minimize raises "ValueError: None values not supported." error when tf.while_loop used inside fn() of tf.cond()

On TF 0.9.0, The following code with tf.while_loop inside tf.cond raises ValueError when minimized.
import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops

with tf.Session() as sess:
    a = tf.constant([[1., 2.], [3.,4.]])
    w = tf.get_variable('w', [2, 1], tf.float32)
    c = tf.Variable(1)

    def loss_a(a, w):
        a_ta = tensor_array_ops.TensorArray(dtype=tf.float32, size=2,
                                            tensor_array_name="a_ta")
        a_ta = a_ta.unpack(a)

        b_ta = tensor_array_ops.TensorArray(dtype=tf.float32, size=2,
                                            tensor_array_name="b_ta")

        time = tf.constant(0, dtype=tf.int32, name="time")

        def _time_step(time, a_ta_t, b_ta_t):
          a = a_ta_t.read(time)
          b_ta_t = b_ta_t.write(time, a * w)
          return (time+1, a_ta_t, b_ta_t)

        (_, _, final_b) = tf.while_loop(
            cond=lambda time, _1, _2: time < 2,
            body=_time_step,
            loop_vars=(time, a_ta, b_ta),
            parallel_iterations=32,
            swap_memory=True)
        b = tf.reduce_sum(final_b.pack(), 0)
        return b

    def loss_b(a, w):
        return 2 * a * w

    loss = tf.cond(tf.equal(c, 0), lambda: loss_a(a, w), lambda: loss_b(a,w))
    train_op = tf.train.AdamOptimizer(1e-4).minimize(loss)

    sess.run(tf.initialize_all_variables())

    sess.run(train_op)

Traceback (most recent call last):
File "test.py", line 39, in
train_op = tf.train.AdamOptimizer(1e-4).minimize(loss)
File "/.../tensorflow/python/training/optimizer.py", line 193, in minimize
grad_loss=grad_loss)
File "/.../tensorflow/python/training/optimizer.py", line 250, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/.../tensorflow/python/ops/gradients.py", line 481, in gradients
in_grads = _AsList(grad_fn(op, *out_grads))
File "/.../tensorflow/python/ops/tensor_array_grad.py", line 115, in _TensorArrayWriteGrad
grad = g.read(index)
File "/.../tensorflow/python/ops/tensor_array_ops.py", line 191, in read
dtype=self._dtype, name=name)
File "/.../tensorflow/python/ops/gen_data_flow_ops.py", line 905, in _tensor_array_read
flow_in=flow_in, dtype=dtype, name=name)
File "/.../tensorflow/python/ops/op_def_library.py", line 704, in apply_op
op_def=op_def)
File "/.../tensorflow/python/framework/ops.py", line 2260, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/.../tensorflow/python/framework/ops.py", line 1234, in init
self._control_flow_context.AddOp(self)
File "/.../tensorflow/python/ops/control_flow_ops.py", line 1479, in AddOp
self._AddOpInternal(op)
File "/.../tensorflow/python/ops/control_flow_ops.py", line 1499, in _AddOpInternal
self.AddValue(x)
File "/.../tensorflow/python/ops/control_flow_ops.py", line 1438, in AddValue
real_val = grad_ctxt.grad_state.GetRealValue(val)
File "/.../tensorflow/python/ops/control_flow_ops.py", line 781, in GetRealValue
real_value = self.AddBackPropAccumulatedValue(h_value, value)
File "/.../tensorflow/python/ops/control_flow_ops.py", line 731, in AddBackPropAccumulatedValue
history_value = _SwitchRefOrTensor(history_value, pred)[branch]
File "/.../tensorflow/python/ops/control_flow_ops.py", line 324, in _SwitchRefOrTensor
return ref_switch(data, pred, name=name)
File "/.../tensorflow/python/ops/gen_control_flow_ops.py", line 341, in ref_switch
result = _op_def_lib.apply_op("RefSwitch", data=data, pred=pred, name=name)
File "/.../tensorflow/python/ops/op_def_library.py", line 459, in apply_op
as_ref=input_arg.is_ref).dtype.name
File "/.../tensorflow/python/framework/ops.py", line 620, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/.../tensorflow/python/ops/constant_op.py", line 179, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/.../tensorflow/python/ops/constant_op.py", line 162, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
File "/.../tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.

It works fine if I add la, lb as follows, although this would force execution of loss_a and loss_b regardless of the pred in tf.cond().
    la = loss_a(a, w)
    lb = loss_b(a, w)

    loss = tf.cond(tf.equal(c, 0), lambda: la, lambda: lb)
    train_op = tf.train.AdamOptimizer(1e-4).minimize(loss)

    sess.run(tf.initialize_all_variables())

    sess.run(train_op)

Is this a known issue or did I miss something in how to use tf.while_loop within tf.cond?

Could you try using the (somewhat experimental) @Defun functionality to see if it works?  See testWhileFuncBasic() in control_flow_ops_py_test.py.

Automatically closing due to lack of recent activity. Please reopen if additional information becomes available.

The error is also occurring when restoring a model trained using layers.stack with a Estimator class(providing as  model directory where the model is saved, and model_fn as the conv_model written in skflow mnist example).
 File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 394, in predict     as_iterable=as_iterable)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 663, in _infer_model     predictions = self._get_predict_ops(features)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 897, in _get_predict_ops     predictions, _, _ = self._call_model_fn(features, targets, ModeKeys.INFER)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 806, in _call_model_fn     return self._model_fn(features, targets)   File "test.py", line 34, in conv_model     return learn.models.logistic_regression(h_fc1, y)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/models.py", line 137, in logistic_regression     logging_ops.histogram_summary('%s.y' % vs.get_variable_scope().name, y)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/logging_ops.py", line 125, in histogram_summary     tag=tag, values=values, name=scope)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/gen_logging_ops.py", line 100, in _histogram_summary     name=name)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/op_def_library.py", line 458, in apply_op     as_ref=input_arg.is_ref).dtype.name   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py", line 621, in convert_to_tensor     ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function     return constant(v, dtype=dtype, name=name)   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/constant_op.py", line 163, in constant     tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))   File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto     raise ValueError("None values not supported.") ValueError: None values not supported.

I get a similar error when I have a map_fn modifying a variable, and the map_fn is inside of a cond.
Traceback (most recent call last):
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 454, in apply_op
    as_ref=input_arg.is_ref)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 621, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto
    raise ValueError("None values not supported.")
ValueError: None values not supported.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "cifar10_shallow.py", line 161, in <module>
    optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss, global_step=global_step)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 193, in minimize
    grad_loss=grad_loss)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 250, in compute_gradients
    colocate_gradients_with_ops=colocate_gradients_with_ops)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gradients.py", line 484, in gradients
    in_grads = _AsList(grad_fn(op, *out_grads))
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/tensor_array_grad.py", line 115, in _TensorArrayWriteGrad
    grad = g.read(index)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/tensor_array_ops.py", line 191, in read
    dtype=self._dtype, name=name)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 1344, in _tensor_array_read
    flow_in=flow_in, dtype=dtype, name=name)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op
    op_def=op_def)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2298, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1236, in __init__
    self._control_flow_context.AddOp(self)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1493, in AddOp
    self._AddOpInternal(op)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1513, in _AddOpInternal
    self.AddValue(x)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 1452, in AddValue
    real_val = grad_ctxt.grad_state.GetRealValue(val)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 788, in GetRealValue
    real_value = self.AddBackPropAccumulatedValue(history_value, value)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 737, in AddBackPropAccumulatedValue
    history_value = _SwitchRefOrTensor(history_value, pred)[branch]
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 325, in _SwitchRefOrTensor
    return ref_switch(data, pred, name=name)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_control_flow_ops.py", line 341, in ref_switch
    result = _op_def_lib.apply_op("RefSwitch", data=data, pred=pred, name=name)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 458, in apply_op
    as_ref=input_arg.is_ref).dtype.name
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 621, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
  File "/home-nfs/dan/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto
    raise ValueError("None values not supported.")

I'm encountering the same error when calling learn.Estimator.predict on a restored, already trained model, unless I call learn.Estimator.fit beforehand. The following code is largely based on this example. My guess is that some important stuff isn't initialized by calling predict. For example, I don't see "Creating TensorFlow device" or anything like that when only calling predict.
To reproduce:

Run ./text_classification_character_rnn.py -m model to train a model stored in the "model" directory
Run ./text_classification_character_rnn.py -m model -t to load the model and make a prediction only, without calling fit first, which causes the error.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from optparse import OptionParser
import numpy as np
from sklearn import metrics
import pandas

import tensorflow as tf
from tensorflow.contrib import learn

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_bool('test_with_fake_data', False,
                         'Test the example code with fake data.')

MAX_DOCUMENT_LENGTH = 200
HIDDEN_SIZE = 60

parser = OptionParser()
parser.add_option("-m", "--model", dest="model_dir",
                  help="specify current model directory")
parser.add_option("-t", "--testonly", action="store_true", dest="testonly",
                  default=False, help="test the model without training")

(options, args) = parser.parse_args()

class ProgressMonitor(learn.monitors.BaseMonitor):
    def __init__(self):
        print('[Monitor] Init')
    def begin(self, max_steps):
        print('[Monitor] Starting run.')
    def end(self):
        print('[Monitor] Completed run.')
    def step_begin(self, step):
        print('[Monitor] Step %d...' % step)
        return []
    def step_end(self, step, outputs):
        pass

def char_rnn_model(x, y):
  """Character level recurrent neural network model to predict classes."""

  # restore saved model if provided
  y = tf.one_hot(y, 15, 1, 0)
  byte_list = learn.ops.one_hot_matrix(x, 128)
  byte_list = tf.unpack(byte_list, axis=1)

  cell = tf.nn.rnn_cell.GRUCell(HIDDEN_SIZE)
  #cell = tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE)
  #cell = tf.nn.rnn_cell.LSTMCell(HIDDEN_SIZE)
  _, encoding = tf.nn.rnn(cell, byte_list, dtype=tf.float32)

  prediction, loss = learn.models.logistic_regression(encoding, y)

  train_op = tf.contrib.layers.optimize_loss(
      loss, tf.contrib.framework.get_global_step(),
      optimizer='Adam', learning_rate=0.01)

  return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op

def main(unused_argv):

  # Prepare training and testing data
  dbpedia = learn.datasets.load_dataset(
      'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
  x_train = pandas.DataFrame(dbpedia.train.data)[1]
  y_train = pandas.Series(dbpedia.train.target)
  x_test = pandas.DataFrame(dbpedia.test.data)[1]
  y_test = pandas.Series(dbpedia.test.target)

  # Process vocabulary
  char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
  x_train = np.array(list(char_processor.fit_transform(x_train)))
  x_test = np.array(list(char_processor.transform(x_test)))

  # Build model
  classifier = learn.Estimator(model_fn=char_rnn_model, model_dir=options.model_dir)

  # Train and predict
  monitor = ProgressMonitor()
  if not options.testonly:
    classifier.fit(x_train, y_train, max_steps=10, batch_size=10000, monitors=[monitor])
  y_predicted = classifier.predict(x_test, batch_size=10000)
  score = metrics.accuracy_score(y_test, y_predicted['class'])
  print('Accuracy: {0:f}'.format(score))

if __name__ == '__main__':
  tf.app.run()

The error:
[Monitor] Init
Traceback (most recent call last):
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 454, in apply_op
    as_ref=input_arg.is_ref)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 628, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto
    raise ValueError("None values not supported.")
ValueError: None values not supported.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "./text_classification_character_rnn.py", line 119, in <module>
    tf.app.run()
  File "/usr/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv))
  File "./text_classification_character_rnn.py", line 113, in main
    y_predicted = classifier.predict(x_test, batch_size=10000)
  File "/usr/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 323, in predict
    as_iterable=as_iterable)
  File "/usr/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 592, in _infer_model
    predictions = self._get_predict_ops(features)
  File "/usr/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 824, in _get_predict_ops
    predictions, _, _ = self._call_model_fn(features, targets, ModeKeys.INFER)
  File "/usr/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 733, in _call_model_fn
    return self._model_fn(features, targets)
  File "./text_classification_character_rnn.py", line 73, in char_rnn_model
    y = tf.one_hot(y, 15, 1, 0)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 2717, in one_hot
    name)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1421, in _one_hot
    axis=axis, name=name)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 458, in apply_op
    as_ref=input_arg.is_ref).dtype.name
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 628, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
  File "/usr/lib/python3.5/site-packages/tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto
    raise ValueError("None values not supported.")

The original issue posted by liusiqi43@ should already be fixed at HEAD.

the same issue.
python train_cifar.py
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally

Downloading cifar-10-binary.tar.gz 100.0%
Successfully downloaded cifar-10-binary.tar.gz 170052171 bytes.
Filling queue with 20000 CIFAR images before starting to train. This will take a few minutes.
Traceback (most recent call last):
File "train_cifar.py", line 311, in
tf.app.run()
File "/home/ozzie/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run
sys.exit(main(sys.argv[:1] + flags_passthrough))
File "train_cifar.py", line 307, in main
train(is_training, logits, images, labels)
File "/media/New_bt/ML/tensorflow-resnet/resnet_train.py", line 33, in train
loss_ = loss(logits, labels)
File "/media/New_bt/ML/tensorflow-resnet/resnet.py", line 148, in loss
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)
File "/home/ozzie/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 745, in sparse_softmax_cross_entropy_with_logits
logits = ops.convert_to_tensor(logits)
File "/home/ozzie/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 657, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/ozzie/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.py", line 180, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/ozzie/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
File "/home/ozzie/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 346, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.

Did anyone find solution to this problem?

I meet this bug too, it seems about the tf.Variable, you can try it with  tf.Variable(0, trainable = False)

I see this too with r0.12
    nn = tf.contrib.learn.Estimator(
          model_fn=model_fn, params=model_params, model_dir='./' + field,
          config=tf.contrib.learn.RunConfig(
           save_checkpoints_steps=1000, save_checkpoints_secs=None))
    validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(
        input_fn=test_fn, every_n_steps=1000, early_stopping_metric="loss",
        early_stopping_metric_minimize=True, early_stopping_rounds=5,
        eval_steps=1)

# If I comment out the line below
    nn.fit(input_fn=train_fn, monitors=[validation_monitor])

# Then this line gives me the error
    prediction = nn.predict(input_fn=predict_fn, as_iterable=False)

Traceback (most recent call last):
File "titanic.py", line 306, in
master.update(estimate('Fare', fareCats, fareReals, master))
File "titanic.py", line 156, in estimate
prediction = nn.predict(input_fn=predict_fn, as_iterable=False)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/util/deprecation.py", line 191, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 469, in predict
as_iterable=as_iterable)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 839, in _infer_model
infer_ops = self._get_predict_ops(features)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1107, in _get_predict_ops
return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1019, in _call_model_fn
params=self.params)
File "titanic.py", line 75, in model_fn
loss = tf.contrib.losses.mean_squared_error(predictions["Value"], labels)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/util/deprecation.py", line 191, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py", line 592, in mean_squared_error
labels = _labels(labels, targets)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/losses/python/losses/loss_ops.py", line 56, in _labels
raise ValueError("Must provide 1 of labels and targets.")
ValueError: Must provide 1 of labels and targets.

Found that this is a known issue: https://github.com/tensorflow/tensorflow/issues/3340
And a ticketed enhancement: https://github.com/tensorflow/tensorflow/issues/5548
See above tickets for work around.

@yuanbyu I think I saw something recently about that. Was that fixed at some point recently?

I met the same exception: ValueError: None values not supported.
It is impossible to use train model in the way:
 nn = tf.contrib.learn.Estimator(model_fn=model_fn,model_dir='/path')     predictions = nn.predict(x=prediction_set.data, as_iterable=True)
the hack to solve it is to run an evaluation before a prediction.
 nn = tf.contrib.learn.Estimator(model_fn=model_fn,model_dir='/path')     ev = nn.evaluate(x=test_set.data, y=test_set.target, steps=1)     predictions = nn.predict(x=prediction_set.data, as_iterable=True)

@agniszczotka
Great ! That solved my problem !
But I still do not know why ~

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


feat(dal): check for cycles when connecting components

Ensures we check for graph cycles when connecting components (to prevent connecting components in a cycle), and when attaching children to parent frames.

bors r+

bors r+

bors r+


[NewPlugin]  shareef12 driveranalyzer v1.0.0

Please add my plugin to the repository.
Yes, I've read the instructions, especially the part about creating an actual release and creating the tag.
I used the UI to create a real release.
Author: Me
Repo name: https://github.com/shareef12/driveranalyzer/releases/tag/v1.0.0
Release: v1.0.0

Thanks for the submission -- added.


[release/1.3] Update cri-tools to v1.16.1.

Let's update cri-tools to the newest release, which includes many useful bug fixes and features.
Signed-off-by: Lantao Liu<EMAIL_ADDRESS>

Verify test is not running because of https://github.com/kubernetes/test-infra/pull/14488

/test pull-cri-containerd-verify

/lgtm


Added : More detailed information for beginners

Added : shell command --> "npm install -g angular"
Added : Description for creating first app in Angular for Beginners

[x] I have read freeCodeCamp's contribution guidelines.
[x] My pull request has a descriptive title (not a vague title like Update index.md)
[x] My pull request targets the master branch of freeCodeCamp.
[x] None of my changes are plagiarized from another source without proper attribution.
[x] My article does not contain shortened URLs or affiliate links.

If your pull request closes a GitHub issue, replace the XXXXX below with the issue number.
Closes #XXXXX

Checks have failed, please provide some info.


unary operator expected

/Library/Application Support/JAMF/tmp/AppleUpdateDefer1.sh: line 80: [: 3: unary operator expected
No new software available.
2019-Jul-01 19:44:20 : No updates pending. Setting plist remainingDeferral to 3.  It was 3.  Exiting

Getting the above "unary operator expected" when running the script from Jamf Pro and visible in a "jamf policy" command.

can you add additional context here?  I am unable to duplicate the error.

@taboc741 there is not much more to add. I setup the script as you described. It works great but in the output I get this unary operator expected error.

I was able to solve this problem with the help of some of the admins on macadmins Slack.
I changed the shebang to #!/bin/bash and made a change to line 83. if [ -z "$remainDeferrals" ]; then


PGP Encrypt using ResolveThenIdentify2

@maxtaco
also verified that this fixes https://keybase.atlassian.net/browse/CORE-2329 as well.

Took it for a test drive. LGTM!


Refactor GPU transfer

Codecov Report

Merging #99 into master will increase coverage by 0.13%.
The diff coverage is 66.40%.

@@            Coverage Diff             @@
##           master      #99      +/-   ##
==========================================
+ Coverage   38.51%   38.64%   +0.13%
==========================================
  Files          83       83
  Lines        9898     9861      -37
  Branches     2084     2063      -21
==========================================
- Hits         3812     3811       -1
+ Misses       5869     5846      -23
+ Partials      217      204      -13

Impacted Files
Coverage Δ

...ral_sp/models/modules/positionwise_feed_forward.py
100.00% <ø> (ø)

..._sp/models/modules/relative_multihead_attention.py
97.26% <ø> (ø)

neural_sp/models/seq2seq/decoders/ctc.py
33.43% <0.00%> (+0.20%)
:arrow_up:

neural_sp/models/seq2seq/encoders/encoder_base.py
44.06% <ø> (-3.56%)
:arrow_down:

neural_sp/models/seq2seq/encoders/subsampling.py
100.00% <ø> (ø)

neural_sp/models/seq2seq/speech2text.py
0.00% <0.00%> (ø)

neural_sp/models/seq2seq/decoders/las.py
50.61% <33.33%> (+0.22%)
:arrow_up:

neural_sp/models/seq2seq/decoders/transformer.py
59.74% <58.82%> (+0.64%)
:arrow_up:

neural_sp/models/seq2seq/encoders/conformer.py
76.64% <64.28%> (ø)

...eural_sp/models/seq2seq/decoders/rnn_transducer.py
75.23% <66.66%> (-0.56%)
:arrow_down:

... and 15 more

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 a39a6e6...77e3aef. Read the comment docs.


Are refactor PRs allowed?

Assalamu Alaikum brothers --
I wish to inshAllah get involved in contributing to Quran.com whatever way I can, and the first step for me would be to get more familiar with the codebase, its structure, etc..
In the process of reading through it I reckon I'll find random things that could be refactored, and usually a new reader or a developer coming back to read his own code has the best eye for these things, so I think it may be a useful contribution in preparing for more significant ones later.
Would you brothers be okay with accepting a primarily refactor-based PR? I know some orgs don't like that that's why I'm asking before I do it and realize I have to throw it away :)
If all's good, I'm perfectly happy with a strict review that ensures I don't change something that was in fact intended.

Please do! Refractors are more than welcome, but the only thing I'd caution is making it easier for us to review it and making it easier for us to transition it in.  Lastly, if you foresee it will be slightly larger, maybe create PRs against an open PR of your own (branch called refactor, branch called refactor1 that is to merge into branch refactor).
Other than that, please do! We need lots of help and I been meaning to do some refactoring too

@mmahalwy Great to hear! Definitely willing to help. Will take your advice into mind -- no one wants to a deal with a diff-mess-fest, understandably. I'll try to just incrementally push a PR when it looks like it's getting significant, to keep each one relatively light, inshAllah.
Looking forward to helping :)

Going to close this!


extend FormGroup and FormPanel to accept all other types of Widgets

The idea is to extend the FormGroup and FormPanel to accept other types of Widgets
since i needed to include panels inside that can be shown or hidden based on other input values

I have added a generic method to add inputs to any kind of mutable composite/panel. This allows to add them to a regular UiVerticalPanel. However, FormPanel still has its unique features such as data binding and layout options so it still needs to be changed.
My plan is to change the API such that it is like any other mutable panel (e.g. UiVerticalPanel):
addChild(UiRegularWidget); // will behave like in a `UiVerticalPanel`
addChild(UiInput); // will behave like it currently does in `UiFormPanel`

Hope this plan makes sense and will finally resolve this issue.


systemverilog-plugin: enums are always 32-bit

Enums like enum logic [2:0] are seen as 32 bit wide types. This is a problem only inside the plugin - yosys converts them to properly sized types in later stages.
Test case:
module mod(output int foo_size, output int bar_size, output int enum_size);
  typedef enum logic [2:0] {
      e_zero  = 0
    , e_one   = 1
    , e_two   = 2
    , e_three = 3
    , e_four  = 4
  } numbers_e;

  typedef struct packed {
    logic [31:0] f_member;
  } foo_t;

  typedef struct packed {
    numbers_e    b_member;
    logic [28:0] b_padding;
  }  bar_t;

  union packed {
    foo_t member_foo;
    bar_t member_bar;
  } union_s;

  assign foo_size = $bits(foo_t);
  assign bar_size = $bits(bar_t);
  assign enum_size = $bits(numbers_e);
endmodule

causes an error:
enum_width.sv:10: ERROR: member member_foo of a packed union has 32 bits, expecting 61

After commenting out the union and using write_verilog in yosys the sizes are correct:
  assign foo_size = 32'h00000020;
  assign bar_size = 32'h00000020;
  assign enum_size = 32'h00000003;

@mglb any insights on this one?

@hzeller - Can you chat with Antmicro about this?

Awesome!
On Tue, Dec 6, 2022 at 8:55 AM Mariusz Glebocki @.***>
wrote:

@taylor-bsg https://github.com/taylor-bsg the PR with a fix will be
ready today: #416
https://github.com/chipsalliance/yosys-f4pga-plugins/pull/416
—
Reply to this email directly, view it on GitHub
https://github.com/chipsalliance/yosys-f4pga-plugins/issues/400#issuecomment-1339675513,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AEFG5AGSX5D4RFRQTQPZIMTWL5VZFANCNFSM6AAAAAASCGEZNE
.
You are receiving this because you were mentioned.Message ID:
@.***>


Tests and fix for Node.depth() not treating EXTRACTs correctly.

This fixes the issue where after an EXTRACT in the Definitions section the rest of the definitions would be omitted.

Addresses https://github.com/18F/atf-eregs/issues/85

Makes sense to me. Thanks for covering the other cases in your test


Add watchOS deployment target to the podspec

Hi There,
Would be great to use emoji-data-ios with watchOS as well as iOS - have added a watchOS deployment target to the podspec, and validated it building with Xcode 11.
Thanks, all the best.

Thank you @crafterm !


MSID: 88054 Version: 2 DOI: 10.1101/2<IP_ADDRESS>5275

MSID: 88054
Version: 2
Preprint DOI: https://doi.org/10.1101/2<IP_ADDRESS>5275
Step 1. Awaiting reviews
Editorial to post reviews via hypothesis
Useful links:

DocMap: https://data-hub-api.elifesciences.org/enhanced-preprints/docmaps/v2/by-publisher/elife/get-by-manuscript-id?manuscript_id=88054
New model tracking: https://docs.google.com/spreadsheets/d/1_fHaoOy7hjyocptKtVJRijeNpUY4hBS7Ck_aVmx6ZJk/
Reviews on sciety: https://sciety.org/articles/activity/10.1101/2<IP_ADDRESS>5275

For trouble shooting (e.g. no Docmaps available):

DocMap issue addressing: https://miro.com/app/board/uXjVNCwK6EI=/
Explore DataHub DocMaps API: https://lookerstudio.google.com/reporting/4c2f0368-babb-4beb-b5b3-497e7e7b0f08/page/ejphD
Unmatched submissions and preprints: https://lookerstudio.google.com/u/0/reporting/9f86204f-3bf7-477c-9b18-5c5ef141bf69/page/p_gxi57ha93c
Unmatched manuscripts spreadsheet: https://docs.google.com/spreadsheets/d/15QcK8w-ssB7109RQEDtFpJPZ0J5HTGxoHa_2TtpMBbg/edit#gid=1336081641

Step 2. Preview reviewed preprint
Production QC content ahead of publication
Instructions:

QC preview: https://prod--epp.elifesciences.org/previews/88054v2
Update ticket with any problems (add blocked label)
When QC OK, add QC OK label to ticket and add publication date and time to https://docs.google.com/spreadsheets/d/1amAlKvdLcaDp5W8Z8g77NmkwbMF5n_u89ArSqPMO8jg
Move card to next column
(At end of the day post link in #enhanced-preprint and ask for PDF to be generated)

Useful links:

Preprint DOI :  https://doi.org/10.1101/2<IP_ADDRESS>5275
Confirm reviews returned by EPP: https://prod--epp.elifesciences.org/api/reviewed-preprints/88054/v2/reviews
To update the MECA path in the docmap: https://docs.google.com/spreadsheets/d/1mctCQuNFBjSn97Lihy7_vBO6z7-N-oqyLv4clyi6zHg

Step 3: Awaiting search reindex
This step adds the reviewed preprint to the homepage: https://elifesciences.org
The search reindex is triggered once an hour. We need the reviewed preprint to be indexed as the search application serves the journal homepage.
Useful links:

Jenkins pipeline to reindex search can be triggered sooner or monitored here: https://alfred.elifesciences.org/job/process/job/process-reindex-reviewed-preprints/

Step 4: Published! PDF requested
Waiting for PDF to be generated
Useful links:

PDF tracking: https://docs.google.com/spreadsheets/d/106_XeDjmuBae7gexOTNzg60lapeqjl2aRn9DzupGyS8/

Step 5: Introduce PDF to data folder and git repo
Upload PDF to relevent folder in git repo https://github.com/elifesciences/enhanced-preprints-data/
Step 6: Done!

[ ] Kettle is on!

@fred-atherden the authors have included their public reviews at the end of their preprint, is this ok?

I think that we should remove the response, as it will cause confusion. I've edited the package. Should update on the next few hours


Export individual methods

Make the old dependencies useless.

This is done.
$ node src/cli.js -r name-your-contributors -u RichardLitt > results
$ cat results | jq '.issueCreators'

Going to reopen as a note to close down all of the old dependenices.

Done. I closed all of their issues and PRs, labelled them as unmaintained.


Minor build fixes/cleanup

This applies just a few minor cleanups to the build:

Deletes a duplicated FoundationInternationalization/LockedStateTests.swift (merging it into FoundationEssentials/LockedStateTests.swift)
Deletes an unused/incorrect JSON spec file that in some environments conflicted with the duplicated file with the same name in the correct subdirectory

@swift-ci please test


Add basic debug functionality

Add a --debug switch to the entry point. This initially should just enable logging and full stack traces but in future could do more.

closed by #77


Upgrade Composer dependencies

Pull Request Test Coverage Report for Build 887828755

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 96.0%

Totals

Change from base Build 887807580:
0.0%

Covered Lines:
24

Relevant Lines:
25

💛  - Coveralls


membership-requests [#855]: waiting flow: add community-membership-request discussion page

add necessary template variable
add style
generalize header macro

membership-requests [#855]: waiting flow: dashboard
depends on: https://github.com/inveniosoftware/invenio-communities/pull/1132

close in favor of https://github.com/inveniosoftware/invenio-app-rdm/pull/2764 that contains it.


Conflict with the Corpse mod.

When installed along side the Corpse mod, the Corpse becomes unable to be interacted with.
This only happens when Create Automated is present as of version 1.2.0. The problem persists with v1.2.1.
Version 1.1.0 doesn't cause this issue.
There is no error thrown in the logs.

that happens because of the new node movement config, i messed with entity interaction and broke something

temporary fix would be to change create's spawner movement settings to something other than No_pickup


The recommanded processes number?

RT
How many processes are you using? I worry about baidu ban my IP or account.

Good question, but... I also have no idea, have to ask Baidu.


Improved Documentation for Using Stripe Connect Accounts

Summary
It would be awesome to have improved documentation on the README.md for usage with Stripe Connect accounts. Judging by #129 , #50 it seems that the focus on of this library is not on Connect, so it may fall under the Advanced Integrations section. But even in this section, nothing directly references the inclusion or exclusion of the Connect platform.
I had to hop onto the IRC as well as read through the source code to figure out how to get it to work.  I assumed that the StripeProvider would accept the stripeAccount parameter. The error messages were helpful, but navigating by error messages took some time.
In order to use react-stripe-elements  with stripe connect I had to call the window.Stripe function within a component to create a stripeObject and pass that down to the StripeProvider component as shown below.
const SubscribeToCoachCardForm = ({ coachId, accountId }) => {
  const stripeObject = window.Stripe(
    process.env.REACT_APP_STRIPE_PUBLISHABLE_KEY,
    { stripeAccount: accountId },
  );
  return (
    <StripeProvider stripe={stripeObject}>
      <Elements>
        <AuthConsumer>
          {({ user: { userId, athleteId } }) => (
            <SubscribeForm
              userId={userId}
              athleteId={athleteId}
              coachId={coachId}
            />
          )}
        </AuthConsumer>
      </Elements>
    </StripeProvider>
  );
};

The docs do point out that we can call the Stripe Window object, but as Connect usage is fairly common (i think...), it would save a lot of people time and would be extremely helpful to just have a sentence or two that indicates that the way to go is to pass in a stripeAccount parameter to window.Stripe function as referenced here: stripe-js in order to use Connect.
In its current form, it seems as if there were no real options if you wanted to use the Connect Platform with React-Stripe-Elements.
Other information
I'd be happy to submit a PR for documentation if agreed upon!

Apologies for taking a while to return to this issue. I checked out the code you linked and passed through the props instead. It seems to be working currently.
I see now that the docs do say:
The ... above represents that this component accepts props for any option that can be passed into Stripe(apiKey, options).

I did notice this convention of adding the stripe_account as a param for the Stripe API (which is really convenient) but didn't expect the same param stripeAccount for React-Stripe-Elements. Though, now that I do know that, I think it's super convenient. Perhaps adding a sentence just mentioning that the ... can be used to accept a connect account id? Let me know what you think!
Thanks for your help.

@stephenhuh Thanks for this suggestion! I've just opened a PR to improve the docs for this. Please comment on whether that would have helped you or not. Since that docs PR is the action item out of this Issue, I'm going to close this, but feel free to re-open if you run into anything else related.

Awesome @asolove-stripe and thanks for taking the time to do that.


Support Overload View for autocomplete

Right now, it does not show overload methods, it is highly unlikely that default method autocomplete is what you want and there is no way of seeing overloaded methods. Any way we can support to see all overaloaded methods? may be by pressing down/up arrow when autocomplete is displayed.
Or any other way supported by autocomplete plugin would be fine.

:+1: also asked in #309

also asked in #309

Agreed, basically show overloads at the first level of autocomplete (I misunderstood that intent in 309)

Any updates on this?

I believe the core of this request is more or less fulfilled by signature help. Feel free to open new issues for improvements.


Use the first 3 sentences from a bio.

Alexa talks too much.

while you are there can you also add log for https://github.com/artsy/elderfield/issues/34

On it @ashkan18.

Thanks

You can merge this if it looks good, don't wait for #34.

Looks good, I think it would be fun to add option for longer version bio later. Something like either give more option at the end of 3 sentences or Ask Artsy about Andy Warhol long bio type of thing.

Thanks for opening https://github.com/artsy/elderfield/issues/44 @ashkan18


Unable to unit test casting MapClaims

I am unable to write a unit test that validates scenarios where token.Claims.(jwt.MapClaims) would fail, since both resources are owned by jwt-go and cannot be abstracted.
A short chunk of code for context:
func (self *Router) allowed(token *jwt.Token, permissions ...string) bool {

	if len(permissions) == 0 {
		return true
	} else if claims, ok := token.Claims.(jwt.MapClaims); !ok {
		return false
	} else if _, ok := claims["permissions"]; !ok {
		return false
	}

I cannot abstract jwt.MapClaims or token.Claims (as a property of jwt.Token and not a method) I am not sure how I should be getting code coverage for this.
Any suggestions?

I don't understand what you're trying to do. Can you expand a bit?

Is there any scenario in which the Claims token property cannot be cast to a jwt.MapClaims type?
I am unable to figure out how to test it and get coverage for that conditional statement with go test -cover since I cannot abstract or control the Claims property or the MapClaims type.

In what scenario will ok not be true for this:
_, ok := token.Claims.(jwt.MapClaims)

And how would you go about testing that in the function I provided?

As a developer, you have total control over the type of token.Claims.  Tokens returned from the Parse method will always have a MapClaims object or nil.  The only way claims will be another type is if you use ParseWithClaims, in which case, you are specifying the type.

Parse almost always returns a token, though it will be mostly unpopulated if parsing fails.  If you want to create a condition where Claims is nil, try jwt.Parse("this.is.invalid", ...).  You should get a token with only the Raw property populated and a parse error.

Also Token has no private fields, which means you can craft one any way you want for testing.  I think it's overkill, but go for it if you like.
If you want adequate coverage of handling the results of Parse, you need these things:

Non-nil error returned
Valid token, with a MapClaims object in Claims

@dgrijalva thank you very much for the suggestions and use-case explanation.  Using a raw token with no properties I was able to easily solve my tests.


Add link to original data

This should also be available from the plan chooser.
If the link to original data is only on the "share" tab, someone has to choose a specific plan to get the original data for any plans. There is no underlying business logic for this requirement -- so it  seems unnatural to require it in the UI.

I agree that the "share" tab isn't a great choice for this data.  Abby and I spoke about an appropriate place for it and concluded that the link works best on the front page.  It's separate from the workflow of creating districts, choosing plans and working with DB in general.  So we put a link on the front page to resources on the Public Mapping page and to the data hosted there.

Somewhere in moving this discussion from ticket to ticket we've lost sight
of the requirement, which is not at all addressed by the solution proposed
below.
The requirement is to expose for public download the core data {shapefile
layers  + subjects} required to reproduce that current working instance.
thanks,
Micah
On Wed, Feb 29, 2012 at 2:31 PM, Andrew Jennings <
<EMAIL_ADDRESS>
wrote:

I agree that the "share" tab isn't a great choice for this data.  Abby and
I spoke about an appropriate place for it and concluded that the link works
best on the front page.  It's separate from the workflow of creating
districts, choosing plans and working with DB in general.  So we put a link
on the front page to resources on the Public Mapping page and to the data
hosted there.

Sorry, Micah.  I put the wrong link in the first time and didn't link my second commit to this issue.  The data is actually on EC2 and that's where the link on the front page should point.
Here's the right link/commit: 68f8f13507eaab75440b2bf9d32c5ec3a073e645.

No. Pointing to EC2 does not help.
Recall this was a variant of "shapefile" export, but applied to the entire
database rather than a single plan. Rather than repeating the requirement,
let me give an example of the key use case:
( 1 ) Administrator: downloads & installs districtbuilder
( 2 ) Administrator: loads shapefiles and subject data into districtbuilder
using setup.py
( 3 ) Administrator: at any later time administrator modifies data: (we
assumed with version 2.0, this would be using  David's subject management
tools)
( 4 ) Non privileged user (including guest): at any time, a user goes to a
designated page (e.g. Share), is presented with a  URL and/or "button",
getting this url/clicking the button results in delivery of  a zip file
containing shapefiles comprising the  layers and subjects currently used by
the system.
Clearly, pointing at an external static page is not going to satisfy this
requirement. The content must be generated within DistrictBuilder or it
will
On Thu, Mar 1, 2012 at 10:49 AM, Andrew Jennings <
<EMAIL_ADDRESS>
wrote:

Sorry, Micah.  I put the wrong link in the first time and didn't link my
second commit to this issue.  The data is actually on EC2 and that's where
the link on the front page should point.
Here's the right link/commit: 68f8f13507eaab75440b2bf9d32c5ec3a073e645.


Contribute to upstream

Bugfixes:

Whitelist should use exact match when there is no wildcard (*). We don't want to pass a foo-like-user when we white list foo.
When updating a linked repo, we should validate input. And should allow whiteListPattern and privacyPolicy as empty string.
Fix small issues when updating org/repo in UI.
Do not get pull request files to check significant changes, there is enough information in the pull request itself. In this way, we could avoid the Github abuse exception when there are thousands of commits in a pull request.
Should not log error message when we don't config raven and slack.

Security:

Setup cookie secure

New feature:

Add readiness route so that Kubernetes could better swap pods.
Show CLA updated date in the CLA signing page.

Refactor:
The refactor is mainly about placing callbacks with promises. It helps to clean the code and easily add key metrics.

Coverage decreased (-2.003%) to 88.01% when pulling 5267edf3d747cde734e9aa94108b52419bc01914 on Microsoft:miczeng/contributeToUpStream into f89b886d09db39c90eb90ad6cc65d80adf94f59d on cla-assistant:master.

Thank you!


xpdf: Don't try to wrap xpdf binary on Darwin if it wasn't requested (enableGUI = false)

Motivation for this change
The postInstall clause wants to wrap xpdf on Darwin even if it was't built (enableGUI = false)
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option build-use-sandbox in nix.conf on non-NixOS)
Built on platform(s)

[ ] NixOS
[x] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[x] Fits CONTRIBUTING.md.

@GrahamcOfBorg build xpdf

Success on x86_64-darwin (full log)
Attempted: xpdf
Partial log (click to expand)
these paths will be fetched (1.52 MiB download, 15.75 MiB unpacked):
  /nix/store/ja766lgasw8kfc2p24m7a22rgjgf7hjr-xpdf-4.00
copying path '/nix/store/ja766lgasw8kfc2p24m7a22rgjgf7hjr-xpdf-4.00' from 'https://cache.nixos.org'...
/nix/store/ja766lgasw8kfc2p24m7a22rgjgf7hjr-xpdf-4.00

Success on x86_64-linux (full log)
Attempted: xpdf
Partial log (click to expand)
these paths will be fetched (1.58 MiB download, 17.35 MiB unpacked):
  /nix/store/myaz9nlr161h2mxzra773knav6078a1r-xpdf-4.00
copying path '/nix/store/myaz9nlr161h2mxzra773knav6078a1r-xpdf-4.00' from 'https://cache.nixos.org'...
/nix/store/myaz9nlr161h2mxzra773knav6078a1r-xpdf-4.00

Success on aarch64-linux (full log)
Attempted: xpdf
Partial log (click to expand)
these paths will be fetched (1.53 MiB download, 17.73 MiB unpacked):
  /nix/store/fxqgn9y3mx6d5pqb20y3di0wd3n95760-xpdf-4.00
copying path '/nix/store/fxqgn9y3mx6d5pqb20y3di0wd3n95760-xpdf-4.00' from 'https://cache.nixos.org'...
/nix/store/fxqgn9y3mx6d5pqb20y3di0wd3n95760-xpdf-4.00


Testing, please ignore. lvuqse

This is a bug bounty test. Please do not approve this! lvuqse

Please, keep BadgerDoc PR's clear


much less pokemon found since July 31 update

After yesterday (July 31) update of Pokemon Go, I found much less Pokemon species could be found. I used be able to find much more rare species, but yesterday, almost zero. In the region I often scanned to find many pokemon, yesterday, almost nothing. Has Niantic found a way to avoid the scanning or they don't spawn that many good pokemon any more as before?

Thsi is related to Niantic's Speed Check, once you jump from the last scan of your radius back to the center of it you trip the speed check and no pokemon will effectively spawn until the triggered speed check is reset. for better results use multiple instances with smaller scan radiuses.

I have disabled all commons and i still get plenty of uncommon and rare spawns...

I am new to all this how do you use multiple instances?
I am also getting allot less.
Please helps

@G0rzilla multiple instances means run more than one scanner and use a unique account for each one with different coordinates in each one.

Ah thanx @AVellucci how many accounts should I create I have one currently (not my main one) and scanning at about 4 zones? Thank you for the quick response.

@AVellucci are there any method to run with multiple accounts but use the same window? I think that would be great information knowing now about the restrictions in place.

All the instances use the same map already, I don't understand the question.
Just start them all up with their coordinates and minimize all but 1.

@Ezoteric what version you using?

the screenshot was from Version 0.1.3  but i use that and the new one to see if i indeed get all spawns.

@AVellucci thanks for comments. What is the speed check? Did you mean that one spot
can only spawn after a certain time period since your last scan? I don't quite get it. I followed your suggestion to use multiple small radius scans, it's not improving the searching results at all.

@aquastrike Are you using multiple accounts or just one PTC account run through multiple instances?

Do you need multiple Google Maps API keys, as well as multiple accounts in order to run multiple instances?

@AVellucci I used only one account

Interesting. I actually find that after the update I see more rare pokemon around me.

@aquastrike if you run multiple instnaces you should use multiple accounts as jumping from one location to another too quickly now trips a safeguard in PGO that stop pokemon from spawning on your map.
using multiple accounts with smaller radii and setup to scan an area you'll see improvements in scans.


Debug IPsec e2e failures

Do not assume that the ping tests are executed on the first Node. Instead, check from the Node where the first Pod is running. This PR also optimizes the deploying/redeploying process while running the IPsec tests.

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

/test-e2e

I am fine with the actual bug fix. However, I am not sure some of the other code changes (to setupTest / redeployAntrea) are such an improvement. In the PR description you write:

This PR also optimizes the deploying/redeploying process while running the IPsec tests.

Could you clarity what you mean by optimize?

The test will apply antrea.yml and then apply antrea-ipsec.yml to run the IPsec related tests. Previously I thought that skipping the first step would reduce the waiting time. But seems it would be a no-op if the previous tests have already applied antrea.yml. So I revered the changes to the framework.

/test-e2e

/test-e2e

/test-all


Moved High rotation compression to Low. Added new High

See the attached video for a comparison of all three compression at different rotation rates. If you cant tell a difference then hopefully that means its not an issue to compress a little more :). The cubes are rotating between 50/100/200 speed*deltaTime in an Update with no VSync. Video at 60fps.
Video: https://www.dropbox.com/s/h2op1du6qdk8gg2/RotationCompressionTest.flv?dl=0
Hint:
Left = No Compression (Float)
Middle = Low Compression (Int16)
Right = High Compression (byte)

Codecov Report

Merging #158 into mirror will decrease coverage by <.01%.
The diff coverage is 0%.

@@            Coverage Diff            @@
##           mirror    #158      +/-   ##
=========================================
- Coverage    6.51%   6.51%   -0.01%
=========================================
  Files          29      29
  Lines        6232    6235       +3
  Branches      867     867
=========================================
  Hits          406     406
- Misses       5813    5816       +3
  Partials       13      13

Impacted Files
Coverage Δ

Mirror/Runtime/NetworkTransform.cs
0% <0%> (ø)
: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 a747b85...17cf7e4. Read the comment docs.

Looks good, thanks

:tada: This PR is included in version 1.0.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


post: Add article about reporter-mantisbt

Signed-off-by: Martin Kutlak<EMAIL_ADDRESS>

Closed by 83639a4. Thank you


Archive Dojo Users

I was talking to someone at a recent mentor meetup and they suggested this feature:
The idea is to be able to archive your previous user relationships to dojos on demand. The benefit of this is that allows Dojos to have a manage users interface for only their currently active users, while still retaining stats and the ability to contact users.

Users should get an email to unarchive themselves for next term (should create a new usersdojo)
Users can rejoin a Dojo through the Dojo listing (new usersdojo)

Some message should be displayed to returning users to explain why they need to rejoin

When archiving the user should be able to name the archive group (e.g. "Class of 2016")
Display/search of individual archives would be nice
Manual archive of individual users should allow you to select a specific archive (latest default selected) would be nice to have. An email probably shouldn't be sent for this, since it's a specific action against a specific user.

@rosalanghammer Any thoughts/comments on this? @Wardormeur and I think it's a great idea and would be very valuable for dojo admins.

Think this is a great idea, essentially inactive users.
All seems perfect in spec but 'Users should get an email to unarchive themselves for next term (should create a new usersdojo)' - are you thinking when they are 'archived' this email goes?
Also needs work for any email functionality where active users are default and you only use inactive users by choice (e.g. notify users).

Also needs work for any email functionality where active users are default and you only use inactive users by choice (e.g. notify users).

Do you mean to ensure that every emailing function from dojo perspective must filter on the current term? That's a valid concern, but not only for emails but any dojo user listing

are you thinking when they are 'archived' this email goes
Yes, basically; when the user is archived by a dojoadmin, he'll receive an email inviting him to continue for next term. Whatever his response, he has been archived on the previous term

Yes every emailing function (within a Dojo) should filter based only on active users, also features like filter should only work within a specific section.
Thoughts could be this could be an additional panel to 'Active', and 'Pending' but we need to look at the full UI.
Yes we can work on this 'archiving notification' when this is being built out. I think it should be a one time email that users can go back on at any point as opposed to every 'term'.


Conversion of symbolic tf.Tensor

I switched from Keras 2 to 3.
When I want to fit a Keras sequential model I get the following error:
---> [17](model.fit(X_train,
     [18]           y_train,
     [19]       validation_data=(X_test, y_test),
     [20]           epochs=10,
     [21]         batch_size=64,
...
    [133]venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/core.py:133) elif isinstance(x, tf.RaggedTensor):
    [134]venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/core.py:134)     x = x.to_tensor()
--> [135]venv/lib/python3.11/site-packages/keras/src/backend/tensorflow/core.py:135) return np.asarray(x)

Cannot convert a symbolic tf.Tensor (Cast_19:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.
This may be a similar error as this issue 19407.
This is the model I am using:

def train_model(X_train, X_test, y_train, y_test):
    model = get_model()
    loss_function = keras.losses.CategoricalCrossentropy()

    model.compile(optimizer=keras.optimizers.Adam(),
                loss=loss_function,
                metrics=[
                # tf.keras.metrics.CategoricalAccuracy(),
                tf.keras.metrics.SensitivityAtSpecificity(0.5), # true positive rate (tp / (tp + fn)) aka recall
                tf.keras.metrics.SpecificityAtSensitivity(0.5), # true negative rate (tn / (tn + fp))
                tf.keras.metrics.F1Score(),
                tf.keras.metrics.AUC(),
                ])

    model.fit(X_train,
                y_train,
                validation_data=(X_test, y_test),
                epochs=10,
                batch_size=64,
                shuffle=True)
    return model

model = train_model(X_train, X_test, y_train, y_test)

The type of all input data is numpy.ndarray.

Hi,
Could you please provide complete reproducible code to replicate the reported behavior. Thanks!

Sorry for the late reply. Here is a working example where the error occurs.
As you already figured out this may be related to the metrics. If I use other metrics it works.
import numpy as np
import tensorflow as tf

X = np.array([[[0., 0.],
        [1., 0.]],
       [[0., 0.],
        [1., 0.]]], dtype=np.float32)

y = np.array([1, 1])

shape = (2, 2)
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(shape=shape),
    tf.keras.layers.Dense(1, activation=keras.activations.softmax),
])
model.compile(optimizer=keras.optimizers.Adam(),
            loss=keras.losses.MSE,
            metrics=[
            tf.keras.metrics.SensitivityAtSpecificity(0.5),
            tf.keras.metrics.SpecificityAtSensitivity(0.5),
            ])

model.fit(X, y)

Basically there is an issue with the tf.keras.metrics.SensitivityAtSpecificity(0.5), tf.keras.metrics.SpecificityAtSensitivity(0.5) if you include any other metrics the issue will go away.
Team is aware of the issue, you can close this issue and track the progress here https://github.com/keras-team/keras/issues/19376


Clarifying what kind of effect blocking has

Description
Clearly state that blocking channel only affects what content the one who blocked can see.
I thought it would be good to have some black on white of how it works, as some think that blocking prevents owner of blocked channel from accessing to your channel.
(There was question about this in discord server, and info provided was bit mixed. I tested the blocking feature myself and it appeared to work as I've added to file.)
How I tested:

Created virtual machine running Ubuntu and download lbry AppImage
Launched lbry on virtual machine and send few LBCs to that wallet so channel could be created.
On lbry running on host machine, I blocked newly created channel. And waited like 15min just to be sure that if something happens it has some time to do that.
Back to virtual machine, Tried to search videos which are tied to wallet on host machine and was able to access content normally

Thanks for this, appreciate it!

Thanks for this, appreciate it!


Fixes #88 Updated dependencies to address security warnings

Pull Request Test Coverage Report for Build 206

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.003%) to 91.417%

Totals

Change from base Build 201:
0.003%

Covered Lines:
3087

Relevant Lines:
3346

💛  - Coveralls


NIF-PRO-Bridge.ttl reasoning issues

https://github.com/SciCrunch/NIF-Ontology/blob/nif3/ttl/NIF-PRO-Bridge.ttl#L382 tries to make a role equivalent to a protein. This is a common pattern with our remaining reasoning issues, roles cause issues with imported ontologies that do not model those entities as roles.

Resolved by b3f4e29b9b2b9c5d4801a00febde4a012e79df4e.


CIP-0001 | Revision (Version 3)

This update revises certain aspects of the current CIP-0001 document(Version 2). Changes and additions include:

The extension of the Header Preamble and framework for grouping CIPs via the introduction of a distinct taxonomy system.

The introduction of a new Living status to better identify certain proposals such as this one (CIP-0001) as living documents.

The reintroduction of Type as a grouping and the addition of new groupings such as Classes, CRCs, and other subcategories to better signal and organise proposals according to their precise scope of focus in the Cardano project.

The introduction of Cardano Request for Comments(CRC for short) as a standard provision to better allocate and compartmentalise application-level proposals, improvements, and standardisation such as smart contract design conventions (for langs such as Plutus-TX, Aiken, Plu-TS, etc.), Metadata standards, NFT token standards, Fungible Token standards, etc.

Note: The appropriate changes have also been made to the document schema for writing a CIP and CPS, in accordance with Version 3.
This revision takes a lot of inspiration from Ethereum's EIP system(as it is one the most vibrants systems of its kind).
The goal is to receive input on these modifications and successfully merge them to advance the CIP standards and the broader application-level (as well as core) standards of Cardano.

@NetWalker108 upon trying to read the changes to CIP-0001 I felt exactly as @KtorZ says above - that the extra vocabulary doesn't allow us to do anything we aren't already doing.  Everything else I also agree with (e.g. throwing out "types") based on experience with previous iterations of the CIP process.
My short reaction is that I also don't see any reason why we should do this.  But if this idea is to stay on the boards for general consideration then would you please refactor your changes to CIP-0001/README.md so it has the same white space and formatting as the original?  Reviewers will need to see a usable "diff" and right now we don't have that.

Hi @KtorZ

I am unsure what problem of the current process the introduction of a taxonomy comes to solve?

I explain this in the Prelude section found in the Rationale.

We ditched types in the last revision of the process and introduced categories instead as there was a general consensus that filtering by topic was more useful than filtering by nature.

I make note of this concern of confusion and elaborate on how this proposed revision solves for the many shortcomings(or lacking elements) that were in the earlier versions(v1 specifically).

CIPs types were also quite unclear and often ambiguous for many CIPs sitting in-between two types.
you're suggesting to not only introduce types, but also classes which I am afraid will lead to even more confusion.

This proposed revision solves such confusions and significantly mitigates(if not completely eliminates) all forms of ambiguity with a dedicated taxonomy intended to cater for various proposals per their specific focuses.

it seems to me that introducing a "core" class will again lead people to believe that any parts of the core stack is open to CIP -- which isn't true.

Currently, most(if not all) work on Core components such as the Consensus stack of the Cardano protocol are reserved to IOG and thus receive no improvement proposals from the outside world(although the CIP process is gradually changing this norm).
However, with the imminent arrival of changes such as the Voltaire era and the potential alternative implementations of the cardano-node, as well as the goal to make the Cardano project more of a truly open source project by the CF and IOG teams, it is imperative that the CIP process be evolved to accommodate such a future.
This proposed revision primes the CIP process to be adequately and appropriately compatible with the post-Voltaire era of contributions. We prepare for tomorrow, today.

Finally, I am not sure to get the rationale behind the "Cardano Request for Comments" category given that, all CIPs are request for comments.

Yes, in practise all CIPs are akin to Request for Comments(as seen in other industries). What the categorisation of "Cardano Request for Comments" does is introduce an artificial abstraction of proposals as a rank for application-level specific proposals.
This abstraction is intended to suggest a clear separation of proposals from the scope of core_protocol changes and assert a sense of flexibility in how these proposals can be adopted so they may not be misunderstood/received as strict rules that are meant to be followed by all application-level developers.
Case in point:

the reception of CIP-68 and how many in the community(users and developers alike) assumed it to be an overarching design ruleset for the NFTs on the Cardano protocol.
the lack of an appropriate distinction between fungible native assets(eg. DeFi tokens) and non-fungible native assets(eg. avatars and other collectibles), at a high-level.

There's good reason why ERCs exist in Ethereum and their use in practise has further solidified the need to adopt similar standards and practises.
Must be noted: Per this proposed revision and context of the CIP process, all ERCs are EIPs but not all EIPs should be identified as ERCs.
All-in-all, it is my hope that this explanation does justice at answering your questions, in addition to detailed documentation provided in the proposed revision itself.
Cc: @rphair

@NetWalker108 I still don't believe the "confusion" that this CIP purports to address has actually been presented by the community.  I can assure you it has not been presented by the editors and in fact as @KtorZ already pointed out we have dropped some of the typing which turned out to be not useful in practice... especially when compared to the Category added in https://github.com/cardano-foundation/CIPs/pull/366 which establishes practical responsibility for acceptance and implementation.
Still I might be in favour of this if it were presented on the basis of a community need.  Individual notions of "the way things should be" can often be accommodated if they come from more than one unrelated source... which we don't yet have in this case.
You can compare a somewhat related situation when some people were asking for more explicit CIP abbreviations which were based on typing as well as versioning... we didn't honour these requests but they remained open for debate for a while, and our reasons for not honouring them were publicly given and can still be challenged at any time:

https://github.com/cardano-foundation/CIPs/pull/366#issuecomment-1309650355
https://github.com/cardano-foundation/CIPs/pull/446#pullrequestreview-1293896917


[ENH] Type hints for primitives and string arguments:Part1

Reference Issues/PRs
Fixes #1454
What does this implement/fix? Explain your changes.
Adding type hints to 2 methods in the script: aeon.classification.distance_based._time_series_neighbors.
Methods are : init(),  get_test_params()
Does your contribution introduce a new dependency? If yes, which one?
None
Any other comments?
Type hints are NOT added in other methods since they were not primitive types.

All the checks are passed successfully. Please review.


Test Result Graphs

Currently we have a testing module that logs test results in a csv file. Eventually these tests will be run every time a commit is made. We would like to be able to auto-generate graphs which will allow us to view regressions/improvements in the code visually.
This should be created as a separate program and thus can be written in any programming language.
It should also be written so that it is easily extensible.
I would suggest looking at various graphing APIs first before dealing with CSV parsing.

Dropped in favour of using Jenkins


[0.9.1] Drop database deletes data/ directory

On master (56d3add), when I issue a "DROP DATABASE name", the folder ~/.influxdb/data is removed, meaning all data files are removed. So after a restart (because influxdb keep some file opened) I've lost all data from all databases.
Version 0.9.1 is also affected.
Step to reproduce:

clean ~/.influxdb to start from fresh setup (rm -fr ~/.influxdb)
Start influxdb
With UI, issue CREATE DATABASE db1 and CREATE DATABASE db2
With UI, write into both database (I'm writting cpu_load_short,host=server01,region=us-west value=0.64<PHONE_NUMBER>000000000, from the doc)
Check with SHOW MEASUREMENTS that writes succeed.
Also check that two folder "db1" and "db2" exists under ~/.influxdb/data
With UI Issue DROP DATABASE db1
Folder ~/.influxdb/data no longer exists ! (so db2's file are deleted from disk)
Restart influxdb, db2 still exists but is empty (SHOW MEASUREMENTS return nothing)

@pauldix @otoolep any ideas?

One for @dgnorton, @jwilder and @benbjohnson too.

Fixed by https://github.com/influxdb/influxdb/pull/3335

@mjdesa -- we don't usually close issues until a PR is merged.

Merged to master.
Thanks the reports @mjdesa and @PierreF


feat: add support for internal functions directory

- Summary
Adds support for bundling functions from .netlify/functions-internal. Needs https://github.com/netlify/build/pull/3213.
- Test plan
Added new tests.
- A picture of a cute animal (not mandatory but encouraged)

With https://github.com/netlify/cli/pull/2975, this is now ready for review.


[Feature] Add image build args to record semver of kubevela for latest image

Currently, if use latest image of kubevela, we will get github.com/kubevela/version.velaversion=latest.
latest doesn't provide any helpful information except the user was using the latest version when he pulled the image.
Only when user specify a specific tag while deploying vela, we can a specific version of vela.
Consider adding a new field VELASEMVER to track the latest git tags while building vela image.

Already added now, closing


palette fixes/edits

\triangle is incorrect and doesn't match symbol
"cross out with replacement" should be changed to "cross out and replace"; missing shortcut key (consider using "shift+insert")
degree symbol shortcut should be \degree (currently has extra "l" at end)
calc needs shortcut key (consider using "==")
"Add Step" should have shortcut key (consider using the "enter" or "shift+enter" key on keyboard)

Questions for Neil:

"floor" and "ceiling" are missing shortcut keys (any suggestions on what to use?)
question for Neil: symbols to consider removing from palette (because they're all on the keyboard): parenthesis, brackets, absolute value

Addressing the issues one by one:

\triangle is incorrect and doesn't match symbol

This is a mathlive bug (it's grabbing the longer command \triangleq). See https://github.com/arnog/mathlive/issues/44.

"cross out with replacement" should be changed to "cross out and replace"; missing shortcut key (consider using "shift+insert").

I fixed the name.
The keyboard shortcut is a little tricky. The cross out actually has two shortcuts: shift+delete and shift+backspace which mirrors what delete & backspace do (they do the same thing when there is a selection). By extension, some modification of delete and backspace make sense for this rather than using 'insert'. Insert has the other problem that several screen readers use it as their special key.
I ended up using shift+ctrl+backspace/delete.

degree symbol shortcut should be \degree (currently has extra "l" at end)

Fixed

calc needs shortcut key (consider using "==")

After starting to work on it, I realized that a keyboard shortcut, at least one that uses multiple keys, doesn't make sense. You need a selection and typing == means you don't have a selection. It could potentially select everything to the left, but then it works differently than the palette items. We could go with cntrl+=

"Add Step" should have shortcut key (consider using the "enter" or "shift+enter" key on keyboard)

I went with 'shift+enter' because 'enter' will get used for multi-line equations.

Fixed except for separate issue


KOGITO-5675 - Rename Kogito prometheus metrics prefix from kie_ to ko…

…gito_

The (build) Apps check has failed. Please check the logs.

The (build) Examples check has failed. Please check the logs.

Jenkins rerun apps tests

Jenkins rerun examples tests


Install MySQL and PG Deps for Rust when Postgres and MySQL are used

This PR installs dependencies depending on the detected library

Seems to be failing for some reason and can't tell
------
 > [stage-0  9/10] RUN --mount=type=cache,id=9UgkYzoS89g-/root/cargo/git,target=/root/.cargo/git --mount=type=cache,id=9UgkYzoS89g-/root/cargo/registry,target=/root/.cargo/registry --mount=type=cache,id=9UgkYzoS89g-target,target=target cargo build --release --target aarch64-unknown-linux-musl:
#16 0.228     Updating crates.io index
#16 1.180    Compiling rust-diesel v0.1.0 (/app)
#16 1.267 warning: unused import: `process::Command`
#16 1.267  --> src/main.rs:1:16
#16 1.267   |
#16 1.267 1 | use std::{env, process::Command};
#16 1.267   |                ^^^^^^^^^^^^^^^^
#16 1.267   |
#16 1.267   = note: `#[warn(unused_imports)]` on by default
#16 1.267
#16 2.401 error: linking with `cc` failed: exit status: 1
#16 2.401   |
#16 2.401   = note: "cc" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained/crt1.o" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained/crti.o" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained/crtbegin.o" "/tmp/rustc9hH4xJ/symbols.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.0.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.1.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.10.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.11.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.2.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.3.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.4.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.5.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.6.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.7.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.8.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.rust_diesel.67e3622e-cgu.9.rcgu.o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7.3sbjddl50pmbyspt.rcgu.o" "-Wl,--as-needed" "-L" "/app/target/aarch64-unknown-linux-musl/release/deps" "-L" "/app/target/release/deps" "-L" "/usr/lib/aarch64-linux-gnu" "-L" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib" "-Wl,-Bstatic" "/app/target/aarch64-unknown-linux-musl/release/deps/libdiesel-2dfb3d0707dfde2f.rlib" "/app/target/aarch64-unknown-linux-musl/release/deps/libpq_sys-3d649afea44a8e81.rlib" "/app/target/aarch64-unknown-linux-musl/release/deps/libbyteorder-9ff3ec6741dea488.rlib" "/app/target/aarch64-unknown-linux-musl/release/deps/libbitflags-e8ef1c3e6c8e7295.rlib" "-Wl,--start-group" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libstd-9a6bce422f0000ee.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libpanic_unwind-159df072af691315.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libobject-384907cca4f5909b.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libmemchr-c4cdcfa83263bff2.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libaddr2line-5ea6f9ba4a8a1cae.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libgimli-33c8299f0e34415c.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/librustc_demangle-9f6d554162f3daa1.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libstd_detect-f233b509811a3b1c.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libhashbrown-9bad9e9ee53588e5.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libminiz_oxide-0aaba3d269092688.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libadler-84369741afc56366.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/librustc_std_workspace_alloc-a771e22077bae3b9.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libunwind-a4f4d8b1097e894a.rlib" "-lunwind" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libcfg_if-8a591157c24046a9.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/liblibc-7171e8d387a44fc7.rlib" "-lc" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/liballoc-df8b9962134a270a.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/librustc_std_workspace_core-fab6effc05523a23.rlib" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libcore-3fe103e3cb247495.rlib" "-Wl,--end-group" "/nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libcompiler_builtins-413a2b918f1f8443.rlib" "-Wl,-Bdynamic" "-lpq" "-Wl,--eh-frame-hdr" "-Wl,-znoexecstack" "-nostartfiles" "-L" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib" "-L" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained" "-o" "/app/target/aarch64-unknown-linux-musl/release/deps/rust_diesel-0d08aa6112be4ce7" "-Wl,--gc-sections" "-static" "-no-pie" "-Wl,-zrelro,-znow" "-Wl,-O1" "-nodefaultlibs" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained/crtend.o" "/nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained/crtn.o"
#16 2.401   = note: /nix/store/4m0ql1nd3kahkcj9ah1s78pc689mfmhf-binutils-2.38/bin/ld: /nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libstd-9a6bce422f0000ee.rlib(std-9a6bce422f0000ee.std.3f650011-cgu.0.rcgu.o): in function `std::sys::unix::os::home_dir::fallback':
#16 2.401           /rustc/4b91a6ea7258a947e59c6522cd5898e7c0a6a88f/library/std/src/sys/unix/os.rs:627: warning: Using 'getpwuid_r' in statically linked applications requires at runtime the shared libraries from the glibc version used for linking
#16 2.401           /nix/store/4m0ql1nd3kahkcj9ah1s78pc689mfmhf-binutils-2.38/bin/ld: /nix/store/131iqai06w79dc8pxfz3hndxqrqhdq6l-rust-std-1.63.0-aarch64-unknown-linux-musl/lib/rustlib/aarch64-unknown-linux-musl/lib/libstd-9a6bce422f0000ee.rlib(std-9a6bce422f0000ee.std.3f650011-cgu.0.rcgu.o): in function `<std::sys_common::net::LookupHost as core::convert::TryFrom<(&str,u16)>>::try_from':
#16 2.401           /rustc/4b91a6ea7258a947e59c6522cd5898e7c0a6a88f/library/std/src/sys_common/net.rs:205: warning: Using 'getaddrinfo' in statically linked applications requires at runtime the shared libraries from the glibc version used for linking
#16 2.401           /nix/store/4m0ql1nd3kahkcj9ah1s78pc689mfmhf-binutils-2.38/bin/ld: /nix/store/xywnx0f0fcc3wc69mr4zr298yk97pw3l-rust-default-1.63.0/lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained/libunwind.a(libunwind.o): undefined reference to symbol 'dladdr@@GLIBC_2.34'
#16 2.401           /nix/store/4m0ql1nd3kahkcj9ah1s78pc689mfmhf-binutils-2.38/bin/ld: /nix/store/il2mpl6qjc4aa42mbiy3p3wicm3d6nz5-glibc-2.35-163/lib/libc.so.6: error adding symbols: DSO missing from command line
#16 2.401           collect2: error: ld returned 1 exit status
#16 2.401
#16 2.401   = help: some `extern` functions couldn't be found; some native libraries may need to be installed or have their path specified
#16 2.401   = note: use the `-l` flag to specify native libraries to link
#16 2.401   = note: use the `cargo:rustc-link-lib` directive to specify the native libraries to link with Cargo (see https://doc.rust-lang.org/cargo/reference/build-scripts.html#cargorustc-link-libkindname)
#16 2.401
#16 2.412 warning: `rust-diesel` (bin "rust-diesel") generated 1 warning
#16 2.412 error: could not compile `rust-diesel` due to previous error; 1 warning emitted
------
executor failed running [/bin/bash -ol pipefail -c cargo build --release --target aarch64-unknown-linux-musl]: exit code: 101
Error: Docker build failed

Get farther with NO_MUSL
     Running `target/debug/nixpacks build ./examples/rust-diesel-postgres --env NO_MUSL=false`

╔═══════════════════════════ Nixpacks v0.3.4 ═══════════════════════════╗
║ Setup      │ gcc, rust-bin.stable.latest.default, libpq-dev, binutils ║
║───────────────────────────────────────────────────────────────────────║
║ Install    │                                                          ║
║───────────────────────────────────────────────────────────────────────║
║ Build      │ mkdir -p bin                                             ║
║            │ cargo build --release                                    ║
║            │ cp target/release/rust-diesel bin                        ║
║───────────────────────────────────────────────────────────────────────║
║ Start      │ ./bin/rust-diesel                                        ║
╚═══════════════════════════════════════════════════════════════════════╝

[+] Building 0.6s (15/15) FINISHED
 => [internal] load build definition from Dockerfile                                                                                0.1s
 => => transferring dockerfile: 1.17kB                                                                                0.0s
 => [internal] load .dockerignore                                                                                0.0s
 => => transferring context: 2B                                                                                0.0s
 => [internal] load metadata for ghcr.io/railwayapp/nixpacks:debian-1660071566                                                                                0.3s
 => [stage-0  1/10] FROM ghcr.io/railwayapp/nixpacks:debian-1660071566@sha256:aa5a574d94cd1c72b367921d0dd6b586b89490f81ada2e6cfba352bf300b1026                                                                                0.0s
 => [internal] load build context                                                                                0.0s
 => => transferring context: 2.82kB                                                                                0.0s
 => CACHED [stage-0  2/10] WORKDIR /app/                                                                                0.0s
 => CACHED [stage-0  3/10] COPY .nixpacks/setup.nix .nixpacks/setup.nix                                                                                0.0s
 => CACHED [stage-0  4/10] RUN nix-env -if .nixpacks/setup.nix                                                                                0.0s
 => CACHED [stage-0  5/10] RUN apt-get update && apt-get install -y libpq-dev binutils                                                                                0.0s
 => CACHED [stage-0  6/10] COPY . /app/                                                                                0.0s
 => CACHED [stage-0  7/10] COPY . /app/                                                                                0.0s
 => CACHED [stage-0  8/10] RUN --mount=type=cache,id=9UgkYzoS89g-/root/cargo/git,target=/root/.cargo/git --mount=type=cache,id=9UgkYzoS89g-/root/cargo/registry,target=/root/.cargo/registry --mount=type=cache,id=9UgkYzoS89g-target,target=target mkdir -p bin                             0.0s
 => CACHED [stage-0  9/10] RUN --mount=type=cache,id=9UgkYzoS89g-/root/cargo/git,target=/root/.cargo/git --mount=type=cache,id=9UgkYzoS89g-/root/cargo/registry,target=/root/.cargo/registry --mount=type=cache,id=9UgkYzoS89g-target,target=target cargo build --release                    0.0s
 => CACHED [stage-0 10/10] RUN --mount=type=cache,id=9UgkYzoS89g-/root/cargo/git,target=/root/.cargo/git --mount=type=cache,id=9UgkYzoS89g-/root/cargo/registry,target=/root/.cargo/registry --mount=type=cache,id=9UgkYzoS89g-target,target=target cp target/release/rust-diesel bin        0.0s
 => exporting to image                                                                                0.0s
 => => exporting layers                                                                                0.0s
 => => writing image sha256:427ac0ec5b8ee348a530049e18cb4d1ee3d85ed99ee82e49821f9b69cce5b08d                                                                                0.0s
 => => naming to docker.io/library/5cd3156f-a221-4557-864f-606f62e251c9                                                                                0.0s

Use 'docker scan' to run Snyk tests against images to find vulnerabilities and learn how to fix them
=== Successfully Built! ===

Run:
  docker run -it 5cd3156f-a221-4557-864f-606f62e251c9
➜  nixpacks git:(cooper/diesel) ✗ docker run -it 5cd3156f-a221-4557-864f-606f62e251c9
./bin/rust-diesel: error while loading shared libraries: libssl.so.1.1: cannot open shared object file: No such file or directory

CC @nebulatgs

By adding NIXPKGS=openssl,openssl.dev,libkrb5 with NO_MUSL
I managed to get it to ask for ./bin/rust-diesel: error while loading shared libraries: libldap_r-2.4.so.2: cannot open shared object file: No such file or directory
If you run it with musl, it gets stuck on ./rust-diesel: error while loading shared libraries: libpq.so.5: cannot open shared object file: No such file or directory
Dunno what to do here so probably gonna have to just close this out

CC @FinnDore if you wanna take a stab

If you run it with musl, it gets stuck on ./rust-diesel: error while loading shared libraries: libpq.so.5: cannot open shared object file: No such file or directory

This is likely the case because with MUSL enabled the binary is copied to a new debian slim image where the shared header files will not exist

Thanks for looking into this @JakeCooper 🙏, I wish dep battleships were this easy.
Ive just tried tryed to translate this  ( or the relevant parts ) into the rust provider + editing the generate_dockerfile function. But no luck, Ill continue to look at this in the background but im really waiting for a rust god to step in. It really does feel like this library is built on a stack of paper cards lol.
Interestingly in my docker image i do copy the built binary, but it just works when i re-install the deps in the debian:buster-slim container. Also notice how i don't need to install the deps in the first build container. Odd!
The docker file that works:
ARG ROCKET_ADDRESS=<IP_ADDRESS>
ARG DATABASE_URL
ARG AUTH_SECRET

FROM amd64/rust:1.62.10@sha256:4f1d43c216d995c2f734d7c682bc3e2abe3e110961cda4ae0743ce3944e673a2 as builder

WORKDIR /usr/src/something
COPY . .

EXPOSE 80

RUN cargo build --release

FROM debian:buster-slim

COPY --from=builder /usr/src/something/target/release/something /bin
COPY --from=builder /usr/src/something/apps/something/rocket.toml /usr

RUN apt-get update && apt-get install -y libssl-dev ca-certificates libmariadb-dev && rm -rf /var/lib/apt/lists/*

ENV AUTH_SECRET=${AUTH_SECRET}
ENV ROCKET_ADDRESS=${ROCKET_ADDRESS}
ENV DATABASE_URL=${DATABASE_URL}

ENV ROCKET_CONFIG=/usr/rocket.toml

CMD ["something"]

Interestingly in my docker image i do copy the built binary, but it just works when i re-install the deps in the debian:buster-slim

I assume because the build actually happens in that rust container which, at some point, someone added the correct header files for :/