[Bug] Tailscale Exit Node DNS Doesnt Provide DNS

Is this a support request?

[X] This is not a support request

Is there an existing issue for this?

[X] I have searched the existing issues

Current Behavior
When connected to an exit node that is routing all traffic through said node, I cannot access the public internet as DNS does not work.

I can run nslookups with <IP_ADDRESS> and google.com and they succeed
I cannot ping <IP_ADDRESS> as it responds with a dns lookup error
I can access local devices and internet-connected servers via their ip address

If I disable Tailscale DNS

DNS works as expected, but is not under the control of headscale.

Note I am only able to test this with the exit node on windows and the client running android.
Expected Behavior
Using the tailscale dns, my headscale server should route traffic to <IP_ADDRESS> as is in the config.
Steps To Reproduce

Set up basic headscale server
set the dns servers in the config
Run an exit node
Connect a device to the exit node with tailscale dns enabled
DNS no worky.

Environment
- OS: Windows 10 & Android
- Headscale version: 0.23.0-beta
- Tailscale version: 1.70.0

Runtime environment

[X] Headscale is behind a (reverse) proxy
[X] Headscale runs in a container

Anything else?
No response

Does this happen with the alpha, if it does not, I would think this is the same issue as https://github.com/juanfont/headscale/issues/2026

I'm not using the custom nameservers mentioned in that issue (setting a custom ip for a domain in the headscale config), but it might be related.

Can confirm, alpha-12 does not have this issue.


Allow configuring packages which are known to be failing

Update is broken for these packages. Exclude them from being processed
for now, until the reason is figured out and fixed.
Signed-off-by: Hunor Csomortáni<EMAIL_ADDRESS>

The failing test passed locally. Merging.


OOM when allocating tensor with shape

I'm using tensorflow 1.3, tested on a linux machine with 2 NVIDIA Tesla K80 cards,
however, I keep getting OOM error on GPU, but it does not happen when using cpu for training:

---log below
Exception happened during training, message: OOM when allocating tensor with shape[1792,4096]
[[Node: projectx/trainig_gpu_0/gradients/projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/MatMul_36_grad/MatMul_1 = MatMul[T=DT_FLOAT, transpose_a=true, transpose_b=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/concat_36, projectx/trainig_gpu_0/gradients/projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/BiasAdd_36_grad/tuple/control_dependency)]]
[[Node: projectx/trainig_gpu_0/gradients/concat/_2929 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_646323_projectx/trainig_gpu_0/gradients/concat", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]
Caused by op 'projectx/trainig_gpu_0/gradients/projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/MatMul_36_grad/MatMul_1', defined at:
File "sync_train.py", line 383, in
main()
File "sync_train.py", line 380, in main
train(config)
File "sync_train.py", line 64, in train
train_model = projectx.projectx(n_gpu, config, is_training=True, reuse=False)
File "/kaldi/exp/tacotron/exp_2/projectx.py", line 291, in init
grads_and_vars = self.optimizer.compute_gradients(loss)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/optimizer.py", line 414, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 581, in gradients
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 353, in _MaybeCompile
return grad_fn()  # Exit early
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gradients_impl.py", line 581, in
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_grad.py", line 922, in _MatMulGrad
grad_b = math_ops.matmul(a, grad, transpose_a=True)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 1891, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 2437, in _mat_mul
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1470, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
...which was originally created as op 'projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/MatMul_36', defined at:
File "sync_train.py", line 383, in
main()
[elided 1 identical lines from previous traceback]
File "sync_train.py", line 64, in train
train_model = projectx.projectx(n_gpu, config, is_training=True, reuse=False)
File "/kaldi/exp/tacotron/exp_2/projectx.py", line 189, in init
feed_previous=feed_previous)
File "/kaldi/exp/tacotron/exp_2/projectx.py", line 427, in seq2seq
pre_alignments)
File "/kaldi/exp/tacotron/exp_2/decoder.py", line 99, in call
attention_rnn_outputs, new_attention_rnn_state, context, alignments = self._attention_rnn_cell(prenet_output, state, pre_alignments)
File "/kaldi/exp/tacotron/exp_2/attention.py", line 427, in call
lstm_output, next_lstm_state = cell(lstm_inputs, states[i + 1])
File "/kaldi/exp/tacotron/exp_2/zoneout_lstm.py", line 48, in call
output, new_state = self._cell(inputs, state, scope)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 183, in call
return super(RNNCell, self).call(inputs, state)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 575, in call
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 611, in call
lstm_matrix = self._linear1([inputs, m_prev])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1189, in call
res = math_ops.matmul(array_ops.concat(args, 1), self._weights)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 1891, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 2437, in _mat_mul
name=name)
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[1792,4096]
[[Node: projectx/trainig_gpu_0/gradients/projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/MatMul_36_grad/MatMul_1 = MatMul[T=DT_FLOAT, transpose_a=true, transpose_b=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/concat_36, projectx/trainig_gpu_0/gradients/projectx/trainig_gpu_0/decoder/projectxDecoderCell_1/lstm_0/lstm_0/lstm_cell/BiasAdd_36_grad/tuple/control_dependency)]]
[[Node: projectx/trainig_gpu_0/gradients/concat/_2929 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_646323_projectx/trainig_gpu_0/gradients/concat", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

Well, I think it's because you're running out of memory. Unfortunately there isn't any easy fix. If you provide more detailed information we could take a quick look to see if there's something obvious.

@drpngx  thx for attention;
I tried batch size 64 or 32, both failed, I also tried:
custom_config=tf.ConfigProto(log_device_placement=True, allow_soft_placement=True)
custom_config.gpu_options.allocator_type = 'BFC'
custom_config.gpu_options.per_process_gpu_memory_fraction = 0.90
failed too;
the error happened when computing the gradient,
here're my GPUs:
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 259C:00:00.0     Off |                    0 |
| N/A   30C    P8    26W / 149W |      0MiB / 11439MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla K80           Off  | 3A8D:00:00.0     Off |                    0 |
| N/A   26C    P8    32W / 149W |      0MiB / 11439MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

Normally I think we would allocate the memory on all detected devices. Could you check the initial message to see if they are detected and used? You can use with tf.device('/gpu:1) to manually place.

yes, I manually placed the training set, here on my machine n_gpu is 2, tower_encoder_inputs and tower_decoder_inputs is splitted according to n_gpu
    for i in range(n_gpu):
        with tf.variable_scope(variable_scope, reuse=True if i > 0 else reuse):
            with tf.device("/gpu:{}".format(i)):
                with tf.name_scope("trainig_gpu_{}".format(i)):
                    print("building train graph on GPU {}".format(i))

                    #outputs, attentions, stop_tokens, n_predicted = self.seq2seq(
                    outputs, attentions= seq2seq(
                        self.tower_encoder_inputs[i],
                        self.tower_decoder_inputs[i])

also, for better reference, I uploaded my code to:
https://github.com/unwritten/tensorflow_oom/blob/master/sync_train.py
thx

it  turns out my lstm dimension is too big, I used 2 layer lstm in my network with dim 1024, when I change it to 256, OOM error gone;

Hi, I also encountered this problem. I ran my code using the GPU version and got this error. However, when I used my other laptop using the CPU version, there was no error. Without changing anything. Have you found the reason for this?

@chaine09 It depends on what your exact hardware setting is. It'll take ages to train your network on the CPU. Just trying lowering the batch size until you can get it to run on the GPU.

I am doing performance testing inception model flask api with gunicorn (creating multiple process) Error: OOM when allocating tensor with shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8, _device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Caused by op 'Cast', defined at: File "/usr/local/bin/gunicorn", line 11, in sys.exit(run()) File "/usr/local/lib/python3.5/dist-packages/gunicorn/app/wsgiapp.py", line 61, in run WSGIApplication("%(prog)s [OPTIONS] [APP_MODULE]").run() File "/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line 223, in run super(Application, self).run() File "/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line 72, in run Arbiter(self).run() File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line 212, in run self.manage_workers() File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line 545, in manage_workers self.spawn_workers() File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line 616, in spawn_workers self.spawn_worker() File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line 583, in spawn_worker worker.init_process() File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/base.py", line 134, in init_process self.run() File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line 124, in run self.run_for_one(timeout) File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line 68, in run_for_one self.accept(listener) File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line 30, in accept self.handle(listener, client, addr) File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line 135, in handle self.handle_request(listener, req, client, addr) File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line 176, in handle_request respiter = self.wsgi(environ, resp.start_response) File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1997, in call return self.wsgi_app(environ, start_response) File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1982, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1612, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1598, in dispatch_request return self.view_functionsrule.endpoint File "/home/ubuntu/cv_workspace/computer_vision_services.py", line 1480, in classify_bulk input_operation, output_operation, tf_session=sess) File "/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 228, in classifyImageInSess t = read_tensor_from_image_file(file_name) File "/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 51, in read_tensor_from_image_file float_caster = tf.cast(image_reader, tf.float32) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 758, in cast return gen_math_ops.cast(x, base_type, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 919, in cast "Cast", x=x, DstT=DstT, name=name) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3160, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1625, in init self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
################ GPU

Kute harramte dele
On 11 Jun 2018 16:35, "daoud"<EMAIL_ADDRESS>wrote:

I am doing performance testing inception model flask api with gunicorn
(creating multiple process) Error: OOM when allocating tensor with
shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0
by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you
want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
Caused by op 'Cast', defined at: File "/usr/local/bin/gunicorn", line 11,
in sys.exit(run()) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/app/wsgiapp.py", line 61, in run
WSGIApplication("%(prog)s [OPTIONS] [APP_MODULE]").run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line 223,
in run super(Application, self).run() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/app/base.py", line 72, in run Arbiter(self).run()
File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line
212, in run self.manage_workers() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/arbiter.py", line 545, in manage_workers
self.spawn_workers() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/arbiter.py", line 616, in spawn_workers
self.spawn_worker() File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py",
line 583, in spawn_worker worker.init_process() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/base.py", line
134, in init_process self.run() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 124, in run
self.run_for_one(timeout) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 68, in run_for_one
self.accept(listener) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 30, in accept
self.handle(listener, client, addr) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 135, in handle
self.handle_request(listener, req, client, addr) File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line
176, in handle_request respiter = self.wsgi(environ, resp.start_response)
File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1997, in
call return self.wsgi_app(environ, start_response) File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1982, in
wsgi_app response = self.full_dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1612, in
full_dispatch_request rv = self.dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1598, in
dispatch_request return self.view_functionsrule.endpoint File
"/home/ubuntu/cv_workspace/computer_vision_services.py", line 1480, in
classify_bulk input_operation, output_operation, tf_session=sess) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 228, in
classifyImageInSess t = read_tensor_from_image_file(file_name) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 51, in
read_tensor_from_image_file float_caster = tf.cast(image_reader,
tf.float32) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py",
line 758, in cast return gen_math_ops.cast(x, base_type, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py",
line 919, in cast "Cast", x=x, DstT=DstT, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/
python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 3160, in create_op op_def=op_def) File "/usr/local/lib/python3.5/
dist-packages/tensorflow/python/framework/ops.py", line 1625, in init
self._traceback = self._graph._extract_stack() # pylint:
disable=protected-access
################ GPU
[image: image]
https://user-images.githubusercontent.com/2271152/41229156-a212955c-6d98-11e8-8a7e-8d41001f3036.png
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Kute harramte dele
On 11 Jun 2018 16:35, "daoud"<EMAIL_ADDRESS>wrote:

I am doing performance testing inception model flask api with gunicorn
(creating multiple process) Error: OOM when allocating tensor with
shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0
by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you
want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
Caused by op 'Cast', defined at: File "/usr/local/bin/gunicorn", line 11,
in sys.exit(run()) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/app/wsgiapp.py", line 61, in run
WSGIApplication("%(prog)s [OPTIONS] [APP_MODULE]").run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line 223,
in run super(Application, self).run() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/app/base.py", line 72, in run Arbiter(self).run()
File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line
212, in run self.manage_workers() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/arbiter.py", line 545, in manage_workers
self.spawn_workers() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/arbiter.py", line 616, in spawn_workers
self.spawn_worker() File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py",
line 583, in spawn_worker worker.init_process() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/base.py", line
134, in init_process self.run() File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 124, in run
self.run_for_one(timeout) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 68, in run_for_one
self.accept(listener) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 30, in accept
self.handle(listener, client, addr) File "/usr/local/lib/python3.5/
dist-packages/gunicorn/workers/sync.py", line 135, in handle
self.handle_request(listener, req, client, addr) File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line
176, in handle_request respiter = self.wsgi(environ, resp.start_response)
File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1997, in
call return self.wsgi_app(environ, start_response) File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1982, in
wsgi_app response = self.full_dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1612, in
full_dispatch_request rv = self.dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1598, in
dispatch_request return self.view_functionsrule.endpoint File
"/home/ubuntu/cv_workspace/computer_vision_services.py", line 1480, in
classify_bulk input_operation, output_operation, tf_session=sess) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 228, in
classifyImageInSess t = read_tensor_from_image_file(file_name) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 51, in
read_tensor_from_image_file float_caster = tf.cast(image_reader,
tf.float32) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py",
line 758, in cast return gen_math_ops.cast(x, base_type, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py",
line 919, in cast "Cast", x=x, DstT=DstT, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/
python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 3160, in create_op op_def=op_def) File "/usr/local/lib/python3.5/
dist-packages/tensorflow/python/framework/ops.py", line 1625, in init
self._traceback = self._graph._extract_stack() # pylint:
disable=protected-access
################ GPU
[image: image]
https://user-images.githubusercontent.com/2271152/41229156-a212955c-6d98-11e8-8a7e-8d41001f3036.png
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Kute. Harramte. Dele
On 11 Jun 2018 16:37, "Laraib Zubair"<EMAIL_ADDRESS>wrote:

Kute harramte dele
On 11 Jun 2018 16:35, "daoud"<EMAIL_ADDRESS>wrote:

I am doing performance testing inception model flask api with gunicorn
(creating multiple process) Error: OOM when allocating tensor with
shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0
by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you
want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
Caused by op 'Cast', defined at: File "/usr/local/bin/gunicorn", line 11,
in sys.exit(run()) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/app/wsgiapp.py", line 61, in run
WSGIApplication("%(prog)s [OPTIONS] [APP_MODULE]").run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line 223,
in run super(Application, self).run() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/app/base.py", line 72, in run Arbiter(self).run()
File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line
212, in run self.manage_workers() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/arbiter.py", line 545, in manage_workers
self.spawn_workers() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/arbiter.py", line 616, in spawn_workers
self.spawn_worker() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/arbiter.py", line 583, in spawn_worker
worker.init_process() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/base.py", line 134, in init_process
self.run() File "/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py",
line 124, in run self.run_for_one(timeout) File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line
68, in run_for_one self.accept(listener) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 30, in accept
self.handle(listener, client, addr) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 135, in handle
self.handle_request(listener, req, client, addr) File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line
176, in handle_request respiter = self.wsgi(environ, resp.start_response)
File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1997,
in call return self.wsgi_app(environ, start_response) File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1982, in
wsgi_app response = self.full_dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1612, in
full_dispatch_request rv = self.dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1598, in
dispatch_request return self.view_functionsrule.endpoint File
"/home/ubuntu/cv_workspace/computer_vision_services.py", line 1480, in
classify_bulk input_operation, output_operation, tf_session=sess) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 228, in
classifyImageInSess t = read_tensor_from_image_file(file_name) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 51, in
read_tensor_from_image_file float_caster = tf.cast(image_reader,
tf.float32) File "/usr/local/lib/python3.5/dist
-packages/tensorflow/python/ops/math_ops.py", line 758, in cast return
gen_math_ops.cast(x, base_type, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py",
line 919, in cast "Cast", x=x, DstT=DstT, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py",
line 787, in _apply_op_helper op_def=op_def) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 3160, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist
-packages/tensorflow/python/framework/ops.py", line 1625, in init
self._traceback = self._graph._extract_stack() # pylint:
disable=protected-access
################ GPU
[image: image]
https://user-images.githubusercontent.com/2271152/41229156-a212955c-6d98-11e8-8a7e-8d41001f3036.png
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Kute harramte. Dele ma de pode
On 11 Jun 2018 16:37, "Laraib Zubair"<EMAIL_ADDRESS>wrote:

Kute. Harramte. Dele
On 11 Jun 2018 16:37, "Laraib Zubair"<EMAIL_ADDRESS>wrote:

Kute harramte dele
On 11 Jun 2018 16:35, "daoud"<EMAIL_ADDRESS>wrote:

I am doing performance testing inception model flask api with gunicorn
(creating multiple process) Error: OOM when allocating tensor with
shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0
by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you
want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
Caused by op 'Cast', defined at: File "/usr/local/bin/gunicorn", line
11, in sys.exit(run()) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/app/wsgiapp.py", line 61, in run
WSGIApplication("%(prog)s [OPTIONS] [APP_MODULE]").run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line
223, in run super(Application, self).run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line 72,
in run Arbiter(self).run() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/arbiter.py", line 212, in run self.manage_workers()
File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line
545, in manage_workers self.spawn_workers() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line 616,
in spawn_workers self.spawn_worker() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/arbiter.py", line 583, in spawn_worker
worker.init_process() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/base.py", line 134, in init_process
self.run() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 124, in run
self.run_for_one(timeout) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 68, in run_for_one
self.accept(listener) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 30, in accept
self.handle(listener, client, addr) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 135, in handle
self.handle_request(listener, req, client, addr) File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py", line
176, in handle_request respiter = self.wsgi(environ, resp.start_response)
File "/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1997,
in call return self.wsgi_app(environ, start_response) File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1982, in
wsgi_app response = self.full_dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1612, in
full_dispatch_request rv = self.dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1598, in
dispatch_request return self.view_functionsrule.endpoint File
"/home/ubuntu/cv_workspace/computer_vision_services.py", line 1480, in
classify_bulk input_operation, output_operation, tf_session=sess) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 228, in
classifyImageInSess t = read_tensor_from_image_file(file_name) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 51, in
read_tensor_from_image_file float_caster = tf.cast(image_reader,
tf.float32) File "/usr/local/lib/python3.5/dist
-packages/tensorflow/python/ops/math_ops.py", line 758, in cast return
gen_math_ops.cast(x, base_type, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py",
line 919, in cast "Cast", x=x, DstT=DstT, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py",
line 787, in _apply_op_helper op_def=op_def) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 3160, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist
-packages/tensorflow/python/framework/ops.py", line 1625, in init
self._traceback = self._graph._extract_stack() # pylint:
disable=protected-access
################ GPU
[image: image]
https://user-images.githubusercontent.com/2271152/41229156-a212955c-6d98-11e8-8a7e-8d41001f3036.png
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Kute harramte
On 11 Jun 2018 16:39, "Laraib Zubair"<EMAIL_ADDRESS>wrote:

Kute harramte. Dele ma de pode
On 11 Jun 2018 16:37, "Laraib Zubair"<EMAIL_ADDRESS>wrote:

Kute. Harramte. Dele
On 11 Jun 2018 16:37, "Laraib Zubair"<EMAIL_ADDRESS>wrote:

Kute harramte dele
On 11 Jun 2018 16:35, "daoud"<EMAIL_ADDRESS>wrote:

I am doing performance testing inception model flask api with gunicorn
(creating multiple process) Error: OOM when allocating tensor with
shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0
by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8,
_device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you
want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current
allocation info.
Caused by op 'Cast', defined at: File "/usr/local/bin/gunicorn", line
11, in sys.exit(run()) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/app/wsgiapp.py", line 61, in run
WSGIApplication("%(prog)s [OPTIONS] [APP_MODULE]").run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line
223, in run super(Application, self).run() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/app/base.py", line
72, in run Arbiter(self).run() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/arbiter.py", line 212, in run self.manage_workers()
File "/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py",
line 545, in manage_workers self.spawn_workers() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line
616, in spawn_workers self.spawn_worker() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/arbiter.py", line
583, in spawn_worker worker.init_process() File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/base.py",
line 134, in init_process self.run() File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 124, in run
self.run_for_one(timeout) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 68, in run_for_one
self.accept(listener) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 30, in accept
self.handle(listener, client, addr) File "/usr/local/lib/python3.5/dist
-packages/gunicorn/workers/sync.py", line 135, in handle
self.handle_request(listener, req, client, addr) File
"/usr/local/lib/python3.5/dist-packages/gunicorn/workers/sync.py",
line 176, in handle_request respiter = self.wsgi(environ,
resp.start_response) File "/usr/local/lib/python3.5/dist-packages/flask/app.py",
line 1997, in call return self.wsgi_app(environ, start_response) File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1982, in
wsgi_app response = self.full_dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1612, in
full_dispatch_request rv = self.dispatch_request() File
"/usr/local/lib/python3.5/dist-packages/flask/app.py", line 1598, in
dispatch_request return self.view_functionsrule.endpoint File
"/home/ubuntu/cv_workspace/computer_vision_services.py", line 1480, in
classify_bulk input_operation, output_operation, tf_session=sess) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 228,
in classifyImageInSess t = read_tensor_from_image_file(file_name) File
"/home/ubuntu/cv_workspace/src/apis/ImgClassification.py", line 51, in
read_tensor_from_image_file float_caster = tf.cast(image_reader,
tf.float32) File "/usr/local/lib/python3.5/dist
-packages/tensorflow/python/ops/math_ops.py", line 758, in cast return
gen_math_ops.cast(x, base_type, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py",
line 919, in cast "Cast", x=x, DstT=DstT, name=name) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py",
line 787, in _apply_op_helper op_def=op_def) File
"/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py",
line 3160, in create_op op_def=op_def) File "/usr/local/lib/python3.5/dist
-packages/tensorflow/python/framework/ops.py", line 1625, in init
self._traceback = self._graph._extract_stack() # pylint:
disable=protected-access
################ GPU
[image: image]
https://user-images.githubusercontent.com/2271152/41229156-a212955c-6d98-11e8-8a7e-8d41001f3036.png
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i got the same problem,then used smaller size of lstm hidden dim, i solved it.

It would be run If you set lowering batch size as well Input size

I found that it helps to build your own generator function that yields X and y in the shape needed. You'll have to use model.fit_generator, and make sure you can change around the size of the 'sub-batches' within the generator. For example:
def generator(self, maxbatch=None):

    # Let `alldata` just be all your data, of course.
    for batch in alldata:
        # ... do stuff to your batch if you want
        x, y = batch
        if not maxbatch:
            return x, y

        for i in range(0, size, maxbatch):
            if i + maxbatch > size:
                yield x[i: size], y[i: size]
            else:
                yield x[i: i+maxbatch], y[i: i+maxbatch]

# Then you can fit your model with this function
model.fit_generator(self.generator, ...)

I also found that (if appropriate) using dropout can help.

OOM when allocating tensor with shape[320000,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
I got this message while train CNN
Specifying batch_size 16, 32, 64, 128 did not help

ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[24,1280,10,10] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node gradients/BoxPredictor_1/ClassPredictor/Conv2D_grad/Conv2DBackpropInput}} = Conv2DBackpropInput[T=DT_FLOAT, data_format="NCHW", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](gradients/BoxPredictor_1/ClassPredictor/Conv2D_grad/Conv2DBackpropInput-0-LayoutOptimizer, BoxPredictor_1/ClassPredictor/weights/read/_3117, gradients/BoxPredictor_1/ClassPredictor/Conv2D_grad/Conv2DBackpropFilter-2-TransposeNHWCToNCHW-LayoutOptimizer, ^gradients/BoxPredictor_1/ClassPredictor/BiasAdd_grad/BiasAddGrad)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
     [[{{node gradients/FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise_grad/tuple/control_dependency_1/_6251}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_14788...pendency_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Guys, this is the error that I encounter during training. What do you think is the solution on thois one?

I am getting the same error

For the people stuck with this in models other than mnist.
A good method to drastically lower these parameters is to add:
subsample=(2, 2) (careful it lowers the resolution of images/data) in all the Convolutional layers above that Flatten layer,
if subsample doesn't work then it is stride=(2, 2).

can someone please explain why i get this error with an nvidea rtx 2070super and not with nvidia gtx 1050ti  with the same code and same parameters?
(0) Resource exhausted: OOM when allocating tensor with shape[64,58,28,28] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node batch_normalization_47/FusedBatchNorm}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
 [[loss/mul/_5287]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
(1) Resource exhausted: OOM when allocating tensor with shape[64,58,28,28] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node batch_normalization_47/FusedBatchNorm}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
0 successful operations.
0 derived errors ignored.

Reduce the size of image
On Thu, 19 Sep, 2019, 10:48 PM Misterio0<EMAIL_ADDRESS>wrote:

can someone please explain why i get this error with an nvidea rtx
2070super and not with nvidia gtx 1050ti with the same code and same
parameters?
(0) Resource exhausted: OOM when allocating tensor with shape[64,58,28,28]
and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator
GPU_0_bfc
[[{{node batch_normalization_47/FusedBatchNorm}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
[[loss/mul/_5287]]
Hint: If you want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
(1) Resource exhausted: OOM when allocating tensor with shape[64,58,28,28]
and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator
GPU_0_bfc
[[{{node batch_normalization_47/FusedBatchNorm}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add
report_tensor_allocations_upon_oom to RunOptions for current allocation
info.
0 successful operations.
0 derived errors ignored.
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it turns out my lstm dimension is too big, I used 2 layer lstm in my network with dim 1024, when I change it to 256, OOM error gone;

Could you please explain how did you change the dim to 256?

In the configuration file. Change the dimensions
On Sun, 22 Sep, 2019, 12:00 AM modeepfake<EMAIL_ADDRESS>wrote:

it turns out my lstm dimension is too big, I used 2 layer lstm in my
network with dim 1024, when I change it to 256, OOM error gone;
Could you please explain how did you change the dim to 256?
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It would be run If you set lowering batch size as well Input size

it worked

I had this same issue on Google Colab. Wiredly enough, when I took a few layers out of my network the number of parameters went up (since a few max poolings were gone). This solved the problem however... So the comment regarding parameter number is incorrect. If I'm missing something, let me know!
regards

I had the same problem. I solved this problem by reducing the number of layer neurons.

Even I got the same problem while working on NVIDIA Tegra X1 (nvgpu)/integrated and it has 62GB of memory

Even I got the same problem while working on NVIDIA Tegra X1 (nvgpu)/integrated and it has 62GB of memory
I have also tried reducing the batch_size and image dimensions as well
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,1,2048,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Add] name: conv5_block3_1_conv/kernel/Initializer/random_uniform/
tried with batch_size=32,16
If there is any other alternative to solve the problem
Do share
Thanks in advance

Also if we have any formula to calculate the batch_size or epochs from the Number of input images
Please share

I am facing the same problem. The model is trained well on CPU and the problem only appears on GPU. Isn't there a solution? It is not reasonable to change the Python code to solve it

Your GPU memory is less, decrease the batch size and try.
On Mon, Jun 1, 2020, 12:50 Orwa Kassab<EMAIL_ADDRESS>wrote:

I am facing the same problem. The model is trained well on CPU and the
problem only appears on GPU. Isn't there a solution? It is not reasonable
to change the Python code to solve it
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Your GPU memory is less, decrease the batch size and try.
…
On Mon, Jun 1, 2020, 12:50 Orwa Kassab @.***> wrote: I am facing the same problem. The model is trained well on CPU and the problem only appears on GPU. Isn't there a solution? It is not reasonable to change the Python code to solve it — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#16768 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/ADECJFHQKQUO33RF6SPWUOTRUNJDBANCNFSM4EPEAQVA .

It did not work. I tried multiple values for the batch size and still getting the same exception.
I monitored the usage of my GPU and it only reached about 14% as max at the first moments of training the model!!!!!!!!!
My GPU info: NVIDIA GeForce GTX 1050

1050 has 4 gb memory, which is pretty low. Only reducing batch size won't
work. Resize all your images to 30*30. Run a loop through the images to
resize them. Then try to train with batch size of 16.
On Mon, Jun 1, 2020, 13:52 Orwa Kassab<EMAIL_ADDRESS>wrote:

Your GPU memory is less, decrease the batch size and try.
… <#m_7891378449595475481_>
On Mon, Jun 1, 2020, 12:50 Orwa Kassab @.***> wrote: I am facing the
same problem. The model is trained well on CPU and the problem only appears
on GPU. Isn't there a solution? It is not reasonable to change the Python
code to solve it — You are receiving this because you commented. Reply to
this email directly, view it on GitHub <#16768 (comment)
https://github.com/tensorflow/tensorflow/issues/16768#issuecomment-636659972>,
or unsubscribe
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.
It did not work. I tried multiple values for the batch size and still
getting the same exception.
I monitored the usage of my GPU and it only reached about 14% as max at
the first moments of training the model!!!!!!!!!
My GPU info: NVIDIA GeForce GTX 1050
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OOM means out out of memory, GPU usage and graphics memory usage are two
different things
On Mon, Jun 1, 2020, 13:56 Ritaprava Dutta<EMAIL_ADDRESS>wrote:

1050 has 4 gb memory, which is pretty low. Only reducing batch size won't
work. Resize all your images to 30*30. Run a loop through the images to
resize them. Then try to train with batch size of 16.
On Mon, Jun 1, 2020, 13:52 Orwa Kassab<EMAIL_ADDRESS>wrote:

Your GPU memory is less, decrease the batch size and try.
… <#m_-4117985182330873088_m_7891378449595475481_>
On Mon, Jun 1, 2020, 12:50 Orwa Kassab @.***> wrote: I am facing the
same problem. The model is trained well on CPU and the problem only appears
on GPU. Isn't there a solution? It is not reasonable to change the Python
code to solve it — You are receiving this because you commented. Reply to
this email directly, view it on GitHub <#16768 (comment)
https://github.com/tensorflow/tensorflow/issues/16768#issuecomment-636659972>,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ADECJFHQKQUO33RF6SPWUOTRUNJDBANCNFSM4EPEAQVA
.
It did not work. I tried multiple values for the batch size and still
getting the same exception.
I monitored the usage of my GPU and it only reached about 14% as max at
the first moments of training the model!!!!!!!!!
My GPU info: NVIDIA GeForce GTX 1050
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OOM means out out of memory, GPU usage and graphics memory usage are two different things
…
On Mon, Jun 1, 2020, 13:56 Ritaprava Dutta @.> wrote: 1050 has 4 gb memory, which is pretty low. Only reducing batch size won't work. Resize all your images to 3030. Run a loop through the images to resize them. Then try to train with batch size of 16. On Mon, Jun 1, 2020, 13:52 Orwa Kassab @.> wrote: > Your GPU memory is less, decrease the batch size and try. > … <#m_-4117985182330873088_m_7891378449595475481_> > On Mon, Jun 1, 2020, 12:50 Orwa Kassab @.**> wrote: I am facing the > same problem. The model is trained well on CPU and the problem only appears > on GPU. Isn't there a solution? It is not reasonable to change the Python > code to solve it — You are receiving this because you commented. Reply to > this email directly, view it on GitHub <#16768 (comment) > <#16768 (comment)>>, > or unsubscribe > https://github.com/notifications/unsubscribe-auth/ADECJFHQKQUO33RF6SPWUOTRUNJDBANCNFSM4EPEAQVA > . > > It did not work. I tried multiple values for the batch size and still > getting the same exception. > I monitored the usage of my GPU and it only reached about 14% as max at > the first moments of training the model!!!!!!!!! > My GPU info: NVIDIA GeForce GTX 1050 > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > <#16768 (comment)>, > or unsubscribe > https://github.com/notifications/unsubscribe-auth/ADECJFDKBIWXZAWYDEIJIC3RUNQOBANCNFSM4EPEAQVA > . >

It worked with a batch size of 8. Thanks very much

I am using Google Colab. and i used TPU and it worked.

Well done.
On Sun, Jun 7, 2020, 9:10 AM Arvina Kori<EMAIL_ADDRESS>wrote:

I am using Google Colab. and i used TPU and it worked.
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It would be run If you set lowering batch size as well Input size

dear bro.. can you tell me, how to change batch size... I mean, which file has batch size...

Me too, I got a same ERROR!

This wasn't an issue with the earlier versions of keras and tensorflow. I had tf 2.0 installed originally and it handled much larger than (1000,10554) matrices without batch training.
Some users are reporting this as a potential issue with python v > 3.75.

Sometimes killing jobs helps kill %N

You need to create TRFRecord files:
https://www.tensorflow.org/guide/data
https://www.tensorflow.org/tutorials/load_data/tfrecord
You need tdfs.load to load these types of files:
https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb

You need to create TRFRecord files:
https://www.tensorflow.org/guide/data
https://www.tensorflow.org/tutorials/load_data/tfrecord
You need tdfs.load to load these types of files:
https://colab.research.google.com/github/tensorflow/datasets/blob/master/docs/overview.ipynb

Reducing the batch_size helped me!

Reduce the batch_size doesn't help me. I run using terminal (with tmux) and I got that error, but when I run it in my notebook, it worked smoothly. Anybody can help me? I have to run it in terminal (with tmux)

for me, reducing batch-size solve problem

downgrading tensorflow and tensorflow-gpu to 2.5 if you are already on version 2.6 or later might help, at least it did with me.

downgrading tensorflow and tensorflow-gpu to 2.5 helped. thx!!

opening this issue as @unwritten shows the same error. I tried the batch size and the different solutions online but none of them worked for me.
I trained the model yesterday on a single GPU which was found but when I increase my batch size by converting the model on both GPU then I am facing the same error.
Any kind help will be appriciated
Thank you

is this tensor size [2,512,512,512]  too big to be trained on GPU on colab? I am getting the following error
OOM when allocating tensor with shape[2,512,512,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[{{node Abs-0-0-TransposeNCHWToNHWC-LayoutOptimizer}}]]
I have image of size 512*512 and batch size of 2


HandlePollingErrorAsync stop bot

Hi
When My bot got exception and it catch into HandlePollingErrorAsync my application stopped and I have to re open application
can you help me how can I change my code
`async Task HandlePollingErrorAsync(ITelegramBotClient botClient, Exception exception, CancellationToken cancellationToken)
{
// Get the line number of the exception
var lineNumber = new StackTrace(exception, true).GetFrame(0).GetFileLineNumber();
if (exception is ApiRequestException apiException)
{
    await botClient.SendTextMessageAsync(3, apiException.Message + $" on line {lineNumber}");
    // handle Telegram API errors
    Console.WriteLine($"Telegram API error occurred: {apiException.Message} on line {lineNumber}");
}
else
{
    await botClient.SendTextMessageAsync(53608103, exception.Message + $" on line {lineNumber}");
    // handle all other errors
    Console.WriteLine($"Error occurred: {exception.Message} on line {lineNumber}");
}

// continue polling
await Task.Delay(1000);

}`

The poller doesn't catch exceptions from your own code. So if you have an exception inside your HandlePollingErrorAsync method it won't catch it, you need to ensure that your own code is exception safe. Try using try/catch clause.

Duplicate for #1106


Bluetooth plugin not working on iOS device

I cloned the project and tried to run the demo app. On Android, the demo app works fine without any issue. But same app on my iOS device doesn't work.
When I clicked on "Is Bluetooth Enabled?", is says "Bluetooth is DISABLED". But, I checked the phone settings and bluetooth service is enabled.
iOS device information
iPhone 6s
iOS version 13.2.3

@prabudevarrajan Do you still have the issue, can you share a sample project?

+1

I have the same issue on an iPhone 11 Pro iOS 13.2.3

Same issue here.
Was working fine before the update of IOS.

Fixed on my side by adding the key NSBluetoothAlwaysUsageDescription in the info.plist
https://developer.apple.com/documentation/bundleresources/information_property_list/nsbluetoothalwaysusagedescription

NSBluetoothAlwaysUsageDescription was recently introduced in iOS 13. Without that your app could crash.
I just ran a test on iOS 13.2.3, it worked just fine.

Thanks a lot @toams69 and @manojdcoder !
It works fine now that I added the key as well.
Now it seems to be scanning correctly but the list looks empty.
In any case the original issue is solved.
Thanks
Luca

Using 3.0.0-beta.10 I'm not able to see any bluetooth devices while scanning on ios 12 iphone 5s, anyone able to get it working with that version?

@Joebh Were you able to test it on any other devices, perhaps one with iOS 13?

I am using the latest beta in production on iOS and Android. It is working fine everywhere. Check you logs you might be missing permissions

@manojdcoder verified it's working ios13 ty, haven't found the issue yet for 12.

I have to setup iOS 12, I will update when I do so.


A "native" driver for web, using CSS transitions

x-post from https://github.com/necolas/react-native-web/issues/470
Hi there,
I just wanted to throw out an idea that I've been successfully implementing in my own app. The app was barely usable on the web, because I use a lot of animations (it's a game). Many of them seem to perform very badly when run purely from JavaScript. So what I've started to do is add a lot of Platform specific code to my animation calls. I add a CSS transition and duration with setNativeProps, then I set the animation duration to 0, and run it. I also inject an Animated.delay into the sequence, which just waits for the CSS transition to finish.
This pattern has worked really well for me so far, so I would like to talk about bringing this into the Animated library (or perhaps react-native-web). It would be a new implementation that you could enable by setting useNativeDriver. iOS and Android already have their own native animation drivers, so I think it makes a lot of sense to build a native driver for web using CSS transitions.
@necolas also mentioned the Web Animations API, but I don't know if that will be usable any time soon, since it's not very supported. (No support at all on IE or Safari, and only in nightly / developer builds for Firefox.)
My idea would be to wrap animations with something like this:
// Returns an object that conforms to the Animated.Animated API
// (with start and stop functions)
const wrapAnimatedTimingWithCSSTransition = (animatedValue, { toValue, duration }) => {
  // Sets the value immediately, but also waits for the given duration
  const animation = Animated.sequence([
    Animated.timing(animatedValue, { toValue, duration: 0 }),
    Animated.delay(duration),
  ])

  return {
    start: (callback) => {
      // Not sure how this would be done
      setTransitionPropertiesForAnimatedValue(animatedValue)

      animation.start(() => {
        resetTransitionPropertiesForAnimatedValue(animatedValue)
        if (callback) callback
      })
    },

    stop: () => {
      animation.stop()
    },
  }
}

There's a lot of things I'm not sure how to do, but that's the general idea.
One major caveat: you can't set separate transition durations for multiple transform properties. e.g. rotation and scale can't be controlled independently. In practice, this hasn't been an issue for me. I think a sane default would be to just choose the minimum duration, if multiple are provided. Or if all of the animations are started at the same time, then we could use the longest duration with keyframes.

since it's not very supported

Support in Chrome means you can still reach 40-80% of users (depending on your app's geographic distribution) and vastly improve the experience on most Android phones (where hogging the main thread ruins performance).

That is a very good point. But still, I wouldn't want to put all my energy into this implementation, and still end up with an app that performs really badly on other browsers. I do want to rip out all of my web-specific animation stuff before launching, because it's getting out of hand and bugs are becoming pretty hard to track down. So I think I might start with a proof-of-concept for the CSS transitions. And if the performance is good enough after that, I'm not sure if it would be worth also doing the Web Animations API.

As stated in a few other threads Animated in it's current fashion isn't the best suited for the web. However if it tied into priority queue of latest React it may become a potential viable candidate.
There I don't think there is a ton of interest from many about pushing Animated on the web in it's current form, as @drcmda had mentioned he forked it and has been improving the API surfaced area.
So I'd recommend giving either this, or React Native version a fork and building from there. This version has a few additions to make it actually work on the web but I believe necolas has also done a lot of that for RNW

Oops when I said "here" I meant in RNW. If you want to hack on a native driver for web I think forking RNW is a good way to get started, as the Animated implementation is identical to that in RN

Looking at some of the web animation api primitives, would it even be feasible at all? It looks like these are all duration based easings. How'd animated be able at all to natively render a spring for instance?


Opera issue

Check your script on Opera browser (mac). After browser update doesn't work properly.

I have this same problem on Opera 46 (win 8.1).

I have the same problem, weirdly Opera is built on Chromium. I use it because it's Chrome with some nice extra features Always on Top video, Build in Ad Blocker and the sidebar.
Since only 1.1% of the population use it, I think anime.js is still fine to use. I'm thinking this is more of an Opera issue than a AnimeJS issue.

FYI: Had the problem with Opera 46, but seems fixed in Opera 47 (Linux).

Check on Opera 47 (Mac) and everything is working perfectly.


Uninstall script doesn't remove group

Running the uninstall script with sudo python3 uninstall.py. Everything was reported as successful, however the group anudeepND/whitelist was not removed from PiHole, nor is it possible to remove it manually. Although the Web UI reports success after an attempt to remove it, if we refresh the browser, the group is still there.
While I haven't investigated, I have the feeling that this is somewhat related to #187
My fix was to remove it directly from gravity.db with DELETE FROM "group" WHERE id =<PHONE_NUMBER>;

@Couto Thanks for reporting the issue, I have reverted the PR for now. I will update the script as soon as possible.

Thank you a lot for the quick response, and a special thank you for maintaining this project! <3

If anyone wants to remove the group, you can run this command in the terminal:
sqlite3 /etc/pihole/gravity.db "DELETE FROM 'group' WHERE id =<PHONE_NUMBER>;"


[stable/sonarqube] Configure ingress correctly if no path is set and ensure that default paths are configured correctly

What this PR does / why we need it:
Corrects an issue noted here: https://github.com/helm/charts/commit/22d06bb14e9ca266cc143da9c05d5cae7ab36ca0#commitcomment-33698505
If ingress was enabled and a host was set, but not a path, the ingress paths were not configured correctly (they should default to "/" and "/", and if "/" is specified, we should still get "/" and "/").
Checklist
[Place an '[x]' (no spaces) in all applicable fields. Please remove unrelated fields.]

[x] DCO signed
[x] Chart Version bumped
[x] Variables are documented in the README.md
[x] Title of the PR starts with chart name (e.g. [stable/chart])

/ok-to-test

/lgtm


Can't add two required items from the same compendium.

Tried adding rope and arrows to the same recipe from the SRD Items compendium but whichever I add second just adds to the quantity of the first item. On V11 Build 315, DnD 5e 3.0.3.
Steps to Reproduce

Create a new recipe
Set recipe type to items
Add rope and Arrows to the recipe from a compendium

Intended result:
The recipe requires rope and arrows.
Actual result
The recipe requires 2 rope.

Look's like a bug to me. I'll look into this and fix it later. For now you can try working with items that are not in the compendiums.


Upgrading with npx uses local CLI

🐞 Bug report
Command (mark with an x)

- [ ] new
- [ ] build
- [ ] serve
- [ ] test
- [ ] e2e
- [ ] generate
- [ ] add
- [x] update
- [ ] lint
- [ ] xi18n
- [ ] run
- [ ] config
- [ ] help
- [ ] version
- [ ] doc

Is this a regression?

Yes, the previous version in which this bug was not present was: 8.0.0-rc.3
Description
I'm using npx to update my project that uses Angular v7.2.4. Instead of using 8.0.0-rc.4 to update it, it uses local CLI version
npx -p @angular/cli@next ng update @angular/cli @angular/core rxjs --force
Your global Angular CLI version (8.0.0-rc.4) is greater than your local
version (7.3.0). The local Angular CLI version is used.

To disable this warning use "ng config -g cli.warnings.versionMismatch false".
    Updating package.json with dependency @angular/language-service @ "7.2.15" (was "7.2.4")...
    Updating package.json with dependency @angular/compiler-cli @ "7.2.15" (was "7.2.4")...
    Updating package.json with dependency @angular/animations @ "7.2.15" (was "7.2.4")...
    Updating package.json with dependency @angular/common @ "7.2.15" (was "7.2.4")...
    Updating package.json with dependency @angular/forms @ "7.2.15" (was "7.2.4")...

🔬 Minimal Reproduction

🔥 Exception or Error

🌍 Your Environment

Anything else relevant?

Hi, this is expected as Angular CLI always uses the local packages and not the global.


foreign key alias to foreign key ref look up functionality

We need the ability to look up a foreign key reference based on its different aliases. The plan is to implement a foreignKeyRefMap object for each schema.
@RFSH  Please note that, the schema can sometimes have an empty string. So the code should deal with this.

@karlcz what cause ermrest to return an empty string schema name? E.g. how should @RFSH set up the table to test this case?

@hongsudt We can mock the server response for testing this case.

The empty string schema name is used for psuedo foreign key constraints which cannot be manipulated through the REST API alone.
Normally they would only be involved if you use REST API to define a foreign key constraint involving two SQL views or a SQL view and table. The view would have to be defined directly through the RDBMS since we provide no REST API to define views at this point.

@hongsudt Since we decided to add ForeignKeyMap to schema, I am using the schema string to find the schema and add ForeignKeyRef to its corresponding ForeignKeyMap. What should I do In case of an empty schema string? Should I just ignore it?

You should keep one additional special map somewhere in the model to store the constraints qualified by empty string schema name. This is an important feature we'll encounter in several of our use cases where we've customized our catalogs with SQL views that are presented to users via Chaise, and these are involved in the pseudo foreign key constraints to link them to related tables and views.

Will this be simpler if we implement the map as an equivalence of 2D lookup
table e.g. foriengKeyMap[schema-name][alias-name] and attach the map at the
catalog level?
On Fri, Aug 5, 2016 at 12:21 AM, Karl Czajkowski<EMAIL_ADDRESS>wrote:

You should keep one additional special map somewhere in the model to store
the constraints qualified by empty string schema name. This is an important
feature we'll encounter in several of our use cases where we've customized
our catalogs with SQL views that are presented to users via Chaise, and
these are involved in the pseudo foreign key constraints to link them to
related tables and views.
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GitHub","url":"https://github.com/informatics-isi-edu/ermrestjs
"}},"updates":{"snippets":[{"icon":"PERSON","message":"@karlcz in #137:
You should keep one additional special map somewhere in the model to store
the constraints qualified by empty string schema name. This is an important
feature we'll encounter in several of our use cases where we've customized
our catalogs with SQL views that are presented to users via Chaise, and
these are involved in the pseudo foreign key constraints to link them to
related tables and views.\n"}],"action":{"name":"View Issue","url":"
https://github.com/informatics-isi-edu/ermrestjs/issues/137#
issuecomment-237775074"}}}

We decided to add the constraint lookup function to Catalog;
Catalog.prototype = {
  byConstraintName(schemaName, constraintName){
    ...
 }
}

I think there should be a trivial way to lookup by what is in the annotations, either making this method more clever or making another wrapper like:
function constraintByNamePair(pair){
return constraintByName(pair[0], pair[1]);
}
so you don't have code all over the place picking out the array elements to put them as separate parameters...

@karl are these blank named schemas still represented as a Schema resource  in ERMrest?

No, the "" schema name is being used to qualify psuedo constraints
that don't exist in a real schema.  The empty string is not a valid
schema name in PostgreSQL so it cannot collide with any possible
catalog content...

Should we give it a name like _ermrest so that we could represent it consistently?
Just a thought, but if not, then I can see why we have push this interface up to the Catalog. Makes sense to me.

_ermrest is also not present in the schemas reported by introspection.
No matter what, you're going to have a special name that does not correspond to a schema name that is part of the model that a model-driven client would want to encounter or present to users.


Updated to allow for multiple capabilities to be placed in the device…

(this is my first pull request so please let me know if I should have done something differently.)
Updated to allow for multiple capabilities to be placed in the device_override configuration setting.
Description:
Older devices do not always auto-discover correctly due to a lack of ability to respond correctly to device catetory and subcategory. The "device_override" feature allows this limitation to be overcome. However, it curretly only supports one capability (i.e. 'light'). This change allows for multiple capabilities to be added.
Related issue (if applicable): fixes #
Pull request in home-assistant.github.io with documentation (if applicable): home-assistant/home-assistant.github.io#<home-assistant.github.io PR number goes here>
Example entry for configuration.yaml (if applicable):
insteon_plm:
port: /dev/insteon
device_override:
- address: aabbcc
platform:
- light
- dimmable
Checklist:
If user exposed functionality or configuration variables are added/changed:

[ ] Documentation added/updated in home-assistant.github.io

If the code communicates with devices, web services, or third-party tools:

[ ] Local tests with tox run successfully. Your PR cannot be merged unless tests pass
[ ] New dependencies have been added to the REQUIREMENTS variable (example).
[ ] New dependencies are only imported inside functions that use them (example).
[ ] New dependencies have been added to requirements_all.txt by running script/gen_requirements_all.py.
[ ] New files were added to .coveragerc.

If the code does not interact with devices:

[ ] Local tests with tox run successfully. Your PR cannot be merged unless tests pass
[ ] Tests have been added to verify that the new code works.

Hi @teharris1,
It seems you haven't yet signed a CLA. Please do so here.
Once you do that we will be able to review and accept this pull request.
Thanks!


Feat: Feedback view icon border-radius changing

Describe the feature
Assign me this task under gssoc'24.
I will increase border-radius so it will looks good.
before image :

After iamage 👍

Add ScreenShots
Consider this changing and assign me this issue under gssoc'24.
Record

[X] I agree to follow this project's Code of Conduct
[X] I'm a GSSOC'24 contributor
[X] I want to work on this issue

can you please assign me this task  under gssoc'24 ?


http error 522 on download section daedalus official website

Sorry to post the problem here
but since several hours i cannot fully load the download page of daedalus
https://daedaluswallet.io/en/download/

tried with firefox chrome safari desktop mobile, with classic internet connection and mobile

I managed to fully display this page using opera with VPN activated as strange as it seems
(i am located in france)


tunnels

https://github.com/inkle/ink/blob/master/Documentation/WritingWithInk.md#1-tunnels
There must be an easier way to implement this using hscript variables like how I ported TheIntercept's variable referencing functionality

Maybe these and threads (#38) could be wrapped into one feature implemented as an insertion hook (#55). If a divert target is meant to be inserted like {target}, the Story could spawn an embedded story that starts at that target and continues until it returns Finished. I'd want to test multiply nested stories at that point...

The syntax I just described is ambiguous with what already happens when you insert a divert target: view count insertion.


model  qwen2-7b-instruct

AI reply not makesense

its working fine with another model

I'm using docker-compose direct image from localai latest
Docker-compose.yml
version: "3.9"
services:
api:
image: localai/localai:latest-aio-gpu-nvidia-cuda-12
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"]
interval: 1m
timeout: 20m
retries: 5
ports:
- 8080:8080
environment:
- DEBUG=true
# ...
volumes:
- ./models:/build/models:cached
# decomment the following piece if running with Nvidia GPUs
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]`

Have you entered flash_attention: true in your model yaml file?

do you mean like this ? but it still generate like that after i restarted
models/qwen.yaml
root@a4681b4b3146:/build/models# cat qwen2-7b-instruct.yaml
context_size: 4096
f16: true
mmap: true
name: qwen2-7b-instruct
flash_attention: true
parameters:
model: Qwen2-7B-Instruct-Q4_K_M.gguf
stopwords:

<|im_end|>

template:
chat: |
{{.Input -}}
<|im_start|>assistant
chat_message: |
<|im_start|>{{ .RoleName }}
{{ if .FunctionCall -}}
Function call:
{{ else if eq .RoleName "tool" -}}
Function response:
{{ end -}}
{{ if .Content -}}
{{.Content }}
{{ end -}}
{{ if .FunctionCall -}}
{{toJson .FunctionCall}}
{{ end -}}<|im_end|>
completion: |
{{.Input}}
function: |
<|im_start|>system
You are a function calling AI model. You are provided with functions to execute. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
{{range .Functions}}
{'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
{{end}}
For each function call return a json object with function name and arguments
<|im_end|>
{{.Input -}}
<|im_start|>assistant

Yes, so it works for me.

@cesinsingapore did you solve your problem?

nope its not


Add variable that allow to choose CA certificate

This module allows us to use only rds-ca-2019, which expires on August 22, 2024. I added a variable called ca_cert_identifier that allows you to choose which CA certificate you want to use. By default, I use the value rds-ca-2019, that is why no changes should be done from clients. Only if clients want to change their CA Certificate they are able to do it.

Thanks for your pull request. Is this your first contribution to a Snowplow open source project? Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).
:memo: Please visit https://docs.snowplowanalytics.com/docs/contributing/contributor-license-agreement/ to learn more and sign.
Once you've signed, please reply here (e.g. I signed it!) and we'll verify. Thanks.

Thanks for your pull request. Is this your first contribution to a Snowplow open source project? Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).
📝 Please visit https://docs.snowplowanalytics.com/docs/contributing/contributor-license-agreement/ to learn more and sign.
Once you've signed, please reply here (e.g. I signed it!) and we'll verify. Thanks.

I signed it!


mujoco.Fatal.Error gladLoadGL in Ubuntu18.04

Hi, I met an error similar to https://github.com/deepmind/dm_control/issues/283, but has different behaivor under Ubuntu18.04 and Ubuntu20.04.
In Ubuntu20.04, everything goes well in the docker created by the following Dockerfile and test file. But the error will arise when I change the OS from Ubuntu20.04 to Ubuntu18.04 in the Dockerfile:
Dockerfile.dmc.txt
test_file:
test_dmc2gym_replay.py.txt

Same error. Did you managed to fix this?


Vite environment variables undefined

So when on obfuscatorPlugin is defined in plugins i'm not able to read .env variables
When  i comment it obfuscatorPlugin out env variables works as they should.
New on obfuscating apps so i don't know if this is the way do env variables, should i do it another way?
eg. env.js and and when building overwrite it like i do with .env
export default defineConfig(configEnv => {
  const envVariables = JSON.stringify(loadEnv(configEnv.mode, process.cwd()))

  // prints all variables to console
  console.log('defineConfig envVariables', envVariables)

  return {
    define: {
      __APP_ENV__: envVariables, // import.meta.env === undefined
    },
    plugins: [
      obfuscatorPlugin({....

my react vite app is not working when I have import.meta.env.VITE_XXXXX. and when I uninstall vite-plugin-javascript-obfuscator, my app also stops working

Try it with https://www.npmjs.com/package/vite-plugin-bundle-obfuscator


refactor: remove done and consuming builder

Now uses a builder that takes self and returns Self. That way it
does not require to call done before using the result.
Renamed add_parameter to parameter to match the other builder
functions.
This improves usage of the API and cuts down on code in the implementation.

actually, this has the effect that you can't use a mutable object anymore. since the builder methods are always consuming the Event you cannot just hold one and change something about it. You'll always have to reassign to your variable.
I think that's why I originally didn't do this.

actually, this has the effect that you can't use a mutable object anymore. since the builder methods are always consuming the Event you cannot just hold one and change something about it. You'll always have to reassign to your variable. I think that's why I originally didn't do this.

That is correct.
As done was already consuming it's input by emtying it, I went this route.
Maybe an alternative would be having a seperate set, or just keep it as it is.


LogoImageData::createForLogo Verbose Errors

Hi @endroid, thanks for this library it is very helpful.
I have run into a situation where the standard_5.php and gd.php methods used in createForLogo() (file_get_contents, imagecreatefromstring) have errors suppressed due to the @ prefix. It would be very helpful to have greater insight into the errors with my current implementation and was wondering if something like the proposed changes would be a possibility?
Clearly this is the first/quick solution that came to mind and it is not a necessity but I figured I would open this PR to start a discussion and did not want to come empty handed.
Having the option to not suppress the potential errors would help to gain further insight. We already get the generic error just below the try/catch if the return is not a string or image. But this skips the root cause of the issue from being shown if something goes wrong when the original methods are being called. I'm open to any ideas.
Thanks either way for your consideration and thanks again for this repo!

Hi @smyhill good idea to do something more with the warning message. In 4.8.5 I append the warning message to the exception message so you have more information. Good luck!


install jdk1.8.0_172, dir install dir is jdk1.8.0_172-amd64

new jdk1.8.0_172, the install dir is /usr/java/jdk1.8.0_172-amd64
the errors:
TASK [srsp.oracle-java : link "/usr/java/default"] *************************************************************************************************************************
Wednesday 25 April 2018  15:53:57 +0800 (0:00:00.309)       0:02:29.328 *******
fatal: [localhost]: FAILED! => {"changed": false, "gid": 0, "group": "root", "mode": "0777", "msg": "src file does not exist, use \"force=yes\" if you really want to create the link: /usr/java/jdk1.8.0_172", "owner": "root", "path": "/usr/java/default", "secontext": "unconfined_u:object_r:usr_t:s0", "size": 16, "src": "/usr/java/jdk1.8.0_172", "state": "link", "uid": 0}

It seems to work on Debian. Which OS are you using?

centos7.4

What version of the role are you using?

I install jdk1.8.0_172 with yum again, the default install dir is /usr/java/jdk1.8.0_172-amd64/
Maybe you should adjust the installation directory, or fixed it

I will take a look at it and fix it, but probably not before tonight.

I can confirm the same problem when trying on Amazon Linux.

Tarball and rpm now behave differently. Version 2.18.3 takes that into account.


stdenv: conservative dynamic undefined variables

Motivation for this change
This PR takes into account if a dynamic variable is not set and replaces the += with a more conservative alternative.
This PR can be controversial this the result is quite uglier than what it was before.
Perhaps an alternative here would be to just define the dynamic variable beforehand ? Not sure.
Things done

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

[ ] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
21.11 Release Notes (or backporting 21.05 Relase notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

I don't like the use of eval here, going to close this for now.


Fix typo in description

What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)

What is the current behavior? (You can also link to an open issue here)

What is the new behavior (if this is a feature change)?

Other information:

This already has been PRd into develop. This is a duplicate of #1479


Generate README.md with helm-docs

Related to #9
Actually, only the main values.yaml generates a proper README.md
helm-docs -t README.md.gotmpl

Feel free to update the comments.
I think that the good approach is to generate these README.md in the release pipeline.

@babykart So should we add a ci step that automatically updates the README.md on release and pushes the changes to main?

@babykart So should we add a ci step that automatically updates the README.md on release and pushes the changes to main?

Yes :)


[Feature Request] Auto refresh index pattern

Is your feature request related to a problem? Please describe
Lately i have been adding a lot of new fields to some logs that goes into opensearch and i had a problem with some dashboards that didn't work at all and i got an error the some fields are "problematic". After a research i found out about index pattern and after refreshing it all dashboards were fixed.
Describe the solution you'd like
I would like to know if there are versions that support this feature of auto refresh index patterns.
Related component
Indexing
Describe alternatives you've considered
No response
Additional context
No response

@opensearch-project/admin Can you help move this to https://github.com/opensearch-project/OpenSearch-Dashboards ?


fix(takerOffer): Making more robust

:tada: This PR is included in version 1.4.6 :tada:
Your semantic-release bot :package::rocket:


Even More Kiosk UI Improvements

Adds color, dropshadow, and rounded corners to team buttons

Stylistic improvements to the pending-swipe modal

Adds a (very imperfect) sound test option for administrators

Adds timer for pending-swipe modal for both non-sticky (30 seconds) and sticky (10 minutes) mode (close #1988)

Adds visual progress bar for timed modals

Buttons on SweetAlert modals are bigger for easier interaction on touchscreen

Functionality is all good in test


verbose

Can you please explain me what we mean by verbose
for  example
mapper = km.KeplerMapper(verbose=2)
how to use it...I am confused...please guide...
Thanks

Thank you for your questions. You’re helping to find some glaring holes in our documentation.
Verbose controls the level of logging that happens while Kepler Mapper runs.
If verbose==0, then there is none. If verbose==1, then there is some. If verbose==2, then there is lots of output.


XroadLib error when response is an asynchronous task

Hi, I would like to use the XRoadLib with asynchronous response but it is not working.
I have two interfaces: asynchronous and synchronous. The interface with synchronous response works fine but the asynchronous response fails. The wsdl is not generated, These are their implementations:
[SuppressMessage("ReSharper", "UnusedMember.Global")]
[XRoadService("getPerson")]
Responses.getPersonResponse getPerson(Requests.getPersonRequest getPersonRequest);
[SuppressMessage("ReSharper", "UnusedMember.Global")]
[XRoadService("getPersonAsync")]
System.Threading.Tasks.Task<Responses.getPersonResponse> getPersonAsync(Requests.getPersonRequest getPersonRequest);
XRoad sends the error:
<SOAP-ENV:Envelope xmlns:SOAP-ENV="http://schemas.xmlsoap.org/soap/envelope/">
SOAP-ENV:Body
SOAP-ENV:Fault
Server.InternalError
The '`' character, hexadecimal value 0x60, cannot be included in a name.
</SOAP-ENV:Fault>
</SOAP-ENV:Body>
</SOAP-ENV:Envelope>
If I replace in the file SchemaDefinitionProvider.cs (XRoadLib.Schema) the value \x60 for string.empty in the normalizedType variable, I can see the wsdl (wrong) generated and the response is Task1, instead getPersonResponse.
Could you please help me? I don't know what I'm doing wrong.
Thanks in advance.
Regards,
Eva

Async service definitions are now supported (version 1.4.5) and should work same as sync definitions. WSDL definition should look identical to sync version (since async doesn't change request structure). Request handling has some more context in ResponseDefinition to provide info about when service should be called as async or not.
If you have custom implementation for service invocation (handler overrides), then check WebServiceRequestHandler class InvokeRuntimeMethodAsync method for ideas how to connect requests with service implementation.

Hi, I have tested the new version and it is working perfectly.
Now, I have some doubts about the connection with C# project and webservice XRoad. I'm going to open a new issue.
Many thanks.


Fix spurious konnectivity server deployment updates

Add better container/volume utilities which can update existing objects,
otherwise containers/volumes are continuously reassigned during mutation.
These utilities make it much easier to patch objects in-place when existing
resources are being updated.

Superceded by https://github.com/openshift/hypershift/pull/557


Incorrect alpha blending when drawing an image with an alpha channel over another image

libgdiplus version: 4.2-1+b1 (Debian stable) and the version shipped in this (unable to tell).
I'm not easily able to compile master so apologies if this is already resolved.
I'm working on an application that compiles game maps out of several textures that heavily use alpha blending, it is using .NET Core's System.Drawing.Common which relies on libgdiplus (either the one on system or shipped with the application through the earlier linked NuGet package). The issue happens when I use Graphics.DrawImage to draw an image heavily utilizing the alpha channel over another image. The output appears pretty distorted wherever the alpha channel is used.
I have set up a repo with sample images/code here that I also used to generate the following images (I cropped them for the issues sake, though).
Expected output (generated on Windows):

Actual output (generated on my Debian machine):

If anyone is willing to test the application with master, that would be great. If it is already fixed or a case of PEBKAC, my apologies. If it is not I'm happy to help test any solutions.

Thanks for the report. I will look into it shortly. It seems to be issue with handling ARGB images (as opposed to PARGB, which is premultiplied).

Set up a new VM on my PC to compile the branch with the fix. Result is MUCH improved (no more distortion etc, yay) but not entirely the same yet (there's still a sort of dark border compared to the Windows version).

I've updated the repro repo sample images with an actual map background/layer for better visibility of the issue, border stands out instead of merging with the terrain.

Interestingly enough, I still have this issue (which I guess is a separate one) with the full fledged non-repro application (which reads decompressed textures instead of on-disk images). I guess there's a difference between the image data in memory and when reading from an image written to disk... unsure where that issue is exactly but I wouldn't be surprised if it is on my end. I'll investigate more to see if that issue is libgdiplus or not. If not I can easily get around it by writing stuff to disk first (after above is fixed). 😄

I found a couple more bugs in the code and fixed them in the pull request. It may help with the other issue.

Looks like the other issue is fixed now! Awesome! Hoping to see this in master soon :)

Fix is merged.

Awesome, I'll compile it manually for now and then switch over to the one in Ubuntu repo when that updates. The issue that repro code is reproducing is still a thing (my main app is fine now though), should this issue remain open for that?


[1.11] Include docker ps in diagnostics bundle

High-level description
Backports: https://github.com/dcos/dcos/pull/4499
Corresponding DC/OS tickets (obligatory)
These DC/OS JIRA ticket(s) must be updated (ideally closed) in the moment this PR lands:

DCOS-42928 Include docker ps in diagnostics bundle

Related tickets (optional)
Other tickets related to this change:

DCOS_OSS- Foo the Bar so it stops Bazzing.

Checklist for all PRs

[ ] Added a comprehensible changelog entry to CHANGES.md or explain why this is not a user-facing change:
[ ] Included a test which will fail if code is reverted but test is not. If there is no test please explain here:
[ ] Read the DC/OS contributing guidelines
[ ] Followed relevant code rules Rules for Packages and Systemd

Checklist for component/package updates:
If you are changing components or packages in DC/OS (e.g. you are bumping the sha or ref of anything underneath packages), then in addition to the above please also include:

[ ] Change log from the last version integrated (this should be a link to commits for easy verification and review): example
[ ] Test Results: [link to CI job test results for component]
[ ] Code Coverage (if available): [link to code coverage report]

PLEASE FILL IN THE TEMPLATE ABOVE / DO NOT REMOVE ANY SECTIONS ABOVE THIS LINE
Instructions and review process
What is the review process and when will my changes land?
All PRs require 2 approvals using GitHub's pull request reviews.
Reviewers should be:

Developers who understand the code being modified.
Developers responsible for code that interacts with or depends on the code being modified.

It is best to proactively ask for 2 reviews by @mentioning the candidate reviewers in the PR comments area. The responsibility is on the developer submitting the PR to follow-up with reviewers and make sure a PR is reviewed in a timely manner. Once a PR has 2 ship-it's, no red reviews, and all tests are green it will be included in the next train.

@mesosphere-mergebot label Ready For Review

:wave:  @mrnugget @drewvanstone @br-lewis Can you review, please?


Verify win32 mmap limits

When opening a file, the mmap view is limited to 2^32 bytes.
What if the file is larger? Will this lead to problems?

If mmap fails (b/c the mapped view is too big) then fall back to read/write.

Fixed for 2.1.13


gofmt issue fixes as per goreportcard (#1274)

Signed-off-by: Bhargav Ravuri<EMAIL_ADDRESS>Change Overview

Fix the go formatting issues reported by goreportcardare.
Add gofmt in golint.sh to warn for future commits.
No functional or test logic changes made. This is only for go formatting.

Pull request type
Please check the type of change your PR introduces:

[x] :rainbow: Refactoring (no functional changes, no api changes)

Issues

fixes #1274

Test Plan

[x] :muscle: Manual

Run make golint.
Install goreportcard and run goreportcard-cli -v. It should print stats similar to:

Grade .......... A+ 100.0%
Files .............. 31757
Issues ................. 0
gofmt ............... 100%
go_vet .............. 100%
ineffassign ......... 100%
gocyclo ............. 100%
license ............. 100%
misspell ............ 100%

Note: % of gofmt should be higher than what it was before this commit.
Message for maintainers

golint is archived and so it was disabled from goreportcard in commit.
Also, goreportcard uses gometalinter which is archived. The replacement is golangci-lint which is being used by Kanister already. However, most of the common linter in it (like, gofmt, revive, etc.) are not enabled or skipped for packages. I'd recommend to have a .golangci.yml configured to make most out of it.

@pavannd1 @PrasadG193 The pipeline probably was failing because of commit e956100 or b48647f that I merged from master into this branch without checking the github action's status. I've now rebased with latest successful commit. Can you please trigger the pipeline again?

Thanks again @PrasadG193 @pavannd1 :blush:


FK Support

A recent post from Steve Sandersion mentioned this issue: "capability already exist in the underlying TG.Blazor.IndexedDB package, it’s probably quite feasible for them to be surfaced through Reshiru’s
DB context APIs" from post in https://blog.stevensanderson.com/2019/08/03/blazor-indexeddb/
and
"StoreSchema ... a set of foreign key indexes" "IndexSpec is used to define the primary key and any foreign keys that are required." from https://github.com/wtulloch/Blazor.IndexedDB


group: create a custom Object group implementation

Implement a custom Object group implementation that:

[ ] Allows types other than Node to be stored.
[ ] Doesn't require a reference to the SceneTree
[ ] Provides signals for member addition and removal

These 3 behaviors will make it much more flexible (and simple) to connect nodes far across the scene tree without needing to add a bunch of boilerplate.

Fixed by #48.


How is it possible to parallelize file_get_contents?

Sorry for posting a question in your issue tracker, but I don't understand how the following is possible.
$responses = wait(parallelMap([
    'https://google.com/',
    'https://github.com/',
    'https://stackoverflow.com/',
], function ($url) {
    return file_get_contents($url);
}));

I was told, in no uncertain terms, that such a thing would be impossible using async because file_get_contents() is, and always will be, a synchronous blocking operation. That is why Artax was created. If this is no longer the case then this is a breakthrough, but if not, and this library is actually using threads instead of coroutines, I think this should be clearly stated in the readme to avoid confusion.

Internally, it create php process (or thread depend on your php configuration) that execute this function, which have been serialized. I think the documentation is more clear about this here : https://amphp.org/parallel-functions/

Oh I see, I thought Amp was all about coroutines, but nevertheless that is very clever and may indeed be some kind of breakthrough. 👍

@Bilge Don't worry about questions in the issue tracker. Amp is mainly focused on non-blocking I/O, correct, but it can also do multi-processing. Consumers don't have to care, they get a promise either way. We also already used threading via libuv for filesystem access before.
Care to provide a PR to replicate the note from the docs into the README?

Would love to, but if you observe my contribution history you may notice I'm a little busy at the moment.


[feature] Transpose rows/columns of DataFrame

I have some data in a different format than what DateFrame expects.  As I started looking around I see that something like pandas transpose would be what I would need.  It would be handy in NimData.
For example:

Name
jan
feb
mar

james
1
2
3

sally
4
5
6

wendy
8
8
9

into

Date
james
sally
wendy

jan
1
4
7

feb
2
5
8

mar
3
6
9

I'm not sure if this can work well with static typing (and I never felt this is a particular useful operation on heterogenous data), because the fields need to be known at compile time, and values of the first column are only known at runtime.

That's why my example was basically a matrix transpose on homogenous data.  The user would have to define the target schema as well so it would all be known at compile time and would generate a runtime error if the actual dataset didn't match.
I'm not sure if it's possible with nim's metaprogramming but one idea would be to use object variants to have an abstract notion of a cell such that they are just placeholders and can hold any type of data.  Then transpose would simply be moving cells around in a two dimensional array.

a matrix transpose on homogenous data.

For homogeneous data it's better to go for libraries like Arraymancer where transpose is a natural operation. The point of this library is to deal with the problem of heterogeneous data with single pass iterator semantics, which doesn't fit well to transposing.


Sometimes one or two records failed to collect

Question
Should we log this kind of failure in another file?
Screenshots
If applicable, add screenshots to help explain.
Additional context
Add any other context here.

Any more specific example?