text:
Hi, I use python 3.5 and tensorflow 1.0.1 . and when I download and load the inception model in tutorial no. 7 my python program gives the error " stop working" . can you please help me?

STOP!
Most of the problems people are having are already described in the installation instructions.
Python 3.5
These tutorials were developed in Python 3.5 and may give strange errors in Python 2.7
Missing Files
You need to download the whole repository, either using git clone or as a zip-file. See the installation instructions.
Questions about TensorFlow
General questions about TensorFlow should either be asked on StackOverflow or GitHub.
Suggestions for Changes
The tutorials cannot change too much because it would make the YouTube videos too different from the source-code.
Requests for New Tutorials
These tutorials were made by a single person on his own time. It took a very long time to
research and produce the tutorials. If a topic is not covered then the best thing is to make
a new tutorial by yourself. All you need is a decent microphone, a screen-grabbing tool, and a
video editor. I used the free version of DaVinci Resolve.
Other Issues?
Please carefully read the installation instructions and only open an issue if you are still having problems.

How am I supposed to help you when you just write a subject-line that says the program has stopped working? You need to give more details and print-outs of the actual errors you are getting. Have you tried googling for the errors?

authors: Hvass-Labs kargarisaac

repo: Hvass-Labs/TensorFlow-Tutorials


text:
How to get future predictions that are not in the input data

Hey, i followed your Time-Series Prediction and was wondering how i would be able to get the prediction data that is in the future.
Example: I am using the GRU to analyze charts and i am using 7 day time steps which results into the chart being shifted into the future by 7 days when i plot it. The problem is, that i still only have the same amount of data points aswell as shifting the prediction into the future by 7 days would mean it is shifted even further and thats what i dont understand.
So how can i get the data for the 7 day time steps that are laying in the future?

I don't understand the question. It sounds like this to me :-)
https://youtu.be/kTcRRaXV-fg?t=70
Please ask on StackOverflow instead, but try and make your question more clear if you expect an answer.

source: gharchive/issue

authors: FaBeyyy Hvass-Labs

license: mit

license_type: permissive

license_source: bigquery


text:
Reviews block on submission detail view gets stuck on loading if not in viewport

Caught by a staff member - with the new HTMX implementation sometimes the reviews card gets stuck on the loading animation and will never populate reviews

sometimes refreshing the page will fix this

pretty sure the issue comes from the revealed trigger, if I have staff center on the card & refresh their browser it seems to load without issue

I can confirm this, making my window less tall so the review block is "below the fold" it will not load.
The neatest would be to have it lazy load when scrolled in to view (or just before, browsers handle this differently).
If that is tricky we can just have it always load.
I assume this issue affects any htmx block, it just so happens that the reviews block is the furthest down.

authors: frjo wes-otf

repo: HyphaApp/hypha

license_source: github-api


text:
Update composer-playground.yaml for new ID card metadata format

Changes required for compatibility with Composer 0.13.0.
Signed-off-by: Mark S. Lewis Mark_Lewis@uk.ibm.com

Waiting for bluemix to finish creating a cluster so I can test this

Hmm, it was looking good. I've deployed a test network without problems -> http://184.172.233.78:31080/ but just investigating an error connecting to the new network...

Sounds like that error is a known issue so this fix is good to go if someone can merge

thanks @bestbeforetoday @jt-nti

source: gharchive/pull-request

authors: bestbeforetoday dhyey20 jt-nti

repo: IBM-Blockchain/ibm-container-service


text:
Updated the runtime to python-3.6.2

Updated the runtime to python-3.6.2 as python-2.7.10 is not supported anymore in the latest version of python_buildpack
python_buildpack                         10         true      false    python-buildpack-v1.5.22.zip
DEPENDENCY MISSING IN MANIFEST: python 2.7.10
It looks like you're trying to use python 2.7.10.
Unfortunately, that version of python is not supported by this buildpack.
The versions of python supported in this buildpack are:

3.6.2
3.6.1
3.5.3
3.5.2
3.4.6
3.4.5
3.3.6
3.3.5
2.7.13
2.7.12
If you need further help, start by reading: https://github.com/cloudfoundry/python-buildpack/releases.

Was this tested to see that 3.6 still worked with the code that was in there?

I did push the application after changing the runtime and it worked fine.

authors: prsahoo travissalascox

repo: IBM-Cloud/key-protect-helloworld-python


text:
Improve handling of query results

IBM-Swift/Swift-Kuery-PostgreSQL#12 Improve handling of query results
Return correct types instead of String

Codecov Report

Merging #13 into master will increase coverage by 7.29%.
The diff coverage is 89.51%.

@@            Coverage Diff             @@
##           master      #13      +/-   ##
==========================================
+ Coverage   66.53%   73.82%   +7.29%
==========================================
  Files           3        3
  Lines         257      363     +106
==========================================
+ Hits          171      268      +97
- Misses         86       95       +9

Flag
Coverage Δ

#SwiftKueryPostgreSQL
73.82% <89.51%> (+7.29%)
:arrow_up:

Impacted Files
Coverage Δ

...es/SwiftKueryPostgreSQL/PostgreSQLConnection.swift
63.97% <100%> (+0.06%)
:arrow_up:

...SwiftKueryPostgreSQL/PostgreSQLResultFetcher.swift
84.3% <88.98%> (+10.16%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update b76cc2d...69a2f7c. Read the comment docs.

LGTM

authors: codecov-io irar2 shmuelk

repo: IBM-Swift/Swift-Kuery-PostgreSQL


text:
Replace /config to /userlib for sharedLib Section 4.3.2

Fhir User Guide has incorrect jar location mentioned to keep custom interceptors jar for Persistence interceptors.
Steps to reproduce the behavior:

Go to 'https://ibm.github.io/FHIR/guides/FHIRServerUsersGuide/#432-implementing-a-persistence-interceptor'
Under 4.3.2 Implementing a persistence interceptor step 3, Copy your jar to the <WLP_HOME>/usr/servers/fhir-server/config. This path should be <WLP_HOME>/usr/servers/fhir-server/userlib

closing PR need to add reviewer

@rgupta3349 I can add the reviewer if you reopen the PR

Reviewer added.
PR reopened for review

repo: IBM/FHIR


text:
Regarding wot_comparison.ipynb

I was trying the USD code on the Embryoid Body dataset used in the TrajectoryNet paper. The line
mid_step = find_intersec(mmd_to_ref(collQ, dataQ0), mmd_to_ref(collQ, dataP))[0]
inside the function run_interval(...) of wot_comparison.ipynb returns error due to the intersection being empty. How do we compute mmd & emd for USD in this case?

Hi @Piyushi-0, thanks for your interest in our paper!
Sorry, I am not able to reproduce the error.
Can you please find out if the code runs correctly until the line
dataQ, collQ, coll_mmd = train_unbalanced_descent(D, dataQ0, dataP, None, opt)
in run_interval()?
Could you for instance insert a break point after that line and verify that dataQ is a FloatTensor of size 4556 x 30?

Hi @Piyushi-0, thanks for your interest in our paper!
Sorry, I am not able to reproduce the error.
Can you please find out if the code runs correctly until the line
dataQ, collQ, coll_mmd = train_unbalanced_descent(D, dataQ0, dataP, None, opt)
in run_interval()?
Could you for instance insert a break point after that line and verify that dataQ is a FloatTensor of size 4556 x 30?

Hi @matrig, thanks for the reply. The code ran perfectly to reproduce results of the experiment Developmental Trajectories of Single Cells, thanks for making the code available. However, I had problems running it for a similar experiment given in the TrajectoryNet paper. I was trying to run the USD code on Embryoid Body sc-RNA time course data to get results like Table 3 of the TrajectoryNet paper. I loaded the data as they show in their github repository. I am able to run USD for the Table 3 experiment for timesteps 2 & 3 but while running the code for timestep 1, I get the error that I mentioned in my first post. Before the line mid_step = find_intersec(mmd_to_ref(collQ, dataQ0), mmd_to_ref(collQ, dataP))[0] the code runs perfectly. dataQ was a FloatTensor of size num_samples x num_features.

Hi @matrig, thanks for the reply. The code ran perfectly to reproduce results of the experiment Developmental Trajectories of Single Cells, thanks for making the code available. However, I had problems running it for a similar experiment given in the TrajectoryNet paper. I was trying to run the USD code on Embryoid Body sc-RNA time course data to get results like Table 3 of the TrajectoryNet paper. I loaded the data as they show in their github repository. I am able to run USD for the Table 3 experiment for timesteps 2 & 3 but while running the code for timestep 1, I get the error that I mentioned in my first post. Before the line mid_step = find_intersec(mmd_to_ref(collQ, dataQ0), mmd_to_ref(collQ, dataP))[0] the code runs perfectly. dataQ was a FloatTensor of size num_samples x num_features.

repo: IBM/USD


text:
Compiling error related to RPU config

Description
I have successfully installed in two machines. But in some machines, it failed.
/data/home/pcl/proj/aihwkit/src/rpucuda/rpu_pulsed_device.h:128:3: error: function ‘RPU::PulsedRPUDeviceBase<T>::PulsedRPUDeviceBase(RPU::PulsedRPUDeviceBase<T>&&) [with T = float]’ defaulted on its first declaration with an exception-specification that differs from the implicit declaration ‘RPU::PulsedRPUDeviceBase<float>::PulsedRPUDeviceBase(RPU::PulsedRPUDeviceBase<float>&&)’

it's the whole installation log.
https://drive.google.com/file/d/11thBy6ZjrP8Raw8huUYRjMhF7ibaIIEy/view?usp=sharing
How to reproduce
I use the following scripts to install. Everyting went well except for the last one.
conda create -n aihwkit python=3.8
conda activate aihwkit
conda install -y cmake openblas pybind11
conda install -y -c conda-forge scikit-build
pip install torch torchvision
git clone https://github.com/IBM/aihwkit.git
cd aihwkit
conda install -y -c intel mkl mkl-devel mkl-static mkl-include
MKLROOT=$CONDA_PREFIX/lib CMAKE_PREFIX_PATH=$CONDA_PREFIX make build_cuda flags="-DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc -DRPU_CUDA_ARCHITECTURES='70'"

Expected behavior
successfully installation
Other information

Pytorch version: 2.0
pip package and conda package:
pip list on google drive
conda list on google drive
OS: centos 7.9
Python version: 3.8
Conda version (or N/A): conda 23.3.1
CUDA version: tried 11.7 and 12.1
GPU version: v100

It seems that your compiler is way too old. You are using gcc 4.8.5.  Please try again with at least 7.x . Current gcc is >11.x .

I followed your instructions to install gcc 9,10,and 11. I tried all of them.
But there are still errors related to RPU.
installation log on google drive

Hi @dongwhfdyer,
Thanks for updating GCC. Now it looks like an error related to CUB. Could you try again with CUDA 11.7 ? CUDA 12.1 is very new and I am not sure whether even pytorch works with that.

Really thank you. I thought I tried. Then I tried again. It works!

authors: dongwhfdyer maljoras

repo: IBM/aihwkit

license: MIT


text:
NCCL Tests

Add the ability for Autopilot to perform pairwise NCCL tests across all the active GPU nodes in the cluster. Pairwise NCCL tests are use to identify bad nodes.  This should be available as a pre-flight test (#1)
Autopilot will need the ability to launch new pods on specific nodes (cluster-level awareness)
https://github.com/NVIDIA/nccl-tests

On 10/30/24, it was decided to abandon NCCL support included as a test in autopilot. Due to the complexity of the test results and the required code, it was decided that NCCL should exist as a stand-alone test. The PR will be archived and the branch will remain in case the work is resumed later.

repo: IBM/autopilot


text:
fix(Tile): correct markup for legacy RadioTile

Closes IBM/carbon-components-react#1819
This PR adds an experimental feature flag check for <RadioTile> and <SelectableTile> to make sure the correct markup is rendered

@emyarod Was <input> in ` your fix for the problem...? Could you share more of your findings...? Thanks!

yes @asudoh <input> within <label> matches the legacy vanilla markup if I'm not mistaken

@emyarod does this revert the changes made for https://github.com/IBM/carbon-components-react/issues/1114 ?

@ZachStowellIBM does the netlify deploy preview throw the same errors as before?

@emyarod I had to manually adjust the styles for the input on the netlify deploy preview since it was still showing the display:none issue that was resolved in #1114 , however after adjusting that, I got the following issues in DAP.

Those issues seem to indicate that this does revert the changes from #1114, unless there is a specific issue with the deploy preview.

@ZachStowellIBM just to confirm, we are looking at deploy preview 1820 above right?
it looks like the DAP output for the master branch docs and the deploy preview for this PR are identical for me, unless I am overlooking something

@emyarod -- correct, I was looking at https://deploy-preview-1820--carbon-components-react.netlify.com.  Is your DAP screenshot from 1820, or master?
The "The id 'identifier' referenced by the 'for' attribute" error in your DAP screenshot is the original error from #1114 (the display:none issue).

@ZachStowellIBM gotcha. That screenshot is identical for both master and 1820 for me at least. But I think I'll have to make the change on vanilla carbon repo like I mentioned previously and just revert the changes on this feature branch

I believe this PR should contain the fix https://github.com/IBM/carbon-components/pull/1742

authors: ZachStowellIBM asudoh emyarod

repo: IBM/carbon-components-react


text:
Use UBI image as the base image?

Is that possible to publish ibm-cloud-operator with http://registry.access.redhat.com/ubi8-minimal image?

Since this is a golang application, it should be possible to create a "scratch" image which would be smaller and more secure than even a UBI image.

@dcberg ,  it is just due to requirement of service framework ..

add https://github.com/IBM/cloud-operators/pull/113 for this

@cdlliuy just to understand better ... is it the case that the "scratch" image would not work for you because you need the ca certificates in the image ?

Fixed in v0.1.7

authors: cdlliuy dcberg pdettori vazirim

repo: IBM/cloud-operators


text:
Mlhelib update

Fixed bug in testing code
Improved readmes
Minor additional improvements

Note @boland25 that this should fix issue #89 (specifically the changes in CTileIntTests.cpp).

Note @boland25 that this should fix issue #89 (specifically the changes in CTileIntTests.cpp).

👍

👍

authors: aehud boland25 dubek

repo: IBM/fhe-toolkit-linux


text:
[IBMCloud] Scale Encryption with GKLM

@rajan-mis

Added a new task which adds the host entry for the key servers and will remove it once the GKLM integration gets completed.
Kindly review and merge the changes.

cc: @digvijay-ukirde @mamuthiah @sasikeda

@rajan-mis
Changes are updated. Kindly review.

repo: IBM/ibm-spectrum-scale-install-infra


text:
Knowing the job name of qshell, python and db on IBMi

Hello  when a python script is executed, it spans 3 job's. Is there a way to know job names in python script so it can be logged. Once ibm_db_dbi is connected, using DB2 SQL service, provides the QSQSRVR job details. Wondering if there is a way to get the other.

Not easily. The current job name can be retrieved with QUSRJOBI through ctypes, though it's not for the faint of heart.
You can also reduce this to two jobs if you use exec on your QSH command line, eg. QSH CMD('exec /QOpenSys/pkgs/bin/python3 ...'). Then the python script will run inside the QP0ZSPWT QSH job instead of spawning another one.

Thank you for that information.

authors: kadler sgonchigar

repo: IBM/ibmi-oss-issues


text:
tab switching can be slow on windows

i'm not sure yet why it's worse on windows. maybe it's just the small VM in which i'm running windows. a performance shows this, where the time range about the colored segments is around 2 seconds

The re function (this is from a production build, hence the obfuscation) is Terminal/visible.isInViewport()

i think we can help this quite a bit by pushing the expensive logic to a debounced async. By the time the old tab gets around to asking about focus, it is no longer the active tab!

repo: IBM/kui


text:
Pop-up Notification Icon Placement

Currently, the icon placement in the pop-up notification appears to have static constraints on the position of the icon. I would love to see an added "centered" option that centers the icon vertically based on the height of the text. I'd prefer it ignored the accessory view section should it be present and only center based on the text. I've attached some images of current behavior with and without accessory view and desired behavior with and without accessory view. Thanks for considering the enhancement.

Hi @mpermann, thanks for opening this FR. The current window layout for the Pop-up UI is based on the Catalina/Big Sur Apple HIG for Alerts (the current Apple HIG shows a different layout for Alerts that we're considering to integrate for "light" or "base" pop-ups) where the left icon for the alert was placed on the left side and anchored to the top constraint for the pop-up body. I think it makes the pop-up UI more consistent to not have the icon move up or down even because some of the accessory views could show simple text and that could make the position of the icon ambiguous if it only follow the "main" text elements of the pop-up.
That's why I think that we'll not consider to implement this logic.
I'll leave this FR open anyway so that if some developers from the community think that it's worth to work on it they can use this issue to track the deployment.
Thanks again for contributing to the community!

authors: SMartorelli mpermann

repo: IBM/mac-ibm-notifications


text:
reserve the status for operators which are still in deletion stage (#867)

reserve the status for operators which are still in deletion stage
Signed-off-by: Daniel Fan fanyuchensx@gmail.com

/retest

/lgtm

authors: Daniel-Fan bluzarraga


text:
fix(Global Tagging): re-gen service with latest API definition

PR summary
This PR contains:

a re-gen of the Global Tagging service
a renovation of the integration tests to more closely align with the current pattern being used for int tests
a new "examples" file which contains working examples of each operation (these can be used to produce an examples .json file which can be used in the production of the online reference apidocs)

PR Checklist
Please make sure that your PR fulfills the following requirements:

[x] The commit message follows the Angular Commit Message Guidelines.
[x] Tests for the changes have been added (for bug fixes / features)
[ ] Docs have been added / updated (for bug fixes / features)

PR Type

[ ] Bugfix
[ ] Feature
[ ] Code style update (formatting, local variables)
[ ] Refactoring (no functional changes, no api changes)
[ ] New tests
[ ] Build/CI related changes
[ ] Documentation content changes
[ ] Other (please describe)

What is the current behavior?

What is the new behavior?

Does this PR introduce a breaking change?

[ ] Yes
[x] No

Other information

:tada: This PR is included in version 0.11.2 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:

authors: ibm-devx-automation padamstx

repo: IBM/platform-services-go-sdk


text:
build: bump node core version to 4.0.5

PR summary
Bumps node core version to 4.0.5
PR Checklist
Please make sure that your PR fulfills the following requirements:

[x] The commit message follows the Angular Commit Message Guidelines.
[ ] Tests for the changes have been added (for bug fixes / features)
[ ] Docs have been added / updated (for bug fixes / features)

Current vs new behavior

Does this PR introduce a breaking change?

[ ] Yes
[x] No

Other information

:tada: This PR is included in version 0.33.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

authors: ibm-devx-sdk padamstx

repo: IBM/platform-services-node-sdk


text:
[Question] Make the environment work with stable_baselines

I've tried to make the environment work with the baselines fork stable_baselines (https://github.com/hill-a/stable-baselines). It runs, but the results shown when I'm running plot_energyplus is always the same for all episodes (both for PPO2 and TRPO):
Reward                    ave=-11.80, min=-16.24, max= 0.11, std= 1.76
westzone_temp             ave=67.03, min=23.86, max=86.97, std= 7.80
eastzone_temp             ave=66.73, min=23.86, max=87.65, std= 7.82
Power consumption         ave=322,506.33, min=177,024.88, max=362,582.24, std=24,882.39
pue                       ave= 1.01, min= 1.01, max= 1.11, std= 0.00

westzone_temp distribution
    degree 0.0-0.9 0.0   0.1   0.2   0.3   0.4   0.5   0.6   0.7   0.8   0.9
    -------------------------------------------------------------------------
    17.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    18.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    19.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    20.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    21.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    22.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    23.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    24.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    25.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    26.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%
    27.0C  0.0%    0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%  0.0%

The environment seems to run properly, it outputs "Continuing Simulation at MM/DD for WHOLEYEARDAY" and "Updating Shadowing Calculations, Start Date=MM/DD".
Questions:

I'm using vectorised environments to be able to parallelise the training (One env = DummyVecEnv, many envs = SubprocVecEnv). This might be a problem?
Is there some magic integration with baselines when building patched EnergyPlus and installing the executables?
Can someone point me in a direction of how I can get it to work with stable_baselines?

I have tried to update to Stable Baselines as well and run into the same issues.
The problem comes from the fact that Stable Baselines and Baselines do not normalise the action space in the same way.
In stable-baselines/runners.py, they do the following (l.151):
# Clip the actions to avoid out of bound error
if isinstance(env.action_space, gym.spaces.Box):
            clipped_action = np.clip(action, env.action_space.low, env.action_space.high)

Here, the actions are normalised (they have values in [-1,1] plus some noise) before we clip them. However, the env.action_space.low is not normalised. Hence, the clipped_action will always be [10, 10, 1.75, 1.75]
The action will be scaled back in energyplus_model.py in the set_action function and then clipped again. Hence, the resulting action will always be [40, 40, 7, 7] and therefore, the results of the training are so bad, that they don't appear in the plot anymore (the original plot only shows temperatures in the range [0,40])
My hack for now is that I commented out the two lines in the code above in stable-baselines/runners.py and it seems to work, but if somebody has a better solution, I would be curious to know about it.

@biemann and @triton99 ,
I am trying to use the TRPO algorithm and getting the following error. Could anyone help on this issue.
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 181, in
main()
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 177, in main
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 106, in train
model.learn(total_timesteps=num_timesteps)
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines/trpo_mpi/trpo_mpi.py", line 333, in learn
seg = seg_gen.next()
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines/common/runners.py", line 114, in traj_segment_generator
action, vpred, states, _ = policy.step(observation.reshape(-1, *observation.shape), states, done)
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines/common/policies.py", line 576, in step
{self.obs_ph: obs})
File "/home/ecocc/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 950, in run
run_metadata_ptr)
File "/home/ecocc/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1149, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (1, 1, 5) for Tensor 'input/Ob:0', which has shape '(?, 5)'
Exception ignored in: <bound method EnergyPlusEnv.del of <gym_energyplus.envs.energyplus_env.EnergyPlusEnv object at 0x7fed1e3fc7f0>>
Traceback (most recent call last):
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 81, in del
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 177, in stop_instance
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 151, in count_severe_errors
NameError: name 'open' is not define

I have a misunderstanding. I guess you are trying to use TRPO with Stable Baselines or is it the original Baselines you are using? Was this the error from TD3?
When I did experiments with TRPO, I used the original Baselines implementation (I actually was using Stable Baselines 3 in Pytorch, that does not have TRPO implemented now, but this may change in the next weeks)
Actually, my solution when I posted this was a turnaround and not a practical solution. What I did now was normalising the environment directly in EnergyPlus Gym. This approach is clearly more scalable, as it would make it possible to use the same code for other libraries (e.g. RLLib) in an uniform way. Normalising states is a general good practice, so some libraries assume normalised states as input.
What I modified was the following:
In energyplus_model_2ZoneDataCenterHVAC_wEconomizer_Temp_Fan.py in setup_spaces()
I changed
  lo = 10.0
  hi = 40.0
  flow_hi = 7.0
  flow_lo = flow_hi * 0.25
  self.action_space = spaces.Box(low =   np.array([ lo, lo, flow_lo, flow_lo]),
                                 high =  np.array([ hi, hi, flow_hi, flow_hi]),
                                 dtype = np.float32)
  self.observation_space = spaces.Box(low =   np.array([-20.0, -20.0, -20.0,          0.0,          0.0,          0.0]),
                                      high =  np.array([ 50.0,  50.0,  50.0, 1000000000.0, 1000000000.0, 1000000000.0]),
                                      dtype = np.float32)

to:
self.low_action = np.array([10.0, 10.0, 1.75, 1.75])
self.high_action = np.array([40.0, 40.0, 7.0, 7.0])

self.low_obs = np.array([-20.0, -20.0, -20.0, 0.0, 0.0, 0])
self.high_obs = np.array([50.0, 50.0, 50.0, 1e5, 1e5, 1e5])

self.action_space = spaces.Box(low=-1, high=1, shape=(4,), dtype=np.float32)
self.observation_space = spaces.Box(low=-1, high=1, shape=(6,), dtype=np.float32)

(Add also these additional class variables)
I had then to modify in energyplus_model.py:
def set_action(self, normalized_action):
        # In Stable Baselines, the action seems to be normalized to [-1.0, 1.0].
        # So it must be scaled back into action_space by the environment.

        self.action_prev = self.action
        self.action = np.clip(self.action, self.action_space.low, self.action_space.high)
        self.action = self.low_action + (normalized_action + 1.) * 0.5 * (self.high_action - self.low_action)

I dont remember if this was everything that was needed to make it work. So please let me know if it works. I guess with this all algorithms  should work out of the box without having to use VecNormalise, as I suggested. This works with Stable Baselines, but not with the original Baselines for example.
This answers the question quite indirectly, Your problem is that the shape of the input tensor is wrong, so I suggest trying to use the function 'flatten()` somewhere to get the shapes right (depending if you are using Pytorch or Tensorflow, the function would be different).

Hi i have used the TRPO from stablebaselines 2.  I thought you have successfully implemented the TRPO using the stable baselines2

Yep ..Infact I tried with TD3 also and got the below error.
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 179, in
main()
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 175, in main
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 112, in train
model_td3.learn(total_timesteps=num_timesteps)
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/td3/td3.py", line 204, in learn
reset_num_timesteps=reset_num_timesteps,
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 273, in learn
log_interval=log_interval,
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 469, in collect_rollouts
action, buffer_action = self._sample_action(learning_starts, action_noise)
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 329, in _sample_action
scaled_action = np.clip(scaled_action + action_noise(), -1, 1)
ValueError: operands could not be broadcast together with shapes (1,2) (4,)
Exception ignored in: <bound method EnergyPlusEnv.del of <gym_energyplus.envs.energyplus_env.EnergyPlusEnv object at 0x7fa2b4a1d828>>
Traceback (most recent call last):
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 81, in del
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 177, in stop_instance
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 151, in count_severe_errors
NameError: name 'open' is not defined
In run_energyplus.py:
action_noise = NormalActionNoise(mean=np.zeros(4), sigma=0.1 * np.ones(4))
model_td3 = TD3('MlpPolicy', env, verbose=1, action_noise=action_noise, tensorboard_log="/home/ecocc/eplog/tensorboard/")
model_td3.learn(total_timesteps=num_timesteps)
model_td3.save("/home/ecocc/eplog/model/td3_model")
Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 179, in
main()
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 175, in main
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
File "/home/ecocc/rl-testbed-for-energyplus/baselines_energyplus/run_mpi/run_energyplus.py", line 112, in train
model_td3.learn(total_timesteps=num_timesteps)
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/td3/td3.py", line 204, in learn
reset_num_timesteps=reset_num_timesteps,
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 273, in learn
log_interval=log_interval,
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 469, in collect_rollouts
action, buffer_action = self._sample_action(learning_starts, action_noise)
File "/home/ecocc/.local/lib/python3.6/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 329, in _sample_action
scaled_action = np.clip(scaled_action + action_noise(), -1, 1)
ValueError: operands could not be broadcast together with shapes (1,2) (4,)
Exception ignored in: <bound method EnergyPlusEnv.del of <gym_energyplus.envs.energyplus_env.EnergyPlusEnv object at 0x7fa2b4a1d828>>
Traceback (most recent call last):
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 81, in del
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 177, in stop_instance
File "/home/ecocc/rl-testbed-for-energyplus/gym_energyplus/envs/energyplus_env.py", line 151, in count_severe_errors
NameError: name 'open' is not defined

Hi,
I assume you were looking at my fork and I indeed used that code once, so I guess you were looking at an older version of my code (I did not upload much on github and was doing experiments mostly locally) that was the first version that worked for me. I uploaded a few months ago a newer version, but I modified the case study to fit my needs.
I guess from a quick glance you are using a different version of the environment (the one that aims to control only temperature, whereas I used a version that controls both temperature and airflow rate). So in your case the action space would be two-dimensional, which is probably the reason of the error message
I think I tested TRPO with StableBaselines (but not StableBaselines 2) and I think it worked, but I am not so sure anymore. I was using Stable Baselines 3 for my experiments mostly (I moved there because I wanted an implementation of SAC and TD3 that was not available in the original Baselines and chose the Pytorch version for personal preferences).

Yep.. I am using to control ony the temperature.. that's the reason. i have set as below
    self.action_space = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
    self.observation_space = spaces.Box(low=-1, high=1,shape=(5,), dtype=np.float32)

You still have the same error with TD3? If you use action noise, did you change the dimensions there as well ?
Note that the dirty hack I did with separating the on-policy and off-policy methods was only because I used unnormalised states initially. If the states are normalised, this is not necessary. I am feeling a bit ashamed of the code I wrote :)
You dont need to install Pytorch for Stable Baselines, but you need it for Stable Baselines 3
PS: I was working on a paper that was aiming to compare different algorithms with each other on this case study, that is currently under revision.

Hi
I have changed the action noise and the error is gone. However, I am getting the Energy plus error. Do you need to modify anything in the IDF file.
** Severe  ** DualSetPointWithDeadBand: Effective heating set-point higher than effective cooling set-point - increase deadband if using unmixed air model
**   ~~~   ** occurs in Zone=SOUTH ZONE Environment=WHOLEYEARDAY, at Simulation time=01/03 08:00 - 09:00
**   ~~~   ** LoadToHeatingSetPoint=-120495.947, LoadToCoolingSetPoint=-120737.064
**   ~~~   ** Zone TempDepZnLd=13290.33
**   ~~~   ** Zone TempIndZnLd=386302.60
**   ~~~   ** Zone Heating ThermostatSetPoint=20.00
**   ~~~   ** Zone Cooling ThermostatSetPoint=23.00
**  Fatal  ** Program terminates due to above conditions.
...Summary of Errors that led to program termination:
..... Reference severe error count=1
..... Last severe error=DualSetPointWithDeadBand: Effective heating set-point higher than effective cooling set-point - increase deadband if using unmixed air model
Do you have any idea?
How to fix the normalised clean code. Sorry I am new to Energyplus and RL implementation. If you guide, i can try also.
Have you succeeded in any particular algorithm in the comparison?
Thanks

Is this error specific to an algorithm? Or the same for all? Does the code crash or are the results just wrong like for OP?
It is a bit difficult to judge without code. Which file are you using? In any case, it seems that there is something wrong with the values you are sending to EnergyPlus. The  load to heating setpoints are clearly too low:)
I did not change the idf file (but I used the version with the 4 dimensional action space)
From my experiments, SAC performed best. But, at the beginning I would suggest using PPO. It is the most stable to learn and quite fast in wall-clock time. It is not ideal for real-world applications, due to its bad data efficiency. But for testing new ideas, it is the one I am using. TD3 was the most tricky to make work (For example, action noise may not be a good idea in this environment)

Hi biemann,
Sorry for the late reply. Yes. Managed to fix the error and able to run for SAC,  TD3 and PPO. In tensorboard results, it seems that PPO is doing better in the ep_rew_mean graph.

Please correct me if the normalisation part is correct:
In run_energyplus.py
env = make_energyplus_env(env_id, workerseed)
env = DummyVecEnv([lambda: env])
env = VecNormalize(env)
In EnergyPlusModel2ZoneDataCenterHVAC_wEconomizer_Temp.py
def setup_spaces(self):
print("inside setup_spaces")
# Bound action temperature
lo = 10.0
hi = 40.0
self.low_action = np.array([10.0, 10.0])
self.high_action = np.array([40.0, 40.0])
self.low_obs = np.array([-20.0, -20.0, -20.0, 0.0, 0.0])
self.high_obs = np.array([50.0, 50.0, 50.0, 1e5, 1e5])
    self.action_space = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
    self.observation_space = spaces.Box(low=-1, high=1,shape=(5,), dtype=np.float32)

Could you please give the guidance for the above? Do we need to change the normalisation part for onpolicy and offpolicy?

How do the results look like? Are the results close to the ones of TRPO in the original implementation?
If you normalize the state space and action space, you dont need to normalise it again with VecNormalize.

Sorry, you mean the results from CSV?
In the above code, can we remove the env = VecNormalize(env) line? Is it applicable for onpolicy and offpolicy?

@biemann ,
I have removed vecNormalize and tabulated the zone air temperature and setpoint temperature. The values are appearing as strange, can you please clarify me.
ModelValues.xlsx
For example, for the below values, if we set the action for one zone input as 10, zone air temperature outcome as 21. However, if we set the temperature as 38, how come the  zone air temperature  as 22? Have you experienced this issue?
10 | 21.0346
38.78527 | 22.55974
40 | 28.13745
10 | 25.80909
10 | 21.81964

@takaomoriyama maybe it could be nice to provide a working example that uses stable-baselines (SB), since OpenAI baselines is in maintenance mode for quite some time now.
Few questions / challenges I have in mind:

ensure results obtained with SB are in line with original ones
should we use SB2 (tensorflow, but in maintenance) or SB3 (pytorch only)
use separate dependency requirements files for baselines and SB projects
test presence of relevant python modules when running a training (ie fail fast if you try to train using original trpo_mpi but installed SB dependencies)
opportunity to write some automated tests (using github actions) to make sure we can still train on all proposed frameworks

What do you think?

authors: antoine-galataud biemann maxvfischer revathij

repo: IBM/rl-testbed-for-energyplus


text:
Fixes #3459

Pull Request Review
Checklist

[x] Issue: https://github.ibm.com/TNC/tnc-o-tracking/issues/3459
[x] List of related PRs. : None
[x] Project builds without any errors.
[x] Unit Tests updated (or created where needed) and all pass.
[x] You have ran manual functional "smoke" test (does product as a whole still work?).
[x] User Story meets the acceptance criteria - and passes. acceptance criteria should be understood at outset.
[x] There is a PR for update of internal documentation for the affected Project(s) - README etc.
[x]  Description of the changes: https://github.ibm.com/TNC/tnc-o-tracking/issues/3459

Test Results:
success.zip

Added NotificationLink.java file based on 2.5.1 spec ,as it structure not defined   in 3.5.1 spec

Hi @hhellyer , @owen-lynch-ibm , Could you please review this PR.

Changes are done in script files as per 3.5.1 version

@hhellyer The driver cannot be backward compatible for 2.4.1. To make it compatible the driver will need to be built and installed separately after doing few changes in code.

Hi,
@hhellyer @owen-lynch-ibm , Could you please review this PR as we need to release the driver for EKS.
Thanks,
Loka


text:
is there and option to set display = *off so we can interact with the 3270 terminal in batch mode.

Hello, wondering if this has a feature interact in batch mode.

found the answer. Thank you.
Emulator(visible=True, args=["-trace",
"-tracefile",
"example.log"])

I found the answer. Thank you for the software.

repo: IBM/tnz


text:
problem with installation

Hello,
I followed your setup instructions and installed AMR parser from pip. I checked installation with the python tests/correctly_installed.py and it showed all Ok.
But nevertheless I have two issues:

When I run bash preprocess/install_alignment_tools.sh I get this error:
Error: Could not retrieve sbt 0.13.5`

When I run bash tests/minimal_test.sh it starts loading my GPU (NVidia Titan RTX) and never stops. In your instructions it is noted that it should not run more than a minute...

Maybe I'm still missing some libraries or my python version is incompatible?

Hi @PolKul I would focus on troubleshooting 2 first since the minimal test uses already aligned data. The alignment tools depend on a number of external modules and its harder to find the cause.
Regarding 2. If the code freezes as soon as you move anything to the GPU it is likely an installation problem.

Hi @ramon-astudillo, thanks for the answer. For the problem 2, let me post the full log here:
`(venv1) pavel@pavel-TRX40-DESIGNARE:~/work/nlp/transition-amr-parser$ bash tests/minimal_test.sh
[normalize rules] months
[normalize rules] units
[normalize rules] cardinals
[normalize rules] ordinals
Read DATA/wiki25.jkaln
25 sentences
216/293 node types/tokens
35/285 edge types/tokens
241/383 word types/tokens
AMR contains 4 duplicate edges
{'ARG1': 4}
2021-05-04 12:04:04 [amr] Processing oracle
2021-05-04 12:04:04 [oracle] Parsing data
computing oracle: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:00<00:00, 211.38it/s]
2021-05-04 12:04:04 [oracle] Done
Not whitelisted actions used e.g. arcs for unconfirmed words
Counter({'LA': 2})
Blacklisted actions used e.g. duplicated edges
Counter({'RA': 1})
There were 18 disconnected nodes (:rel)
2021-05-04 12:04:04 [Totals:] 0.61
2021-05-04 12:04:04 [Totals:] Failed Entity Predictions:
Namespace(alignfile=None, batch_normalize_reward=False, bert_layers=None, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='DATA.tests/features/wiki25.jkaln/', entity_rules='DATA.tests/oracles/wiki25.jkaln//entity_rules.json', fp16=False, fp16_init_scale=128, fp16_scale_tolerance=0.0, fp16_scale_window=None, gold_annotations=None, gold_episode_ratio=None, joined_dictionary=False, log_format=None, log_interval=1000, lr_scheduler='fixed', machine_rules='DATA.tests/oracles/wiki25.jkaln//train.rules.json', machine_type='AMR', memory_efficient_fp16=False, min_loss_scale=0.0001, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, only_source=False, optimizer='nag', padding_factor=8, pretrained_embed='roberta.base', seed=1, source_lang='en', srcdict=None, target_lang='actions', task='translation', tbmf_wrapper=False, tensorboard_logdir='', testpref='DATA.tests/oracles/wiki25.jkaln//test', tgtdict=None, threshold_loss_scale=None, thresholdsrc=0, thresholdtgt=0, tokenize_by_whitespace=False, tokenizer=None, trainpref='DATA.tests/oracles/wiki25.jkaln//train', user_dir=None, validpref='DATA.tests/oracles/wiki25.jkaln//dev', workers=1)
| [en] Dictionary: 247 types
| [en] DATA.tests/oracles/wiki25.jkaln//train.en: 25 sents, 408 tokens, 0.0% replaced by
| [en] Dictionary: 247 types
| [en] DATA.tests/oracles/wiki25.jkaln//dev.en: 25 sents, 408 tokens, 0.0% replaced by
| [en] Dictionary: 247 types
| [en] DATA.tests/oracles/wiki25.jkaln//test.en: 25 sents, 408 tokens, 0.0% replaced by
| [actions] Dictionary: 127 types
| [actions] DATA.tests/oracles/wiki25.jkaln//train.actions: 25 sents, 1327 tokens, 0.0% replaced by
| [actions] Dictionary: 127 types
| [actions] DATA.tests/oracles/wiki25.jkaln//dev.actions: 25 sents, 1327 tokens, 0.0% replaced by
| [actions] Dictionary: 127 types
| [actions] DATA.tests/oracles/wiki25.jkaln//test.actions: 25 sents, 1327 tokens, 0.0% replaced by
Using cache found in /home/pavel/.cache/torch/hub/pytorch_fairseq_master
Unable to build Cython components. Please make sure Cython is installed if the torch.hub model you are loading depends on it.
loading archive file http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz from cache at /home/pavel/.cache/torch/pytorch_fairseq/37d2bc14cf6332d61ed5abeb579948e6054e46cc724c7d23426382d11a31b2d6.ae5852b4abc6bf762e0b6b30f19e741aa05562471e9eb8f4a6ae261f04f9b350
| dictionary: 50264 types
Using roberta.base extraction in GPU
25it [00:00, 199.25it/s]
There were missing actions
Counter({'LA(op1)': 1, 'LA(domain)': 1, 'RA(ARG1)': 1})
Using cache found in /home/pavel/.cache/torch/hub/pytorch_fairseq_master
Unable to build Cython components. Please make sure Cython is installed if the torch.hub model you are loading depends on it.
loading archive file http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz from cache at /home/pavel/.cache/torch/pytorch_fairseq/37d2bc14cf6332d61ed5abeb579948e6054e46cc724c7d23426382d11a31b2d6.ae5852b4abc6bf762e0b6b30f19e741aa05562471e9eb8f4a6ae261f04f9b350
| dictionary: 50264 types
Using roberta.base extraction in GPU
25it [00:00, 198.63it/s]
There were missing actions
Counter({'LA(op1)': 1, 'LA(domain)': 1, 'RA(ARG1)': 1})
Using cache found in /home/pavel/.cache/torch/hub/pytorch_fairseq_master
Unable to build Cython components. Please make sure Cython is installed if the torch.hub model you are loading depends on it.
loading archive file http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz from cache at /home/pavel/.cache/torch/pytorch_fairseq/37d2bc14cf6332d61ed5abeb579948e6054e46cc724c7d23426382d11a31b2d6.ae5852b4abc6bf762e0b6b30f19e741aa05562471e9eb8f4a6ae261f04f9b350
| dictionary: 50264 types
Using roberta.base extraction in GPU
25it [00:00, 197.31it/s]
There were missing actions
Counter({'LA(op1)': 1, 'LA(domain)': 1, 'RA(ARG1)': 1})
| Wrote preprocessed data to DATA.tests/features/wiki25.jkaln/
| distributed init (rank 1): tcp://localhost:18693
| distributed init (rank 0): tcp://localhost:18693
| initialized host pavel-TRX40-DESIGNARE as rank 1
| initialized host pavel-TRX40-DESIGNARE as rank 0
Namespace(activation_dropout=0.0, activation_fn='relu', adam_betas='(0.9,0.98)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, arch='stack_transformer_6x6_nopos', attention_dropout=0.0, bert_backprop=False, best_checkpoint_metric='loss', bpe=None, bucket_cap_mb=25, burnthrough=5, clip_norm=0.0, cpu=False, criterion='label_smoothed_cross_entropy', curriculum=0, data='DATA.tests/features/wiki25.jkaln/', dataset_impl=None, ddp_backend='c10d', decoder_attention_heads=4, decoder_embed_dim=256, decoder_embed_path=None, decoder_ffn_embed_dim=512, decoder_input_dim=256, decoder_layers=6, decoder_learned_pos=False, decoder_normalize_before=False, decoder_output_dim=256, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method='tcp://localhost:18693', distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=2, dropout=0.0, encode_state_machine='all-layers_nopos', encoder_attention_heads=4, encoder_embed_dim=256, encoder_embed_path=None, encoder_ffn_embed_dim=512, encoder_layers=6, encoder_learned_pos=False, encoder_normalize_before=False, find_unused_parameters=False, fix_batches_to_gpus=False, fp16=False, fp16_init_scale=128, fp16_scale_tolerance=0.0, fp16_scale_window=None, keep_interval_updates=-1, keep_last_epochs=1, label_smoothing=0.01, lazy_load=False, left_pad_source='True', left_pad_target='False', log_format='json', log_interval=1000, lr=[0.025], lr_scheduler='inverse_sqrt', max_epoch=10, max_sentences=None, max_sentences_valid=None, max_source_positions=1024, max_target_positions=1024, max_tokens=3584, max_tokens_valid=3584, max_update=0, maximize_best_checkpoint_metric=False, memory_efficient_fp16=False, min_loss_scale=0.0001, min_lr=1e-09, no_bert_precompute=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_progress_bar=False, no_save=False, no_save_optimizer_state=False, no_token_positional_embeddings=False, num_workers=1, optimizer='adam', optimizer_overrides='{}', pretrained_embed_dim=768, raw_text=False, required_batch_size_multiple=8, reset_dataloader=False, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, restore_file='checkpoint_last.pt', save_dir='DATA.tests/models/wiki25.jkaln/', save_interval=1, save_interval_updates=0, seed=42, sentence_avg=False, share_all_embeddings=False, share_decoder_input_output_embed=False, skip_invalid_size_inputs_valid_test=False, source_lang=None, target_lang=None, task='translation', tbmf_wrapper=False, tensorboard_logdir='', threshold_loss_scale=None, tokenizer=None, train_subset='train', update_freq=[1], upsample_primary=1, use_bmuf=False, user_dir=None, valid_subset='valid', validate_interval=1, warmup_init_lr=1e-07, warmup_updates=1, weight_decay=0.0)
| [en] dictionary: 248 types
| [actions] dictionary: 128 types
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.en
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.en.bert
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.en.wordpieces
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.en.wp2w
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.actions
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.memory
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.memory_pos
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.target_masks
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/valid.en-actions.active_logits
| DATA.tests/features/wiki25.jkaln/ valid en-actions 25 examples
TransformerModel(
(encoder): TransformerEncoder(
(subspace): Linear(in_features=768, out_features=256, bias=False)
(embed_tokens): Embedding(248, 256, padding_idx=1)
(embed_positions): SinusoidalPositionalEmbedding()
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(1): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(2): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(3): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(4): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(5): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
)
)
(decoder): TransformerDecoder(
(embed_tokens): Embedding(128, 256, padding_idx=1)
(embed_stack_positions): SinusoidalPositionalEmbedding()
(embed_positions): SinusoidalPositionalEmbedding()
(layers): ModuleList(
(0): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(1): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(2): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(3): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(4): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
(5): TransformerDecoderLayer(
(self_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(self_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(encoder_attn): MultiheadAttention(
(out_proj): Linear(in_features=256, out_features=256, bias=True)
)
(encoder_attn_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=256, out_features=512, bias=True)
(fc2): Linear(in_features=512, out_features=256, bias=True)
(final_layer_norm): LayerNorm(torch.Size([256]), eps=1e-05, elementwise_affine=True)
)
)
)
)
| model stack_transformer_6x6_nopos, criterion LabelSmoothedCrossEntropyCriterion
| num. model params: 8232960 (num. trained: 8232960)
| training on 2 GPUs
| max tokens per GPU = 3584 and max sentences per GPU = None
| no existing checkpoint found DATA.tests/models/wiki25.jkaln/checkpoint_last.pt
| loading train data for epoch 0
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.en
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.en.bert
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.en.wordpieces
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.en.wp2w
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.actions
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.memory
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.memory_pos
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.target_masks
| loaded 25 examples from: DATA.tests/features/wiki25.jkaln/train.en-actions.active_logits
| DATA.tests/features/wiki25.jkaln/ train en-actions 25 examples
| NOTICE: your device may support faster training with --fp16
/usr/lib/python3.7/multiprocessing/semaphore_tracker.py:144: UserWarning: semaphore_tracker: There appear to be 1 leaked semaphores to clean up at shutdown
len(cache))
`

I was able to run the test after modifying the overfit.sh and adding --distributed-world-size 1 to the fairseq-train. So it looks like a problem with multi-gpu training.
Anyway, now I'm trying to install the allignment tools with the bash preprocess/install_alignment_tools.sh but I get Error: Could not retrieve sbt 0.13.5 Can you please help me with that?

Are you able to pinpoint which part of the installation process yields that error here?.
https://github.com/IBM/transition-amr-parser/blob/master/preprocess/install_alignment_tools.sh#L10
Is it the JAMR aligner, Kevin aligner? I advise you to run those by separate to localize the error.

It stops at JAMP aligner. Let me post the full log here:

pavel@pavel-TRX40-DESIGNARE:~/work/nlp/transition-amr-parser$ bash preprocess/install_alignment_tools.sh

Downloading JAMR

Cloning into 'jamr'...
remote: Enumerating objects: 6768, done.
remote: Total 6768 (delta 0), reused 0 (delta 0), pack-reused 6768
Receiving objects: 100% (6768/6768), 2.51 MiB | 1.37 MiB/s, done.
Resolving deltas: 100% (4357/4357), done.
Already on 'Semeval-2016'
Your branch is up to date with 'origin/Semeval-2016'.
OpenJDK 64-Bit Server VM warning: ignoring option MaxPermSize=256m; support was removed in 8.0
Getting org.scala-sbt sbt 0.13.5 ...

:: problems summary ::
:::: WARNINGS
		module not found: org.scala-sbt#sbt;0.13.5

	==== local: tried

	  /home/pavel/.ivy2/local/org.scala-sbt/sbt/0.13.5/ivys/ivy.xml

	  -- artifact org.scala-sbt#sbt;0.13.5!sbt.jar:

	  /home/pavel/.ivy2/local/org.scala-sbt/sbt/0.13.5/jars/sbt.jar

	==== typesafe-ivy-releases: tried

	  http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt/0.13.5/ivys/ivy.xml

	==== Maven Central: tried

	  http://repo1.maven.org/maven2/org/scala-sbt/sbt/0.13.5/sbt-0.13.5.pom

	  -- artifact org.scala-sbt#sbt;0.13.5!sbt.jar:

	  http://repo1.maven.org/maven2/org/scala-sbt/sbt/0.13.5/sbt-0.13.5.jar

		::::::::::::::::::::::::::::::::::::::::::::::

		::          UNRESOLVED DEPENDENCIES         ::

		::::::::::::::::::::::::::::::::::::::::::::::

		:: org.scala-sbt#sbt;0.13.5: not found

		::::::::::::::::::::::::::::::::::::::::::::::

:::: ERRORS
	Server access Error: Connection refused (Connection refused) url=http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt/0.13.5/ivys/ivy.xml

	SERVER ERROR: HTTPS Required url=http://repo1.maven.org/maven2/org/scala-sbt/sbt/0.13.5/sbt-0.13.5.pom

	SERVER ERROR: HTTPS Required url=http://repo1.maven.org/maven2/org/scala-sbt/sbt/0.13.5/sbt-0.13.5.jar

:: USE VERBOSE OR DEBUG MESSAGE LEVEL FOR MORE DETAILS
unresolved dependency: org.scala-sbt#sbt;0.13.5: not found
Error during sbt execution: Error retrieving required libraries
  (see /home/pavel/.sbt/boot/update.log for complete log)
Error: Could not retrieve sbt 0.13.5

This seems related to the installing of scala which is a requirement of the JAMR aligner https://github.com/jflanigan/jamr
I have not encountered that error, but it seems to complain from inability to access a particular URL of the scala repo (see Server access Error: Connection refused above). I found some indication in SO it may be related to certificate issues in the machine where this is installed, see
https://stackoverflow.com/questions/27002423/sbt-build-failed-module-not-found-org-scala-sbtsbt0-13-5

well, I'm not experienced with Scala or sbt. But it looks like an old version of sbt (0.13.5) is used for the JAMR project. Here is a similar issue discussed on SO, for example https://stackoverflow.com/questions/59763435/access-maven-repo-over-https-in-sbt
it says that "the issue was that I was using sbt version 0.13.5. And DefaultMavenRepository started pointing to https endpoint from v0.13.6"
And in the logs it is really pointing to the http addresses which are not valid on the SBT site. As pointed out, they use HTTPS since version 0.13.6.
Is it possible to recompile the repository with the latest version of Scala/SBT?

Unfortunately this is a problem in JAMR and not our model and I know little about it, maybe you can reach the JAMR authors in the repo (linked above). They may have encountered this problem and have a fix.

Given other related open issues, I assume this solved

Hello, I followed your setup instructions and installed AMR parser from pip. I checked installation with the python tests/correctly_installed.py and it showed all Ok.
But nevertheless I have two issues:

When I run bash preprocess/install_alignment_tools.sh I get this error:
Error: Could not retrieve sbt 0.13.5`
When I run bash tests/minimal_test.sh it starts loading my GPU (NVidia Titan RTX) and never stops. In your instructions it is noted that it should not run more than a minute...

Maybe I'm still missing some libraries or my python version is incompatible?

Hi there, I ran into the same issue, have you ever solved that?

authors: HoraceXIaoyiBao PolKul ramon-astudillo

repo: IBM/transition-amr-parser


text:
a bug about hidden_dim

According to the code, in DINO, hidden_dim is not only the number of neurals in MLP, but also the length of tokens and querys，but when I try to change the hidden_dim to 64, there is a error in deformable_transformer.py

I found the function .utils.gen_sineembed_for_position return has a fixed shape[-1], which is 512, twice hidden_dim, so I make it adapt to hidden_dim, and it can be trained.
By the way, will decreasing hidden_dim affect the model performance greatly?

It will affect the performance,  we have tried hidden dim 256 and 128, and found 128 will decrease the performance by around 0.5 AP. We are not sure what will happen when further set it to 64.

Thank you for your reply.

authors: FengLi-ust TengQinglong

repo: IDEACVR/DINO


text:
fantasic project, how about changing the open source license?

I notice this project is GPL-3.0 license, however the referenced project PyTorch-GAN is MIT project?
My question is, is it possible to change the open source license from GPL-3.0 into MIT?
Thanks.

Thank you for your advice, it has been done.

repo: IDKiro/MCT


text:
🛑 IDR (well 547609) is down

In e8e05d7, IDR (well 547609) (https://idr.openmicroscopy.org/webclient/?show=well-547609) was down:

HTTP code: 502
Response time: 83 ms

Resolved: IDR (well 547609) is back up in a446345 after 20 minutes.

repo: IDR/upptime


text:
[Bug] Add task not working

Describe the bug
The "Add Task" functionality is not working as expected. When the user attempts to add a task, either nothing happens or an error message is displayed. This prevents users from creating new tasks in the app.
To Reproduce
Steps to reproduce the behavior:
Open the app.
Click on the "Add Task Floating" button.
Fill in the task details (e.g., title, due date).
Click on the "Save" button to add the task.
See error: The task is not added, and no confirmation or success message is shown.

Expected behavior
The task should be added to the list, and the user should see a success message or be redirected to the task list page with the newly created task visible. If there is an issue, an appropriate error message should be displayed.
Screenshots
Smartphone
Device: Iphone 13 Pro Max
OS: iOS 16.1

Additional context
This issue may be related to a problem with saving the task data or issues with the button functionality. It might also be caused by incorrect API calls or a failure in the task saving logic.

How should the task list screen be managed from Home Screen, what should be shown on Home Screen to navigate to the list screen?

authors: pranavkonidena sommye-ctr

repo: IMGIITRoorkee/Taskly


text:
Some WinNT DirectoryEntry objects converted to string by ConvertTo-SimpleProperty

Caused incomplete output from Expand-AccountPermission, without directoryentry objects it was impossible to distinguish users from empty groups (groups with members are obviously groups).
Root cause was missing GetType() method on these DirectoryEntry objects, causing ConvertTo-SimpleProperty to treat them as generic properties and cast them as string.

Fixed in 0.0.152

repo: IMJLA/Export-Permission


text:
🛑 Paisajista is down

In fc5e829, Paisajista (https://paisajistapicanteria.com) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Paisajista is back up in aee18f0.

repo: INNARSSAC/innarsupptime


text:
Analysis of data is aborted without any indication why

Problem: 2021 - new uploads (['KOU', 'PPT'])
KOU is copied to temporary but then:
....
Copying kou_20211214_000000_PT1S_4.cdf to temporary folder /srv/imbot/second/2021/tmp/2021/raw/KOU/kou_20211214_000000_PT1S_4.cdf
=> ONE-SECOND DATA ANALYSIS SUCCESSFULLY FINISHED
Analysis performed
GIN unmounted

It is likely that the server is not capable of running multiple analysis in parallel. Workaround: only analyze data sets from two years within one day.

repo: INTERMAGNET/IMBOT


text:
🛑 IOGT HOSTING is down

In a6ffd1a, IOGT HOSTING ($IOGT_CPANEL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: IOGT HOSTING is back up in 0cf807b after 12 minutes.

repo: IOGT/upptime


text:
Add ChipSelect component

Migrate the chip select component we currently have in one of our apps over to this library and update apps to use it.

Syntax should be identical to the existing Select component in this library.

I think it would be best to also update the current component to have checkboxes in the selector, especially for multi-select as it's not immediately obvious what is currently selected.

I think it would be best to also update the current component to have checkboxes in the selector, especially for multi-select as it's not immediately obvious what is currently selected.

repo: IPG-Automotive-UK/react-ui


text:
A new article on MEP by Simon b

Please consider this article

I fixed the conflict

I was going to fix the conflicts and merge this, when I realized that your repository does not exist anymore 😞

authors: claudiodsf simonbiens

repo: IPGP/newspaper


text:
440 update GitHub actions

What this PR does / why we need it: Updates Github Action triggers for Chromatic and test builds
Which issue(s) this PR closes:
Closes #440
Special notes for your reviewer:
Suggestions on how to test this:
Does this PR introduce a user interface change? If mockups are available, please link/include them here:
Is there a release notes update needed for this change?:
Additional documentation:

coverage: 97.774%. remained the same
when pulling ccb54138c4c08be728c9d76f622435117daa5911 on 319-update-github-actions
into 6bc2261756a466f857cf5507d0c19e03800a9eff on develop.

Closing this to trigger the PR reopen event

Re-opening to test that github actions will run

authors: coveralls ekraffmiller

repo: IQSS/dataverse-frontend


text:
EN background check

Implements the EN background check #56. NB. it requires a data file to operate. I need to work out how to distributed the data file. If it is not present the test is effectively ignored.
This also includes an attempt to solve #62.

I am merging this straight away as it includes code changes that will be useful in the coding sprint. More work is required to add code tests and to make the auxiliary data file available.

repo: IQuOD/AutoQC


text:
double drop

i managed to drop out twice, tp ing me to y -1.500.500, 7 million kilometers down
dont know how exactly

warping again took me back to rsp and warp.


text:
test

test

I don't have pull


text:
🐛 fix searchfile bug and filter tests for old clients

What do these changes do?

Related issue/s

fixes #217

How to test

For internal developers

@pcrespov I ran the e2e tests against master with these changes and checked that they work...

repo: ITISFoundation/osparc-simcore-clients


text:
More test cases needed for the ITensorNetworks module

Currently the ITensorNetworks module only has 3 tests, two for the projectors and 1 for the model.
We need to add more unit tests, for both testing the function robustness and for demonstrating the API/purpose of each function.

Agreed it could use more testing.
A lot of the functionality was exploratory as I was trying out different designs and contraction strategies. It has been cleaned up, reorganized and simplified in #16 so should be easier to understand and test now.

authors: LinjianMa mtfishman


text:
[ITensorGPU] [BUG] DMRG with quantum number conservation not working

Description of bug
I tried to run the code from https://itensor.github.io/ITensors.jl/dev/tutorials/QN_DMRG.html on a GPU using ITensorGPU.jl and encountered an error where the dimensions of the dims of the tensors in the Hamiltonian did not coincide with the arrays inside the Hamiltonian itself.
(Click me) Minimal runnable code
julia> using ITensors, ITensorGPU

julia> let
         N = 100
         sites = siteinds("S=1",N;conserve_qns=true)

         ampo = OpSum()
         for j=1:N-1
           ampo += "Sz",j,"Sz",j+1
           ampo += 1/2,"S+",j,"S-",j+1
           ampo += 1/2,"S-",j,"S+",j+1
         end
         H = MPO(ampo,sites)

         state = [isodd(n) ? "Up" : "Dn" for n=1:N]
         psi0 = productMPS(sites,state)

         sweeps = Sweeps(5)
         setmaxdim!(sweeps, 10,20,100,100,200)
         setcutoff!(sweeps, 1E-10)

         energy, psi = dmrg(cu(H),cu(psi0), sweeps) # Note the use of cu here

         return energy
         end

Expected output or behavior
I expected this to produce the same answer as the CPU version
Actual output or behavior
It errored.
Output of minimal runnable code
flux(psi0) = QN("Sz",0)
ERROR: DimensionMismatch("new dimensions (5, 3, 3) must be consistent with array size (13,)")
Stacktrace:
  [1] reshape(a::CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, dims::Tuple{Int64, Int64, Int64})
    @ CUDA ~/.julia/packages/CUDA/5jdFl/src/array.jl:665
  [2] reshape
    @ ./reshapedarray.jl:116 [inlined]
  [3] permute!(B::NDTensors.DenseTensor{Float64, 3, Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}, NDTensors.Dense{Float64, CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}}, A::NDTensors.DenseTensor{Float64, 3, Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}, NDTensors.Dense{Float64, CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}})
    @ ITensorGPU ~/.julia/dev/ITensors/ITensorGPU/src/tensor/cudense.jl:532
  [4] #1
    @ ~/.julia/dev/ITensors/ITensorGPU/src/tensor/cudense.jl:61 [inlined]
  [5] permutedims!!
    @ ~/.julia/dev/ITensors/ITensorGPU/src/tensor/cudense.jl:65 [inlined]
  [6] permutedims!!
    @ ~/.julia/dev/ITensors/ITensorGPU/src/tensor/cudense.jl:63 [inlined]
  [7] permutedims
    @ ~/.julia/dev/ITensors/NDTensors/src/dense.jl:438 [inlined]
  [8] permutedims
    @ ~/.julia/dev/ITensors/src/itensor.jl:1686 [inlined]
  [9] _permute(as::NDTensors.NeverAlias, T::NDTensors.DenseTensor{Float64, 3, Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}, NDTensors.Dense{Float64, CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}}, new_inds::Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}})
    @ ITensors ~/.julia/dev/ITensors/src/itensor.jl:1691
 [10] permute(as::NDTensors.NeverAlias, T::ITensor, new_inds::Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}})
    @ ITensors ~/.julia/dev/ITensors/src/itensor.jl:1695
 [11] #permute#219
    @ ~/.julia/dev/ITensors/src/itensor.jl:1676 [inlined]
 [12] permute(T::ITensor, new_inds::Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}})
    @ ITensors ~/.julia/dev/ITensors/src/itensor.jl:1661
 [13] permute(M::MPO, #unused#::Tuple{typeof(linkind), typeof(siteinds), typeof(linkind)})
    @ ITensors ~/.julia/dev/ITensors/src/mps/dmrg.jl:14
 [14] #dmrg#954
    @ ~/.julia/dev/ITensors/src/mps/dmrg.jl:45 [inlined]
 [15] dmrg(H::MPO, psi0::MPS, sweeps::Sweeps)
    @ ITensors ~/.julia/dev/ITensors/src/mps/dmrg.jl:41
 [16] top-level scope
    @ REPL[3]:21
 [17] top-level scope
    @ ~/.julia/packages/CUDA/5jdFl/src/initialization.jl:52

Version information

Output from versioninfo():

julia> versioninfo()
Julia Version 1.7.0-rc2
Commit f23fc0d27a (2021-10-20 12:45 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: AMD Ryzen Threadripper 2990WX 32-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-12.0.1 (ORCJIT, znver1)
Environment:
  JULIA_NUM_THREADS = 26

Output from using Pkg; Pkg.status("ITensors"):

(@v1.7) pkg> st ITensors
      Status `~/.julia/environments/v1.7/Project.toml`
  [9136182c] ITensors v0.3.10 `~/.julia/dev/ITensors`

(@v1.7) pkg> st ITensorGPU
      Status `~/.julia/environments/v1.7/Project.toml`
  [d89171c1] ITensorGPU v0.0.5 `~/.julia/dev/ITensors/ITensorGPU`

(@v1.7) pkg> st NDTensors
      Status `~/.julia/environments/v1.7/Project.toml`
  [23ae76d9] NDTensors v0.1.37 `~/.julia/dev/ITensors/NDTensors`

Block sparse operations are not defined on GPU right now, I'll leave this open for others to find.

authors: MasonProtter mtfishman


text:
Think about the names of sparse storage types/tensors

It may be good to rethink the names of the sparse storage tensors and constructors. Right now there is:

Dense
Diag
BlockSparse
DiagBlockSparse

DiagBlockSparse may not be a good name, however. I think it is a bit misleading, because naively one might expect that it has a general block sparse structure (where any blocks may be nonzero), where the blocks are diagonal. However, it is actually a block diagonal tensor (the only nonzero blocks are along the diagonal), where the nonzero blocks themselves are diagonal. Therefore, it may be better to call it DiagBlockDiag. This looks like a strange name, but it is anticipating that we may want to have a BlockDiag storage where the diagonal blocks are dense (many block diagonal matrices already show up in the code, for example as inputs to eigen).
Additionally, we may want to rename these constructors:

https://github.com/ITensor/NDTensors.jl/blob/master/src/diag.jl#L19
https://github.com/ITensor/NDTensors.jl/blob/master/src/blocksparse/diagblocksparse.jl#L19

to explicitly use UniformDiag instead of Diag, so it is clearer that a uniform Diag storage is being made.

I agree: good to think about naming conventions that will "scale" to future designs we want to have.

authors: emstoudenmire mtfishman


text:
Integration of the xmlrpc service in Angular 2

It would be really helpful if you can update the documentation on how to integrate the xmlrpc service with Angular 2.

Hi @Harish-Ramamurthy
Sorry for the delay of this anwer.
The service has been design to work on Angular 1 but I will study that idea, using upgrade functionnality of Angular2.
I will update the doc if the result is acceptable. :)

I spent the whole week going through this upgrade module of Angular 2. I feel if you could use services rather than factories and also split each service into its own file it will be a lot easier to upgrade them.
After that you can start using classes and export them.
Hope it helps.

Rewrite this using classes sounds good and will upgrade that old code.

Any updates on the xmlrpc service for angular 2 ??

Hi @Harish-Ramamurthy!
Sorry for the lack of news in these last days.
I have just pushed a total rewrite of the lib in the branch ts-migration. The lib is now written in TypeScript, the tests have been migrated,  and the compatibility with AngularJS v1 is ok (it's now a service instead of a factory).
I have not tested yet the Angular2 compatibility, but I think it will be a lot easier. I will work on it next, and I will merge the branch when everything will be ok!

Cool stuff @jchakra. I will have at look at it. Hopeful that you will test the angular2 compatibility soon and merge the branch with an updated documentation.

Any updates on the testing part ?? Is the xmlrpc service compatible with Angular 2 ??

@jchakra are you going to release this version?

Is this project going to be touched with life again? Currently this is really a showstopper for me. Currently we are at Angular 10 and I can't use that currently...

Is this project going to be touched with life again? Currently this is really a showstopper for me. Currently we are at Angular 10 and I can't use that currently...

I would be willing to contribute if that is something which is missing. I do need an angular-xmlrpc interface and this repo is apparently the most prominent when Googling Angular and XMLRPC...

I would be willing to contribute if that is something which is missing. I do need an angular-xmlrpc interface and this repo is apparently the most prominent when Googling Angular and XMLRPC...

Hi just wanted to share I ported this library to Angular2+ (still experimental):
https://github.com/francescogabbrielli/angular2-xmlrpc
IE support has been dropped to simplify things

Hi @francescogabbrielli ,
Looked into your library and i was wondering , if there is any way to add additional headers while calling xml rpc method, right now it only accepts method name and params as an argument.

Hi Rohit,
you are right. What do you think about adding a headers field of type HttpHeaders? so you do something like:
this.xmlrpc.headers.set('Your-Header', 'Value')

authors: Harish-Ramamurthy RohitKumar09 SchoolGuy francescogabbrielli jchakra pirasalbe

repo: ITrust/angular-xmlrpc


text:
Questions about BotometerLite

I purchased BotometerLite, it can return botscore, tweet_id and user_id. My problem is that my twitter data only has user_name but no user_id, and in BotometerLite batch analysis, accounts that have been cancelled will be automatically skipped, so I have no way to match the robot analysis result with user_name. How can I add user_name to the returned result?

Unfortunately it's not possible to get the scores for users that have been deleted/suspended.

Unfortunately it's not possible to get the scores for users that have been deleted/suspended.

repo: IUNetSci/botometer-python


text:
🛑 Website is down

In 519c6b1, Website (https://pt.ivao.aero/portal) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Website is back up in c3e7836.

repo: IVAO-Portugal/status-page


text:
Wercker pipeline

Create a wercker pipeline to build the apps and run tests.

I'll use cloud builder instead.

repo: IanLewis/homepage


text:
global listeners, except text inputs

At the moment I'm using my own method for listening on key events globally. This interferes with text inputs where I pause and resume my global listener on their focus and blur events. I do this manually. Can your plugin help me with that? That this will ignore key events on text inputs, when global once are registered?

@gossi we don't currently expose an option to disable/enable shortcuts based on whether or not there is a focused textarea or input. We manage this on a one-off basis per shortcut by checking document.activeElement.
I'd be interested in adding this as a feature. Would you find it more useful to be able to globally disable shortcuts any time an input has focus? Or would you want to be able to granularly disable per shortcut?

Cool! For me particular, a global option would be nice. However, since this is a general purpose plugin, both options should be available, you never know how one will be using your plugin.

Gotcha. Thanks for the info!

Closing this as we have merged the updates with the global config and register flag to disable when an input element is active.

@gossi please let me know if the new option works for you.

I just implemented it and it's a bless. I love you guys! Awesome job.

authors: gossi jordpo patrickberkeley

repo: IcarusWorks/ember-key-manager


text:
Detect Azure's traffic manager subdomains (trafficmanager.net)

Traffic Manager lets you route traffic within Azure. Using cnames to xxx.trafficmanager.net it's sometimes possible to takeover such routing. Portal link: https://portal.azure.com/#create/Microsoft.WebSite
More info: https://azure.microsoft.com/en-us/services/traffic-manager/

In addition to Traffic Manager profiles Cloud Services (.cloudapp.net) and Blob Storages (.blob.core.windows.net) are also vulnerable to Subdomain Takeovers

Sorry for the late response, actually the public project is discontinued. Please check https://github.com/haccer/subjack. I am not maintaining this at the moment.

authors: Ice3man543 alisalmanbilal menzow


text:
Error executing action enable on resource 'icinga2_feature[command]'

Getting this error running chef-client. My setup includes:
chef-icinga2 = 2.9.1
ruby = 2.4.0
yum[icinga2] =   2.6.0-1.el6
yum[icingacli] =  2.4.0-2.el6
icingaweb2 = 2.4.0
This is the exception:
* icinga2_feature[command] action enable

    Error executing action `enable` on resource 'icinga2_feature[command]'

    RuntimeError
    ------------
    feature not available - command

    Cookbook Trace:
    ---------------
    /var/chef/cache/cookbooks/icinga2/libraries/resource_feature.rb:34:in `block in <class:Icinga2Feature>'
    /var/chef/cache/cookbooks/compat_resource/files/lib/chef_compat/monkeypatches/chef/runner.rb:78:in `run_action'
    /var/chef/cache/cookbooks/compat_resource/files/lib/chef_compat/monkeypatches/chef/runner.rb:106:in `block (2 levels) in converge'
    /var/chef/cache/cookbooks/compat_resource/files/lib/chef_compat/monkeypatches/chef/runner.rb:106:in `each'
    /var/chef/cache/cookbooks/compat_resource/files/lib/chef_compat/monkeypatches/chef/runner.rb:106:in `block in converge'
    /var/chef/cache/cookbooks/compat_resource/files/lib/chef_compat/monkeypatches/chef/runner.rb:105:in `converge'

    Resource Declaration:
    ---------------------
    # In /var/chef/cache/cookbooks/icinga2/recipes/server_web2.rb

     95: icinga2_feature 'command'

    Compiled Resource:
    ------------------
    # Declared in /var/chef/cache/cookbooks/icinga2/recipes/server_web2.rb:95:in `from_file'

    icinga2_feature("command") do
      provider Chef::Provider::Icinga2Feature
      action :enable
      retries 0
      retry_delay 2
      default_guard_interpreter :default
      provides :icinga2_feature
      declared_type :icinga2_feature
      cookbook_name "icinga2"
      recipe_name "server_web2"
    end

Running handlers:
[2017-01-18T16:10:01+00:00] ERROR: Running exception handlers
Running handlers complete
[2017-01-18T16:10:01+00:00] ERROR: Exception handlers complete
Chef Client failed. 13 resources updated in 32 seconds
[2017-01-18T16:10:01+00:00] FATAL: Stacktrace dumped to /var/chef/cache/chef-stacktrace.out
[2017-01-18T16:10:01+00:00] FATAL: Please provide the contents of the stacktrace.out file if you file a bug report
[2017-01-18T16:10:01+00:00] ERROR: icinga2_feature[command] (icinga2::server_web2 line 95) had an error: RuntimeError: feature not available - command
[2017-01-18T16:10:02+00:00] FATAL: Chef::Exceptions::ChildConvergeError: Chef run process exited unsuccessfully (exit code 1)

weird, this should not fail.
I do not suspect it is caused by ruby version, chef-client uses embedded ruby.

I also tried a diff version, same result.

@vkhatri do you have any ideas or something I can look into? We're running a big monitoring solution that I'm trying to upload cookbooks to and we're a bit under the water.
Any help would be appreciated.

@vkhatri any timeframe on this? If there's any way I can rollback to a previous version I would that as well. We were on 2.6.5. If I go back to that, I will get the wrong GIT repo issue. So, kind of stuck at this point. If this gets escalated we might drop this solution.

Patching the old cookbook you've installed and update to the current git repo isn't an option? (at least I would apply such a workaround if it is urgent)

Thanks @dnsmichi that's exactly what we did this morning. It's a hack, but it fixes the issue. We'll try this back again when the fix is pushed to the marketplace.

@luijar Could you share the OS details, especially the platform version.
I ran kitchen tests and did not see any issue.
Also, could you please share the output of below command?
# icinga2 feature list

# ls /etc/icinga2/features-available

for some reason, resource icinga2_feature[command] is unable to find the command.conf under /etc/icinga2/features-available

Thanks @vkhatri Those instructions helped me resolve my issue. In fact the features were all missing for some reason. I don't know what that is, but I suspect it's our error somehow. I was able to SCP the features from our second Icinga master server and things are back to normal. Again, I don't know if this is our mistake or something else. Here's the info you requested anyways, perhaps it's an issue in the chef-icinga scripts:
Before the fix, running icinga2 feature list:
Disabled features:
Enabled features: ido-mysql

the /etc/icinga2/features-available only had ido-mysql

@luijar Glad to see you got it working. Cookbook does not create/delete features configuration files directly, it relies on /usr/sbin/icinga2 feature [enable|disable].

authors: dnsmichi luijar vkhatri


text:
the file can not open ,it  got damage

upload the mp4 file to openload success, but it can not been opened .
the format of the file got damage

Could you see me your code?

authors: Ideneal jiahut

repo: Ideneal/OpenLoad


text:
Cordova InAppBrowser crash first open because of automaticSilentRenew

Hey, thanks for the great library.
I have a problem: When I turn on automaticSilentRenew and try to open any URL via cordova-plugin-inappbrowser the first trying is falling and inappbrowser automatically closed (the next ones okay).
Seems like it can be a problem with cordova-plugin-inappbrowser when oidc-client uses option hidden to renew token but I am not sure.
Env: iOS 10, 11
"cordova-plugin-inappbrowser": "~2.0.2",
"oidc-client": "1.4.1",

Thanks for any help.

Not sure, sorry. If you debug into it and find out what the problem is let me know.

After some debug I understood that renew based on a mechanism that we display hidden inAppBrowser instance and in this window we are running silent renew.
I've checked that this is a problem cordova-plugin-inappbrowser when I open one instance of inAppBrowser with hidden=yes with any URL (like https://www.google.com) and one visible instance but everything works fine.
I have walked through native iOS logs and what I have found:
2018-04-11 17:37:11.507671+0300 *******[78598:13843430] THREAD WARNING: ['InAppBrowser'] took '21.510986' ms. Plugin should use a background thread.
2018-04-11 17:37:11.690821+0300 *******[78598:13847209] Task <6A7FB937-D661-449A-8FD7-7BF6674EB27F>.<0> HTTP load failed (error code: -999 [1:89])

I see a repeat of this message on every two seconds and oidc-client in InAppBrowser tries to silent renew token.
Any ideas would be very nice

I honestly don't know the cordova framework, so I'm of little help. Sorry.

@brockallen Thanks for the empathy :)
After some deeper investigation, I understood that I have this problem just when I have set silent_redirect_uri in options and on each checkSessionInterval cordova-plugin-inappbrowser tries to open and do some check session. So when I have not set silent_redirect_uri each opens of InAppBrowser works fine.
I have tried to reimplement silent renew token flow as it was implemented in a file SilentRenewService with passing redirect_uri directly.
this.mgr.events.addAccessTokenExpiring(() => {
    console.log('addAccessTokenExpiring', new Date());
    this.mgr.signinSilent({ redirect_uri: AuthSilentRedirectUri }).then((user) => {
        this.currentUser = user;
    }, (err) => {
        console.log('Error from signinSilent:', err.message, new Date());
    });
});

Everything starts working better, and my token renews,
When I have opened some another window via InAppBrowser, the windows not crashed, it's very cool BUT, when I close this window and after some time trying to renew a token then I see this error
Response was error – "login_required"

Maybe you guys have some suggestions

All set on this issue -- can we close?

@disist can you share your implementation for silent refresh using inappbrowser?

authors: brockallen disist fateh14

repo: IdentityModel/oidc-client-js


text:
Getting Config to update in realtime

Hi Guys,
I just stumbled across this project and I'm very excited to see its happening.
My big question is:
Normally when a change is made to the idsvr options OWIN startup, it is not re-run by IIS unless a change to web.config is also made.
Will this admin tool force some kind of update so that the OWIN startup will reflect the changes in realtime?

Hi,
No, The admin just gets the data from the database and perisists the changes. Why should we want to reset the App Pool? Are you using in memory data?

I'm not using in memory data. I pull my client changes out of config and convert them during the IdentityServerServiceFactory creation that gets called from startup.
The problem I have, is the startup code that creates my IdentityServerOptions, only gets fired when I first load up the application, or when any change (could just be a space) is made to the web.config.
If the admin is just storing the settings and then identity server is retrieving those settings. Then how do you get the startup to fire again when you make a change to them?

Currently the admin repo only supports EntityFramework as persistance layer Link  so in your case you would need to implement a persistance layer for config files, to make use of the Admin site.

I don't think I'm explaining my situation very well.
I'd be more than happy to convert over to using EF as the persistance layer.
However, if I'm in a production environment that uses OWIN and I make changes in the admin site to my idsvr clients. Do I need to restart idsvr site or do anything for them to be reflected? At the moment, even if I use the inMemory samples from idsvr any changes I make to clients or scopes are only picked up if I also make a change to web.config. Otherwise they get ignored.
I basically am wanting to know if there's some way to trigger the OWIN startup code that reads the stored  configuration. My concern is that we could make use of idsvr Admin, but the changes won't be pulled into idsvr unless we do something to the site. In a production environment this changes how we could do our deployments.
It would be awesome if I could make a change in idsvr admin, and idsvr has some way of knowing its been updated so it will rebuild the client and scope repos.

If your IdentityServer is looking at the same database as your admin site, every edit you do in the Admin site will immediately be done for the Idsrv. No  need to restart App Pools or owin applications

So in IdentityServer instead of having an in memory user service, you have one that references the database. Does this get queried everytime someone attempts to authenticate?

Ofcourse

Thank you very much. This sounds very promising.
I'll keep an eye on it and see if I can find time to implement it one day.

FYI, the Admin is only for clients and scopes. Use the IdentityManager to manage your roles and users,

authors: iBoonz parkinsona


text:
Using TestUsers for password flow causes ArgumentNullException

Issue / Steps to reproduce the problem
When using TestUser list, password flow does not work.
Startup.cs/ConfigureServices.cs:
        services.AddIdentityServer()
            .AddDeveloperSigningCredential()
            .AddTestUsers(Config.GetTestUsers())
            .AddInMemoryClients(Config.GetClients());

Config.cs:
    public static IEnumerable<Client> GetClients()
    {
        return new List<Client>
        {
            new Client
            {
                ClientId = "web",

                // no interactive user, use the clientid/secret for authentication
                AllowedGrantTypes = GrantTypes.ResourceOwnerPasswordAndClientCredentials,

                // secret for authentication
                ClientSecrets =
                {
                    new Secret("secret".Sha256())
                },

                // scopes that client has access to
                AllowedScopes = { "api1" }
            }
        };
    }

    public static List<TestUser> GetTestUsers()
    {
        return new[] {
            new TestUser
            {
                Username = "admin",
                Password = "secret",
                Claims = new[]
                {
                    new Claim(ClaimTypes.Name, "Administrator"),
                    new Claim(ClaimTypes.Role, "Administrator"),
                    new Claim(ClaimTypes.Email, "admin@test.com"),
                    // Also tried this one, but to no avail:
                    new Claim("AspNet.Identity.SecurityStamp", Guid.NewGuid().ToString())
                },
            }
        }.ToList();
    }

When POST-ing correct client_id, client_secret, scope, username and password to the http://localhost:5000/connect/token (with grant_type of password), ArgumentNullException occurs in System.Security.Claims.Claim..ctor.
Relevant parts of the log file
IdentityServer4.Hosting.IdentityServerMiddleware:Critical: Unhandled exception: System.ArgumentNullException: Value cannot be null.
Parameter name: value
   at System.Security.Claims.Claim..ctor(String type, String value, String valueType, String issuer, String originalIssuer, ClaimsIdentity subject, String propertyKey, String propertyValue)
   at System.Security.Claims.Claim..ctor(String type, String value)
   at IdentityServer4.Validation.GrantValidationResult..ctor(String subject, String authenticationMethod, DateTime authTime, IEnumerable`1 claims, String identityProvider, Dictionary`2 customResponse)
   at IdentityServer4.Test.TestUserResourceOwnerPasswordValidator.ValidateAsync(ResourceOwnerPasswordValidationContext context)
   at IdentityServer4.Validation.TokenRequestValidator.<ValidateResourceOwnerCredentialRequestAsync>d__16.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
   at IdentityServer4.Validation.TokenRequestValidator.<RunValidationAsync>d__13.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
   at IdentityServer4.Validation.TokenRequestValidator.<ValidateRequestAsync>d__12.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
   at IdentityServer4.Endpoints.TokenEndpoint.<ProcessTokenRequestAsync>d__7.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
   at IdentityServer4.Endpoints.TokenEndpoint.<ProcessAsync>d__6.MoveNext()
--- End of stack trace from previous location where exception was thrown ---
   at System.Runtime.ExceptionServices.ExceptionDispatchInfo.Throw()
   at System.Runtime.CompilerServices.TaskAwaiter.HandleNonSuccessAndDebuggerNotification(Task task)
   at IdentityServer4.Hosting.IdentityServerMiddleware.<Invoke>d__4.MoveNext()

Oops, seems like I missed to set SubjectId. Maybe the error message should be a bit more descriptive?

Thanks. I added an additional check,

authors: leastprivilege tymarats