New Secondary Stat - Fallout VNDPNP

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A new Secondary Stat called Detection has been added.
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Server build fails with code 127 on Fedora 36

README.md says in multiple locations to run setup.py. There is no such file.
sudo docker-compose up fails as follows. Fairly certain all instructions were followed.

$ sudo docker-compose up
WARNING: The PG_DB variable is not set. Defaulting to a blank string.
WARNING: The PG_PASS variable is not set. Defaulting to a blank string.
WARNING: The PG_USER variable is not set. Defaulting to a blank string.
WARNING: The PRIVATE_STRIPE_KEY variable is not set. Defaulting to a blank string.
WARNING: The FRONTEND_URL variable is not set. Defaulting to a blank string.
WARNING: The AUTH0_AUDIENCE variable is not set. Defaulting to a blank string.
WARNING: The AUTH0_URL variable is not set. Defaulting to a blank string.
WARNING: The PUBLIC_STRIPE_KEY variable is not set. Defaulting to a blank string.
WARNING: The AUTH0_CLIENT_ID variable is not set. Defaulting to a blank string.
Building server
Sending build context to Docker daemon  442.9kB
Step 1/7 : FROM node:18-alpine
 ---> 6f44d13dd258
Step 2/7 : RUN apk upgrade --update-cache --available &&     apk add openssl &&     rm -rf /var/cache/apk/*
 ---> Running in 38a5cb4805dd
Error relocating /lib/ld-musl-x86_64.so.1: RELRO protection failed: Permission denied
Error relocating /bin/sh: RELRO protection failed: Permission denied
The command '/bin/sh -c apk upgrade --update-cache --available &&     apk add openssl &&     rm -rf /var/cache/apk/*' returned a non-zero code: 127
ERROR: Service 'server' failed to build : Build failed

This particular problem goes away when I set SELinux to permissive mode. I'd argue that lack of out-of-box support for running the app with SELinux in enforcing mode is a bug.
After setting SELinux to permissive mode, I got a ton of Node warnings but was able to install. However, the bring-up is still having problems:
WARNING: The PG_DB variable is not set. Defaulting to a blank string.
WARNING: The PG_PASS variable is not set. Defaulting to a blank string.
WARNING: The PG_USER variable is not set. Defaulting to a blank string.
WARNING: The PRIVATE_STRIPE_KEY variable is not set. Defaulting to a blank string.
WARNING: The FRONTEND_URL variable is not set. Defaulting to a blank string.
WARNING: The ROOT_URL variable is not set. Defaulting to a blank string.
WARNING: The PUBLIC_STRIPE_KEY variable is not set. Defaulting to a blank string.
Recreating wondertix_client_1   ... done
Recreating wondertix_server_1   ... done
Recreating wondertix_database_1 ... done
Attaching to wondertix_database_1, wondertix_server_1, wondertix_client_1
database_1  | Error: Database is uninitialized and superuser password is not specified.
database_1  |        You must specify POSTGRES_PASSWORD to a non-empty value for the
database_1  |        superuser. For example, "-e POSTGRES_PASSWORD=password" on "docker run".
database_1  |
database_1  |        You may also use "POSTGRES_HOST_AUTH_METHOD=trust" to allow all
database_1  |        connections without a password. This is *not* recommended.
database_1  |
database_1  |        See PostgreSQL documentation about "trust":
database_1  |        https://www.postgresql.org/docs/current/auth-trust.html
wondertix_database_1 exited with code 1
server_1    |
server_1    | ><EMAIL_ADDRESS>dev
server_1    | > nodemon --exec npx ts-node ./src/server.ts
server_1    |
server_1    | [nodemon] 2.0.22
server_1    | [nodemon] to restart at any time, enter `rs`
server_1    | [nodemon] watching path(s): *.*
server_1    | [nodemon] watching extensions: ts,json
server_1    | [nodemon] starting `npx ts-node ./src/server.ts`
client_1    |
client_1    | ><EMAIL_ADDRESS>start
client_1    | > cross-env HTTPS=true cross-env SSL_CRT_FILE=localhost.pem react-scripts start
client_1    |
server_1    | DB_HOST: <IP_ADDRESS>
client_1    | (node:40) [DEP_WEBPACK_DEV_SERVER_HTTPS] DeprecationWarning: 'https' option is deprecated. Please use the 'server' option.
client_1    | (Use `node --trace-deprecation ...` to show where the warning was created)
client_1    | (node:40) [DEP_WEBPACK_DEV_SERVER_ON_AFTER_SETUP_MIDDLEWARE] DeprecationWarning: 'onAfterSetupMiddleware' option is deprecated. Please use the 'setupMiddlewares' option.
client_1    | (node:40) [DEP_WEBPACK_DEV_SERVER_ON_BEFORE_SETUP_MIDDLEWARE] DeprecationWarning: 'onBeforeSetupMiddleware' option is deprecated. Please use the 'setupMiddlewares' option.
server_1    | Error: ENOENT: no such file or directory, open '/wondertix/localhost-key.pem'
server_1    |     at Object.openSync (node:fs:590:3)
server_1    |     at Object.readFileSync (node:fs:458:35)
server_1    |     at Object.<anonymous> (/wondertix/src/server.ts:72:19)
server_1    |     at Module._compile (node:internal/modules/cjs/loader:1267:14)
server_1    |     at Module.m._compile (/wondertix/node_modules/ts-node/src/index.ts:1618:23)
server_1    |     at Module._extensions..js (node:internal/modules/cjs/loader:1321:10)
server_1    |     at Object.require.extensions.<computed> [as .ts] (/wondertix/node_modules/ts-node/src/index.ts:1621:12)
server_1    |     at Module.load (node:internal/modules/cjs/loader:1125:32)
server_1    |     at Function.Module._load (node:internal/modules/cjs/loader:965:12)
server_1    |     at Function.executeUserEntryPoint [as runMain] (node:internal/modules/run_main:83:12) {
server_1    |   errno: -2,
server_1    |   syscall: 'open',
server_1    |   code: 'ENOENT',
server_1    |   path: '/wondertix/localhost-key.pem'
server_1    | }
server_1    | [nodemon] app crashed - waiting for file changes before starting...
client_1    | Starting the development server...
client_1    |
client_1    | Failed to compile.
client_1    |
client_1    | [eslint]
client_1    | src/components/CRM/Tasks/Filter.tsx
client_1    |   Line 24:9:  Unknown property 'sx' found  react/no-unknown-property
client_1    |
client_1    | src/components/CRM/Tasks/Note.tsx
client_1    |   Line 12:7:  Unknown property 'sx' found  react/no-unknown-property
client_1    |
client_1    | src/components/Ticketing/cart/CartItem.tsx
client_1    |   Line 51:57:  Unknown property 'aira-label' found  react/no-unknown-property
client_1    |
client_1    | Search for the keywords to learn more about each error.
client_1    | assets by path static/ 7.39 MiB
client_1    |   assets by path static/js/*.js 6.96 MiB
client_1    |     asset static/js/bundle.js 6.95 MiB [emitted] (name: main) 1 related asset
client_1    |     asset static/js/node_modules_web-vitals_dist_web-vitals_js.chunk.js 7.24 KiB [emitted] 1 related asset
client_1    |   assets by path static/media/ 440 KiB
client_1    |     asset static/media/S5A5574X_web.0b42f233979bb8fccea6.jpg 429 KiB [emitted] [immutable] [from: src/assets/S5A5574X_web.jpg] (auxiliary name: main)
client_1    |     asset static/media/2011_Logo_white.c13c470d7748f823d2c3.png 11.1 KiB [emitted] [immutable] [from: src/Logo/2011_Logo_white.png] (auxiliary name: main)
client_1    | asset index.html 1.68 KiB [emitted]
client_1    | asset asset-manifest.json 648 bytes [emitted]
client_1    | orphan modules 1.5 MiB [orphan] 974 modules
client_1    | runtime modules 32.8 KiB 17 modules
client_1    | cacheable modules 5.26 MiB (javascript) 440 KiB (asset)
client_1    |   modules by path ./node_modules/ 4.53 MiB 896 modules
client_1    |   modules by path ./src/ 752 KiB (javascript) 440 KiB (asset)
client_1    |     modules by path ./src/components/ 634 KiB 86 modules
client_1    |     asset modules 13.8 KiB (javascript) 440 KiB (asset) 4 modules
client_1    |     modules by path ./src/*.tsx 16.5 KiB 2 modules
client_1    |     modules by path ./src/*.css 74.6 KiB 2 modules
client_1    |     modules by path ./src/Logo/*.css 3.88 KiB 2 modules
client_1    |     ./src/reportWebVitals.ts 1.36 KiB [built] [code generated]
client_1    |     ./src/utils/arrays.tsx 8.41 KiB [built] [code generated]
client_1    |
client_1    | ERROR in [eslint]
client_1    | src/components/CRM/Tasks/Filter.tsx
client_1    |   Line 24:9:  Unknown property 'sx' found  react/no-unknown-property
client_1    |
client_1    | src/components/CRM/Tasks/Note.tsx
client_1    |   Line 12:7:  Unknown property 'sx' found  react/no-unknown-property
client_1    |
client_1    | src/components/Ticketing/cart/CartItem.tsx
client_1    |   Line 51:57:  Unknown property 'aira-label' found  react/no-unknown-property
client_1    |
client_1    | Search for the keywords to learn more about each error.
client_1    |
client_1    | webpack 5.80.0 compiled with 1 error in 10906 ms
client_1    | No issues found.

Readme has been updated with new instructions to build with correct .env variables.


Use GoogleAuth class without a credential file

Hello,
We don't want to use a credential file to store our secrets as it doesn't work well with our stack. I'm not quite sure if this should be in bug or feature request, but I'll share it here and the results. I feel like it is already possible to use GoogleAuth without a file, and perhaps I'm just doing it wrong. I would like to use GoogleAuth since the automatic selection of auth type is appealing.
I found an issue that was similar here. I haven't tried that method yet since they mentioned it was specifically for the Compute type which I would rather not use in case this app runs somewhere other than GCP.
Environment details

OS: macOS Monterey 12.4 (M1)
Node.js version: 16.14.0
npm version: 8.3.1
google-auth-library version: 8.5.1

Steps to reproduce
Here's the snippet of code I've currently made it to:
import { GoogleAuth } from 'google-auth-library';

import config from '../config/config.js';

const {
  PROJECT_ID,
  GOOGLE_CLIENT_ID,
  GOOGLE_CLIENT_SECRET,
} = config.google;

// map console to logger since you won't have our logging package
const logger = console;

let googAuth;

async function getAuth () {
  try {
    const auth = new GoogleAuth({
      scopes: 'https://www.googleapis.com/auth/cloud-platform',
      credentials: {
        client_email: GOOGLE_CLIENT_ID,
        private_key: GOOGLE_CLIENT_SECRET,
      },
      projectId: PROJECT_ID,
    });
    logger.debug({ auth });
    const client = await auth.getClient();
    const projectId = await auth.getProjectId();
    const url = `https://dns.googleapis.com/dns/v1/projects/${projectId}`;
    const res = await client.request({ url });
    googAuth = res;
  } catch (e) {
    logger.error('unable to auth', e);
  }
}

await getAuth();

The resultant error message that's caught is this:
'unable to auth' Error: error:0909006C:PEM routines:get_name:no start line
    at Sign.sign (node:internal/crypto/sig:131:29)
    at Object.sign (***/app/node_modules/jwa/index.js:152:45)
    at Object.jwsSign [as sign] (***/app/node_modules/jws/lib/sign-stream.js:32:24)
    at GoogleToken.requestToken (***/app/node_modules/gtoken/build/src/index.js:239:31)
    at GoogleToken.getTokenAsyncInner (***/app/node_modules/gtoken/build/src/index.js:173:21)
    at GoogleToken.getTokenAsync (***/app/node_modules/gtoken/build/src/index.js:152:55)
    at GoogleToken.getToken (***/app/node_modules/gtoken/build/src/index.js:104:21)
    at JWT.refreshTokenNoCache (/***/app/node_modules/google-auth-library/build/src/auth/jwtclient.js:172:36)
    at JWT.refreshToken (***/app/node_modules/google-auth-library/build/src/auth/oauth2client.js:152:24)
    at JWT.getRequestMetadataAsync (***/app/node_modules/google-auth-library/build/src/auth/oauth2client.js:284:28) {
  library: 'PEM routines',
  function: 'get_name',
  reason: 'no start line',
  code: 'ERR_OSSL_PEM_NO_START_LINE'
}

After some more hunting, it seems that this issue, better documentation of the various strategies, could be helpful.

@champ-devops based on your code and my own debugging, I manage to get this to work:
const auth = new GoogleAuth({
  // Please note this is an entirely different scope than yours
  scopes: 'https://www.googleapis.com/auth/spreadsheets.readonly',
  credentials: {
    // `client_email` property from the service account Key file downloaded as JSON
    client_email: GOOGLE_CLIENT_ID,
    // `private_key` property from the service account Key file downloaded as JSON
    private_key: GOOGLE_CLIENT_SECRET,
  },
  // skipped, no need as service account is bound to project (it's encoded in the email)
  // projectId: PROJECT_ID,
});

The PEM routines:get_name:no start line error sounds to me like it's related to your key - is there some additional escaping?
Mine looks like:
private_key: '-----BEGIN PRIVATE KEY-----\nhere-there-be-dragons\n-----END PRIVATE KEY-----\n'

I realise this comment might be of little help for you, but hopefully it does a bit.

This is helpful thank you! I did realize that I was using the wrong secret which was definitely a big part of my problem.


Multi-stage build for minimal image

also upgrade to golang:1.8
still need glibc

@christoe @carlpett

Bugger - I just noticed docker hub does not offer multistage builds yet: https://github.com/docker/hub-feedback/issues/1039

Thanks @davidkarlsen, I will merge this as soon as we get a green light on multistage builds in Docker Hub.

@christoe I think you are good to go now: https://github.com/docker/hub-feedback/issues/1039#issuecomment-323761501

@davidkarlsen: Seems like it was rolled back, but any day now.. https://github.com/docker/hub-feedback/issues/1039#issuecomment-323857796


feat: iavl, db & disk stats logging

Added iavl, db & disk stats logging when IAVL_DEV environment variable is set to the device name to check the disk io activity.
Before we can merge this PR, please make sure that all the following items have been
checked off. If any of the checklist items are not applicable, please leave them but
write a little note why.

[ ] Targeted PR against correct branch (see CONTRIBUTING.md)
[ ] Linked to Github issue with discussion and accepted design OR link to spec that describes this work.
[ ] Code follows the module structure standards.
[ ] Wrote unit and integration tests
[ ] Updated relevant documentation (docs/) or specification (x/<module>/spec/)
[ ] Added relevant godoc comments.
[ ] Added a relevant changelog entry to the Unreleased section in CHANGELOG.md
[ ] Re-reviewed Files changed in the Github PR explorer
[ ] Review Codecov Report in the comment section below once CI passes

Wouldn't it affect performance to open and parse the file every commit? How about to create a config for turning it off and turning it on?

Wouldn't it affect performance to open and parse the file every commit? How about to create a config for turning it off and turning it on?

It's just string pointer comparisons. If it's uninitialized, i.e. logIoDev == nil, then it checks environment variable once and only once.
if logIoDev == nil {
// uninitialized, check env. var.
} else if logIoDev == &logIoDevNop {
// disabled
} else {
// enabled, *logIoDev is set to device name to collect data
}

It only works when IAVL_DEV is set. I missed this.


Accessing the API with http header "Authorization" does not work

Gitea version (or commit ref): 1.3.2
Git version: 2.7.4
Operating system: Ubuntu 16.04
Database (use [x]):

[ ] PostgreSQL
[x] MySQL
[ ] MSSQL
[ ] SQLite

Can you reproduce the bug at https://try.gitea.io:

[ ] Yes (provide example URL)
[x] No (try.gitea.io is down)
[ ] Not relevant

Log gist:

Description
Accessing the API with http header "Authorization" does not work.
curl \
  --resolve gitea.net:3000:<IP_ADDRESS> \
  -X GET \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  'http://gitea.net:3000/api/v1/repos/devs/tmv4/keys?token=4a40ddb4190802e39825f06ec8a04a4ba3af22bf'; echo

[]

The same for access_token=.

No helpfull information in Debug and Trace logs. (?)

Reviewing the code revealed that providing -H 'Authorization: token 4a40ddb4190802e39825f06ec8a04a4ba3af22bf seems to work. This would be something which really required some documentation, I guess.


Add scroll to top button

A button at the bottom of the articles page which will enhance user experience
IMAGE1

IMAGE2

Please assign this issue to me under JWOC.

okhh


ValueError: Empty Range for RandRange() in Train Function for Custom Classifier

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
I used stock example script provided by TensorFlow to write my own custom code.

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
Windows 10 - Colab Notebook

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

TensorFlow installed from (source or binary):
Not sure

TensorFlow version (use command below):
1.9

Python version:
3.6 (I think)

Bazel version (if compiling from source):
Unsure

GCC/Compiler version (if compiling from source):
Unsure

CUDA/cuDNN version:
N/A

GPU model and memory:
N/A

Exact command to reproduce:
  # Train the CNN model
  mnist_classifier.train(input_fn=train_input_fn,steps=train_steps)

You can collect some of this information using our environment capture script:
https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh
You can obtain the TensorFlow version with
python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
Describe the problem
Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.
I want to train multiple CNNs of the same structure (lenet) using randomly selected subsets of varying sizes from the MNIST training set. I was able to use the same line of code successfully in an earlier program. This work is contributing towards the development of a computational model for executive function deficits in ADHD as part of a PhD thesis in Pharmaceutical Sciences.
Source code / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.
Quantitative WM Model.txt

I apologize, but I am having a hard time understanding what the problem is, where the problem is, and what version it affects. What script are you running? What is the error trace that you are seeing?

Hello,
I am having trouble with the command to train a classifier (although I have
been able to use this command successfully in a similar application). I am
using Tensorflow 1.9, and Python 3.6 (I think). I am getting ValueError:
empty range for randrange() using the following command:
mnist_classifier.train(input_fn=train_input_fn,steps=train_steps)
I have copied & pasted the error trace to the bottom of this email.
I have attached a text file with my code that I am running in a Colab
Notebook (I'm not sure how to share this directly so I copied & pasted the
code and the output into the text file). I also attached a word doc where I
have highlighted the error message and the line where the error occurred.
Is there an email address that I could use to share my notebook if this
email does not help clarify the problem?
Thank you.
Below is the error trace:
ValueError                                Traceback (most recent call last)
 in ()
137
138 if name == "main":
--> 139   tf.app.run()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/platform/app.py in
run(main, argv)
123   # Call the main function, passing through any arguments
124   # to the final program.
--> 125   _sys.exit(main(argv))
126
 in main(unused_argv)
111
112         # Train the CNN model
--> 113
mnist_classifier.train(input_fn=train_input_fn,steps=train_steps)
114
115         # Evaluate the models and print results
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py
in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
364
365       saving_listeners = _check_listeners_type(saving_listeners)
--> 366       loss = self._train_model(input_fn, hooks, saving_listeners)
367       logging.info('Loss for final step: %s.', loss)
368       return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py
in _train_model(self, input_fn, hooks, saving_listeners)
1117       return self._train_model_distributed(input_fn, hooks,
saving_listeners)
1118     else:
-> 1119       return self._train_model_default(input_fn, hooks,
saving_listeners)
1120
1121   def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py
in _train_model_default(self, input_fn, hooks, saving_listeners)
1127       features, labels, input_hooks = (
1128           self._get_features_and_labels_from_input_fn(
-> 1129               input_fn, model_fn_lib.ModeKeys.TRAIN))
1130       worker_hooks.extend(input_hooks)
1131       estimator_spec = self._call_model_fn(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py
in _get_features_and_labels_from_input_fn(self, input_fn, mode)
983           lambda: self._call_input_fn(input_fn, mode))
984     else:
--> 985       result = self._call_input_fn(input_fn, mode)
986
987     return estimator_util.parse_input_fn_result(result)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py
in _call_input_fn(self, input_fn, mode)
1072       kwargs['config'] = self.config
1073     with ops.device('/cpu:0'):
-> 1074       return input_fn(**kwargs)
1075
1076   def _call_model_fn(self, features, labels, mode, config):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/inputs/numpy_io.py
in input_fn()
196         num_threads=num_threads,
197         enqueue_size=batch_size,
--> 198         num_epochs=num_epochs)
199
200     batch = (
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/inputs/queues/feeding_functions.py
in _enqueue_data(data, capacity, shuffle, min_after_dequeue, num_threads,
seed, name, enqueue_size, num_epochs, pad_value)
482                 random_start=shuffle,
483                 seed=seed_i,
--> 484                 num_epochs=num_epochs))
485       else:
486         feed_fns.append(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/inputs/queues/feeding_functions.py
in init(self, placeholders, ordered_dict_of_arrays, batch_size,
random_start, seed, num_epochs)
217     self._epoch = 0
218     random.seed(seed)
--> 219     self._trav = random.randrange(self._max) if random_start else 0
220     self._epoch_end = (self._trav - 1) % self._max
221
/usr/lib/python3.6/random.py in randrange(self, start, stop, step, _int)
186             if istart > 0:
187                 return self._randbelow(istart)
--> 188             raise ValueError("empty range for randrange()")
189
190         # stop argument supplied.
ValueError: empty range for randrange()
On Tue, Jul 24, 2018 at 12:52 PM, Karmel Allison<EMAIL_ADDRESS>wrote:

I apologize, but I am having a hard time understanding what the problem
is, where the problem is, and what version it affects. What script are you
running? What is the error trace that you are seeing?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/tensorflow/tensorflow/issues/20997#issuecomment-407474850,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AZZjtJIaQVKqZnLvfaO74x8cOy4bBmXgks5uJ1C2gaJpZM4VYT0n
.

--
Markus Ville Tiitto, Pharm.D.
PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
789 S. Limestone St.
Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192
Quantitative Working Memory Model
Date & Time
Purpose: This program will develop a quantitative model of the working memory deficiency. Identically-structured CNNs (lenet) will be trained with varying numbers of examples from the MNIST training set with batch size (=25) and training steps (=300) held constant. The quantitative relationship between % accuracy of identification of handwritten digits in the MNIST test set and the number of image files will be determined by non-linear regression.
Inputs:

Number of ANNs per training group (set to 20 to generate a normal distribution)

Number of images per training group (n=25, 250, 2500, 25000, 55000) Note: a unique set of training images will be used for each individual ANN in every group

Outputs: N/A
Conclusions

from future import absolute_import
from future import division
from future import print_function
Import Libraries
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import operator
import sys
from datetime import datetime
------------CNN and MNIST Function Definitions---------
"""This section is adapted from Tensorflow 1.9 MNIST tutorial called "Build a CNN Using Estimators" found here: https://www.tensorflow.org/tutorials/layers """
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features, labels, mode):
"""Model function for CNN."""
Input Layer
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=20,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=50,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 50])
dense = tf.layers.dense(inputs=pool2_flat, units=500, activation=tf.nn.relu)
Logits Layer
logits = tf.layers.dense(inputs=dense, units=10)
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
# Add softmax_tensor to the graph. It is used for PREDICT and by the
# logging_hook.
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
Configure the Training Op (for TRAIN mode) ***Learning rate = 0.001
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer()
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
----------------------Main Driver Function---------------------------------------
Purpose: This function will download MNIST data, train ANNs, and evaluate these ANNs
def main(unused_argv):
#Set parameters for #ANNs to train & #steps in training
num_ANNs = 2
batchsize = 25
train_steps = 300
train_set_sizes = [25,250,2500,25000,55000]
Load training and eval data
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
#55,000 images in train set (28x28 grayscale)
train_images = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
#10,000 images in test set (28x28 grayscale)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
#Initialize lists to hold ADHD training examples
train_set_examples = []
train_set_labels = []
#create training input function for control-ANNs & evaluation input function
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.asarray(train_set_examples)},
y=np.asarray(train_set_labels),
batch_size=25,
num_epochs=None,
shuffle=True)
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": eval_data},
y=eval_labels,
num_epochs=1,
shuffle=False)
#Initialize list to hold all of the accuracy data
all_accuracies = []
#For each training set group
for group_number in range(len(train_set_sizes)):
  #For each ANN in training set group
  for i in range(num_ANNs):

    #Initialize group_accuracies list
    group_accuracies = []

    #--------------Generate a training set--------------------------------
    #If size of train set < 100, check to make sure there is at least one
    #example of each digit included

    if train_set_sizes[group_number] <= 100:

      #Initialize contains_all_digits list. This list will contain one entry per digit (n=10 entries total).
      #The entries will be initialized with zeros, and each entry will be filled with a 1 when its corresponding
      #digit is found in adhd_train_labels. When all entries = 1, then adhd_train set will contain all digits
      contains_all_digits = []

      for i in range(10):

        contains_all_digits.append(0)

      while 0 in contains_all_digits:

        #Reinitialize contains_all_digits if a train set containing all digits has not been generated yet
        for i in range(10):

          contains_all_digits[i] = 0

        train_set_examples = []
        train_set_labels = []

        example_select_indices = np.random.randint(0,len(train_images),size=train_set_sizes[group_number])

        #randomly select examples
        for i in range(train_set_sizes[group_number]):

          random_index = example_select_indices[i]
          train_set_examples.append(train_images[random_index])
          train_set_labels.append(train_labels[random_index])

        #check that labels contain all digits 0-9
        for i in range(10):

           if i in train_set_labels:
              contains_all_digits[i] = 1

           else:
              contains_all_digits[i] = 0

    else:

      #Initialize train_set lists
      train_set_examples = []
      train_set_labels = []

      #randomly select indices to pull train set examples
      example_select_indices = np.random.randint(0,55000,size=train_set_sizes[group_number])

      #For number of images in the appropriate training set group
      for j in range(train_set_sizes[group_number]):

        random_index = example_select_indices[j]
        train_set_examples.append(train_images[random_index])
        train_set_labels.append(train_labels[random_index])

    # Create the MNIST Estimator
    mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn)

    # Train the CNN model
    mnist_classifier.train(input_fn=train_input_fn,steps=train_steps)

    # Evaluate the models and print results
    eval_result = mnist_classifier.evaluate(input_fn=eval_input_fn)
    print("Group Number ",group_number+1,", ANN #",i+1," Accuracy: ",eval_result," %")

    #Record ANN accuracy
    group_accuracies.append(eval_result)

#record group accuracies in all_accuracies
all_accuracies.append(group_accuracies)
#Print All Accuracy Results
print()
print("-------------------------------------Accuracy Results--------------------------------------------")
for i in range(len(train_set_sizes)):
for j in range(numANNs):

  print("Group #",i+1,", ANN #",j+1," Accuracy: ",eval_result," %")

if name == "main":
tf.app.run()
--------------------Output--------------
Extracting MNIST-data/train-images-idx3-ubyte.gz
Extracting MNIST-data/train-labels-idx1-ubyte.gz
Extracting MNIST-data/t10k-images-idx3-ubyte.gz
Extracting MNIST-data/t10k-labels-idx1-ubyte.gz
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpge3laept
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpge3laept', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fb6c08b7c88>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
ValueError                                Traceback (most recent call last)
 in ()
137
138 if name == "main":
--> 139   tf.app.run()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/platform/app.py in run(main, argv)
123   # Call the main function, passing through any arguments
124   # to the final program.
--> 125   _sys.exit(main(argv))
126
 in main(unused_argv)
111
112         # Train the CNN model
--> 113         mnist_classifier.train(input_fn=train_input_fn,steps=train_steps)
114
115         # Evaluate the models and print results
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
364
365       saving_listeners = _check_listeners_type(saving_listeners)
--> 366       loss = self._train_model(input_fn, hooks, saving_listeners)
367       logging.info('Loss for final step: %s.', loss)
368       return self
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1117       return self._train_model_distributed(input_fn, hooks, saving_listeners)
1118     else:
-> 1119       return self._train_model_default(input_fn, hooks, saving_listeners)
1120
1121   def _train_model_default(self, input_fn, hooks, saving_listeners):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1127       features, labels, input_hooks = (
1128           self._get_features_and_labels_from_input_fn(
-> 1129               input_fn, model_fn_lib.ModeKeys.TRAIN))
1130       worker_hooks.extend(input_hooks)
1131       estimator_spec = self._call_model_fn(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _get_features_and_labels_from_input_fn(self, input_fn, mode)
983           lambda: self._call_input_fn(input_fn, mode))
984     else:
--> 985       result = self._call_input_fn(input_fn, mode)
986
987     return estimator_util.parse_input_fn_result(result)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/estimator.py in _call_input_fn(self, input_fn, mode)
1072       kwargs['config'] = self.config
1073     with ops.device('/cpu:0'):
-> 1074       return input_fn(**kwargs)
1075
1076   def _call_model_fn(self, features, labels, mode, config):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/inputs/numpy_io.py in input_fn()
196         num_threads=num_threads,
197         enqueue_size=batch_size,
--> 198         num_epochs=num_epochs)
199
200     batch = (
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/inputs/queues/feeding_functions.py in _enqueue_data(data, capacity, shuffle, min_after_dequeue, num_threads, seed, name, enqueue_size, num_epochs, pad_value)
482                 random_start=shuffle,
483                 seed=seed_i,
--> 484                 num_epochs=num_epochs))
485       else:
486         feed_fns.append(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/estimator/inputs/queues/feeding_functions.py in init(self, placeholders, ordered_dict_of_arrays, batch_size, random_start, seed, num_epochs)
217     self._epoch = 0
218     random.seed(seed)
--> 219     self._trav = random.randrange(self._max) if random_start else 0
220     self._epoch_end = (self._trav - 1) % self._max
221
/usr/lib/python3.6/random.py in randrange(self, start, stop, step, _int)
186             if istart > 0:
187                 return self._randbelow(istart)
--> 188             raise ValueError("empty range for randrange()")
189
190         # stop argument supplied.
ValueError: empty range for randrange()

It looks like you are using the deprecated queue API. Can you try with tf.data.Dataset?

Ok, I will try a dataset. Should the code on the Datasets for Estimators
page (https://www.tensorflow.org/guide/datasets_for_estimators) work?
Thank you.
On Fri, Jul 27, 2018 at 3:37 PM, Karmel Allison<EMAIL_ADDRESS>wrote:

It looks like you are using the deprecated queue API. Can you try with
tf.data.Dataset?
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PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
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Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192

Yes, it should. Let me know how it goes--

I found python scripts in the introductory tensorflow download documents
(which also include scripts for the iris classification problem used to
describe estimators) for MNIST in the directory
\TensorFlow_models-master\models-master\official\mnist. Are there any
accompanying materials on the Tensorflow website that describe the code in
the dataset.py and mnist.py scripts found in this folder?
Thank you.
On Mon, Jul 30, 2018 at 7:33 PM, Karmel Allison<EMAIL_ADDRESS>wrote:

Yes, it should. Let me know how it goes--
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Markus Ville Tiitto, Pharm.D.
PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
789 S. Limestone St.
Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192

The documentation and README in that repo are all that is for available for that model, but there are also a number of tutorials and a dataset guide

I'm attempting to use a Dataset as you recommended, but now I am getting a
new error that says " ValueError: Duplicate node name in graph: 'packed'".
I am trying to convert the MNIST data to a tensor, select subsets of the
training data into lists, convert the lists into tensors, and then convert
the "list" tensors into datasets.
However, the classifier.train command is still not working.
Do you have any suggestions on how to fix this or would you recommend I try
a different strategy?
Thank you.
On Wed, Aug 1, 2018 at 1:53 PM, Karmel Allison<EMAIL_ADDRESS>wrote:

The documentation and README in that repo are all that is for available
for that model, but there are also a number of tutorials
https://www.tensorflow.org/tutorials/ and a dataset guide
https://www.tensorflow.org/guide/datasets
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Markus Ville Tiitto, Pharm.D.
PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
789 S. Limestone St.
Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192

It's hard for me to help debug without the code, data, and the like. Maybe start from scratch with the Official MNIST, and see if you can get that running?

Hello again,
I downloaded the Official MNIST scripts and tried running them but I got
an  ImportError: No module named official.mnist.
I went to the Tensorflow Official models website (found here
https://github.com/tensorflow/models/tree/master/official#running-the-models),
and tried to follow the directions under the Requirements section. However,
I learned that these directions are only for Linux. Are there additional
directions available for Windows systems?
Thank you.
On Sat, Aug 4, 2018 at 12:07 PM, Karmel Allison<EMAIL_ADDRESS>wrote:

It's hard for me to help debug without the code, data, and the like. Maybe
start from scratch with the Official MNIST
https://github.com/tensorflow/models/tree/master/official/mnist, and
see if you can get that running?
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Markus Ville Tiitto, Pharm.D.
PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
789 S. Limestone St.
Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192

I was actually able to get the Official models installed and working
properly, although I still need to try to manipulate the training set by
taking out subsets to train separate networks. Could I still follow up on
here if I am not able to do so?
Also, would you have any advice about how to get the official models to
work in a Colaboratory Notebook? I have tried running the MNIST model in my
Notebook
https://colab.research.google.com/drive/13Gl9l-xnIL4OeNgd-Wqrb2bMcG10NUF1,
but I get  ModuleNotFoundError: No module named 'official' on the line "from
official.mnist import dataset".
I have tried uploading all of the scripts in the "official" folder (that
includes all the Official Models) to my google drive but this did not help.
Thank you.
On Wed, Aug 22, 2018 at 3:29 PM Alfred Sorten Wolf<EMAIL_ADDRESS>wrote:

Nagging Assignee @karmel https://github.com/karmel: It has been 14 days
with no activity and this issue has an assignee. Please update the label
and/or status accordingly.
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Markus Ville Tiitto, Pharm.D.
PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
789 S. Limestone St.
Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192

Nagging Assignee @karmel: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

I was able to get the model working properly by using the Keras library so
my issues has been resolved.
Thank you for the help.
On Tue, Jul 24, 2018 at 12:52 PM Karmel Allison<EMAIL_ADDRESS>wrote:

I apologize, but I am having a hard time understanding what the problem
is, where the problem is, and what version it affects. What script are you
running? What is the error trace that you are seeing?
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Markus Ville Tiitto, Pharm.D.
PhD Student
Clinical & Experimental Therapeutics
Department of Pharmaceutical Sciences
University of Kentucky, College of Pharmacy
Lee Todd, Jr. Building Rm 466
789 S. Limestone St.
Lexington, KY 40536
<EMAIL_ADDRESS><EMAIL_ADDRESS>(412) 296-9192

Great, thanks for updating. Closing this.

@mtiitto I suffered the same problem "ValueError: Empty range for randrange()" when I attempted to train my network. The error occurred in this line:
mnist_classifier.train(         input_fn=train_input_fn,         steps=2000,         hooks=[logging_hook]     )
So, do you have any suggestions for me to solve this problem. My tensorflow version and python version are 1.13.1 an 3.7, respectively.


Cannot install numbers-parser with python3.10 -m pip install numbers-parser due to failed installation of python-snappy.  I cannot install python-snappy to Python3.10 environment instead of Python2.7.  Please help.

➜  ~ python3.10 -m pip install numbers-parser
Looking in indexes: https://pypi.python.org/simple, https://pypi.apple.com/simple
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/numbers-parser/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting numbers-parser
Using cached numbers_parser-2.3.3-py3-none-any.whl
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/pyyaml/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting PyYAML
Using cached https://pypi.apple.com/packages/packages/44/e5/4fea13230bcebf24b28c0efd774a2dd65a0937a2d39e94a4503438b078ed/PyYAML-6.0-cp310-cp310-macosx_10_9_x86_64.whl (197 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/pytest/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting pytest
Using cached https://pypi.apple.com/packages/packages/38/93/c7c0bd1e932b287fb948eb9ce5a3d6307c9fc619db1e199f8c8bc5dad95f/pytest-7.0.1-py3-none-any.whl (296 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/python-snappy/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting python-snappy
Using cached https://pypi.apple.com/packages/packages/9f/2f/314ebc4ad5faeab6b3d485be443d1493636026afe72a52400342d7f76003/python-snappy-0.6.0.tar.gz (21 kB)
Preparing metadata (setup.py) ... done
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/protobuf/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting protobuf
Using cached https://pypi.apple.com/packages/packages/cf/73/37e7222e36a2c1863994cbb7f5bd80f95886d26e90e617b92a92b69f8997/protobuf-3.19.4-cp310-cp310-macosx_10_9_universal2.whl (961 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/pluggy/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting pluggy<2.0,>=0.12
Using cached https://pypi.apple.com/packages/packages/9e/01/f38e2ff29715251cf25532b9082a1589ab7e4f571ced434f98d0139336dc/pluggy-1.0.0-py2.py3-none-any.whl (13 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/tomli/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting tomli>=1.0.0
Using cached https://pypi.apple.com/packages/packages/97/75/10a9ebee3fd790d20926a90a2547f0bf78f371b2f13aa822c759680ca7b9/tomli-2.0.1-py3-none-any.whl (12 kB)
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╰─> Problematic URL: https://pypi.apple.com/simple/packaging/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting packaging
Using cached https://pypi.apple.com/packages/packages/05/8e/8de486cbd03baba4deef4142bd643a3e7bbe954a784dc1bb17142572d127/packaging-21.3-py3-none-any.whl (40 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/attrs/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting attrs>=19.2.0
Using cached https://pypi.apple.com/packages/packages/be/be/7abce643bfdf8ca01c48afa2ddf8308c2308b0c3b239a44e57d020afa0ef/attrs-21.4.0-py2.py3-none-any.whl (60 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/iniconfig/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting iniconfig
Using cached https://pypi.apple.com/packages/packages/9b/dd/b3c12c6d707058fa947864b67f0c4e0c39ef8610988d7baea9578f3c48f3/iniconfig-1.1.1-py2.py3-none-any.whl (5.0 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/py/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting py>=1.8.2
Using cached https://pypi.apple.com/packages/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl (98 kB)
warning: missing-index-doctype
× The package index page being used does not have a proper HTML doctype declaration.
╰─> Problematic URL: https://pypi.apple.com/simple/pyparsing/
note: This is an issue with the page at the URL mentioned above.
hint: You might need to reach out to the owner of that package index, to get this fixed. See https://github.com/pypa/pip/issues/10825 for context.
Collecting pyparsing!=3.0.5,>=2.0.2
Using cached https://pypi.apple.com/packages/packages/80/c1/23fd82ad3121656b585351aba6c19761926bb0db2ebed9e4ff09a43a3fcc/pyparsing-3.0.7-py3-none-any.whl (98 kB)
Building wheels for collected packages: python-snappy
Building wheel for python-snappy (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> [10 lines of output]
running bdist_wheel
running build
running build_py
running build_ext
building 'snappy._snappy' extension
snappy/snappymodule.cc:32:10: fatal error: 'snappy-c.h' file not found
#include <snappy-c.h>
^~~~~~~~~~~~
1 error generated.
error: command '/usr/bin/clang' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for python-snappy
Running setup.py clean for python-snappy
Failed to build python-snappy
Installing collected packages: python-snappy, iniconfig, tomli, PyYAML, pyparsing, py, protobuf, pluggy, attrs, packaging, pytest, numbers-parser
Running setup.py install for python-snappy ... error
error: subprocess-exited-with-error
× Running setup.py install for python-snappy did not run successfully.
│ exit code: 1
╰─> [12 lines of output]
running install
/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
running build
running build_py
running build_ext
building 'snappy._snappy' extension
snappy/snappymodule.cc:32:10: fatal error: 'snappy-c.h' file not found
#include <snappy-c.h>
^~~~~~~~~~~~
1 error generated.
error: command '/usr/bin/clang' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: legacy-install-failure
× Encountered error while trying to install package.
╰─> python-snappy
note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.

This is  python-snappy failing to install so not my issue (I think). See also https://github.com/masaccio/numbers-parser/issues/8

Best approach is to use Home-brew and install python yourself otherwise you need to install the snappy binaries yourself and Homebrew will handle this dependency for you.


pull request for e2e test at 11/15/2016 19:56:09

:white_check_mark: Validation status: passed

File
Status
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Details

optest/docs/updated_at.md
:white_check_mark:Succeeded

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


Callback after enabling passcode?

Hi,
Is there a way to call a function where passcode has been enabled?
LTHPasscodeViewController.sharedUser().showForEnablingPasscode(in: self, asModal: true);
Thanks!

Hey,
Sadly, that's one of the few cases where a callback doesn't happen, but when enabling the postcode successfully, the controller closes, and you get a callback for that, passcodeViewControllerWillClose.

Okay, thank you for your confirmation. You should mark this issue with the label "enhancement ".
Have a nice day.

Added in 3.7.10, 958b32265d6f450fc11f884fb839079721363ca8.


Bump fs2 and enable for scala 3

I had to remove zio from the list of scala 3 enabled projects to get sbt to load.

Yeah zio is often quite delayed wrt scala3 support

Should be release soon in 1.1.1 :)


IE7 - Help text popup

The help text popup boxes do not show when on a field.

IE7 usage is below 2% for the gallery.


ssh_only tag fails

When attempting to run the ssh_only tag, it fails to execute:
TASK [roles/kraken.cluster_common : Loop over each node and check that corresponding ssh file exists] ****
fatal: [localhost]: FAILED! => {"failed": true, "msg": "'node' is undefined"}

need to add ssh_only tag to the common role.  I saw this when I was patching up access_only.

Not quite that easy:
TASK [roles/kraken.cluster_common : Fail when cluster.status.lock file exists] ***************************
fatal: [localhost]: FAILED! => {"changed": false, "failed": true, "msg": "Spinning up  jj-rc cluster has been canceled due to its already completed."}

Which should probably be somewhere else anyway. Maybe a "protection" role or something.


Eager loading deeply nested relations

Hi thank You for this easy to use package.
I was wondering if it is possible to also eager load deeply nested relations?
For example: Inspection has_one -> Device has_one ->  Contract
Something like this doesn't seem work also tried nested arrays with different errors.
Inspection::get()->with(['Device.Contract'])
Fingers crossed it's documentation issue 🤞 🙂

Hi @priithansen ,
Sorry for the late reply.
Yes this should work.
Can you, please, elaborate on what effects you are having when executing this code?

Hi and thanks for looking into this.
It indeed seems my errors are caused by trying to eager load a polymorphic relation. Not sure if there is anything this addon can do in this situation.
As the last has_one on the chain is polymorphic
private static $has_one = [
    'DeviceOf' => DataObject::class, // Zone or Output
];

and I'm trying to eager load it with
DeviceInspection::get()->with(['Device.DeviceOf'])

This has nothing to do with the module, sorry.
AFAIK, DataObject is not an end-user class in SS4 and has no DB table, therefore no lookup queries are possible.
I highly recommend to create a parent class for all the possible options behind DeviceOf and use it there.
i.e.
class DeviceConnection extends DataObject {}

class Zone extends DeviceConnection {}
class Output extends DeviceConnection {}

and then
class Device extends DataObject {
    private static $has_one = [
         'DeviceOf' => DeviceConnection::class, // Zone or Output
    ];
}

Thought so, thanks for the tip about using a common parent class and also in hindsight I should have probably just used one class with a type enum :)
Thank You for Your time and thoughts!


Specify Inference language

Hello, great work on your project!
I'm trying to generate lyrics from a melody using the provided example in the Jupyter notebook. However, I'm struggling to get the lyrics in English, even when I specify it in the instructions and use special tokens like . Can you please guide me on how to ensure the output language is always English?
Thank you!

Thank you for your interest in our work. The language-specific prompt functionality is included in the newest version of our model. We will be releasing it soon, so please stay tuned.
Best,
Shuangrui

Thank you for addressing this issue. I noticed the updated results in the demo. Has the updated model been posted on HuggingFace, or is it still pending? Also, could you clarify whether the problem is with the model or the tokenizer? I'm new to LLMs and find it challenging to understand the root cause.

The method for generating entire songs will be released after the paper is accepted :) The root cause is whether you feed the language-specific prompt data during the supervised fine-tuning training.


Attempt to recreate a file for type org.parceler.Parceler$$Parcels

I'm writing an annotation processor that generates some classes that are annotated with @Parcel. No problem so far. In a project that uses this annotation processor, I can also create manually some classes that are annotated with @Parcel.
If only the generated classes are annotated with @Parcel, it works. And the same if only manually created classes are annotated. But I get the following compilation error when both generated and manually created classes are annotated: Attempt to recreate a file for type org.parceler.Parceler$$Parcels
From the README and the issue #89 I understand that the Parceler$$Parcels class may be generated several times in case of @Parcel classes from several modules / libraries. However in my case, the generated and manually created classes are in the same module.
It works when all my annotated classes declares @Parcel(parcelsIndex = false).
If this behaviour is expected, could you clarify a bit what are the difference when not using the parceler mapping class? Especially in terms of performance.
Thanks

Great question.
In general, Parceler gains its speed from avoiding reflection.  Parceler performs all analysis and code generation at compile time to make your wrapped Parcelables as fast as possible.  This concept is extended to the Parcels utility class by writing out the Parceler$$Parcels mapping class.
Taking a look at the Parceler$$Parcels class, it is simply a hash map relating two types, the annotated @Parcel class and the factory to create the wrapper class ParcelableFactory:
public class Parceler$$Parcels implements Repository<org.parceler.Parcels.ParcelableFactory> {

    private final Map<Class, org.parceler.Parcels.ParcelableFactory> map$$0 = new HashMap<Class, org.parceler.Parcels.ParcelableFactory>();

    public Parceler$$Parcels() {
        map$$0 .put(ManuallyRegistered.class, new Parceler$$Parcels.ManuallyRegistered$$Parcelable$$0());
        map$$0 .put(ExternalParcel.class, new Parceler$$Parcels.ExternalParcel$$Parcelable$$0());
        map$$0 .put(Example.class, new Parceler$$Parcels.Example$$Parcelable$$0());
        map$$0 .put(Manual.class, new Parceler$$Parcels.Manual$$Parcelable$$0());
        map$$0 .put(ExampleParcel.class, new Parceler$$Parcels.ExampleParcel$$Parcelable$$0());
        map$$0 .put(ExampleEnumList.class, new Parceler$$Parcels.ExampleEnumList$$Parcelable$$0());
        map$$0 .put(SubParcel.class, new Parceler$$Parcels.SubParcel$$Parcelable$$0());
    }
   //...
}

The Parcels utility class uses reflection to look up this class.  After this one reflection call, every call that is made to Parcels.wrap() is static code.  This reduces any slowdown from this lookup to near zero.
As a fallback, Parceler does use reflection if a call to wrap is made with a class that doesn't exist in the map.  If it finds one of the generated Parcelable classes, then it caches the class and constructor making subsequent calls as fast as possible.
This fallback is used in the case where parcelsIndex = false.  In this case a Parcelable is written out and compiled, but not added to the Parceler$$Parcels class.  If no classes are set to be written out to the Parceler$$Parcels class then the class is simply not generated.  This is why setting this parameter to false avoids the Attempt to recreate... exception during compile time.

If you're interested, I wrote a quick and dirty performance benchmark here: https://github.com/johncarl81/parceler/tree/master/examples/performance

Thanks for the details.
However I still don't understand why all the classes should have parcelsIndex = false?
Shouldn't it be only generated classes, or only manually written classes?
As for working with multiple parcels mapping classes, I'm thinking about a scenario where @Parcel could specify the name of its mapping class. Like for instance @Parcel(indexedIn = "ProcessorParcelerMapping") and @Parcel(indexedIn = "MyAppParcelerMapping").
Thus, the Parceler utility class would only require to lookup for those 2 mapping classes, rather than each @Parcel annotated classes.
On the other hand, it feels a bit like over optimisation. What do you think?

Im not sure what you mean... only one module's classes should have parcelsIndex = true, everything else should have parcelsIndex = false to avoid the Attemp to recreate... exception.
I think, if anything, the move would be to eliminate the Parceler$$Parcels mapping class and use a cached reflection lookup for each call to wrap().

@lukaspili, are you ok to close?


Move the code from sdks-java-fn-execution to sdks-java-core

Part of fixing #30169
I have not completed debugging of the massive change at #29924 that would complete the issue. Instead, I am starting to break off pieces that are easier to complete, to shrink the final change.
This takes just the code from sdks-java-fn-execution and merges it into the core SDK. The dependencies look like this:
sdks-java-core <--  sdks-java-fn-execution <-- runners-core-construction-java
So the merge must necessarily flatten all of those. This smaller merge is easier to accomplish first. I suspect it may also be easier to move all the code from one module to another and separate the gradle build changes.

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[ ] Update CHANGES.md with noteworthy changes.
[ ] If this contribution is large, please file an Apache Individual Contributor License Agreement.

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To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md
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R: @chamikaramj

There are some tiny code changes that I guess were done as part of IntelliJ's "move" operation and subsequent spotlessApply. For now I'll leave them so CI can continue. They seem like fine to leave or I could do the manual work to undo them.

Finally java precommit green - some issue with windmill precommit

OK yea I hit problems - I think there are a LOT of modules that do not declare their dependencies. I think people are counting on IntelliJ to automatically edit the build.gradle and that is part of why they are so disorganized. And it doesn't always add what it should perhaps. Or who knows but either way, removing this module is its own challenge and I will get to it next.


[jsfm] support component methods in another way

In current html5 render, the callComponentMethod method in runtime/listener.js for virtual dom should be reimplemented in the dom/componentManager.js, because the document's listener is replaced by it. @Jinjiang @fkysly @Hanks10100

@MrRaindrop Thanks for your reminder, I will add this feature in html5-feature-0.5.

It can be merged, and the modification of vm.$el should be reverted. #1665


Missing old ribbon

I cannot find how to go to the query editor and the article doesn't explain what it has been removed in detail.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 93a9e0e5-9166-bbd6-5ab2-7dc552d2d5d7
Version Independent ID: 5478be1c-64f8-2b5e-f51f-06d3311f89b9
Content: Use the updated ribbon in Power BI Desktop (preview) - Power BI
Content Source: powerbi-docs/desktop-ribbon.md
Service: powerbi
Sub-service: powerbi-desktop
GitHub Login: @davidiseminger
Microsoft Alias: davidi

You can open Power Query Editor on the main (new) ribbon, in the Home ribbon, by selecting the Transform data button.


Flutter sdk, gradle and customtabslauncher upgrade

Flutter sdk upgrade: >=2.12.0 <4.0.0
Gradle upgrade: v8.1.1
com.github.droibit:customtabslauncher upgrade: v1.7.0

Thank you for the PR!
This plugin has not been maintained for a long time
so don't worry about the error on CI.
I will fix the workflows later.


Making py3 compatible aph edits

@bsipocz - I finally resolved the bug that was causing your PR to fail on my local tests. So this PR introduces full-fledge python 3.x compatibility, at long last.
However, I am a bit mystified by the resolution, and I don't want to merge the PR until I understand it. As shown in this commit, https://github.com/aphearin/halotools/commit/469a7f96c2202ffedc75198dd82d49977a47b128, all I had to do is wrap yield_entry with the bool function. Without doing this, yield_entry stores an integer, and so the way the concluding control flow was written, the generator always yielded an empty list, causing test failures that are local to my machine where my local cache.log is not empty. What I'm confused about is what change led to this. Why did this only crop up now? Let me know if you have any insight. I'll be glad to put this PR into the repo when I get to the bottom of it.

Ok, I see it now @bsipocz. Some of the changes included changing condition == True to condition is True. This change is responsible for the difference because (1 == True) is True but (1 is True) is False. Mystery resolved, PR ready to merge, huzzah!

This PR resolves #266 and #190.

many thanks, @bsipocz!

@aphearin - Sorry about this PEP8 mess, I should have read the code to figure out what it does and use e.g. this change instead of is True:
if yield_entry:
    yield entry

It should have the same output as the current version with bool(), however I think it's better readable with bool().

No problem at all, this is entirely on me because some tests only run on my personal machine and this error does not get caught unless the APH_MACHINE flag in my test suite is True.


Validate comScore and Commanders Act implementations

As a GD / Mediapulse analyst I want to be sure that major changes made to Pillarbox implementation do not change analytics in a negative way.
Acceptance criteria

Mediapulse validated the current implementation.
ADI validated the implementation.

Tasks

[x] Contact Mediapulse.
[x] Contact ADI.
[x] Obtain Mediapulse validation.
[x] Obtain ADI validation.

comScore and ADI validated our updated implementation. Closed.


mininum/no gap between chapters in webtoon mode

Describe your suggested feature
Example from mangadex:

Example from tachiyomi:

Beside in neko:

Other details
i try the vertical reading mode but it didn't suit me, try to adjust the setting but still. Perhaps you can give me solution
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open issue.
[X] I have written a short but informative title.
[X] I have updated the app to the newest version Latest.
[X] I have checked through the app settings for my feature.
[X] I will fill out all of the requested information in this form.

The gap size there is intentional to break up the page noticeably.


Scene Navigation Disappearing

When using the Compact UI option, the Scene Navigation tends to disappear upon canvas load. It doesn't always happen, but most of the time it does. Instead of collapsing, showing the currently viewed and activated scenes, it just disappears completely, including the drawer button and everything so you can't get it back.
Tested with only Dorako UI enabled, no errors or warnings shown in the Console log.

Did you ever figure out reproduction steps for this?

I saw this message when it was posted but couldn't log in at the moment and then I forgot about it. Anyway, no I never managed to reproduce it, and it seldom happens anymore. Still no idea what causes it.

This is happening to me right now.  I have to disable Compact UI if I want to see my scene navigation bar.


read anon user role from config, remove reference to public role

The get_user_roles should get the role specified for the anon user in the config.py at AUTH_ROLE_PUBLIC. Without this fix, the list of tables the anon user can see is limited to the tables visible to the "Public" role.

@willgroves build is broken

@willgroves please rebase your changes on top of master

@xrmx There is a new PR #1878 with these changes rebased onto the latest master.


support py2

this shouldn't be hard, just untested at this point.
there might be some issues with fetching elements from bytes (py2: char, py3: int), for example.

closed in release v0.2


Replace XGA's use of corner with getdist

Getdist produces considerably nice plots than corner (in my option at least), so I think I'll replace uses of corner with getdist:
https://github.com/cmbant/getdist

I didn't replace it with getdist, but I added getdist as another option


extend age check on all snapshots in the repo

Hi,
I've extended the Freshness check to all snapshots in the repository without having to specify them individually. By this it is much easier to  monitor backups of multiple computers with different paths.
When everything is o.k. it shows the age of the oldest snapshot.

Huh, I must admit I don't use the Perl version anymore (there is also a Python one in this repo) but will try to have a look at your changes soon


Direct Access Help

Hi Love the plug-in.  Having issues with direct access.  I cannot seem to get rid of the wrong IP address associated with one of the mini splits which is disabling direct access.

Address.=[1834P008B1801xxx]=[<IP_ADDRESS>]
Address.=[1834P008B1800xxx]=[<IP_ADDRESS>]
Address.=[1834P008B1800xxx]=[<IP_ADDRESS>]

RESOLVED.
fixed the syntax.
Address.zone_name=ipaddress
Thanks!


Update of charging sessions and statistics URLs to v2

Proposed change
The charging statistics and charging sessions URLs have changed in April 2024. Although they are not yet used in this project, this is probably helpful for other projects to be aware of.
Type of change

[x] Bugfix (non-breaking change as this Consts aren't used yet)

Checklist

[x] The code change is tested and works locally.

Thanks! I've also seen that some other endpoints have been updated, but haven't had the chance to go through everything. This PR is a good reminder to do that though.

No worries. I was just playing around with it and started building my own Python scripts to collect data and forward it to my TIG stack while I was coming along the statistics stuff and saw, that also other integrations of openHAB, HA, ioBroker and so on always seem to reference here as well. So probably tackling it in the point of truth might help also the other maintainers.
Are there any plans on having a look into the sessions/statistics from your side on the roadmap or should I maybe give it a try?

I don't have an EV, so don't need it personally and cannot test it.
There was #569 which implemented a lot but then it got stale. I would still prefer this as a type of remote service (https://github.com/bimmerconnected/bimmer_connected/pull/569#issuecomment-1902630313) as we don't need to get this every 5 minutes with the rest of the state.
So yeah, if you want to look at it, sure!


Showing html as plain text

I have integrated it successfully and I am trying to show html in tour box but its showing only plain text with tags as text. Is there a way to show html in tour box ?

Angular won't let you inject html as a string in your templates, but you can override the tour step template as described in the "Custom Template" section of the docs.
If you want to provide a custom template for each step, you could do it like this:
// app.component.ts  or wherever you initialise your steps
@ViewChild('firstStepTemplate') firstStepTemplate: TemplateRef;
steps = [{
  title: 'First step',
  template: this.firstStepTemplate,
}, {
  title: 'Step with plain text',
  content: 'Some plain text',
}]

<ng-template #firstStepTemplate let-step="step">
  This is html and you can use <my-custom-component>too</my-custom-component>.  And even reference the step object: {{step.content}}
</ng-template>

<tour-step-template>
  <ng-template let-step="step">
    <md-card (click)="$event.stopPropagation()">
      <md-card-title>
        {{step?.title}}
      </md-card-title>
      <md-card-content>

        <ng-container *ngIf="step.template">
          <ng-container *ngTemplateOutlet="step.template; context: { step: step }"></ng-container>
        </ng-container>
        {{step?.content}}

      </md-card-content>
      <md-card-actions>
        <button md-icon-button [disabled]="!tourService.hasPrev(step)" (click)="tourService.prev()">
          <md-icon>chevron_left</md-icon>
        </button>
        <button md-icon-button [disabled]="!tourService.hasNext(step)" (click)="tourService.next()">
          <md-icon>chevron_right</md-icon>
        </button>
        <button md-button (click)="tourService.end()">End</button>
      </md-card-actions>
    </md-card>
  </ng-template>
</tour-step-template>

I gave this a shot and I am getting this error: Object literal may only specify known properties, and 'template' does not exist in type 'INgxmStepOption'
It doesn't like the template property. Did you remove this feature? If so, is there another way to do it?

@isaacplmann Well, angular does allow this, using [innerHTML]:
https://www.digitalocean.com/community/tutorials/angular-innerhtml-binding-angular
And supposedly sanitization is built in now. Could this be implemented? It would make the tour templates much easier to customize


SW-1842 Exact-then-fuzzy logic for search summaries

Commit ee97dd57 updated the search code so that if you do a fuzzy search, it first
looks for exact matches and returns those if there are any. This had the desired
effect of letting people type exact accession numbers into the accession search
UI, but the summary statistics were still based on the fuzzy matches.
Update the summary endpoint so it uses the same "try an exact search first, and
fall back to fuzzy if that doesn't return results" logic so the summary stats will
match the detailed search results.

Current dependencies on/for this PR:

main

PR #765   👈

This comment was auto-generated by Graphite.


"folder with images" : syntaxe ?

Hi,
I managed to make it work with:

Torch 1.0.0 as instructed
256*256 jpg source image
256*256 mp4 driving vidéo

However, the result is not so great (almost no mouth movement, and overall onbvious face distorsion to move the eyes and eyebraws etc)
So i have converted my mp4 video into about 1000 png pictures (30+ secondes, 30+ fps = ajout 1000 frames), into a dedicated folder.
However I must have overlooked something, because i can't find the exact syntaxe, un order to put a image folder as a parameter for "driving_video" location.
What should i use ?
I have tried:
" /path/to/the/folder/ "
and
"/path/to/the/folder/* "
But nothing seem to work.
Thanks
Kind regards,

Are you talking about training time? At test time it mp4 can be used.

No, i mean :
In the Readme we can read the following:
"the videos can be in '.gif', '.mp4' or folder with images. We recommend the later, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance."

This is a description of training dataset. Are you training?


error: failed to run custom build command for onig_sys v69.2.0

[root@docker opt]# cat /etc/centos-release
CentOS Linux release 7.7.1908 (Core)
[root@docker opt]# docker version
Client: Docker Engine - Community
 Version:           19.03.2
 API version:       1.40
 Go version:        go1.12.8
 Git commit:        6a30dfc
 Built:             Thu Aug 29 05:28:55 2019
 OS/Arch:           linux/amd64
 Experimental:      false

Server: Docker Engine - Community
 Engine:
  Version:          19.03.2
  API version:      1.40 (minimum version 1.12)
  Go version:       go1.12.8
  Git commit:       6a30dfc
  Built:            Thu Aug 29 05:27:34 2019
  OS/Arch:          linux/amd64
  Experimental:     false
 containerd:
  Version:          1.2.6
  GitCommit:        894b81a4b802e4eb2a91d1ce216b8817763c29fb
 runc:
  Version:          1.0.0-rc8
  GitCommit:        425e105d5a03fabd737a126ad93d62a9eeede87f
 docker-init:
  Version:          0.18.0
  GitCommit:        fec3683
[root@docker opt]# docker images
REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
rust                latest              cff8025aa0fa        3 weeks ago         1.62GB

in Dockerfile,i just "cargo -V && cargo install bat" for test
cargo 1.39.0 (1c6ec66d5 2019-09-30)
    Updating crates.io index
 Downloading crates ...
  Downloaded bat v0.12.1
  Installing bat v0.12.1
 Downloading crates ...
  Downloaded syntect v3.3.0
  Downloaded atty v0.2.13
  Downloaded ansi_colours v1.0.1
  Downloaded clap v2.33.0
  Downloaded dirs v2.0.2
  Downloaded encoding v0.2.33
  Downloaded content_inspector v0.2.4
  Downloaded wild v2.0.2
  Downloaded shell-words v0.1.0
  Downloaded lazy_static v1.4.0
  Downloaded error-chain v0.12.1
  Downloaded console v0.8.0
  Downloaded ansi_term v0.12.1
  Downloaded git2 v0.10.2
  Downloaded vec_map v0.8.1
  Downloaded serde v1.0.104
  Downloaded plist v0.4.2
  Downloaded unicode-width v0.1.7
  Downloaded cfg-if v0.1.10
  Downloaded term_size v0.3.1
  Downloaded libc v0.2.66
  Downloaded ansi_term v0.11.0
  Downloaded encoding-index-tradchinese v1.20141219.5
  Downloaded flate2 v1.0.13
  Downloaded memchr v2.2.1
  Downloaded bitflags v1.2.1
  Downloaded fnv v1.0.6
  Downloaded textwrap v0.11.0
  Downloaded cc v1.0.48
  Downloaded bincode v1.2.1
  Downloaded encoding-index-singlebyte v1.20141219.5
  Downloaded encoding-index-korean v1.20141219.5
  Downloaded encoding-index-simpchinese v1.20141219.5
  Downloaded serde_json v1.0.44
  Downloaded yaml-rust v0.4.3
  Downloaded walkdir v2.2.9
  Downloaded serde_derive v1.0.104
  Downloaded onig v5.0.0
  Downloaded regex-syntax v0.6.12
  Downloaded lazycell v1.2.1
  Downloaded dirs-sys v0.3.4
  Downloaded strsim v0.8.0
  Downloaded encoding-index-japanese v1.20141219.5
  Downloaded num_cpus v1.11.1
  Downloaded regex v1.3.1
  Downloaded version_check v0.1.5
  Downloaded log v0.4.10
  Downloaded clicolors-control v1.0.1
  Downloaded miniz_oxide v0.3.5
  Downloaded xml-rs v0.8.0
  Downloaded quote v1.0.2
  Downloaded jobserver v0.1.17
  Downloaded itoa v0.4.4
  Downloaded linked-hash-map v0.5.2
  Downloaded same-file v1.0.5
  Downloaded ryu v1.0.2
  Downloaded humantime v1.3.0
  Downloaded syn v1.0.11
  Downloaded encoding_index_tests v0.1.4
  Downloaded proc-macro2 v1.0.6
  Downloaded byteorder v1.3.2
  Downloaded line-wrap v0.1.1
  Downloaded base64 v0.10.1
  Downloaded termios v0.3.1
  Downloaded url v2.1.0
  Downloaded crc32fast v1.2.0
  Downloaded libgit2-sys v0.9.2
  Downloaded onig_sys v69.2.0
  Downloaded aho-corasick v0.7.6
  Downloaded thread_local v0.3.6
  Downloaded unicode-xid v0.2.0
  Downloaded quick-error v1.2.2
  Downloaded adler32 v1.0.4
  Downloaded safemem v0.3.3
  Downloaded percent-encoding v2.1.0
  Downloaded pkg-config v0.3.17
  Downloaded libz-sys v1.0.25
  Downloaded bindgen v0.50.1
  Downloaded matches v0.1.8
  Downloaded idna v0.2.0
  Downloaded cexpr v0.3.6
  Downloaded proc-macro2 v0.4.30
  Downloaded shlex v0.1.1
  Downloaded env_logger v0.6.2
  Downloaded quote v0.6.13
  Downloaded clang-sys v0.28.1
  Downloaded fxhash v0.2.1
  Downloaded peeking_take_while v0.1.2
  Downloaded which v2.0.1
  Downloaded unicode-bidi v0.3.4
  Downloaded unicode-normalization v0.1.11
  Downloaded unicode-xid v0.1.0
  Downloaded nom v4.2.3
  Downloaded termcolor v1.0.5
  Downloaded failure v0.1.6
  Downloaded smallvec v1.0.0
  Downloaded glob v0.3.0
  Downloaded backtrace v0.3.40
  Downloaded libloading v0.5.2
  Downloaded failure_derive v0.1.6
  Downloaded synstructure v0.12.3
  Downloaded rustc-demangle v0.1.16
  Downloaded backtrace-sys v0.1.32
   Compiling libc v0.2.66
   Compiling cfg-if v0.1.10
   Compiling log v0.4.10
   Compiling memchr v2.2.1
   Compiling proc-macro2 v1.0.6
   Compiling unicode-xid v0.2.0
   Compiling syn v1.0.11
   Compiling version_check v0.1.5
   Compiling pkg-config v0.3.17
   Compiling bitflags v1.2.1
   Compiling byteorder v1.3.2
   Compiling unicode-width v0.1.7
   Compiling lazy_static v1.4.0
   Compiling glob v0.3.0
   Compiling failure_derive v0.1.6
   Compiling vec_map v0.8.1
   Compiling strsim v0.8.0
   Compiling ansi_term v0.11.0
   Compiling rustc-demangle v0.1.16
   Compiling quick-error v1.2.2
   Compiling regex-syntax v0.6.12
   Compiling proc-macro2 v0.4.30
   Compiling unicode-xid v0.1.0
   Compiling bindgen v0.50.1
   Compiling termcolor v1.0.5
   Compiling shlex v0.1.1
   Compiling serde v1.0.104
   Compiling encoding_index_tests v0.1.4
   Compiling peeking_take_while v0.1.2
   Compiling crc32fast v1.2.0
   Compiling ryu v1.0.2
   Compiling smallvec v1.0.0
   Compiling matches v0.1.8
   Compiling safemem v0.3.3
   Compiling adler32 v1.0.4
   Compiling linked-hash-map v0.5.2
   Compiling itoa v0.4.4
   Compiling same-file v1.0.5
   Compiling xml-rs v0.8.0
   Compiling percent-encoding v2.1.0
   Compiling fnv v1.0.6
   Compiling lazycell v1.2.1
   Compiling ansi_term v0.12.1
   Compiling shell-words v0.1.0
   Compiling wild v2.0.2
   Compiling nom v4.2.3
   Compiling error-chain v0.12.1
   Compiling thread_local v0.3.6
   Compiling clang-sys v0.28.1
   Compiling humantime v1.3.0
   Compiling encoding-index-simpchinese v1.20141219.5
   Compiling encoding-index-japanese v1.20141219.5
   Compiling encoding-index-singlebyte v1.20141219.5
   Compiling encoding-index-tradchinese v1.20141219.5
   Compiling encoding-index-korean v1.20141219.5
   Compiling unicode-normalization v0.1.11
   Compiling unicode-bidi v0.3.4
   Compiling line-wrap v0.1.1
   Compiling miniz_oxide v0.3.5
   Compiling yaml-rust v0.4.3
   Compiling walkdir v2.2.9
   Compiling encoding v0.2.33
   Compiling idna v0.2.0
   Compiling jobserver v0.1.17
   Compiling num_cpus v1.11.1
   Compiling term_size v0.3.1
   Compiling atty v0.2.13
   Compiling termios v0.3.1
   Compiling dirs-sys v0.3.4
   Compiling clicolors-control v1.0.1
   Compiling aho-corasick v0.7.6
   Compiling content_inspector v0.2.4
   Compiling quote v1.0.2
   Compiling fxhash v0.2.1
   Compiling base64 v0.10.1
   Compiling quote v0.6.13
   Compiling flate2 v1.0.13
   Compiling url v2.1.0
   Compiling bincode v1.2.1
   Compiling serde_json v1.0.44
   Compiling cc v1.0.48
   Compiling textwrap v0.11.0
   Compiling dirs v2.0.2
   Compiling regex v1.3.1
   Compiling cexpr v0.3.6
   Compiling plist v0.4.2
   Compiling clap v2.33.0
   Compiling env_logger v0.6.2
   Compiling console v0.8.0
   Compiling backtrace-sys v0.1.32
   Compiling libloading v0.5.2
   Compiling libz-sys v1.0.25
   Compiling libgit2-sys v0.9.2
   Compiling ansi_colours v1.0.1
   Compiling synstructure v0.12.3
   Compiling serde_derive v1.0.104
   Compiling bat v0.12.1
   Compiling backtrace v0.3.40
   Compiling failure v0.1.6
   Compiling which v2.0.1
   Compiling onig_sys v69.2.0
error: failed to run custom build command for `onig_sys v69.2.0`

Caused by:
  process didn't exit successfully: `/tmp/cargo-installyIFHSU/release/build/onig_sys-5d74a82b7523dc4d/build-script-build` (exit code: 101)
--- stdout
cargo:warning=couldn't execute `llvm-config --prefix` (error: No such file or directory (os error 2))
cargo:warning=set the LLVM_CONFIG_PATH environment variable to a valid `llvm-config` executable

--- stderr
thread 'main' panicked at 'Unable to find libclang: "couldn\'t find any valid shared libraries matching: [\'libclang.so\', \'libclang-*.so\', \'libclang.so.*\'], set the `LIBCLANG_PATH` environment variable to a path where one of these files can be found (invalid: [])"', src/libcore/result.rs:1165:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace.

warning: build failed, waiting for other jobs to finish...
error: failed to compile `bat v0.12.1`, intermediate artifacts can be found at `/tmp/cargo-installyIFHSU`

Caused by:
  build failed

why?

It looks like you are missing libclang.
You may want to follow up on one of the user forums, or the rust-onig repository.

for reference only, plaese see here

Quite a few crates have switched over to fancy-regex from onig for regex as it's pure rust so builds a little more easily.


Mark autopostgresqlbackup as absent

It's not something we're actively using anymore so I don't think we need to keep it around. By keeping it around we have to deal with alerts and various other problems about it. Also, if we needed to restore from a backup in most cases we can now use RDS snapshots, and for any databases still left on Carrenza we can use WAL-E.
This matches with the proposed strategy for backups and it agrees with our documentation which marks it as deprecated.
Once this has been deployed across our machines, I'll open a follow up PR to remove the bits of code left in Puppet.

Can anyone think of a reason to keep this around?


name function error nmgetlib.c

Non-existent function error preventing compilation on Windows

While this change allows fasta36 to compile under Windows, it does not provide the appropriate functionality, and breaks things for Unix like operating systems that provide popen().
Better would be to replace "popen" with "_popen" for Windows compilation, inside an #ifdef.
#ifdef UNIX
libf=popen(acc_script,"r");
#endif
#ifdef WIN32
libf=_popen(acc_script,"r");
#endif
Begin forwarded message:
From: ZLoo<EMAIL_ADDRESS>Subject: [wrpearson/fasta36] Update nmgetlib.c (PR #44)
Date: August 31, 2022 at 1:15:03 AM MDT
To: wrpearson/fasta36<EMAIL_ADDRESS>Cc: Subscribed<EMAIL_ADDRESS>Reply-To: wrpearson/fasta36<EMAIL_ADDRESS>Non-existent function error preventing compilation on Windows

You can view, comment on, or merge this pull request online at:
https://github.com/wrpearson/fasta36/pull/44
Commit Summary

a78a5b8https://github.com/wrpearson/fasta36/pull/44/commits/a78a5b84e1da230245e72fc0cb5187acc34323e8 Update nmgetlib.c

File Changes
(1 filehttps://github.com/wrpearson/fasta36/pull/44/files)

M src/nmgetlib.chttps://github.com/wrpearson/fasta36/pull/44/files#diff-2797b84a443e02f1f040a742bf0e8398a69e0ea80bd1641cff566881269deb4f (2)

Patch Links:

https://github.com/wrpearson/fasta36/pull/44.patch
https://github.com/wrpearson/fasta36/pull/44.diff

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@wrpearson Thank you for your reply. Indeed, this would be the best solution.


Incorrect peer dependency

Just installed stylelint-a11y as part of a fresh install on a new project using yarn with:
yarn add stylelint stylelint-config-standard stylelint-a11y --dev

Noticed the following in the output of Yarn:
warning " ><EMAIL_ADDRESS>has incorrect peer dependency<EMAIL_ADDRESS>|| ^9.0.0 || ^10.0.0 || ^11.0.0".

When PR #53 is published, this issue should be resolved.

#53 is merged. When a new version is published, we can close this issue.
/cc @YozhikM

@ybiquitous I'll release it in 10 mins. Check it later

@YozhikM Thank you so much! I've confirmed that a new version has been published and resolved this peer-dep issue! 😊