0 to destroy.
 2024/07/10 17:41:15 Command finished successfully.

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@Ak-sky please provide more info in the release notes. There are several variables removed, added and updated here. So please summarise what consumers need to know when upgrading

Hi @vburckhardt, I have added the test detail for ServiceRef zone with regions here- https://github.com/terraform-ibm-modules/terraform-ibm-cbr/pull/472#discussion_r1700353024, please let me know if this is ok.

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