Not working with linux 4.1.2-2

The module does not work on my machine. Firmware version of my devices is 1.1.0
# node test.js
connectAndSetup
disconnected!

# uname -a
Linux pc 4.1.2-2-ARCH #1 SMP PREEMPT Wed Jul 15 08:30:32 UTC 2015 x86_64 GNU/Linux

# hciconfig -a
hci0:	Type: BR/EDR  Bus: USB
	BD Address: 48:D7:05:CB:09:20  ACL MTU: 1021:8  SCO MTU: 64:1
	UP RUNNING PSCAN
	RX bytes:5429 acl:28 sco:0 events:298 errors:0
	TX bytes:4638 acl:28 sco:0 commands:206 errors:0
	Features: 0xbf 0xfe 0xcf 0xfe 0xdb 0xff 0x7b 0x87
	Packet type: DM1 DM3 DM5 DH1 DH3 DH5 HV1 HV2 HV3
	Link policy: RSWITCH SNIFF
	Link mode: SLAVE ACCEPT
	Name: 'pc'
	Class: 0x10010c
	Service Classes: Object Transfer
	Device Class: Computer, Laptop
	HCI Version: 4.0 (0x6)  Revision: 0x22eb
	LMP Version: 4.0 (0x6)  Subversion: 0x4167
	Manufacturer: Broadcom Corporation (15)

# hcitool lescan
LE Scan ...
90:03:B7:C9:A2:04 (unknown)
90:03:B7:C9:A2:04 Flower power A204
A0:14:3D:08:FF:EE (unknown)
A0:14:3D:08:FF:EE Flower power FFEE
A0:14:3D:09:1A:DF (unknown)
A0:14:3D:09:1A:DF Flower power 1ADF
[...]

small test-script:
var fp = require('flower-power');

fp.discover(function(fp){
  console.log(fp);
  fp.connectAndSetup(function(e){
    console.log("connect",e);
  });
  fp.readSerialNumber(function(e, sn){
    console.log(e, sn);
  });
});

output:
{ _peripheral:
   { _noble:
      { state: 'poweredOn',
        _bindings: [Object],
        _peripherals: [Object],
        _services: [Object],
        _characteristics: [Object],
        _descriptors: [Object],
        _discoveredPeripheralUUids: [Object],
        _events: [Object],
        _allowDuplicates: false },
     id: 'a0143d08ffee',
     uuid: 'a0143d08ffee',
     address: 'a0:14:3d:08:ff:ee',
     addressType: 'public',
     connectable: true,
     advertisement:
      { localName: 'Flower power FFEE',
        txPowerLevel: 0,
        manufacturerData: <Buffer 03>,
        serviceData: [],
        serviceUuids: [Object] },
     rssi: -41,
     services: null,
     state: 'disconnected',
     _events: { disconnect: [Function] } },
  _services: {},
  _characteristics: {},
  id: 'a0143d08ffee',
  uuid: 'a0143d08ffee',
  address: 'a0:14:3d:08:ff:ee',
  addressType: 'public',
  connectedAndSetUp: false,
  name: 'Flower power FFEE' }

[Error: service uuid 180a not found!] undefined

@eni23 this is due to: https://github.com/sandeepmistry/noble/issues/242. Please see that issue for a temporary workaround for now.

@sandeepmistry thanks for your reply. Sadly im trying to run it on a x64-host and not on a raspi. i tested a bit more and it seems to work with an external bt-dongle, the problems just occurs when trying to access the internal bt-device. One thing i mentioned too is that the range is really low compared to the official flower-power-app (less than 5M). but this may be a problem of the dongle.
The hardware is a macbook air 2013 which runs arch linux
.

@eni23 there's a fix for the kernel disconnect issue in noble master, it hasn't been published to npm yet though. If you can get an HCI dump capture, I can tell you if the issue will be resolved by the upcoming release: sudo hcidump -t -x
As for the range issue, that comes down to the antenna design. My iPhone 6 has much better BLE range than my MacBook air.

v1.2.1 of noble has been published with a fix for https://github.com/sandeepmistry/noble/issues/242.
Installing a fresh version of node-flower-power should automatically pull the new version of noble.


Better SmallCapsFont processing

If you specify a global SmallCapsFont, it should do its best to pick up the bold/italic shapes as well, if they exist.
E.g., \newfontfamily\Scala[SmallCapsFont={*Caps}]{Scala}

Let's say this is too much of an edge case to bother with at this stage


Some new machines & fixes

Add:
Commodore SL386SX, Packard Bell Legend 300SX, Thomson TO16, Amstrad PC5086, Shuttle Hot-419, Hp Vectra VL on dev branch.
Dynamic & sparse VHD support through external minivhd
WinCHIP2 & 2A
APM
i2c devices & DDC
MCA IBM GD5428
Fix :
issues #1, #93, #121
Cue files not loading.
Serial port and mouse
Update:
386+ cpu interpreter & dynarec
VGA, Cirrus Logic & S3 video cards
SCAT chipset
WD1007 ESDI controller

Merged manually, we discussed why.
Thanks! Lots of work done here!!


Fix build-uglify

The build-uglify script was failing because uglifyjs should be uglify-js.

Thanks!


Background resizes with output

Backgrounds are now stored in Output containers
Background are part of the layout code, and will now resize properly when the output size changes.
~If a new background is added while an old one still exists, the previous one is now killed. This is not a security issue, because hypothetically when security is actually implemented clients would never get this far.  If a background needs to be replaced, that client should instead be killed. Currently, there is no mechanism to do this from Way Cooler (and may never be). Instead, if a background needs to be replaced then there needs to be coordination done with the program that provides the background.

Coverage decreased (-0.3%) to 58.372% when pulling e1323cc060063b72870cbcdb306cad8274ed0c3b on background-resize into f3a1b7ccb7ceb2c3ec780c26f2ee9f6486dd0065 on master.

Coverage decreased (-0.3%) to 58.451% when pulling 0113e215c6713c6155727eb474a5a87570e8f274 on background-resize into f3a1b7ccb7ceb2c3ec780c26f2ee9f6486dd0065 on master.

Coverage decreased (-0.5%) to 58.198% when pulling 633f23e9e9115ba2b762e4fe66103de5ea85eae0 on background-resize into f3a1b7ccb7ceb2c3ec780c26f2ee9f6486dd0065 on master.

Coverage decreased (-0.5%) to 58.246% when pulling aa5468be4cd4a09b6bbc90396bb64d2ce9c44c72 on background-resize into f3a1b7ccb7ceb2c3ec780c26f2ee9f6486dd0065 on master.


commands: filter out dotfiles from output

[x] Have you followed the guidelines in our Contributing document?
[x] Have you checked to ensure there aren't other open Pull Requests for the same change?
[x] Have you added an explanation of what your changes do and why you'd like us to include them?
[x] Have you successfully run brew tests with your changes locally?

Noticed in this that DS_Store was getting chucked in with the commands list 🤷♂.

not all dot files?

I can if you want? Just thought this was the only one that seemed likely to end up inside a cmd folder unless someone is doing something a little strange with their tap/etc.

It will currently list e.g. .info.rb.swp so I would do all dot files, yeah

Alright. One sec.

How about
  Pathname.glob(directory/"*")
          .select(&:file?)
          .map { |f| f.basename.to_s.sub(/\.(?:rb|sh)$/, "") }

?

I don't have a major objection if people prefer that format. Whatever folks are happy with. I am not a huge fan of chaining together across multiple lines like that but it's become a common enough thing I can't really start complaining about it now 😄.

I find it easier to follow than a next inside an if inside an each_with_object. 😉

@ilovezfs Any objections? Keen to avoid getting in the middle of a maintainer disagreement over style. I don't miss those 😅.

Thanks @DomT4 and @reitermarkus!

Thanks for merging Mike. Was trying to give ILZ a little bit to see if he had any objections, but I'm assuming we're good.

LGTM. Thanks, Mr. T


Remove second call to create_lightning_module on torch estimator

Issue #2833
Description of changes:
Remove redundant call to create_lightning_module on train_model inside torch/model/estimator.py
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
Please tag this pr with at least one of these labels to make our release process faster: BREAKING, new feature, bug fix, other change, dev setup

@lostella  This needs a label.


Estimator API and transfer learning/fine-tuning

I've been using the Estimator API with the model_fn and input_fn as shown in the official examples (https://github.com/tensorflow/models/blob/master/official/resnet/cifar10_main.py for instance).
This all looks great and wonderful. However, I'm now facing an issue for going further with it. I'd like to use a model trained on a dataset and transfer it to another dataset. In practice, I would like to take the weights from the trained model up to the softmax layer and only initialize randomly this final layer. Then, I can do fine-tuning on the new dataset, which has different labels for instance.
I haven't found a way to do what I want. Is it something missing in the interface? Can we have something  like a variable list to restore from a checkpoint and some other not? Ideally, it would be also good to specify variables to be frozen. Does that all make sense?

I'have been able to restore weights via a custom tf.train.SessionRunHook. I customize the after_create_session method to run an init function generated with tf.contrib.framework.assign_from_checkpoint_fn. You can give it a list of variables you wish to restore. You can then return the hook with yor EstimatorSpec structure.
class RestoreHook(tf.train.SessionRunHook):
    def __init__(self, init_fn):
        self.init_fn = init_fn

    def after_create_session(self, session, coord=None):
        if session.run(tf.train.get_or_create_global_step()) == 0:
            self.init_fn(session)

def model_fn():
    ...
    init_fn = assign_from_checkpoint_fn(model_path, var_list, ignore_missing_vars=True)
    ...
    return EstimatorSpec(..., training_hooks=[RestoreHook(init_fn)])

About generating the variables to restore list, tf.contrib.framework has some useful functions like get_variables_to_restore, get_variables_by_name, etc.

Thanks for the hint, I'll try it as soon as possible. What I just implemented:
def mode_fn():
....
var_to_restore = []
for var in tf.trainable_variables():
if var.name.startswith('my_var'):
var_to_restore.append(var)
checkpoint_state = tf.train.get_checkpoint_state('pretrained')
input_checkpoint = checkpoint_state.model_checkpoint_path
pretrain_saver = tf.train.Saver(var_to_restore)

def init_fn(scaffold, session):
    pretrain_saver.restore(session, input_checkpoint)

scaffold = tf.train.Scaffold(init_fn=init_fn)

return tf.estimator.EstimatorSpec(
    mode=mode,
    predictions=predictions,
    loss=loss,
    train_op=train_op,
    eval_metric_ops=metrics,
    scaffold=scaffold)

Let's see whether this works too. But anyway, this requires more documentation or examples. Even a simpler interface, because this seems to me like a basic use.

My method seems to work.
I leave this issue open if a member from the tensorflow development team want to jump in or tag some enhancement.

Glad you worked out your issue.  But for future reference,

Please go to Stack Overflow for help and support:
https://stackoverflow.com/questions/tagged/tensorflow
If you open a GitHub issue, here is our policy:

It must be a bug or a feature request.
The form below must be filled out.
It shouldn't be a TensorBoard issue. Those go here.

Here's why we have that policy: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.

@tatatodd I don't agree to your post. The issue I've posted here is to be understood as a feature request, either on the documentation side or on the code side. Typically, I would love to see a parameter in the constructor of the Estimator that lets us use pre-trained models, with variable selector. It is a very common use case and I find it ugly the way it has to be done.
Another remark, I spent quite some time digging a solution in Google search, stack overflow, etc. Strangely, I've found many users having the same question as I have, but no answer so far. So, I think my answer here should benefit the whole community.

@jmaye My apologies, it wasn't clear to me that you were requesting a documentation or code feature.  And note that closing an issue doesn't make it less beneficial to the community, it just helps us keep track of what issues still need to be resolved.
Perhaps @ispirmustafa can comment on your proposed changes to Estimator.

@jmaye best way to do transfer learning for now is using tf.train.init_from_checkpoint. Example:
with tf.variable_scope('transferred'):
  # build transferred part of the graph
tf.train.init_from_checkpoint(checkpoint_path, {'old_scope': 'transferred'})
# rest should work seamless.

FYI, we're working on better solutions.

@ispirmustafa Any other reference to track this?

I think that we could reopen this untill we have a new issue or a PR to track this feature.

Hi @bhack
I've closed it since I think answer with current utilities is sufficient. If you think tf.train.init_from_checkpoint does not solve your problem, please re open it.

I've was just referring to your last comment . So is there a follow-up?

Also vaguely related https://github.com/tensorflow/tensorflow/issues/10155

@jmaye I had the exact same use-case and tried to figure out how to use init_from_checkpoint as suggested (and it's really not as trivial as it sounded).
Eventually I got it working, see my answer here: https://stackoverflow.com/q/47867748/3214872

Can we close this issue?

Fine for me, I'm still on 1.5 with the aforementioned code. Not urgent as it works as it is. I'll update the code as soon as I switch to 1.6. Thanks

Can we have an example of how to use tf.estimator.WarmStartSettings when we want to warn start all variables under a given scope. The current documentation says that we have to set the var_name_to_prev_var_name but this dictionary is not easy to create when the model is large (e.g a resnet). It would be better if we could do the same thing as with init_from_checkpoint(ckpt, {'/': 'resnet'})

Can you clarify exactly how your variables are named, both in your model and in the checkpoint you're trying to warm-start from?

Hello, everyone!
I'm new to TensorFlow and meet these problem too. I build VGG16 Network for CUB-200 and try to fine-tuning it with .npy weights from caffe(maybe).
Now, I try like that:
def load_weights(self, weight_file, sess):
        weights = np.load(weight_file)
        keys = sorted(weights.keys())
        for i, k in enumerate(keys):
            print (i, k, np.shape(weights[k]))
            if self.num_output != 1000 and (k == 'fc8_W' or k =='fc8_b'):
                print('pass layer', k)
                continue
            sess.run(self.parameters[i].assign(weights[k]))
            print (self.parameters[i].name, self.parameters[i].shape)

But I will need a session to do the parameters assignment. I'm confused about how to get the same  session with tf.estimator.

@Bowenwu1 that question belongs to stackoverflow, you won't get a reply here, since your problem has nothing to do with this specific issue.
I suggest you to open a question on SO, it's usually rather fast to get an answer there for these questions ;)

How about loading weights from checkpoints in the slim or object detection zoo.?
I'm working on shifting most of my networks from tf.contrib.slim to Estimators+Layers and this would be really useful.

For transfer learning with Estimator, I recommend to checkout following two options:

Tensorflow Hub either directly hub layers or hub feature columns
warm_start_from argument of Estimator.

@ispirmustafa I think both don't support estimators coming from tf.keras models (model_to_estimator).

then one path would be using keras utilities before running model_to_estimator. @fchollet to comment about transfer learning functionalities you can use with tf.keras.Model.

Or take more care on what is supported or not in model_to_estimator: https://github.com/tensorflow/tensorflow/issues/19295 cause model_to_estimator is becoming to be a sort of "second class" estimator.

@GPhilo
Thank you for you reply. I still think TensorFlow should have more doc on Transfer Learning. Since seldom people train model from scratch, it's a quite common request.

Hello!
I've been trying to use warm start to load my model and fine-tune it on some other dataset. It works fine, except when I want to "freeze" layers in the model by making the relevant variables non-trainable, and train only the other unfrozen layers. In this case, the non-trainable variables do not get warm-started, even when the variable names match. It doesn't do it even when I specify those variables manually in the dictionary for var_name_to_prev_var_name argument.
Is there any way to warm-start the non-trainable variables?

You can explicitly pass in a list of Variables or list of strings if you want to avoid regexp + TRAINABLE_VARIABLES (docstring).

@eddie-zhou This feature does not seem to be available in version 1.8.0, which is the latest version of tensorflow-gpu on PyPI. It worked when I downloaded the file tensorflow/python/training/warm_starting_util.py, but now I'm getting errors with importing trainable variables. Now, it gives me the following error:
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmpzrdp8yhb/', vars_to_warm_start=['0_5_two/conv_w', 'batch_normalization_21/gamma', '1_8_one/conv_hebb', 'batch_normalization_50/moving_mean', 'batch_normalization_21/moving_mean', 'batch_normalization_14/moving_variance', '2_5_two/conv_b', '1_7_two/conv_w', '1_6_two/conv_hebb', '2_8_two/conv_b', 'batch_normalization_8/moving_mean', '2_3_two/conv_alpha', 'batch_normalization_24/gamma', 'batch_normalization_3/gamma', '0_1_one/conv_hebb', 'batch_normalization_4/moving_variance', 'batch_normalization_52/gamma', '0_4_two/conv_alpha', '0_2_one/conv_w', 'batch_normalization_45/beta', 'batch_normalization_4/gamma', 'batch_normalization_2/gamma', '2_1_two/conv_alpha', '2_8_two/conv_w', 'batch_normalization_24/moving_variance', '2_6_one/conv_w', '1_6_one/conv_alpha', 'batch_normalization_5/gamma', '1_2_two/conv_b', 'batch_normalization_44/moving_variance', '2_6_two/conv_b', '1_0_two/conv_b', 'batch_normalization_31/moving_mean', 'batch_normalization_10/moving_mean', 'batch_normalization_45/gamma', '0_7_two/conv_w', '2_2_one/conv_w', 'batch_normalization_50/beta', 'batch_normalization_4/moving_mean', 'batch_normalization_17/beta', '2_4_two/conv_alpha', '0_2_one/conv_alpha', '1_0_one/conv_alpha', 'batch_normalization_33/gamma', 'batch_normalization_35/moving_variance', '0_6_one/conv_alpha', '2_3_two/conv_w', '0_8_two/conv_alpha', 'batch_normalization_4/beta', '2_4_one/conv_b', 'batch_normalization_49/moving_mean', '1_7_one/conv_b', 'batch_normalization_35/beta', '2_2_one/conv_b', 'batch_normalization_22/moving_mean', '2_2_two/conv_alpha', 'batch_normalization_34/moving_variance', 'batch_normalization_53/moving_mean', '2_6_one/conv_b', 'batch_normalization_9/gamma', '1_0_two/conv_w', '2_3_one/conv_alpha', 'batch_normalization_26/moving_variance', 'batch_normalization_20/moving_variance', 'batch_normalization_17/gamma', '1_2_one/conv_alpha', 'batch_normalization_33/moving_mean', 'batch_normalization_39/beta', 'batch_normalization_23/moving_mean', 'batch_normalization_6/gamma', 'batch_normalization_17/moving_mean', 'Variable_3', '1_8_one/conv_alpha', 'batch_normalization_2/moving_mean', '1_1_one/conv_b', '0_4_one/conv_hebb', 'batch_normalization_53/gamma', 'batch_normalization_7/gamma', 'batch_normalization/moving_mean', '2_2_one/conv_hebb', 'batch_normalization_32/moving_mean', 'batch_normalization_13/beta', 'batch_normalization_35/moving_mean', '0_6_two/conv_w', '1_0_one/conv_hebb', 'batch_normalization_52/beta', 'batch_normalization_31/beta', '1_1_one/conv_alpha', 'batch_normalization_26/gamma', 'batch_normalization_18/gamma', '1_7_two/conv_b', 'batch_normalization_15/beta', '1_3_one/conv_alpha', '0_7_one/conv_hebb', '0_0_two/conv_w', 'batch_normalization_47/gamma', '0_3_one/conv_b', '0_2_one/conv_b', 'batch_normalization_24/beta', '2_7_one/conv_hebb', 'batch_normalization_5/beta', 'batch_normalization_10/beta', 'batch_normalization_34/beta', '1_0_one/conv_w', 'batch_normalization_51/moving_mean', '2_7_one/conv_b', '2_8_two/conv_hebb', '0_8_one/conv_b', '0_6_two/conv_b', 'batch_normalization_5/moving_mean', '2_4_one/conv_hebb', 'batch_normalization_19/moving_mean', '1_1_two/conv_b', '1_4_one/conv_w', '0_5_two/conv_hebb', 'beta2_power', 'batch_normalization_23/moving_variance', '2_0_three/conv_b', '0_0_one/conv_alpha', 'batch_normalization_6/moving_variance', '0_7_one/conv_w', '2_0_one/conv_alpha', 'first/conv_b', 'Variable_4/Adam', '1_1_one/conv_w', 'batch_normalization_25/beta', '0_4_one/conv_b', 'batch_normalization_20/moving_mean', '1_0_three/conv_w', '0_0_one/conv_hebb', 'batch_normalization_48/beta', '0_3_two/conv_w', '0_1_two/conv_w', 'batch_normalization_37/beta', 'batch_normalization_47/moving_mean', '0_0_two/conv_hebb', 'batch_normalization_28/moving_variance', '2_0_one/conv_b', '2_8_one/conv_alpha', 'Variable_2/Adam_1', 'batch_normalization_44/moving_mean', 'batch_normalization_44/gamma', 'batch_normalization_42/beta', '0_2_two/conv_w', '0_5_one/conv_b', 'batch_normalization_36/beta', 'batch_normalization_54/moving_mean', 'batch_normalization_42/gamma', 'batch_normalization_15/gamma', '1_8_two/conv_alpha', '0_4_two/conv_b', '2_7_one/conv_w', 'batch_normalization_23/beta', 'batch_normalization_33/beta', 'batch_normalization_49/beta', '2_0_one/conv_w', '2_8_one/conv_b', 'batch_normalization_54/beta', 'batch_normalization_29/moving_variance', '1_0_two/conv_alpha', 'first/conv_w', 'batch_normalization_13/gamma', 'batch_normalization_41/beta', 'Variable_4', 'batch_normalization_48/moving_variance', '0_1_one/conv_w', 'batch_normalization_41/gamma', 'batch_normalization_10/moving_variance', '1_7_two/conv_alpha', 'batch_normalization_29/moving_mean', '1_5_one/conv_b', 'batch_normalization_47/moving_variance', '1_6_one/conv_hebb', 'batch_normalization_13/moving_mean', 'batch_normalization_50/gamma', 'batch_normalization_18/moving_variance', '2_7_two/conv_hebb', 'batch_normalization_25/moving_variance', 'batch_normalization_37/moving_variance', 'batch_normalization_5/moving_variance', 'batch_normalization_46/moving_variance', '1_3_one/conv_w', 'batch_normalization_25/moving_mean', 'batch_normalization_20/gamma', '0_6_one/conv_hebb', '2_0_two/conv_hebb', '2_8_two/conv_alpha', 'batch_normalization_31/moving_variance', 'batch_normalization_7/moving_variance', '0_3_one/conv_hebb', '2_0_one/conv_hebb', 'batch_normalization_51/gamma', 'first/conv_hebb', '0_7_two/conv_alpha', 'batch_normalization_6/moving_mean', 'batch_normalization_32/beta', '1_3_two/conv_alpha', 'batch_normalization_1/moving_mean', '1_2_one/conv_w', '2_6_two/conv_w', '1_7_two/conv_hebb', 'batch_normalization_29/gamma', '2_6_one/conv_alpha', '1_6_one/conv_b', '0_2_two/conv_alpha', 'batch_normalization/moving_variance', '0_4_one/conv_alpha', '0_5_one/conv_w', 'batch_normalization_54/gamma', 'batch_normalization_41/moving_variance', 'batch_normalization_43/moving_variance', 'batch_normalization_22/gamma', '0_8_one/conv_alpha', '0_8_two/conv_hebb', 'batch_normalization_48/moving_mean', '1_4_two/conv_alpha', '0_3_one/conv_alpha', '2_7_one/conv_alpha', 'batch_normalization_12/beta', '2_0_two/conv_alpha', '1_8_two/conv_w', '2_5_one/conv_w', '1_3_one/conv_hebb', '0_4_one/conv_w', 'batch_normalization_54/moving_variance', 'batch_normalization_3/moving_variance', '0_5_one/conv_hebb', 'batch_normalization_9/beta', '2_4_two/conv_b', 'batch_normalization_42/moving_mean', '1_8_two/conv_hebb', '1_6_two/conv_b', 'batch_normalization_9/moving_variance', 'batch_normalization_39/gamma', 'batch_normalization_6/beta', 'batch_normalization_38/gamma', 'batch_normalization_46/beta', '0_8_two/conv_b', '2_2_two/conv_hebb', 'batch_normalization_40/moving_variance', '1_1_one/conv_hebb', 'batch_normalization_8/gamma', 'batch_normalization_27/moving_mean', '0_5_two/conv_alpha', '2_8_one/conv_w', '0_3_one/conv_w', 'batch_normalization_46/moving_mean', '1_6_one/conv_w', 'batch_normalization_38/beta', '2_2_two/conv_w', '0_7_one/conv_b', 'batch_normalization_28/gamma', 'batch_normalization_45/moving_variance', 'batch_normalization/beta', '0_1_one/conv_b', '2_6_two/conv_hebb', '2_3_one/conv_b', '1_1_two/conv_hebb', 'batch_normalization_16/moving_variance', '1_3_two/conv_b', 'batch_normalization_44/beta', 'batch_normalization_22/moving_variance', 'batch_normalization_11/beta', '1_5_one/conv_alpha', '2_7_two/conv_alpha', 'batch_normalization_16/gamma', 'batch_normalization_7/beta', '1_1_two/conv_alpha', 'batch_normalization_24/moving_mean', '0_1_two/conv_b', 'batch_normalization_37/moving_mean', 'Variable_2/Adam', 'Variable_1/Adam', 'batch_normalization_8/beta', '0_0_two/conv_alpha', '2_3_one/conv_hebb', 'batch_normalization_49/moving_variance', '1_2_one/conv_b', '2_3_one/conv_w', '0_7_one/conv_alpha', '2_4_one/conv_w', '0_1_one/conv_alpha', 'batch_normalization_30/beta', '1_0_three/conv_b', 'batch_normalization_32/gamma', '1_4_two/conv_w', 'batch_normalization_52/moving_variance', '2_1_two/conv_b', '2_1_one/conv_b', '2_5_two/conv_alpha', '1_0_two/conv_hebb', '1_4_one/conv_b', '1_1_two/conv_w', 'batch_normalization_43/moving_mean', '2_0_two/conv_b', '2_3_two/conv_hebb', 'Variable', 'batch_normalization_53/beta', 'batch_normalization_25/gamma', '1_2_two/conv_hebb', 'beta1_power', 'batch_normalization_38/moving_variance', 'batch_normalization_36/moving_variance', 'batch_normalization_2/moving_variance', '2_4_one/conv_alpha', 'batch_normalization_45/moving_mean', '0_3_two/conv_b', '0_1_two/conv_alpha', '1_8_one/conv_b', '0_0_one/conv_b', 'batch_normalization_2/beta', 'batch_normalization_12/moving_variance', '2_5_one/conv_hebb', 'batch_normalization_19/moving_variance', 'batch_normalization_3/moving_mean', '0_8_one/conv_hebb', 'batch_normalization_26/beta', '2_4_two/conv_w', 'batch_normalization_36/moving_mean', '1_3_two/conv_hebb', '2_0_three/conv_w', '1_5_two/conv_b', '0_8_one/conv_w', 'batch_normalization_12/gamma', '2_0_three/conv_alpha', '2_6_two/conv_alpha', '1_5_one/conv_hebb', 'batch_normalization/gamma', 'batch_normalization_1/gamma', 'batch_normalization_26/moving_mean', '2_2_two/conv_b', 'batch_normalization_14/beta', '1_3_two/conv_w', 'batch_normalization_28/moving_mean', 'batch_normalization_14/moving_mean', '0_4_two/conv_w', 'batch_normalization_11/gamma', '1_8_one/conv_w', '0_3_two/conv_hebb', 'batch_normalization_34/gamma', '1_5_two/conv_alpha', '2_5_two/conv_w', 'batch_normalization_31/gamma', '0_8_two/conv_w', 'batch_normalization_18/moving_mean', '1_4_one/conv_hebb', 'batch_normalization_1/moving_variance', '2_1_one/conv_w', 'batch_normalization_15/moving_variance', 'batch_normalization_40/gamma', '2_1_one/conv_alpha', '2_1_one/conv_hebb', '2_2_one/conv_alpha', 'batch_normalization_37/gamma', 'batch_normalization_19/beta', '2_0_three/conv_hebb', '0_3_two/conv_alpha', '2_1_two/conv_w', 'first/conv_alpha', 'batch_normalization_11/moving_variance', '0_6_one/conv_b', 'batch_normalization_43/beta', 'batch_normalization_3/beta', 'batch_normalization_51/moving_variance', 'batch_normalization_27/moving_variance', 'batch_normalization_11/moving_mean', 'batch_normalization_33/moving_variance', '2_5_one/conv_b', 'batch_normalization_16/moving_mean', 'batch_normalization_51/beta', '0_7_two/conv_b', '0_1_two/conv_hebb', '1_6_two/conv_w', '2_5_one/conv_alpha', '0_0_one/conv_w', 'batch_normalization_18/beta', 'batch_normalization_10/gamma', 'batch_normalization_30/gamma', 'batch_normalization_20/beta', 'Variable_2', 'batch_normalization_34/moving_mean', 'batch_normalization_19/gamma', 'batch_normalization_14/gamma', 'batch_normalization_8/moving_variance', 'batch_normalization_13/moving_variance', '1_8_two/conv_b', 'Variable_4/Adam_1', 'batch_normalization_27/gamma', 'batch_normalization_38/moving_mean', 'Variable_1/Adam_1', '1_3_one/conv_b', 'batch_normalization_16/beta', 'batch_normalization_53/moving_variance', '1_2_one/conv_hebb', 'batch_normalization_12/moving_mean', 'batch_normalization_7/moving_mean', '1_4_two/conv_hebb', '1_4_one/conv_alpha', '2_7_two/conv_b', 'batch_normalization_23/gamma', '0_6_two/conv_hebb', '0_4_two/conv_hebb', '0_7_two/conv_hebb', 'batch_normalization_36/gamma', 'batch_normalization_35/gamma', 'batch_normalization_43/gamma', 'batch_normalization_21/beta', '1_4_two/conv_b', 'batch_normalization_48/gamma', 'batch_normalization_41/moving_mean', '1_0_one/conv_b', '0_5_one/conv_alpha', 'batch_normalization_22/beta', 'batch_normalization_29/beta', 'batch_normalization_40/moving_mean', '0_2_two/conv_b', 'batch_normalization_21/moving_variance', 'batch_normalization_32/moving_variance', 'batch_normalization_1/beta', 'batch_normalization_46/gamma', 'batch_normalization_9/moving_mean', 'batch_normalization_42/moving_variance', '1_7_one/conv_hebb', 'batch_normalization_40/beta', '2_6_one/conv_hebb', '1_0_three/conv_alpha', '1_7_one/conv_w', '0_0_two/conv_b', 'batch_normalization_15/moving_mean', '1_5_one/conv_w', '0_2_two/conv_hebb', '1_5_two/conv_hebb', 'batch_normalization_50/moving_variance', 'batch_normalization_52/moving_mean', '1_7_one/conv_alpha', 'batch_normalization_28/beta', '2_1_two/conv_hebb', '2_7_two/conv_w', 'batch_normalization_17/moving_variance', 'batch_normalization_27/beta', '0_2_one/conv_hebb', 'batch_normalization_30/moving_mean', '0_5_two/conv_b', 'batch_normalization_39/moving_variance', '1_0_three/conv_hebb', '0_6_one/conv_w', '1_5_two/conv_w', 'batch_normalization_49/gamma', 'batch_normalization_30/moving_variance', '1_6_two/conv_alpha', 'Variable_1', 'batch_normalization_39/moving_mean', '2_5_two/conv_hebb', '2_0_two/conv_w', 'batch_normalization_47/beta', '1_2_two/conv_alpha', '2_4_two/conv_hebb', '1_2_two/conv_w', '2_8_one/conv_hebb', '2_3_two/conv_b', '0_6_two/conv_alpha'], var_name_to_vocab_info={}, var_name_to_prev_var_name={})

INFO:tensorflow:Warm-starting from: ('/tmp/tmpzrdp8yhb/',)
INFO:tensorflow:Warm-starting variable: 0_5_two/conv_w; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_5_two/conv_w:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_5_two/conv_w
INFO:tensorflow:Warm-starting variable: 2_0_one/conv_w/Adam; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_0_one/conv_w/Adam:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_0_one/conv_w/Adam
INFO:tensorflow:Warm-starting variable: 2_3_two/conv_b/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_3_two/conv_b/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_3_two/conv_b/Adam_1
INFO:tensorflow:Warm-starting variable: batch_normalization_9/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_9/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_9/moving_variance
INFO:tensorflow:Warm-starting variable: batch_normalization_21/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_21/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_21/gamma
INFO:tensorflow:Warm-starting variable: 1_8_one/conv_hebb; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_8_one/conv_hebb:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_8_one/conv_hebb
INFO:tensorflow:Warm-starting variable: batch_normalization_6/beta; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_6/beta:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_6/beta
INFO:tensorflow:Warm-starting variable: 2_0_three/conv_b/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_0_three/conv_b/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_0_three/conv_b/Adam_1
INFO:tensorflow:Warm-starting variable: batch_normalization_38/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_38/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_38/gamma
INFO:tensorflow:Warm-starting variable: batch_normalization_53/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_53/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_53/moving_variance
INFO:tensorflow:Warm-starting variable: batch_normalization_50/moving_mean; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_50/moving_mean:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_50/moving_mean
INFO:tensorflow:Warm-starting variable: batch_normalization_46/beta; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_46/beta:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_46/beta
INFO:tensorflow:Warm-starting variable: 0_8_two/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_8_two/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_8_two/conv_b
INFO:tensorflow:Warm-starting variable: 0_7_two/conv_w; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_7_two/conv_w:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_7_two/conv_w
INFO:tensorflow:Warm-starting variable: 2_6_one/conv_alpha/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_6_one/conv_alpha/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_6_one/conv_alpha/Adam_1
INFO:tensorflow:Warm-starting variable: batch_normalization_21/moving_mean; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_21/moving_mean:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_21/moving_mean
INFO:tensorflow:Warm-starting variable: batch_normalization_15/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_15/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_15/moving_variance
INFO:tensorflow:Warm-starting variable: batch_normalization_3/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_3/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_3/gamma
INFO:tensorflow:Warm-starting variable: batch_normalization_48/gamma/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_48/gamma/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_48/gamma/Adam_1
INFO:tensorflow:Warm-starting variable: 2_5_one/conv_hebb; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_5_one/conv_hebb:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_5_one/conv_hebb
INFO:tensorflow:Warm-starting variable: batch_normalization_36/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_36/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_36/gamma
INFO:tensorflow:Warm-starting variable: batch_normalization_23/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_23/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_23/moving_variance
INFO:tensorflow:Warm-starting variable: batch_normalization_14/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_14/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_14/moving_variance
INFO:tensorflow:Warm-starting variable: 2_5_two/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_5_two/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_5_two/conv_b
INFO:tensorflow:Warm-starting variable: 1_7_two/conv_w; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_7_two/conv_w:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_7_two/conv_w
INFO:tensorflow:Warm-starting variable: 1_5_two/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_5_two/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_5_two/conv_b
INFO:tensorflow:Warm-starting variable: 2_7_two/conv_w/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_7_two/conv_w/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_7_two/conv_w/Adam_1
INFO:tensorflow:Warm-starting variable: 1_1_one/conv_hebb; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_1_one/conv_hebb:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_1_one/conv_hebb
INFO:tensorflow:Warm-starting variable: batch_normalization_39/gamma/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_39/gamma/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_39/gamma/Adam_1
INFO:tensorflow:Warm-starting variable: 1_6_two/conv_hebb; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_6_two/conv_hebb:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_6_two/conv_hebb
INFO:tensorflow:Warm-starting variable: 2_8_two/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_8_two/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_8_two/conv_b
INFO:tensorflow:Warm-starting variable: 1_0_three/conv_alpha; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_0_three/conv_alpha:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_0_three/conv_alpha
INFO:tensorflow:Warm-starting variable: batch_normalization_27/moving_mean; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_27/moving_mean:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_27/moving_mean
INFO:tensorflow:Warm-starting variable: batch_normalization_39/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_39/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_39/gamma
INFO:tensorflow:Warm-starting variable: batch_normalization_8/moving_mean; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_8/moving_mean:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_8/moving_mean
INFO:tensorflow:Warm-starting variable: batch_normalization_46/beta/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_46/beta/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_46/beta/Adam_1
INFO:tensorflow:Warm-starting variable: 2_8_one/conv_w; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_8_one/conv_w:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_8_one/conv_w

INFO:tensorflow:Warm-starting variable: 0_0_two/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_0_two/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_0_two/conv_b
INFO:tensorflow:Warm-starting variable: 2_5_one/conv_alpha/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_5_one/conv_alpha/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_5_one/conv_alpha/Adam_1
INFO:tensorflow:Warm-starting variable: 0_3_one/conv_w; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_3_one/conv_w:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_3_one/conv_w
INFO:tensorflow:Warm-starting variable: batch_normalization_46/gamma/Adam; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_46/gamma/Adam:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_46/gamma/Adam
INFO:tensorflow:Warm-starting variable: 2_3_two/conv_alpha; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_3_two/conv_alpha:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_3_two/conv_alpha
INFO:tensorflow:Warm-starting variable: 2_0_two/conv_alpha/Adam; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_0_two/conv_alpha/Adam:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_0_two/conv_alpha/Adam
INFO:tensorflow:Warm-starting variable: batch_normalization_24/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_24/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_24/gamma
INFO:tensorflow:Warm-starting variable: 2_1_two/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_1_two/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_1_two/conv_b
INFO:tensorflow:Warm-starting variable: 2_0_two/conv_b/Adam; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_0_two/conv_b/Adam:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_0_two/conv_b/Adam
INFO:tensorflow:Warm-starting variable: 2_8_two/conv_w/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_8_two/conv_w/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_8_two/conv_w/Adam_1
INFO:tensorflow:Warm-starting variable: 0_1_one/conv_hebb; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_1_one/conv_hebb:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_1_one/conv_hebb
INFO:tensorflow:Warm-starting variable: batch_normalization_4/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_4/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_4/moving_variance
INFO:tensorflow:Warm-starting variable: batch_normalization_28/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_28/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_28/gamma
INFO:tensorflow:Warm-starting variable: batch_normalization_45/moving_variance; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_45/moving_variance:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_45/moving_variance
INFO:tensorflow:Warm-starting variable: batch_normalization_42/gamma/Adam_1; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_42/gamma/Adam_1:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_42/gamma/Adam_1
INFO:tensorflow:Warm-starting variable: batch_normalization_15/moving_mean; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_15/moving_mean:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_15/moving_mean
INFO:tensorflow:Warm-starting variable: 0_4_two/conv_alpha; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_4_two/conv_alpha:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_4_two/conv_alpha
INFO:tensorflow:Warm-starting variable: 1_0_two/conv_hebb; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_0_two/conv_hebb:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_0_two/conv_hebb
INFO:tensorflow:Warm-starting variable: batch_normalization/beta; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization/beta:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization/beta
INFO:tensorflow:Warm-starting variable: 0_2_one/conv_w; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 0_2_one/conv_w:0 from checkpoint /tmp/tmpzrdp8yhb/ with 0_2_one/conv_w
INFO:tensorflow:Warm-starting variable: 1_6_two/conv_alpha; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 1_6_two/conv_alpha:0 from checkpoint /tmp/tmpzrdp8yhb/ with 1_6_two/conv_alpha
INFO:tensorflow:Warm-starting variable: batch_normalization_45/beta; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_45/beta:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_45/beta
INFO:tensorflow:Warm-starting variable: batch_normalization_4/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_4/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_4/gamma
INFO:tensorflow:Warm-starting variable: 2_2_two/conv_alpha/Adam; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_2_two/conv_alpha/Adam:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_2_two/conv_alpha/Adam
INFO:tensorflow:Warm-starting variable: 2_3_one/conv_b; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_3_one/conv_b:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_3_one/conv_b
INFO:tensorflow:Warm-starting variable: 2_7_two/conv_b/Adam; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable 2_7_two/conv_b/Adam:0 from checkpoint /tmp/tmpzrdp8yhb/ with 2_7_two/conv_b/Adam
INFO:tensorflow:Warm-starting variable: batch_normalization_2/gamma; prev_var_name: Unchanged
INFO:tensorflow:Initialize variable batch_normalization_2/gamma:0 from checkpoint /tmp/tmpzrdp8yhb/ with batch_normalization_2/gamma
INFO:tensorflow:Warm-starting variable: Variable_2; prev_var_name: Unchanged

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-a56b5d459051> in <module>()
----> 1 model.train(input_fn=lambda:svhn_input_fn(test_mat), hooks=[logging_hook], max_steps=1)

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    361
    362     saving_listeners = _check_listeners_type(saving_listeners)
--> 363     loss = self._train_model(input_fn, hooks, saving_listeners)
    364     logging.info('Loss for final step: %s.', loss)
    365     return self

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
    841       return self._train_model_distributed(input_fn, hooks, saving_listeners)
    842     else:
--> 843       return self._train_model_default(input_fn, hooks, saving_listeners)
    844
    845   def _train_model_default(self, input_fn, hooks, saving_listeners):

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
    857       return self._train_with_estimator_spec(estimator_spec, worker_hooks,
    858                                              hooks, global_step_tensor,
--> 859                                              saving_listeners)
    860
    861   def _train_model_distributed(self, input_fn, hooks, saving_listeners):

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)
    984                    (self._warm_start_settings,))
    985       # pylint: disable=protected-access
--> 986       warm_starting_util.warm_start(*self._warm_start_settings)
    987       # pylint: enable=protected-access
    988     # Check if the user created a loss summary, and add one if they didn't.

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/training/warm_starting_util.py in warm_start(ckpt_to_initialize_from, vars_to_warm_start, var_name_to_vocab_info, var_name_to_prev_var_name)
    391         if len(variable) == 1:
    392           variable = variable[0]
--> 393         _warm_start_var(variable, ckpt_to_initialize_from, prev_var_name)
    394
    395   prev_var_name_not_used = set(

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/training/warm_starting_util.py in _warm_start_var(var, prev_ckpt, prev_tensor_name)
    130     current_var_name = _infer_var_name([var])
    131   elif isinstance(var, list) and all(_is_variable(v) for v in var):
--> 132     current_var_name = _infer_var_name(var)
    133   elif isinstance(var, variables_lib.PartitionedVariable):
    134     current_var_name = _infer_var_name([var])

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/training/warm_starting_util.py in _infer_var_name(var)
    103     Name of the `var`
    104   """
--> 105   name_to_var_dict = saver.BaseSaverBuilder.OpListToDict(var)
    106   if len(name_to_var_dict) > 1:
    107     raise TypeError("`var` = %s passed as arg violates the constraints.  "

~/resnet_venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py in OpListToDict(op_list, convert_variable_to_tensor)
    636           if name in names_to_saveables:
    637             raise ValueError("At least two variables have the same name: %s" %
--> 638                              name)
    639           names_to_saveables[name] = var
    640

ValueError: At least two variables have the same name: Variable_2

The error occurs everytime I re-run the kernel in the notebook, with the error thrown for different variables everytime (all defined with tf.Variable). This happens when I'm trying to train an estimator just after defining the estimator with warm starting.

Generally, steer towards using tf.get_variable instead of tf.Variable -- I suspect that's where your problems with duplicate names are coming from.

The issue is sort-of solved. It still existed even after changing all instances of tf.Variable to tf.get_variable. Also, it only seemed to occur with trainable variables in the graph.
Therefore I simply did not specify the trainable variables in the list passed to the vars_to_warm_start parameter. Now, even though they aren't in the list, they're still being warm-started. This is exactly what I had intended, but it still wouldn't allow me to warm-start non-trainable variables and ignore warm-starting for trainable ones.
Although my issue has been solved, is there a way of getting the trainable variables to not warm-start automatically?

I have the same problem with you@Xyza1972 , when using "tf.train.init_from_checkpoint"  I found that the ckpt file has two types ,if the ckpt file is only one file like xxx.ckpt ,it can work well . But when the ckpt split to three files like "model.ckpt-xxx.data、model.ckpt-xxx.index、model.ckpt-xxx.meta", the problem would appear, for detail , it will restore the ckpt weights over and over again when a new epoch starts, which can not lead to a right way to get a perfect accuracy . Is that a bug or i missed something ?? Thanks for helping me .

I have the same problem with you@Xyza1972 , when using "tf.train.init_from_checkpoint" I found that the ckpt file has two types ,if the ckpt file is only one file like xxx.ckpt ,it can work well . But when the ckpt split to three files like "model.ckpt-xxx.data、model.ckpt-xxx.index、model.ckpt-xxx.meta", the problem would appear, for detail , it will restore the ckpt weights over and over again when a new epoch starts, which can not lead to a right way to get a perfect accuracy . Is that a bug or i missed something ?? Thanks for helping me .

Did you solve this yet ?, I've google everywhere with no luck

I have the same problem with you@Xyza1972 , when using "tf.train.init_from_checkpoint" I found that the ckpt file has two types ,if the ckpt file is only one file like xxx.ckpt ,it can work well . But when the ckpt split to three files like "model.ckpt-xxx.data、model.ckpt-xxx.index、model.ckpt-xxx.meta", the problem would appear, for detail , it will restore the ckpt weights over and over again when a new epoch starts, which can not lead to a right way to get a perfect accuracy . Is that a bug or i missed something ?? Thanks for helping me .

Hello, I meet the same problem. Did you find the solution? Thanks

@thalitadru Hi, what's your version of tensorflow? I restore variables like what you do leveraging tf.train.SessionRunHook. with tensorflow 1.15, but it throws the error that key h3/bias not in the checkpoint, key h3/bias is the variable in my new network which is not in the checkpoint. Anyone has the same problem?

So is warmstartsettings or init_from_checkpoint recommended?


Error Importing Excel Spreadsheets w/Crash

What happens?
I'm a new user. I installed duckdb version 0.10.0 and followed instructions to import excel spreadsheet.  My files contain multiple sheets inside.  Simple files load ok however for my private file with multiple sheets I get fatal error and need to restart in some cases.
To Reproduce
download a complex excel spreadsheet with multiple sheets. I downloaded
SaleData.xlsx # Note I did this after first trying and failing to load my own private xlsx sheet.   Steps I took 1. install spatial and load spatial. Its possible on my first attempt I did not load spatial but I am not sure.  I received an error trying to load:   SELECT * FROM st_read('sludge.xlsx');  This produced an error:  SELECT * FROM st_read('sludge.xlsx');
Error: INTERNAL Error: Failed to bind "ST_Read": Table function must return at least one column

Then I tried with SaleData.xlsx and got identical area.
Open another window and started duckdb and ran a very simple spreadsheet names.xlsx that was one row and one column. It worked fine.  I split SaleData into separate files one per sheet and it loaded it.

This worked. So I tried the singled the single sheet file from SalesData. That also worked ok.  but it loaded over a thousand rows. Perhaps when i downloaded it from google it created extra rows? not sure. Here is the actual file
namelist.xlsx
Maybe it has over 1k rows but I dont think so.   So I then tried loading SaleData file and it loaded ok  in a way but it loaded 45 rows. In fact there are 8 rows in the first sheet and 44 rows in the second sheet. Why does it appear to have selected the second sheet?  After this sort of worked (except for the number of rows) I tried the original sheet that it had trouble loading and got the following errors:  Internal Error failed to bind st_reader.  Table must return at least one column. (Which table?) can it please say which table it wants to try to get this from? Because spreadsheets can have many right?  When I try to load another spreadsheet the SaleData again it gives a fatal error saying it was invalidated by previous error.

I will take a look at my private xslx that seemed to cause the original issue but in any event I think the behavior is odd and not expected.  Plus the documentation is unclear on how to load a document with multiple sheets inside. Its simply not clear enough. most spreadsheets DO have multiple sheets inside. So the documentation is off. The logic of which sheets and how many rows get loaded is at best poorly documented and more likely simply does not work at a professional level.
OS:
M1 Mac OS Silicon Ventura 13.3.1
DuckDB Version:
v0.10.0
DuckDB Client:
Python
Full Name:
Dean Oz
Affiliation:
None
What is the latest build you tested with? If possible, we recommend testing with the latest nightly build.
I have not tested with any build
Did you include all relevant data sets for reproducing the issue?
No - Other reason (please specify in the issue body)
Did you include all code required to reproduce the issue?

[X] Yes, I have

Did you include all relevant configuration (e.g., CPU architecture, Python version, Linux distribution) to reproduce the issue? Yes I have. However, I do need to check the original file as well that caused the original error.

[X] Yes, I have

I will load up my original spreetsheet sludge.xlsx that caused the original error and check it and report back whats in it or not in it.

I can't share the whole sheet but 1) it loaded ok into google sheets so its a valid spreedsheet. 2) its first three columns do not have values until for several iniital rows. Maybe 10 or 20 but they do have values farther down.   Here is a picture of the columns

ABC have do not have values for the three rows but they do after that.  D-AE have values in all rows. There is only one sheet in the document.

Only other thing besides not having values for the first three columns in the first three rows is that there is some vertically aligned text in some columns as opposed to horizontally aligned.

I added a1, b1, b3
a2, b2, b3
a3, b3, c3
and the sheet loaded after that. It does appear to report up to 1000k plus rows? Not sure why it loads that many. here is the sheet as downloaded. Please see that sd.xlsx also loads 1k plus rows.

In summary it seems that first colunn rows must be filled in or it will not even import as distinct from create a table.  The error message related to there not being at least one column is not descriptive as the table has many columns and all have values at some point. It was not decided that the first column for example is the primary etc. So I think this error message is inaccurate. Also there needs to be more reported for a full crash like this.


Any news for OpenCL support for AMD and mobile gpus ?

Hi , when can we use dl4j with opencl support ?
any news for that ?
which version of dl4j will support amd and embedded gpus ?
With robovm support and ios also metal 2 .
if you make the interface , i can help you on apple ( ios and osx ) side
https://github.com/dehesa/Metal
for bulk multiplication etc...
and opencl side i try my best.
But need interface what to do.
For big bulk calculations.

OpenCL support won't be added any time soon, it would be literally thousands of hours of work to implement.
We are looking at other options that could provide AMD GPU support amongst other things (LLVM and Intel One API / data parallel C) but have nothing to announce at this time.
CoreML (which uses Metal internally) is on the roadmap for 2020, but will still be a very large task.
Integrated GPUs - Intel MKLDNN (now DNNL as of version 1.1) has support for Intel integrated graphics - but we haven't tested it. In theory we should get that "for free"
https://github.com/intel/mkl-dnn
Upgrading to MKLDNN/DNNL 1.1 is already planned, keep an eye on this issue: https://github.com/eclipse/deeplearning4j/issues/8316

@AlexDBlack Have you consider porting the CUDA code to HIP?
It shouldn't be too time consuming with the HIPIFY tools (which can automatically convert source from CUDA to HIP) and the result source code could be compiled to run on both NVIDIA or AMD GPUs.

@s-bernard We have looked at a few of these options a while back.
Given the size of our codebase, our expectation is that such tools will never work quite as well as advertised - i.e., will still need manual post-processing. And even if they do work (in terms of providing correct output), it's unlikely they will be optimized as well as we can do so manually.
There's a lot more to adding AMD support than merely running CUDA code through a converter (java side, CI, any changes to memory management, not to mention a ton of QA/testing).
All that said, still want to look at AMD support and looking at it in 2020 is a possibility. But we haven't made a commitment to do so yet.

@AlexDBlack BTW, Intel oneAPI also offers something similar these days:
https://software.intel.com/en-us/oneapi/dpc-compiler

Yep, we're aware of that OneAPI has a similar CUDA code conversion tool.
We haven't evaluated OneAPI in depth yet, but it's an option worth considering. to expand our hardware support

Thanks a lot @AlexDBlack for your detailed answers. I hope I don't bother you with my questions.
I've recently come across TornadoVM, a plug-in to OpenJDK and GraalVM that allows programmers to automatically run Java programs on heterogeneous hardware aka CPUs, GPUs, and FPGAs.
Have you heard about it? What do you think?

All these simplified tools are great if all you need is element-wise operations. Once you need anything slightly more complex, anything  that can’t be expressed as elementary operation along some dimension, or require platform-specific algorithms/tweaks  - you have to get back to old good C.

Another possibility: SYCL
https://github.com/illuhad/hipSYCL

Another possibility: SYCL
https://github.com/illuhad/hipSYCL

@cyberbeat  @quickwritereader  is experimenting with hip to see if the tooling there is any good. @saudet  is also experimenting with TVM support which would allow at least inference.

@cyberbeat  @quickwritereader  is experimenting with hip to see if the tooling there is any good. @saudet  is also experimenting with TVM support which would allow at least inference.


Do we support calling With on an interface?

Specifically, if the variable has an interface type, calling With should return an instance with the same interface. Do we support that?

Migrated to https://github.com/Miista/Typesafe.With/issues/2


docs or dumb user: Can't successfully make template changes

I haven't been able to figure out where to put my modified layout.html such that it will be picked up and used.
https://docs.raneto.com/templates/customizing-the-template indicates that the updated html files should go in "themes/default" but I'm not sure exactly where due to the relative path. Putting "themes" at the same level as "content", "images", and "public" feels correct but doesn't seem to work. Putting "themes" inside "public" doesn't seem to work either.

I ended up setting -
theme_dir = '/config/themes'
theme_name = 'default'
and moving 'public' into '/config/themes/default/public' and my templates into '/config/themes/default/templates'
That works, but it feels like it's nowhere near the "intended" setup for theme template files.


Connect-AtwsWebAPI - Intermittent errors when running in Azure Function (using secret ENV: variables)

I found that Connect-AtwsWebAPI.ps1 had a nice little secret logic flow that looks for Environment Variables of interest when running in an Azure Function; something that I have been leveraging with a serverless automation system I've been developing for my workplace.
However, I have intermittently been running into an issue, where it seems that the AutoTask connection needs to be re-established, so "Get-ATWSData" (or an equivalent CMDlet) calls Connect-AtwsWebAPI without any parameters; this seems to follow the DefaultParameterSet of 'ConfigurationFile'
The error is thrown as a result of the $ProfilePath using the 'Join-Path' CMDlet, with the generic error: "Path" cannot be bound because it is null
 [Parameter(             ParameterSetName = 'ConfigurationFile'         )]         [ArgumentCompleter( {                 param($Cmd, $Param, $Word, $Ast, $FakeBound)                 $(Get-ChildItem -Path $Global:AtwsModuleConfigurationPath -Filter "*.clixml").FullName             })]         [ValidateScript( {                  Test-Path $_             })]         [Alias('Path')]         [IO.FileInfo]         # The path to an alternate clixml file with connection profiles         $ProfilePath = $(Join-Path -Path $Global:AtwsModuleConfigurationPath -ChildPath AtwsConfig.clixml)
I have been able to get around this issue by editing the DefaultParameterSet to one I defined, which doesn't actually take any relevant variables - allowing the command to go through, reach the "$ENV:FUNCTIONS_WORKER_RUNTIME" clause, and then obtain the Environment variables I set from there.
Is it possible to get a new Parameter Set that is targeted for official Azure Function use? Otherwise, could the assignment of $profilePath occur in the $PSCmdlet.ParameterSetName clause for 'ConfigurationFile' instead? Either solution should work.

Hi @TwiggyDev, this seems to be faulty logic at my part. Also, I am being unecessarily "clever" by setting the default value of ProfilePath in the parameter definition when there exist cases where the configuration path will not be set. So in the update I intend to post today I have moved the join-path command to the section where the code has established that it will indeed use a saved profile to connect, hopefully solving this issue permanently.


DTLSv1.3: Adds DTLS unified header support

Checklist

[ ] documentation is added or updated
[x] tests are added or updated

ping @openssl/committers

@t8m rebased, please reconfirm
@openssl/committers the is ready for a second review.

@openssl/committers ping for second review.


configurable text colors for guild tree and messages

Hi,
I added some more config options for text colors. It may fix #389 and definitely allows me to read the text (and not have white text on white background as before).
Best,

Don't forget to add defaults to the readme config!

Thanks peeps! This helps! :D


update odf storage deletion

Type of change
Note: Fill x in []

[x] bug
[ ] enhancement
[ ] documentation
[ ] dependencies

Description

Update ODF deletion storage type
For security reasons, all pull requests need to be approved first before running any automated CI

Right, fixed


⚠ Bofrost.at has degraded performance

In 648d1e4, Bofrost.at (https://www.bofrost.at) experienced degraded performance:

HTTP code: 200
Response time: 1435 ms

Resolved: Bofrost.at performance has improved in 360e0cd after 26 minutes.


CodeCop Rule: AA0100 Do not have identifiers with quotes in the name

Describe the bug
We are currently updating our extension to BC16 in the ${CodeCop} declared under code analyzer it is showing multiple warnings. Below is one "AL0100 Do not have identifiers with quotes in the name"
To Reproduce
Steps to reproduce the behavior:

AL Language extension is installed (5.0.254558)
Create a page extension of standard page "Customer Card" (21)
Modify the existing field """Balance (LCY)"" - ABS(""Post Dated Checks (LCY)"")"
A warning saying "Do not have identifiers with quotes in the name" will appear

Screenshots

Standard Code BC16

Versions:
AL Language 5.0.254558
Business Central : 16.0.11209.0 (Preview)

Well, even Microsoft is not yet compliant with their own rules :)
If this warning annoys you, you can disable it in a ruleset file until Microsoft fix the control name.
https://docs.microsoft.com/en-us/dynamics365/business-central/dev-itpro/developer/devenv-rule-set-syntax-for-code-analysis-tools

It should be ok to refer to to something with a quote in.
Warning should only appear on the actual definition.

fixed in master


Create hello world (System Programming)

Thanks for giving a look to my repo. Kindly commit it.

Hi, thanks for the effort but a program in c has already been accepted. You can try out any new language which you don't know much about and which hasn't been added already!


Remove SSH hostKeyFormat from the config file

It isn't used anywhere in the code.

@jbonofre Looks like it is already removed from there, I did a grep and can't find a reference to it anywhere else in the source.


Preference list sort order seems wrong

Here is an example of a sort:

All of these results are flagged for preference, but it seems like the one on the bottom should actually be on top. Maybe when sorting the nodes, it should not only take into account the preference list order, but the position it once was in prior to the sort.

fixed in fb8e091


Recipe issue when immersive engineering is in the pack

We have built a large mod pack and we are having an issue with the enriching factory after adding immersive engineering, when you run through nether quartz ore you used to get 6 nether quartz now we are getting immersive engineering quartz dust which is not ore dict any ideas on how to change it back to its old recipe
I hope you can help
Many Thanks
Occelot666

U can try to use modtweaker to change it back

looking into the modtweaker way i found this but it does not seem right
//Enrichment Chamber
//InputStack, OutputStack
mods.mekanism.Enrichment.addRecipe(minecraft:diamond_ore, minecraft:diamond * 8);
//InputStack
mods.mekanism.Enrichment.removeRecipe(minecraft:diamond);
obviously changing the diamond_ore to quartz_ore and so on but both say input on the modteaker site
http://minetweaker3.powerofbytes.com/wiki/ModTweaker:Mekanism_Support for reference
any further help would be appreciated as the pack is running live and dont want to mess it up completely :)
many thanks
occelot666

Remove the recipe first and u will be able to make the recipe


[Bug]:failed to query using dblink in ODC

ODC version
ODC4.1.3BP3
OB version
OB<IP_ADDRESS> for Oracle tenant
What happened?
SQL： select a.* from t1@test_dblink a;
ODC ERROR：
select ROWID AS "ODC_INTERNAL_ROWID", a.* from t1@test_dblink a;
ErrorCode = 1410, SQLState = HY000, Details = ORA-01410: invalid ROWID
What did you expect to happen?
SQL statement execute without errors
How can we reproduce it (as minimally and precisely as possible)?
Using dblink to execute SQL in ODC4.1.3BP3
Anything else we need to know?
No response
Cloud
No response

odc will add an user config to solve this problem, user can disable this feature to work around:

Fixed In Commit e79dfe7d35a7a5c28b7fbfcb576424015ae0e358
------
xiaokang
Mon Mar 25 10:53:11 2024 +0800

test pass


Schema loss after executing a failing sql script

System information:

Windows 10 Pro
DBeaver 21.1.4

Connection specification:

Postgres 9.4
PostgreSQL

Describe the problem you're observing:
After executing an sql script, if an error occur the selected schema is lost and I'm able to reexecute the script only opening a new script tab.
Steps to reproduce, if exist:

Editor SQL -> New SQL script
Select a database and a schema though the dropdown
Execute a failing script
Fix the script and run it again to get the error

Errore SQL [42P01]: ERROR: relation "tank_mix" does not exist
Posizione: 15", but it does exist

And if I try to change the schema another error is showing:

Error changing default database
Error setting search path
Error setting search path
ERROR: current transaction is aborted, commands ignored until end of transaction block
ERROR: current transaction is aborted, commands ignored until end of transaction block
ERROR: relation "tank_mix" does not exist
Posizione: 15
ERROR: relation "tank_mix" does not exist
Posizione: 15"
This errors seems to appear only selecting the database and the schema after opening the sql script tab.
If I select in advance the default database and schema through the Database Navigator, nothing happens after executing a failing script

@vincendep unfortunately, cannot reproduce it. could you provide a video of an issue?


Update VSourcesTable.vue

Please review the code and approve

Fixes

Fixes #469 by @obulat
Description

This pull request will solve the sorting (case-insenstive) issue in the source table.

Testing Instructions

Checklist

[x] My pull request has a descriptive title (not a vague title likeUpdate index.md).
[x] My pull request targets the default branch of the repository (main) or a parent feature branch.
[x] My commit messages follow best practices.
[x] My code follows the established code style of the repository.
[x] I added or updated tests for the changes I made (if applicable).
[ ] I added or updated documentation (if applicable).
[x] I tried running the project locally and verified that there are no visible errors.
[ ] I ran the DAG documentation generator (if applicable).

Developer Certificate of Origin

Developer Certificate of Origin
Developer Certificate of Origin
Version 1.1

Copyright (C) 2004, 2006 The Linux Foundation and its contributors.
1 Letterman Drive
Suite D4700
San Francisco, CA, 94129

Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.

Developer's Certificate of Origin 1.1

By making a contribution to this project, I certify that:

(a) The contribution was created in whole or in part by me and I
    have the right to submit it under the open source license
    indicated in the file; or

(b) The contribution is based upon previous work that, to the best
    of my knowledge, is covered under an appropriate open source
    license and I have the right under that license to submit that
    work with modifications, whether created in whole or in part
    by me, under the same open source license (unless I am
    permitted to submit under a different license), as indicated
    in the file; or

(c) The contribution was provided directly to me by some other
    person who certified (a), (b) or (c) and I have not modified
    it.

(d) I understand and agree that this project and the contribution
    are public and that a record of the contribution (including all
    personal information I submit with it, including my sign-off) is
    maintained indefinitely and may be redistributed consistent with
    this project or the open source license(s) involved.

@Chirag57 Thanks for the contribution.
Please run linting locally (just lint) and commit the changes.
Additionally, please edit the PR body and fill the template as requested. Each section has comments explaining what should be included, and they are critical to getting PRs reviewed quickly.

Thanks @sarayourfriend for the help, I did (run linting) and made the changes required.

I'm not sure why TypeScript does not narrow the value from string|number to string if it knows the key

TypeScript doesn't know the specific key at this point. All it knows is that sorting.field is keyof MediaProvider, at least at the point where field1 and field2 are created (before the === 'display_name' check). See this TypeScript playground link as an illustration: https://www.typescriptlang.org/play?#code/C4TwDgpgBA8gRgKygXigbwFBW1AhgLigGdgAnASwDsBzLHOQygVwFs4JSMBfDDAYwD2lElAGJC8JKjQEoAIlxyANFAZQAjD35CRAM3IQANgBMAcrhbRUAcmt4iUANYQQA3bEQYA9F5wA9AH5eQWFgKH0jYxRRRABtCJNzSwBdb19sQN5ydwAKBLMLK2QbXGsASnQ6bBCREgoaADEDE2ixBHjmgpSqqB8cKEz+-IBuHr7MniA
Note how field is string | number because fieldName is still just "any key", whereas inside the conditional stringField is string, because we knew the specific field name when retrieving the value from the object. However, even inside the conditional, field remains string | number, because TypeScript isn't refining the types for already defined values, only new ones.
There's no real convenient way to facilitate this other than pulling field1 and field2 from the objects inside the conditional again.

i m a newbie to open source can you please @sarayourfriend  explain me that why this linting is failing. Thanks for the help.

@Chirag57 prettier is making changes to the file that you'll need to commit. You should be able to run linting using the just lint script from the repository root. I haven't run it locally and prettier doesn't say what it changed in the CI logs, but I'm 99% sure the only change is to switch the new code to use double quotes instead of single quotes: that's the only thing that jumps out at me as something prettier would care about.
Please run just lint locally and commit any files that have changed. If you haven't run just precommit yet, please do so as well, that's what sets up linting to happen automatically whenever you commit or push (and prevents accidentally committing code that will fail linting in CI).


Write up a set of guidelines we should follow when creating build and release pipelines in our devops

I started putting the guidelines at https://dev.azure.com/azure-sdk/internal/_wiki/wikis/internal.wiki?wikiVersion=GBwikiMaster&pagePath=%2FAzure Devop Pipeline Guidance&pageId=2 but we need to finish them and structure the document a little more.

Need to include a section on publishing artifacts that are shipping under a particular artifact name.

All currently known guidelines have been written and fixed in our yaml files, so closing.


Feature: Add compat option to disable preact/compat aliases

Can be enabled by default, but would be nice to allow users to have a bit more control without having to splice the plugins returned by the preset.
I saw that Astro ran into this as well so I think it would be a reasonable addition.

Thanks @rschristian, I was going to open an issue as well. Having a compat: false or noCompat: true or whatever else would be nice.


basic scoring built on event system

Added basic scoring system built on an event system (only setup for local multiplayer currently). Lowered height of ballspawner to reduce bouncing ball after scoring.

Thanks!

@Cheeseness, you're welcome! :)

For reference, this addresses half of #43 (still needs multiplayer sync)


Double click event in a row

You have written activate event for body cell. If the cell has no value the event is not emitting. Pls check. If you have any answers please share with me

I also need this feature as well. Pls


Linker does not link constructors of instances located in files without linked function (IDFGH-10141)

Answers checklist.

[X] I have read the documentation ESP-IDF Programming Guide and the issue is not addressed there.
[X] I have updated my IDF branch (master or release) to the latest version and checked that the issue is present there.
[X] I have searched the issue tracker for a similar issue and not found a similar issue.

IDF version.
v4.3.4
Operating System used.
Windows
How did you build your project?
VS Code IDE
If you are using Windows, please specify command line type.
PowerShell
What is the expected behavior?
Creating an Instance of a Class in global scope should call its constructor prior app_main() execution.
This should always be true, even if inside the file containing the Instance is no other function.
What is the actual behavior?
If there is a Function inside the .cpp file that is somehow linked to the app_main() (throu direct call or a call to a function nested in other functions) this behaviour is correct. Otherwise, if there is only the global Instance inside the .cpp file, the constructor will never be called and the instance will never be created.
Steps to reproduce.
Please have a look at this repo: https://github.com/timoxd7/ConstructorLinkageBug
There is a config inside the main component called MAIN_CPP_FORCE_COMPILATION. This will only add a call to an empty_function() defined inside the .cpp file. It does not add anything else.
If you then use the size command, you can see that only the main.c.obj is produced, while with this config enabled, there are main.c.obj, main.cpp.obj and custom_class.cpp.obj produced.
Build or installation Logs.
No response
More Information.
I think this might be a linkage problem. The linker sees no function to link and the instances itself are also not referenced anywhere, so it throws away the instances, including the calls to its constructors.
Doing the same with gcc on windows while removing any ESP specific code (gcc main.c main.cpp custom_class.cpp -O2 -o out.exe) does indeed work as expected and links and executes all constructors even if there is no function.

Hi @timoxd7, please check the explanation of the issue here: https://github.com/espressif/esp-idf/tree/master/examples/build_system/cmake/plugins#ensuring-that-the-plugin-code-is-included-into-the-executable.
The reason why you observe this difference between direct gcc invocation and IDF build system is that your gcc invocation links object files directly into the executable. IDF build system first combines the object files into static libraries and then links them. The logic of GNU linker when dealing with objects in static libraries is different from that when object files are linked directly. The example README linked above has some suggestions on how to avoid this issue.
(Do note that the WHOLE_ARCHIVE option mentioned in that example has been added in IDF v5.0, and you won't be able to use it in v4.3.x.)

Thank you, did not know this is expected behaviour. I will close the issue then.


Tidy Up Further

Best reviewed commit by commit:

c283c35 Streamline documentation
622bce2 Use standard error classes
4df26a1 Abstract duplicate code in a local function

Thanks.

Implemented your feed back in be9ef10.
Added a minor commit 4a6a428 to clarify required dependencies.


Release TEAMNADO-4697 to Prod Beta

Description
Associated Jira ticket: # (issue)
Please include a summary of the change, what this fixes/creates/improves.
How to test the PR
Please include steps to test your PR.
Before the change
After the change
Dependent work link
Checklist:

[ ] The commit message has the Jira ticket linked
[ ] PR has a short description
[ ] Screenshots before and after the change are added
[ ] Tests for the changes have been added
[ ] README.md is updated if necessary
[ ] Needs additional dependent work

Codecov Report
Patch coverage: 100.00% and project coverage change: -0.50 :warning:

Comparison is base (412f90d) 54.21% compared to head (cf0ee35) 53.72%.

Additional details and impacted files
@@              Coverage Diff              @@
##           prod-beta     #216      +/-   ##
=============================================
- Coverage      54.21%   53.72%   -0.50%
=============================================
  Files             81       81
  Lines           1103     1249     +146
  Branches         225      254      +29
=============================================
+ Hits             598      671      +73
- Misses           505      578      +73

Impacted Files
Coverage Δ

...alRepositoriesTable/AdditionalRepositoriesTable.js
97.91% <100.00%> (ø)

... and 20 files with indirect coverage changes
Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.

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NavigatorIOS component view too tall

The component view for a NavigatorIOS view is exactly 64 points too tall. This is exactly the height of the iOS status bar plus UINagivationBar. Is it not accounting for this in the height calculation?
A bottom margin of 64 on the container view is a temporary solution.
Sample code and screenshots included below:
NavigatorIOS Layout
 var TestApp = React.createClass({
   render: function() {
     StatusBarIOS.setStyle('light-content', false);
     return (
       <NavigatorIOS
         style={styles.nav}
         initialRoute={{
           title: 'Test',
           component: TestView,
         }}
         titleTextColor='white'
         tintColor='white'
         barTintColor='red'
         translucent={false}
         itemWrapperStyle={styles.wrappedView}
       />
     );
   }
 });

 var styles = StyleSheet.create({
   nav: {
     flex: 1,
   },
   wrappedView: {
     backgroundColor: Colors.GreyBackground,
     // NavigationIOS container height workaround
    //  marginBottom: 64,
   }
 });

Child View layout:
var TestView = React.createClass({
   render: function() {
     return (
       <View style={styles.container}>
       <View style={styles.emptyView}>
       </View>
         <View style={styles.bottomBar}>
         </View>
       </View>
     );
   },
 });

 var styles = StyleSheet.create({
   container: {
     flex: 1,
     flexDirection: 'column',
   },
   emptyView: {
     flex: 1,
     backgroundColor: 'yellow',
   },
    bottomBar: {
      backgroundColor: 'red',
      height: 64,
    }
 });

With bottom view height 80:

With bottom view height 64:

I believe it is recommended that you use Navigator rather than NavigatorIOS. See the last sentence on this page: https://facebook.github.io/react-native/docs/navigator-comparison.html

I can reproduce, it happens when you disable transparency transparency={false}, set to true and it works correctly.

Hi there! This issue is being closed because it has been inactive for a while.
But don't worry, it will live on with ProductPains! Check out its new home: https://productpains.com/post/react-native/navigatorios-component-view-too-tall
ProductPains helps the community prioritize the most important issues thanks to its voting feature.
It is easy to use - just login with GitHub.
Also, if this issue is a bug, please consider sending a PR with a fix. We rely on the community to provide
bugfixes as the core team is focused on performance.


CRASH REPORT Process <0.1498.0> with 0 neighbours crashed with reason: {ssl_error,{tls_alert,"certificate unknown"}} in esockd_connection:upgrade

Environment

OS: centos7.2
Erlang/OTP:
EMQ: 2.0.7/2.0.5

Description
2017-02-21 16:46:05.274 [error] <0.1510.0> CRASH REPORT Process <0.1510.0> with 0 neighbours crashed with reason: {ssl_error,{tls_alert,"certificate unknown"}} in esockd_connection:upgrade/1 line 117
2017-02-21 16:46:05.274 [error] <0.1193.0> Supervisor 'esockd_connection_sup - <0.1193.0>' had child connection started with emqttd_client:start_link([{max_clientid_len,1024},{max_packet_size,65536},{client_idle_timeout,30}]) at <0.1510.0> exit with reason {ssl_error,{tls_alert,"certificate unknown"}} in context connection_crashed
2017-02-21 16:46:13.386 [error] <0.1513.0> SSL: certify: ssl_alert.erl:93:Fatal error: certificate unknown
2017-02-21 16:46:13.387 [error] <0.1512.0> CRASH REPORT Process <0.1512.0> with 0 neighbours crashed with reason: {ssl_error,{tls_alert,"certificate unknown"}} in esockd_connection:upgrade/1 line 117
2017-02-21 16:46:13.387 [error] <0.1193.0> Supervisor 'esockd_connection_sup - <0.1193.0>' had child connection started with emqttd_client:start_link([{max_clientid_len,1024},{max_packet_size,65536},{client_idle_timeout,30}]) at <0.1512.0> exit with reason {ssl_error,{tls_alert,"certificate unknown"}} in context connection_crashed
2017-02-21 16:46:13.459 [error] <0.1515.0> SSL: certify: ssl_alert.erl:93:Fatal error: certificate unknown
user connect to emqttd is OK using ssl, but report error between two server in cluster?
$ /data/emqttd/bin/emqttd_ctl cluster status
Cluster status<EMAIL_ADDRESS><EMAIL_ADDRESS>

@maxshucn The issue has been fixed in esockd 4.2 which is released with EMQ 2.1.0-beta.2.
The SSL connection process should exit quietly when the '{tls_alert, Reason}' errors happened.


Updatepriority

changing priority from 999 to 900 for better execution place.

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.1 out of 2 committers have signed the CLA.:white_check_mark: jeremyjpj0916:x: rsbrisciYou have signed the CLA already but the status is still pending? Let us recheck it.


Black screen - No Verbose nor OpenCore

Hey, there.
How are you?
First of all, I would like to thank you for all your work.
It is astonishing work indeed, from what I can tell by the screenshots.
I myself have lost countless hours to try and get my EFI folder up and running flawless (apart from dGPU and HDMI, of course).
I had everything running flawlessly up until 2 months ago, including Wifi and Bluetooth, but for some reason Bluetooth stopped working for some reason and I cannot, for the life of me, get it going.
I am running OpenCore 0.9.1 with the latest Kexts on Ventura 13.3.1 and I decided to give your EFI a shot, using my SMBIOS info.
However, I cannot get it to boot at all, even after settings hideverbose to false and ensuring the -v argument is present in boot-args.
I just get a black screen and never even get to see verbose or OpenCore.
Is there something that I'm doing wrong?
Would you be willing to help?
I can share my EFI, if you want, seeing as we took different approaches and something might be of use to you.
The only thing that I see different is that my dGPU reports as a GTX 1050 (non-TI) under Windows 11 22H2, so I'm not sure that's the issue.
Can you shed some light on this matter?
Thank you in advance,
Bruno.

What is your smbios info ? It should be MacBookPro14,3
and for the wifi you should use
https://github.com/OpenIntelWireless/itlwm/releases , v.2.2.0 alpha
If you update your efi and kext with ocat https://github.com/ic005k/OCAuxiliaryTools
Do not tick on AirportItlwm.kext It will download v.2.1.0 instead and it not work on ventura
Sorry for late reply I just log back to github

For Bluetooth
Use IntelBTPatcher.kext & IntelBluetoothFirmware.kext + BlueToolFixup.kext
do not put IntelBluetoothInjector.kext
this will make a Bluetooth work but also took a system a bit long to boot

What is your smbios info ? It should be MacBookPro14,3 if you use my efi
because of the USBMap.kext is set target to this smbios
and for the wifi you should use
https://github.com/OpenIntelWireless/itlwm/releases , v.2.2.0 alpha
If you update your efi and kext with ocat https://github.com/ic005k/OCAuxiliaryTools
Do not tick on AirportItlwm.kext It will download v.2.1.0 instead which is not work on ventura
For Bluetooth
follow
https://openintelwireless.github.io/IntelBluetoothFirmware/FAQ.html#what-does-this-kext-do
In short
Use IntelBTPatcher.kext & IntelBluetoothFirmware.kext + BlueToolFixup.kext
do not put IntelBluetoothInjector.kext
this will make a Bluetooth work but also took a system a bit long to boot
ofc "I just get a black screen and never even get to see verbose or OpenCore."
Yeah That's a very common problem if you dl pre-build efi from the internet
a laptop with the same model sometime in different region some component isn't the same
I had got the same problem too in good old clover bootloader days
P.S.#1 I do not have the device/own the device This is my friend laptop
But I could help with your problem
P.S.#2 My personal Hac has similar spec (ASUS FX504) running OC 0.9.1 and ventura 13.3.1
My wifi&BT (Intel Wireless-AC 9560) (same card) is working just fine but it took a bit long time to boot (Cause by Bluetooth Kext) But it work flawlessly
My suggestion is buy a native wifi card if you are daily drive your Hac
Sorry for (Very)late reply I just log back to github

What is your smbios info ? It should be MacBookPro14,3 if you use my efi because of the USBMap.kext is set target to this smbios
and for the wifi you should use https://github.com/OpenIntelWireless/itlwm/releases , v.2.2.0 alpha If you update your efi and kext with ocat https://github.com/ic005k/OCAuxiliaryTools Do not tick on AirportItlwm.kext It will download v.2.1.0 instead which is not work on ventura
For Bluetooth follow https://openintelwireless.github.io/IntelBluetoothFirmware/FAQ.html#what-does-this-kext-do In short Use IntelBTPatcher.kext & IntelBluetoothFirmware.kext + BlueToolFixup.kext do not put IntelBluetoothInjector.kext this will make a Bluetooth work but also took a system a bit long to boot
ofc "I just get a black screen and never even get to see verbose or OpenCore." Yeah That's a very common problem if you dl pre-build efi from the internet a laptop with the same model sometime in different region some component isn't the same I had got the same problem too in good old clover bootloader days
P.S.#1 I do not have the device/own the device This is my friend laptop But I could help with your problem
P.S.#2 My personal Hac has similar spec (ASUS FX504) running OC 0.9.1 and ventura 13.3.1 My wifi&BT (Intel Wireless-AC 9560) (same card) is working just fine but it took a bit long time to boot (Cause by Bluetooth Kext) But it work flawlessly
My suggestion is buy a native wifi card if you are daily drive your Hac
Sorry for (Very)late reply I just log back to github

Hey there.
Thank you for your reply.
It's perfectly alright that you took your time to reply, no worries.
Regarding the issue:
I believe I didn't quite make myself clear.
I did have the exact same hardware as stated and generated my personal SMBIOS with MacBook Pro 14,3 details.
It simply didn't boot.
Regardless, it is a personal issue, as I run a multiple boot environment (Kali, Ubuntu, Windows, macOS and ChromeOS).
I reverted back to my personal EFI and I got Bluetooth working again after fiddling with it under Kali (because it got disabled there too when I updated Kali's kernel, for some reason).
After a successful boot into macOS with BT working, I cycled trough all OS'es (except Kali) and then back to macOS and had BT working under all of them and then again in macOS.
However, after booting back into Kali, I did have BT under it, but after rebooting (and shutting down and all that), I could not get BT working under macOS again.
Kali does something to the BT module that prevents it from starting under macOS.
I am unsure as to what that is and I got tired of debugging it.
I went ahead and got a BCM94360NG Fenvi card and now I have BT working natively under macOS and under all other OS'es.
I bounce around between all the before mentioned OS'es on a daily basis, so for me, it was worth the investment.
I tried my best to keep the stock hardware as much as I could, but to be honest, under Ventura, the Wifi was indeed taking a veeeeeeeeery long time to boot up and that kind of annoyed me too.
With the native BCM94360NG Fenvi card, it just works as soon as I log in, which is great.
This also allowed to me cleanup my EFI folder quite a bit, as I only use the BlueToolFixUp.kext now.
For Fenvi cards, no other kexts are needed, which is great.
So, in summary, I gave up on the Intel AC-9560 and got a Broadcom 94360NG Fenvi card.
lol
Anyway, thank you for your help and for the time you took to write your proposed solution details.

TBH Me neither understand about black screen problem but
It always happen when I download a pre-build efi from other people lol
Thanks for reply, Have a good day.

What is your smbios info ? It should be MacBookPro14,3 if you use my efi because of the USBMap.kext is set target to this smbios
and for the wifi you should use https://github.com/OpenIntelWireless/itlwm/releases , v.2.2.0 alpha If you update your efi and kext with ocat https://github.com/ic005k/OCAuxiliaryTools Do not tick on AirportItlwm.kext It will download v.2.1.0 instead which is not work on ventura
For Bluetooth follow https://openintelwireless.github.io/IntelBluetoothFirmware/FAQ.html#what-does-this-kext-do In short Use IntelBTPatcher.kext & IntelBluetoothFirmware.kext + BlueToolFixup.kext do not put IntelBluetoothInjector.kext this will make a Bluetooth work but also took a system a bit long to boot
ofc "I just get a black screen and never even get to see verbose or OpenCore." Yeah That's a very common problem if you dl pre-build efi from the internet a laptop with the same model sometime in different region some component isn't the same I had got the same problem too in good old clover bootloader days
P.S.#1 I do not have the device/own the device This is my friend laptop But I could help with your problem
P.S.#2 My personal Hac has similar spec (ASUS FX504) running OC 0.9.1 and ventura 13.3.1 My wifi&BT (Intel Wireless-AC 9560) (same card) is working just fine but it took a bit long time to boot (Cause by Bluetooth Kext) But it work flawlessly
My suggestion is buy a native wifi card if you are daily drive your Hac
Sorry for (Very)late reply I just log back to github

Hey there.
Thank you for your reply. It's perfectly alright that you took your time to reply, no worries.
Regarding the issue: I believe I didn't quite make myself clear. I did have the exact same hardware as stated and generated my personal SMBIOS with MacBook Pro 14,3 details. It simply didn't boot.
Regardless, it is a personal issue, as I run a multiple boot environment (Kali, Ubuntu, Windows, macOS and ChromeOS).
I reverted back to my personal EFI and I got Bluetooth working again after fiddling with it under Kali (because it got disabled there too when I updated Kali's kernel, for some reason). After a successful boot into macOS with BT working, I cycled trough all OS'es (except Kali) and then back to macOS and had BT working under all of them and then again in macOS. However, after booting back into Kali, I did have BT under it, but after rebooting (and shutting down and all that), I could not get BT working under macOS again.
Kali does something to the BT module that prevents it from starting under macOS. I am unsure as to what that is and I got tired of debugging it.
I went ahead and got a BCM94360NG Fenvi card and now I have BT working natively under macOS and under all other OS'es.
I bounce around between all the before mentioned OS'es on a daily basis, so for me, it was worth the investment.
I tried my best to keep the stock hardware as much as I could, but to be honest, under Ventura, the Wifi was indeed taking a veeeeeeeeery long time to boot up and that kind of annoyed me too.
With the native BCM94360NG Fenvi card, it just works as soon as I log in, which is great.
This also allowed to me cleanup my EFI folder quite a bit, as I only use the BlueToolFixUp.kext now. For Fenvi cards, no other kexts are needed, which is great.
So, in summary, I gave up on the Intel AC-9560 and got a Broadcom 94360NG Fenvi card.
lol
Anyway, thank you for your help and for the time you took to write your proposed solution details.

Hi
I have a problem with the bluetooth and I tried everything, so I thought to install this card, but when I search on amazon I see multiple article of this card but with different brands like: Wresetly, Exoggo, Bassulouda. Which one did you buy and how can I now which one is genuine?


"Ciulnița", "Ion Ghica", "Ivănești" and "Poiana" are listed in the drop-down menu

Description:
When user writes ”Ciulnița”, "Ion Ghica", "Ivănești" and "Poiana" the locations appear in the drop-down menu next to the comune and the county which is a part according to the law.
Precondition:
The website is up an running.

Step 1
Write in the search bar "Ciulnița".

Expected results
The location is listed in the drop-down menu as "Sat Ciulnița IALOMIȚA (Ciulnița)".

Step 2
Write in the search bar "Ion Ghica".

Expected results
The location is listed in the drop-down menu as "Sat Ion Ghica IALOMIȚA (Ciulnița)".

Step 3
Write in the search bar "Ivănești".

Expected results
The location is listed in the drop-down menu as "Sat Ivănești IALOMIȚA (Ciulnița)".

Step 4
Write in the search bar "Poiana".

Expected results
The location is listed in the drop-down menu as "Sat Poiana IALOMIȚA (Ciulnița)".


Run an error report on mac： Library not loaded: @rpath/libvips-cpp.42.dylib

Possible bug
Is this a possible bug in a feature of sharp, unrelated to installation?

[x] Running npm install sharp completes without error.
[x] Running node -e "require('sharp')" completes without error.

If you cannot confirm both of these, please open an installation issue instead.
Are you using the latest version of sharp?

[x] I am using the latest version of sharp as reported by npm view sharp dist-tags.latest.

If you cannot confirm this, please upgrade to the latest version and try again before opening an issue.
If you are using another package which depends on a version of sharp that is not the latest, please open an issue against that package instead.
What is the output of running npx envinfo --binaries --system --npmPackages=sharp --npmGlobalPackages=sharp?
System:
OS: macOS 10.15.5
CPU: (12) x64 Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
Memory: 177.38 MB / 16.00 GB
Shell: 5.7.1 - /bin/zsh
Binaries:
Node: 16.13.0 - /usr/local/bin/node
Yarn: 1.22.17 - /usr/local/bin/yarn
npm: 8.5.1 - /usr/local/bin/npm
npmPackages:
sharp: ^0.30.3 => 0.30.3

What are the steps to reproduce?
yarn dev
Please provide a minimal, standalone code sample, without other dependencies, that demonstrates this problem

Please provide sample image(s) that help explain this problem

I didn't install vips.

Please don't use node-loader. There's information about use with webpack at https://sharp.pixelplumbing.com/install#bundlers

I hope this information helped. Please feel free to re-open with more details if further assistance is required.


Anyone know of a similar plugin (that is supported!)

It looks as though the latest version of this plugin cannot be installed, because it hasn't been updated with Grafana, so does anyone know of a button/switch which has similar functionality, and is maintained?

You could use the Canvas Panel with disable_sanitize_html set to true and enable_alpha set to true ( See  https://grafana.com/docs/grafana/latest/setup-grafana/configure-grafana/#panels)
You can then move the lever for "Show advanced element types" to on and see an element type "Button" in the dropdown of available elements.

https://grafana.com/grafana/plugins/speakyourcode-button-panel/ could also be used in combination with a datasource, in this way any credentials required to execute the query are stored in secureJson rather than as plaintext.


[CPO] Remove post run for e2e conformance

This commit removes post run for e2e conformance , as its been removed.

cc @lingxiankong

/lgtm
@ramineni thanks, I totally forgot that.

@wangxiyuan @bzhaoopenstack can we merge this?

@ramineni  sorry, forgot it.


Protobuf change and action functions for scenario

Protobuf definition change - add tenant_id and project_id for InternalVpcInfo
add entities for scenario actions and event
add status for each service
ignore some entity's fields from swagger
add deploy functions in scenario

LGTM