Training

Is there possibility to train for some time, stop (other tasks for GPU)  and then start from the checkpoint?
-EDIT : train.py --restore_step

After trying to restore checkpoint on 18000 steps it declares lack of memory on Tesla K80!

Resuming from checkpoint: /home/nbuser/tacotron/logs-tacotron/model.ckpt-18000 at commit: None
Generated 32 batches of size 32 in 14.691 sec
2017-10-09 11:33:11.242203: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.242337: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 136.67MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.250553: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.250653: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 136.73MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.261062: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.261147: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 136.80MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.268897: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.268969: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 136.61MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.275714: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:11.275782: W tensorflow/core/common_runtime/bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 136.55MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
2017-10-09 11:33:21.367717: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.23MiB.  Current allocation summary follows.
2017-10-09 11:33:21.368842: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *************************************************************************************************xxx
2017-10-09 11:33:21.368877: W tensorflow/core/framework/op_kernel.cc:1158] Resource exhausted: OOM when allocating tensor with shape[32,1,79,128]
Exiting due to exception: OOM when allocating tensor with shape[32,1,79,128]
[[Node: model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims, model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims_1)]]
[[Node: model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter/_2765 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_22231_model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]]

Caused by op 'model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D', defined at:
File "train.py", line 153, in
main()
File "train.py", line 149, in main
train(log_dir, args)
File "train.py", line 64, in train
model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.linear_targets)
File "/home/nbuser/tacotron/models/tacotron.py", line 47, in initialize
encoder_outputs = encoder_cbhg(prenet_outputs, input_lengths, is_training)  # [N, T_in, 256]
File "/home/nbuser/tacotron/models/modules.py", line 22, in encoder_cbhg
projections=[128, 128])
File "/home/nbuser/tacotron/models/modules.py", line 52, in cbhg
proj1_output = conv1d(maxpool_output, 3, projections[0], tf.nn.relu, is_training, 'proj_1')
File "/home/nbuser/tacotron/models/modules.py", line 100, in conv1d
padding='same')
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py", line 376, in conv1d
return layer.apply(inputs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 492, in apply
return self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 441, in call
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py", line 158, in call
data_format=utils.convert_data_format(self.data_format, self.rank + 2))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 670, in convolution
op=op)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 338, in with_space_to_batch
return op(input, num_spatial_dims, padding)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 662, in op
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 116, in _non_atrous_convolution
name=scope)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 2010, in conv1d
data_format=data_format)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 399, in conv2d
data_format=data_format, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1269, in init
self._traceback = _extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[32,1,79,128]
[[Node: model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims, model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims_1)]]
[[Node: model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter/_2765 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_22231_model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]]
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1139, in _do_call
return fn(*args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
status, run_metadata)
File "/usr/lib/python3.5/contextlib.py", line 66, in exit
next(self.gen)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[32,1,79,128]
[[Node: model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims, model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims_1)]]
[[Node: model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter/_2765 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_22231_model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 93, in train
step, loss, opt = sess.run([global_step, model.loss, model.optimize])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[32,1,79,128]
[[Node: model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims, model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims_1)]]
[[Node: model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter/_2765 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_22231_model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]]
Caused by op 'model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D', defined at:
File "train.py", line 153, in
main()
File "train.py", line 149, in main
train(log_dir, args)
File "train.py", line 64, in train
model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.linear_targets)
File "/home/nbuser/tacotron/models/tacotron.py", line 47, in initialize
encoder_outputs = encoder_cbhg(prenet_outputs, input_lengths, is_training)  # [N, T_in, 256]
File "/home/nbuser/tacotron/models/modules.py", line 22, in encoder_cbhg
projections=[128, 128])
File "/home/nbuser/tacotron/models/modules.py", line 52, in cbhg
proj1_output = conv1d(maxpool_output, 3, projections[0], tf.nn.relu, is_training, 'proj_1')
File "/home/nbuser/tacotron/models/modules.py", line 100, in conv1d
padding='same')
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py", line 376, in conv1d
return layer.apply(inputs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 492, in apply
return self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 441, in call
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py", line 158, in call
data_format=utils.convert_data_format(self.data_format, self.rank + 2))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 670, in convolution
op=op)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 338, in with_space_to_batch
return op(input, num_spatial_dims, padding)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 662, in op
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 116, in _non_atrous_convolution
name=scope)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 2010, in conv1d
data_format=data_format)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 399, in conv2d
data_format=data_format, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1269, in init
self._traceback = _extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[32,1,79,128]
[[Node: model/inference/encoder_cbhg/proj_1/conv1d/convolution/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/gpu:0"](model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims, model/inference/encoder_cbhg/proj_1/conv1d/convolution/ExpandDims_1)]]
[[Node: model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter/_2765 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_22231_model/inference/decoder/while/BasicDecoderStep/decoder/output_projection_wrapper/output_projection_wrapper/multi_rnn_cell/cell_0/cell_0/output_projection_wrapper/output_projection_wrapper/concat_output_and_attention_wrapper/concat_output_and_attention_wrapper/attention_wrapper/assert_equal/Equal/Enter", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"]]
2017-10-09 11:33:21.376900: W tensorflow/core/kernels/queue_base.cc:294] _0_datafeeder/input_queue: Skipping cancelled enqueue attempt with queue not closed
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1139, in _do_call
return fn(*args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
status, run_metadata)
File "/usr/lib/python3.5/contextlib.py", line 66, in exit
next(self.gen)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled
[[Node: datafeeder/input_queue_enqueue = QueueEnqueueV2[Tcomponents=[DT_INT32, DT_INT32, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](datafeeder/input_queue, _arg_datafeeder/inputs_0_1, _arg_datafeeder/input_lengths_0_0, _arg_datafeeder/mel_targets_0_3, _arg_datafeeder/linear_targets_0_2)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/nbuser/tacotron/datasets/datafeeder.py", line 74, in run
self._enqueue_next_group()
File "/home/nbuser/tacotron/datasets/datafeeder.py", line 96, in _enqueue_next_group
self._session.run(self._enqueue_op, feed_dict=feed_dict)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled
[[Node: datafeeder/input_queue_enqueue = QueueEnqueueV2[Tcomponents=[DT_INT32, DT_INT32, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](datafeeder/input_queue, _arg_datafeeder/inputs_0_1, _arg_datafeeder/input_lengths_0_0, _arg_datafeeder/mel_targets_0_3, _arg_datafeeder/linear_targets_0_2)]]
Caused by op 'datafeeder/input_queue_enqueue', defined at:
File "train.py", line 153, in
main()
File "train.py", line 149, in main
train(log_dir, args)
File "train.py", line 58, in train
feeder = DataFeeder(coord, input_path, hparams)
File "/home/nbuser/tacotron/datasets/datafeeder.py", line 45, in init
self._enqueue_op = queue.enqueue(self._placeholders)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 321, in enqueue
self._queue_ref, vals, name=scope)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 1191, in _queue_enqueue_v2
name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1269, in init
self._traceback = _extract_stack()
CancelledError (see above for traceback): Enqueue operation was cancelled
[[Node: datafeeder/input_queue_enqueue = QueueEnqueueV2[Tcomponents=[DT_INT32, DT_INT32, DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](datafeeder/input_queue, _arg_datafeeder/inputs_0_1, _arg_datafeeder/input_lengths_0_0, _arg_datafeeder/mel_targets_0_3, _arg_datafeeder/linear_targets_0_2)]]

@mathias3
Are you using a different dataset, or did you add new characters in symbols.py?
I encountered these errors when I augmented my dataset and added a few characters to symbols.py; and
I resolved them by taking out the characters I added in symbols.py (seems you need to re-train from scratch or at least from base if you want to add new characters), and by also using the appropriate max_iters in hparams.py, which I got after preprocessing my new dataset (max output length / outputs_per_step) ...

Not at all- it is still the same original dataset : LJ Speech Dataset.

I'm sure you've tried a smaller batch size, just to see if it resolves the problem?

I went down to 8. For now i will try to preprocess data again just to be sure there was no mistakes there.

@mathias3  Make sure you kill/interrupt the process NOT only stop. You can use 'watch nvidia-smi to monitor the GPU memory.

Hmmm... that's unexpected. Like @493238731 said, use nvidia-smi to make sure that there aren't other processes eating up your GPU memory.

OK , sorted. It was problem with dev environment( which i can not solve) . It works perfect on another one. My bad . It just shows it is hard to run code in dev env that you don't have fully in control :(


virtctl console not show any

What happened:
userDeploying the vm according to user-guide, the console does not show any information
vm.yaml
apiVersion: kubevirt.io/v1
kind: VirtualMachine
metadata:
  name: testvm
spec:
  running: false
  template:
    metadata:
      labels:
        kubevirt.io/size: small
        kubevirt.io/domain: testvm
    spec:
      domain:
        devices:
          disks:
            - name: containerdisk
              disk:
                bus: virtio
            - name: cloudinitdisk
              disk:
                bus: virtio
          interfaces:
          - name: default
            masquerade: {}
        resources:
          requests:
            memory: 64M
      networks:
      - name: default
        pod: {}
      volumes:
        - name: containerdisk
          containerDisk:
            image: quay.io/kubevirt/cirros-container-disk-demo
        - name: cloudinitdisk
          cloudInitNoCloud:
            userData: |-
              #cloud-config
              ssh_pwauth: true
              disable_root: false
              chpasswd:
                expire: False
                list: |-
                  root:dangerous
                  test:dangerous

virtctl console testvm
Successfully connected to testvm console. The escape sequence is ^]

into testvm pod
kubectl exec -it virt-launcher-testvm-4fkjp  bash
kubectl exec [POD] [COMMAND] is DEPRECATED and will be removed in a future version. Use kubectl exec [POD] -- [COMMAND] instead.
bash-4.4# virsh list
Authorization not available. Check if polkit service is running or see debug message for more information.
 Id   Name             State
--------------------------------
 1    default_testvm   running

bash-4.4# virsh console default_testvm
Authorization not available. Check if polkit service is running or see debug message for more information.
Connected to domain 'default_testvm'
Escape character is ^] (Ctrl + ])
error: internal error: character device console0 is not using a PTY

What you expected to happen:
A clear and concise description of what you expected to happen.
How to reproduce it (as minimally and precisely as possible):
Steps to reproduce the behavior.
Additional context:
Add any other context about the problem here.
Environment:

KubeVirt version (use virtctl version): 0.58
Kubernetes version (use kubectl version): v1.23.10
VM or VMI specifications: N/A
Cloud provider or hardware configuration: N/A
OS (e.g. from /etc/os-release): N/A
Kernel (e.g. uname -a): Linux controller-node-1 3.10.0-1160.el7.x86_64
Install tools: N/A
Others: N/A

Thanks for raising the bug, scydas. Quick question: you mention virtctl console command but the command listed in the bug is virsh console. Just checking that that is a typo and you are using virtctl console. Also it looks like the VM name in the posted spec does not match the virsh console command.

Thank you for your reply, it has been revised Ask a question Details

@scydas That makes it clearer but I'm still a bit confused.
It looks like you are able to use virtctl console <vm> command to access the VM.
Outside of the VM, are you then exec'ing into the launcher pod and then running virsh console? I could be wrong but I don't think that's possible, which is why you're getting the error: internal error: character device console0 is not using a PTY error.
Just to clarify: In description you say " userDeploying the vm according to user-guide, the console does not show any information". Is there something wrong with the VM session when you access the testvm VM using virtctl console <vm> ?
@xpivarc - can you keep me honest, please?

@aburdenthehand
When using virtctl console <vm>, the console only shows  Successfully connected to testvm console. The escape sequence is ^], there is no more information. When I go into the vm container, using virsh console shows the error: internal error: character device console0 is not using a PTY  message.
@xpivarc
virtctl vnc testvm
{"component":"","level":"info","msg":"connection timeout: 1m0s","pos":"vnc.go:157","timestamp":"2023-05-11T22:54:33.399322Z"}
Error encountered: could not find remote-viewer or vncviewer binary in $PATH

And the memory has been expanded to 512m

I have solved this problem
virt-host-validate qemu
  QEMU: 正在检查 for hardware virtualization                                 : PASS
  QEMU: 正在检查 if device /dev/kvm exists                                   : PASS
  QEMU: 正在检查 if device /dev/kvm is accessible                            : PASS
  QEMU: 正在检查 if device /dev/vhost-net exists                             : PASS
  QEMU: 正在检查 if device /dev/net/tun exists                               : PASS
  QEMU: 正在检查 for cgroup 'memory' controller support                      : PASS
  QEMU: 正在检查 for cgroup 'memory' controller mount-point                  : PASS
  QEMU: 正在检查 for cgroup 'cpu' controller support                         : PASS
  QEMU: 正在检查 for cgroup 'cpu' controller mount-point                     : PASS
  QEMU: 正在检查 for cgroup 'cpuacct' controller support                     : PASS
  QEMU: 正在检查 for cgroup 'cpuacct' controller mount-point                 : PASS
  QEMU: 正在检查 for cgroup 'cpuset' controller support                      : PASS
  QEMU: 正在检查 for cgroup 'cpuset' controller mount-point                  : PASS
  QEMU: 正在检查 for cgroup 'devices' controller support                     : PASS
  QEMU: 正在检查 for cgroup 'devices' controller mount-point                 : PASS
  QEMU: 正在检查 for cgroup 'blkio' controller support                       : PASS
  QEMU: 正在检查 for cgroup 'blkio' controller mount-point                   : PASS
  QEMU: 正在检查 for device assignment IOMMU support                         : WARN (No ACPI DMAR table found, IOMMU either disabled in BIOS or not supported by this hardware platform)

kubectl -n kubevirt patch kubevirt kubevirt --type=merge --patch '{"spec":{"configuration":{"developerConfiguration":{"useEmulation":true}}}}'

I have solved this problem
virt-host-validate qemu
  QEMU: 正在检查 for hardware virtualization                                 : PASS
  QEMU: 正在检查 if device /dev/kvm exists                                   : PASS
  QEMU: 正在检查 if device /dev/kvm is accessible                            : PASS
  QEMU: 正在检查 if device /dev/vhost-net exists                             : PASS
  QEMU: 正在检查 if device /dev/net/tun exists                               : PASS
  QEMU: 正在检查 for cgroup 'memory' controller support                      : PASS
  QEMU: 正在检查 for cgroup 'memory' controller mount-point                  : PASS
  QEMU: 正在检查 for cgroup 'cpu' controller support                         : PASS
  QEMU: 正在检查 for cgroup 'cpu' controller mount-point                     : PASS
  QEMU: 正在检查 for cgroup 'cpuacct' controller support                     : PASS
  QEMU: 正在检查 for cgroup 'cpuacct' controller mount-point                 : PASS
  QEMU: 正在检查 for cgroup 'cpuset' controller support                      : PASS
  QEMU: 正在检查 for cgroup 'cpuset' controller mount-point                  : PASS
  QEMU: 正在检查 for cgroup 'devices' controller support                     : PASS
  QEMU: 正在检查 for cgroup 'devices' controller mount-point                 : PASS
  QEMU: 正在检查 for cgroup 'blkio' controller support                       : PASS
  QEMU: 正在检查 for cgroup 'blkio' controller mount-point                   : PASS
  QEMU: 正在检查 for device assignment IOMMU support                         : WARN (No ACPI DMAR table found, IOMMU either disabled in BIOS or not supported by this hardware platform)

kubectl -n kubevirt patch kubevirt kubevirt --type=merge --patch '{"spec":{"configuration":{"developerConfiguration":{"useEmulation":true}}}}'

I encountered the same problem， virtcl console only show 'Successfully connected to testvm console. The escape sequence is ^],', When I go into the vm container, using virsh console shows the error: internal error: character device console0 is not using a PTY  message..  There is no solution according to the method you mentioned.

@BAMSHK  virt-handler log ? error  messag?


Animation Bug

After Update 1.8.6, we're having this bug in every game
We create the lobby and select the moon (everything is fine till there)
As we start the game our characters are stuck in a standing still animation:

The characters just slide onto the terrain
If we try to crouch, the game logic recognize that we're crouching (lower movement speed and can pass under things) but the first person pov is like as we're standing up
whenever you pick up an item or you have an item in your hands, you're not able to see it. Even with bigger items, you just get the text "hands full"
whenever you try to enter the terminal the pov is like far away from the terminal screen, making it difficult to read. And not close like default when the character lean towards the screen

I tried to be as accurate as possible when describing the issue. Hope it helps!

Sorry for the delay. I'm performing a code restructure, which will change how emotes are handled. This should fix many bugs, and maybe this one included, so maybe we can check to see if this issue is still there after I push the update, hopefully this weekend.

Don't worry, i just saw the reply. Anyway as you'll push the update i'll let you know if is everything good. Thanks mate!

I have updated the mod now, and it's currently on 1.9.1. Hopefully everything will be fine with your issue, but please let me know!

Ok sorry for the delayed reply. I just tested out the 1.9.3 version and everything seems working fine. Thanks for the update! ^^


bug: sanitize and validate JSON before using it in routes

REMEMBER: Issues related to the cli client or frontend will not be tolerated in this repo. This is for server/backend issues only. Check the box below if you understand:

[x] I understand.

Describe the bug
A clear and concise description of what the bug is.. In other words, what happened?
Validation isn't completed on routes.
Expected behavior
A clear and concise description of what you expected to happen. (what didn't it do?)
Inputs should be validated to not contain escape characters, code, among other XSS vulnerabilities.

This will eventually be implemented using Joi and escaping of fields.
There are also currently headers in place to deter XSS attacks thanks to koa-helmet.

XSS is purely client-side therefore spacebin will not do anything more than set headers to deter it.

CSP
I may hate CSP's guts when it comes to userscripts, but setting a CSP header would help deter outside scripts (and perhaps people running scripts on the page, too!)

koa-helmet can handle contentSecurityPolicy for us, we just need to enable it.

sounds good to me: this may cause a few issues with selfhosted instances then. We should make it clear it allows scripts/css from a specific folder, perhaps a folder named media?

sounds good to me: this may cause a few issues with selfhosted instances then. We should make it clear it allows scripts/css from a specific folder, perhaps a folder named media?

I think assets would be a better name.
Also, see: bfa96f4

The only browser I could see CSP becoming a problem on would be Safari, but Cascadea pretty much solves the injecting CSS issue.
Also, sounds good. I'll be "safari tester", I guess.

sounds good to me: this may cause a few issues with selfhosted instances then. We should make it clear it allows scripts/css from a specific folder, perhaps a folder named media?

I think assets would be a better name.
Also, see: bfa96f4

Also, I think we'll want a config option for enabling/disabling CSP.

Sure; CSP can be the bane of developer existence sometimes.

Instead of

This will eventually be implemented using Joi and just basic escaping of fields.
There are also currently headers in place to deter XSS attacks thanks to koa-helmet.

I said here that we're going to use Joi, I'm now unsure. Joi is by far the most popular but it doesn't seem to have good support for Koa. I'm researching yup as an alternative that will most likely support Koa.

Alright. So, we're going to move to @koa/joi-router, this allows us to implement validation relatively easy using Joi. Implementing this requires a small refactoring in how we do routes but I think it will be easy enough and definitely worth it.

CSP is also currently a bit broken, I need to setup directives. I'll get to work on fixing it soon.

In progress.

Closed due to de1464f.


[Error Handling] Avoid access response data when response not exists

Should we have better error message in this ECONNRESET case.
https://github.com/Yoctol/bottender/issues/246

Maybe we could do _.get with the data needed and fallback msg to original error.message in handleError of the packages.
Also need to update axios-error.
https://github.com/Yoctol/messaging-apis/blob/v0.6.16/packages/axios-error/src/index.js#L33


After upgrade to v2: invalid args rid for command get: command get missing required key rid

I'm seeing many of these console errors in the webview2 inspector after updating tauri and tauri-plugin-store from 2.0.0-rc to 2.0.0 final. The error points to the plugin-store, or my call point: store.get("my-key"). No code changed, just Cargo and npm package upgrades to @tauri-apps v2 release.
My code:
import { Store } from "@tauri-apps/plugin-store"

const Setting = {
  editorOpen: "editor-visible",
  windowSplit: "window-split"
}

class WindowSettings {
  #defaults
  #store

  constructor(defaults, store = new Store(".settings.dat")) {
    this.#defaults = defaults
    this.#store = store
  }

  #saveSetting(key, value) {
    this.#store.set(key, value).then(() => this.#store.save())
  }

  /**
   * Return bounded split value in pixels
   * @returns number
   */
  getSplit = () =>
    this.#store.get(Setting.windowSplit).then(split =>
      typeof split !== "number"
        ? this.#defaults.split.min
        : Math.min(
            Math.max(split, this.#defaults.split.min),
            this.#defaults.split.max
          )
    )

  setSplit = px => {
    this.#saveSetting(Setting.windowSplit, px)
  }
}

I've tried changing my key names and store file (.dat), neither helped.

Can you post the output of the tauri info command please?

This method of declaring store no longer works: https://v2.tauri.app/plugin/store/.
I've followed the migration but I'm now stuck because the create_store command apparently expects a u64 rather than a boolean
Unhandled Promise Rejection: invalid args `autoSave` for command `create_store`: invalid type: boolean `true`, expected u64

Some issues with stable it seems

Further to yesterday, I'm now seeing this error on Store construction:
invalid args ridfor commandget: invalid type: string ".settings.dat", expected u32
tauri info:
[✔] Environment
    - OS: Windows 10.0.22631 x86_64 (X64)
    ✔ WebView2: 129.0.2792.79
    ✔ MSVC: Visual Studio Build Tools 2022
    ✔ rustc: 1.78.0 (9b00956e5 2024-04-29)
    ✔ cargo: 1.78.0 (54d8815d0 2024-03-26)
    ✔ rustup: 1.27.1 (54dd3d00f 2024-04-24)
    ✔ Rust toolchain: stable-x86_64-pc-windows-msvc (default)
    - node: 20.10.0
    - npm: 10.2.3

[-] Packages
    - tauri 🦀: 2.0.0
    - tauri-build 🦀: 2.0.0
    - wry 🦀: 0.44.1
    - tao 🦀: 0.30.2
    - @tauri-apps/api : 2.0.1
    - @tauri-apps/cli : 2.0.0-rc.18 (outdated, latest: 2.0.1)

[-] Plugins
    - tauri-plugin-http 🦀: 2.0.0
    - @tauri-apps/plugin-http : 2.0.0
    - tauri-plugin-dialog 🦀: 2.0.0
    - @tauri-apps/plugin-dialog : 2.0.0
    - tauri-plugin-fs 🦀: 2.0.0
    - @tauri-apps/plugin-fs : 2.0.0
    - tauri-plugin-store 🦀: 2.0.0
    - @tauri-apps/plugin-store : 2.0.0
    - tauri-plugin-shell 🦀: 2.0.0
    - @tauri-apps/plugin-shell : 2.0.0
    - tauri-plugin-window-state 🦀: 2.0.0
    - @tauri-apps/plugin-window-state : 2.0.0

[-] App
    - build-type: bundle
    - CSP: default-src 'self' data: blob:; script-src 'self' 'unsafe-inline' http://localhost:*; style-src 'self' 'unsafe-inline'; connect-src 'self' ipc: http://ipc.localhost http://localhost:*; img-src 'self' data: http: https:
    - frontendDist: ../dist
    - devUrl: http://localhost:3055/
    - framework: React
    - bundler: Rollup

I can't load data after upgrading from v2.0.0-rc to v2.0.1 .
The console prints errors as gknapp said.

the same problem😭

@will5933 @antkit @gknapp
You guys need to change the way you declare the store -> https://v2.tauri.app/plugin/store/

You guys need to change the way you declare the store after updating to plugin-store v2 -> https://v2.tauri.app/plugin/store/

The bug still happens for me with this code snippet

Thanks, it works.
from
const clipboardStore = new window.__TAURI_PLUGIN_STORE__.Store('clipboard.bin');

to
const { createStore } = window.__TAURI__.store;
const clipboardStore = await createStore('clipboard.bin');

I've updated my code to follow the V2 docs, this generated a new error passing the { autoSave: true } option object:

Uncaught (in promise) invalid args autoSave for command create_store: invalid type: boolean true, expected u64

I omitted that option and just call store.save() after setting a value.
I updated my capabilities for the plugin too (to use store:default) and rewrote the constructor of my class. I no longer construct the store if it's not passed in. I moved store construction to a hook, as createStore() is async.
const Setting = {
  windowSplit: "window:split"
}

class WindowSettings {
  #defaults
  #store

  constructor(defaults, store) {
    this.#defaults = defaults
    this.#store = store
  }

  #saveSetting(key, value) {
    this.#store.set(key, value).then(() => this.#store.save())
  }

  getSplit = () =>
    this.#store.get(Setting.windowSplit).then(split =>
      typeof split !== "number"
        ? this.#defaults.split.min
        : Math.min(
            Math.max(split, this.#defaults.split.min),
            this.#defaults.split.max
          )
    )

  setSplit = px => {
    this.#saveSetting(Setting.windowSplit, px)
  }
   ...
}

I use a hook for store instantiation and updated my app to show a loading component if the store isn't set / ready.
import React from "react"
import { createStore } from "@tauri-apps/plugin-store"
import Settings from "../settings"

function useSettings(defaults, storeFile = "settings.bin") {
  const [settings, setSettings] = React.useState(null)

  React.useEffect(() => {
    const initSettings = async () => {
      const store = await createStore(storeFile)
      setSettings(new Settings(defaults, store))
    }
    initSettings()
  }, [])

  return settings
}

export default useSettings

Conditional render in App:
  ...
  const defaultSettings = React.useMemo(() => {
    return {
      split: {
        max: leftSideMax + resizeBarWidth,
        min: minWidth
      }
    }
  }, [minWidth, leftSideMax, resizeBarWidth])
  const settings = useWindowSettings(defaultSettings)
  ...
  if (!settings) {
    return (<Loading />)
  }
  ...
  // render app UI as usual, settings available

Oh oops I opened https://github.com/tauri-apps/plugins-workspace/issues/1931 before seeing this. But it seems that createStore is now the blessed api? So we should update the readme here to match that?
But for autoSave, it seems like the rust is using it as a u64 because it's using it to define debounce. But those changes aren't reflected in the JS, and I'm not sure which we should want to do:
a. change types/docs to match this behavior in js (autoSave is now a boolean, describe it to be used for debounce)
b. transform the autoSave boolean in js, and properly pass an int to bridge
c. change the rust to treat them as two different properties
Although it also looks like more changes are in the pipeline already, so maybe we should just make sure they know about this.

I also encountered this issue, should we update this version?

I am currently experiencing the same problem.
I have implemented the code as documented below, but it fails when I run load().
Rust
let config_path = PathBuf::from("config.bin");
let store =  app.store( config_path );
if store.load().is_err() {
  store.set("general".to_string(), json!(INITIAL_CONFIG));
  store.save().unwrap();
}

TypeScript
import { type Store, load as loadStore } from "@tauri-apps/plugin-store";
/// --------
const store = useRef<Store | null>( null );
store.current = await loadStore( CONFIG_File );

TAURI info
[✔] Environment
    - OS: Windows 10.0.22631 x86_64 (X64)
    ✔ WebView2: 129.0.2792.89
    ✔ MSVC:
        - Visual Studio Build Tools 2022
        - Visual Studio Community 2022
    ✔ rustc: 1.82.0 (f6e511eec 2024-10-15)
    ✔ cargo: 1.82.0 (8f40fc59f 2024-08-21)
    ✔ rustup: 1.27.1 (54dd3d00f 2024-04-24)
    ✔ Rust toolchain: stable-x86_64-pc-windows-msvc (default)
    - node: 20.12.1
    - pnpm: 9.12.2
    - npm: 10.5.0

[-] Packages
    - tauri 🦀: 2.0.6
    - tauri-build 🦀: 2.0.2
    - wry 🦀: 0.46.1
    - tao 🦀: 0.30.3
    - @tauri-apps/api : 2.0.3
    - @tauri-apps/cli : 2.0.5

[-] Plugins
    - tauri-plugin-dialog 🦀: 2.0.3
    - @tauri-apps/plugin-dialog : 2.0.1
    - tauri-plugin-window-state 🦀: 2.0.1
    - @tauri-apps/plugin-window-state : not installed!
    - tauri-plugin-shell 🦀: 2.0.2
    - @tauri-apps/plugin-shell : 2.0.1
    - tauri-plugin-stronghold 🦀: 2.0.1
    - @tauri-apps/plugin-stronghold : 2.0.0
    - tauri-plugin-http 🦀: 2.0.3
    - @tauri-apps/plugin-http : 2.0.1
    - tauri-plugin-sql 🦀: 2.0.1
    - @tauri-apps/plugin-sql : 2.0.0
    - tauri-plugin-fs 🦀: 2.0.3
    - @tauri-apps/plugin-fs : 2.0.1
    - tauri-plugin-store 🦀: git+https://github.com/tauri-apps/plugins-workspace?branch=v2#7e1c17a635f10d69e10ea9f35081a68c71276203 (2.0.1)
    - @tauri-apps/plugin-store : 2.1.0
    - tauri-plugin-deep-link 🦀: 2.0.1
    - @tauri-apps/plugin-deep-link : not installed!
    - tauri-plugin-process 🦀: 2.0.1
    - @tauri-apps/plugin-process : 2.0.0
    - tauri-plugin-single-instance 🦀: 2.0.1
    - @tauri-apps/plugin-single-instance : not installed!
    - tauri-plugin-global-shortcut 🦀: 2.0.1
    - @tauri-apps/plugin-global-shortcut : 2.0.0

[-] App
    - build-type: bundle
    - CSP: connect-src 'self' https://google.github.io/magika/model/ https://www.youtube.com/ https://googleads.g.doubleclick.net/ ipc: http://ipc.localhost; worker-src 'self' blob:; frame-src 'self' https://www.youtube.com/embed/; style-src 'self' 'unsafe-hashes' 'sha256-QLk7hiiq3b6XVhLz2U+7CDM2vHCse7K+ELeFYemxS1w='; style-src-elem 'self' 'nonce-idn354fskf25dt' 'sha256-47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=' 'sha256-ejxaNqI9HE4qAX7B2hAfln6pnS37ktMxKSd3kZCKrvU=' 'sha256-nzTgYzXYDNe6BAHiiI7NNlfK8n/auuOAhh2t92YvuXo='; script-src 'self'; default-src 'self'; img-src * blob: blob: data:
    - frontendDist: ../dist
    - devUrl: http://localhost:1420/
    - framework: React
    - bundler: Vite

Your rust side store plugin's version seems to be before the most recent rework, do you mind upgrading it to the latest and try again?

Thank you very much.
I set the version of the store plugin to fine center and the problem is solved.

Thank you very much.  I set the version of the store plugin to fine center and the problem is solved.
hello which version ?2.1.0?

Yes, 2.1.0, both js package and rust crate


RuntimeError: CUDA out of memory

There is an issue of RAM during the training of the FasterRCNN_FPN on Colab.
How to optimise the code?

The images in the dataset have resolution 4000x3000. In RCNN, image patches are first resized before being passed to the backbone. However, in FasterRCNN_FPN, there is no resizing. Unfortunately, you can't pass a 4000x3000 image to the backbone – you have to resize it. You can do that using the res keyword during training. Here's an example.

The issue also happens when setting res=800. Using Colab with GPU Tesla P100-PCIE-16GB

Did you manage to solve the problem? Based on the edit history of your comment, I see that you still encountered some issues.

Did you manage to solve the problem? Based on the edit history of your comment, I see that you still encountered some issues.

Yes. It was a problem with the GPU Ram not deleted. With the resolution of 800 it works.


[FLINK-26120][tests] Relax CheckpointIDCounterTestBase assertions

The test was verifying that the counters produced perfect sequences, but we only require strictly monotonous sequences.

How would a manual executor allow interleaving operations?

How would a manual executor allow interleaving operations?

I didn't think that through. The ExecutorService is not used internally but only utilized in test itself. So, it doesn't make it difference. Forget what I said. 😇


chore: remove run_all_examples_no_poetry.sh

Closes #63.

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 100.00%. Comparing base (664872c) to head (79b6c97).

Additional details and impacted files
@@            Coverage Diff            @@
##            master       #95   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files           13        13
  Lines          243       243
  Branches        27        27
=========================================
  Hits           243       243

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


chore(main): release remnote-plugin-template-react 0.2.6

:robot: I have created a release beep boop
0.2.6 (2024-11-30)
Bug Fixes

build error (84d9c2e)

This PR was generated with Release Please. See documentation.

:robot: Created releases:

remnote-plugin-template-react-v0.2.6
:sunflower:


CDE: Google Sheets Sample Data and Templates

CDE: Google Sheets Sample Data and Templates Needed
Hi @J-Yash, I am tasking you with providing sample files and templates in Google Sheets for the Cell Distance Explorer. Let me know if there is anyone else I need to loop in to complete this task.
Please use this GitHub comment thread for uploading files. Bruce, Daniel, or Edward can close out this issue when development has what they need.
Requirements

Share all sheets in the Google Drive folder: Cell Distance Explorer

Individually grant view access for each Google Sheet. The CDE folder and its directory can't be made public.

Collaborate with development as needed to complete this task

Resources

FigJam: UX Studies: Bruce, Yash, and Libby's Comment thread on Files
Google Drive: CDE Folder

Task List
Sample Files
Sample files are needed to explore visualizations. The below were specified in Figma:

[ ] 2D Skin
[ ] 3D Intestine

Templates for the Create Visualization workflow

[ ] Cell Type Table (note: Katy wants endothelial cells and a couple of other rows of sample data loaded into this template)
[ ] Color Map Table

@LibbyUX

The two template files have been uploaded to the Google Drive mentioned above. I have granted view access for both files. Feel free to update the color map template file based on how such a file would be used as input.
The sample files for intestine and skin data are already on S3 image-store:

2D intestine sample:  vccf-data-cell-nodes/published/intestine-codex-stanford/B006_Ascending-nodes.csv
3D skin sample: vccf-data-cell-nodes/published/skin-celldive-ge/region_6-nodes.csv

Thank you so much for the speedy reply and the templates, @J-Yash!
I have follow-up questions about the sample files:

I am unfamiliar with the S3 image-store, can you provide a link to this? Is this something our development team is tracking and familiar with, so I shouldn't worry about it?
Do the sample files contain all metadata fields being reported in the specified Metadata module?
In the Metadata Module: Clicking on the Source Data sample file title routes users to the sample file's Google Sheet.

View the design for the Sample Metadata module below or in the Figma design file.

Hey @LibbyUX ,
Dev team should be able to get the sample files from S3. But here are the links as well:

Intestine 2D: https://cdn.humanatlas.io/image-store/vccf-data-cell-nodes/published/intestine-codex-stanford/B006_Ascending-nodes.csv
Skin 3D: https://cdn.humanatlas.io/image-store/vccf-data-cell-nodes/published/skin-celldive-ge/region_6-nodes.csv

Note that the sample files only contain the cell coordinates and cell types. They do not contain the metadata since that is not part of the file format for nodes files. Below is the metadata for the two samples:
Intestine 2D:
Organ: Intestine
Technology: CODEX
Thickness: 7
Pixel Size: 0.866
Skin 3D:
Organ: Skin
Technology: Cell DIVE
Thickness: 5
Pixel Size: 0.325
In the Metadata Module: Clicking on the Source Data sample file title routes users to the sample file's Google Sheet.
Does it route them to a Google Sheet or simply downloads the source CSV file from the link above? If the former, I will have to create two more Google Sheets (based on the same CSV files linked above).

Hi @J-Yash, it should route them to a Google Sheet. Our experts were most comfortable viewing and using Google Sheets.

Hi @LibbyUX , I have placed the two sample files in the Google Drive linked above.

Thank you so much, @J-Yash!

Hi @bherr2, @edlu77, @axdanbol, @bhushankhope
Do you have everything you need for this issue?

We just made the data part of the repository


Add: Trumpet of Faith TV [UG]

Channel Name
Trumpet of Faith TV
Alternative Names (optional)
No response
Network (optional)
No response
Owners (optional)
Apostle Dr. Jean Saflo,
Country
UG
Subdivision (optional)
No response
City (optional)
No response
Broadcast Area
c/UG
Languages
eng
Categories (optional)
religious
NSFW
FALSE
Launched (optional)
No response
Closed (optional)
No response
Replaced By (optional)
No response
Website (optional)
https://trumpetnet.com/
Logo
https://i.imgur.com/L7aP4ai.png
Notes
No response

TrumpetofFaithTV.ug: "owners[0]" with value "Apostle Dr. Jean Saflo," fails to match the required pattern: /^[^",]+$/


[Fix][3.5][Ready] Allow developers to pass arrays as field names

The only thing that doesn't work when trying to use a field named category[0][name] is that the old() value isn't populated. Otherwise, it works as expected: in store() and update() the input ends up as a proper array.
This adds a square_brackets_to_dots() helper, which then should be used for ALL field type that support bracket notation.
Merged @pestopancake 's  #1654 into this branch, to work on it.

Done. Implemented for all field types.
@pestopancake , I would love it if you could test this PR too, in your particular use case at least, to confirm it's working as expected. It's a change in a lot of field types, wouldn't want to break people's apps.
Thanks again for the suggestion and PR!
Cheers!

Merged into 3.5. Will be released later this week. Thanks again @pestopancake !


PR#661 master-live merge conflict resolution

Docs Build status updates of commit 674fecf:
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feat(pisa-daemon):  init pisa-daemon project

Signed-off-by: xuanyuan300<EMAIL_ADDRESS>
What is changed and how it works?
What's Changed:

init pisa-daemon

Please check the license


Disable ability to have digest emails

I want to make the server completely private so that users cannot even choose to send themselves digest emails.  So they would have to use one of the apps, or the web version.
This command was useful to turn it off for all users:
./manage.py turn_off_digests -a -r 2
But how can I prevent users from switching it back on? Sorry I could not work this out.

It appears this isn't properly documented, and we shouldn't close this issue until we add proper documentation for Zulip's digest emails feature, but you can make digest emails unavailable for all users here:

Hi @timabbott this only turns off weekly digest emails. And users themselves can still opt in to get emails for every single message. The turn off digests setting above didn't change that at all and people still get emails. Is there some way to turn off all emails that have any posting content? Obviously emails to reset passwords etc still need to go out so I can't turn it off all together. Looking for a way to prevent data "leakages" to 100s of email inboxes.

@timabbott he wants to turn off email-notification settings (for mentions and stream notifications) for all users. We don't have a provision for that. Meanwhile, should I document the digest emails setting?

@maltokyo makes sense.  The easiest way to do that, at present would be to just not run the queue worker for this.  Are you self-hosting?
If so, you can arrange that temporarily with:
supervisorctl stop zulip-workers:zulip_events_missedmessage_emails
But the right longer-term workaround would involve a small fork of Zulip that removes that queue worker (or makes it discard events).  I can open a clearly specified issue for how that should work soon.
@vaibhavrajsingh2001 yes opening a PR to document the notification setting is helful.

Hi @timabbott @vaibhavrajsingh2001
Many thanks for the advice. Indeed I thought the "turn of digests" setting would do exactly that (stop the missed message emails etc). What does it do exactly then?  Will turning off the queue also stop email reset (administrative) emails? These are still needed.

@maltokyo the digest emails are more of a newsletter than notifications about places the user was specifically mentioned. So disabling it won't actually promote usage of the web and mobile apps as such.

@timabbott made #14258 for the documentation issue.

Hi @timabbott @vaibhavrajsingh2001 - my objective (request for help) here is to ensure that Zulip content STAYS on Zulip server only.  I want  to disable completely even the ability for users to send any user posts to outside email servers.
My users tell that they are not comfortable to use my Zulip installation, when there is the fear that someone is sending all the content in an automated way to their emails (sitting on Google/corporate servers, that we have no control over).
Please could you look into this as a privacy feature?

@maltokyo thanks for the follow-up.  I think there are 2 features being mixed up here.  One is Zulip's digest emails (which you can see a preview for how they work at e.g. https://chat.zulip.org/digest/), which an organization administrator can disable for the whole organization using the setting documented by the Help Center document we just merged.
The second is the "missed-message" emails, which are email notifications for new messages, which it sounds like is what you were actually talking about, and perhaps explains the confusion here.
Individual users can of course disable email notifications across all platforms (or configure content-free emails that just let them know they got a notification), but it sounds like you're looking for an organization-level or server-level way to do it for everyone?
That's detailed here: https://zulipchat.com/help/configure-missed-message-emails.  Let me know if it doesn't cover your use case; there may be a feature we want to add if so.

(We can finish the discussion on this thread and then open a new issue for the feature if there's a feature we need).

Hi @timabbott
I understood all of the above, and now also setup the "missed-message" emails that have no content.  So at least privacy is confirmed, and no zulip post content is stored in corporate servers...
I just wish that I could also just turn off "missed-message" functionality centrally, all together.  So not a biggie at all.  Main thing was the privacy aspect.


Fix: duplicate pairing strings in config

This pull request adds a preventive measure in the HomeKit
server's  AddPair  function. A conditional check is performed before adding
a new pair to ensure that it doesn't already exist within the pairings. This
preventative measure avoids redundancy and can potentially improve the
efficiency of pairing processes in the HomeKit server.

Thanks!

https://github.com/AlexxIT/go2rtc/releases/tag/v1.8.0


Name resources according to instance.Name

... instead of using static names. This allows us to make naming more consistent across operators. Although we don't support deploying multiple control planes in a single name space, we have to deploy multiple instances of some CR(eg GlanceAPI) in the single namespace to deploy a single control plane, so instance.Name is the preferred method to name resources. This does not care about the naming conflicts between CRs in case different CRs are deployed with the same name, because adding prefix causes redundant names. (for example if a user deploys a KeystoneAPI instance with name keystone, we prefer creating a deployment with name keystone instead of keystone-keystone). To avoid such collisions in sub CRs this ensures the top service CR adds proper suffixes when creating sub CR instances.
This also fixes inconsistency in naming related to GlanceAPI CR (mainly about glance-external-api vs glance-api-external).
Also, services and endpoints are still using static names because of the current implementations in lib-common.

/retest


Support stream-based tokenization

For large inputs we want to be able to process one line at a time, so we don't have to read the entire thing in to memory.

Use phrase_from_file.  This reads ahead only enough to perform the reduce.
If you don't leave choice points when you emit a token it should neatly dispose of list.
Also, it works with lazy_list_location and so on for neatly reporting errors.
The only time it would consume a lot of memory  would be eg an unclosed string if you allow newlines in files, or trying to read a file by lines, and a line by line mode breaks under same conditions.
I would advocate for not doing this, it hard codes line breaks are token breaks into the tokenizer in a way you may come to regret. EG if you attempt to tokenize SWI-Prolog code, you have to deal with multiline strings.


Change the destination disk

I don't know exactly how to change the destination disk of the backup.
I tried to follow the Spatie instructions but in the filament-*.php file and I just only success to change the "name".
Thanks in advance.

same question

@cgiupponi Read this https://spatie.be/docs/laravel-backup/v7/taking-backups/overview#content-determining-the-content-of-the-backup

I already mentioned please check.

Anyhow
php artisan vendor:publish --provider="Spatie\Backup\BackupServiceProvider"


Checking and modifying the statement of Can turn off SSL

[Enter feedback here]
Hi,
Regarding the statement in the comparison table, Can turn off SSL for Flexible Server is ”No”.
However, Can turn off SSL for Flexible Server is possible by require_secure_transport from server parameter.
Please kindly check and modify this statement.
Thank you,
Naoki
Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 3e4deba6-4073-0174-e4cf-ee02458fb764
Version Independent ID: 13b2c9e1-283b-41b7-99f2-9008e53ee7f3
Content: Compare Azure Database for PostgreSQL - Single Server and Flexible Server
Content Source: articles/postgresql/flexible-server/concepts-compare-single-server-flexible-server.md
Service: postgresql
Sub-service: flexible-server
GitHub Login: @sr-msft
Microsoft Alias: srranga

@naokitakeda14 Thank you for the feedback. We are actively investigating and will get back to you soon.

@naokitakeda14 Thanks for patience. Yes rightly mentioned by you and as per the article as well "_Although its not recommended, if needed, you have an option to disable TLS\SSL for connections to Azure Database for PostgreSQL - Flexible Server by updating the require_secure_transport server parameter to OFF."_
@sr-msft Could you please suggest if we can make this change to document?

Thanks @naokitakeda14 for pointing that out. I will fix it in the next update.

#please-close


Creating table in footer puts it in the header

Code I am using:
pdf.addTable(.footerCenter, table: table)

Similar issue, but for me, tables in the footer don't show up at all. Tables do seem to work for headers.

Hi there, can you test this again with the pre-release 2.0.0? Tables have changed, therefore this might behave differently

@EliteTechnicalCare @96kaitlinburke anthing new on this issue?

Closing this due to inactivity. Please feel free to reopen if necessary and (even better) create a small sample for me to reproduce the issue


Peripheral initiated MTU Changes can cause OutOfOrderGattCallbackException

The BluetoothGattCallback() method for onMtuChanged needs to be serialized with the execute method when the libraries consumer calls readMtu(), but unlike other onBlankEvent() methods, this one can also be called in response to a peripheral device requesting an MTU change.
Note the UPPERCASED part of the documentation:
    /**
     * Callback indicating the MTU for a given device connection has changed.
     *
     * This callback is triggered in response to the requestMtu function,
     * OR IN RESPONSE TO A CONNECTION EVENT.
     *
     * @param gatt GATT client invoked {@link BluetoothGatt#requestMtu}
     * @param mtu The new MTU size
     * @param status {@link BluetoothGatt#GATT_SUCCESS} if the MTU has been changed successfully
     */
    public void onMtuChanged(BluetoothGatt gatt, int mtu, int status) {
    }

I forked the old able library and fixed it there by exposing MTU Updates through a separate stateflow that is exposed publicly and is used for retrieving a serialized response to readMtu() with the execute method.

I should note that unless your peripheral device can request an MTU change on its own behalf, you likely would never see this problem show up. Our device happens to do this automatically after a few seconds into the connection, or if the OS happens to perform this request for a profile that it manages.

Thanks for reporting. We had only ever initiated MTU changes from the client side, so I was unaware that it could be initiated from the server side. TIL.
I'll brainstorm good ways to handle this, but as you described, using a Flow, is probably a good approach.


Updated Collected Registration Data

Updated the frontend and lambda functions to accept a user position and modified the collected expected graduation date.

The user can pick a position ("Undergraduate", "Graduate", "Faculty/Staff", "Other") when filling out the registration form.
The expected graduation date input and the major/minor input will only be visible when the "Undergraduate" position is selected.
The graduation input was modified so that the user can now select a graduating semester and year, instead of a specific date.

I tested these changes in my dev stack cloudfront registration page. I entered the information that is collected when the "Undergraduate" field is chosen, as well as the information when the "Faculty/Staff" field is chosen. The data from both cases was correctly sent to my dev stack dynamoDB table.

This PR should be good to go whenever the data migration is ready,
I also changed the GradYear back to a string because it would cause errors when the year was left blank because a Faculty/Staff user would leave it blank.

I rebased it and removed the edits to cdk/visit/console/ , I must have accidently left those in while testing.
I wasn't able to test this change on my dev stack, I'm running into the issue the others are running into with the DynamoDB tables, but it seems like that is being worked on, but I don't believe any of the items changed with this commit should mess with anything I had before.

I changed 'Position' to 'position' for the new table.
Grad semester, year, and major are all required now when "Undergraduate Student" is selected, and will not be required from the others.
I tested this on my dev account, and it worked as intended with both undergraduate and graduate selected


Scripts not found

Hello,
i was pretty inactive last time but now i updated to latest github revision and wanted to launch my scripts i wrote but my self implemented console didnt showed up which made me look into the scripthookvdotnet.log and it says that my dll or my .cs files are found but they doesnt contain any scripts. Scripts found (0) ....
I tried the SimpleTrainer script from example project and it also doesnt find any scripts.
What to do now?


few japanese translated

and checked Validate,

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.1 out of 2 committers have signed the CLA.:white_check_mark: hogemaru321:x: MoombaDSYou have signed the CLA already but the status is still pending? Let us recheck it.


[BUG]  BlobContainerClient.CreateIfNotExists records 409s as failures in distributed traces and logs

Library name
Azure.Storage.Blobs
Please describe the feature.

Azure.Core needs a bit of help from the library to understand when specific responses don't indicate a failure and should not be traced/logged as errors.
This can be done using response classifiers similar to this PR https://github.com/Azure/azure-sdk-for-net/pull/33639 for sync and async versions of this method.

This also applies to blob existence checks, e.g.  BlobBaseClient.Exists() where the service returns a 404 on a HEAD request when the blob does not exist.    In this case, I would not expect to see a failed dependency call in my telemetry.


#[serde(rename=...)] should apply to containers

As in:
#[derive(Serialize, Deserialize)]
#[serde(rename = "foo")]
struct Foo {
    x: u32,
}

Should serialize the structure with the name "foo".

What about renaming fields?

This has been implemented in the 0.7 branch.


Hdds 11784

What changes were proposed in this pull request?
Provide a one-liner summary of the changes in the PR Title field above.
It should be in the form of HDDS-1234. Short summary of the change.
Please describe your PR in detail:

What changes are proposed in the PR? and Why? It would be better if it is written from third person's
perspective not just for the reviewer.
Provide as much context and rationale for the pull request as possible. It could be copy-paste from
the Jira's description if the jira is well defined.
If it is complex code, describe the approach used to solve the issue. If possible attach design doc,
issue investigation, github discussion, etc.

Examples of well-written pull requests:

#3980
#5265
#4701
#5283
#5300

What is the link to the Apache JIRA
Please create an issue in ASF JIRA before opening a pull request, and you need to set the title of the pull
request which starts with the corresponding JIRA issue number. (e.g. HDDS-XXXX. Fix a typo in YYY.)
(Please replace this section with the link to the Apache JIRA)
How was this patch tested?
(Please explain how this patch was tested. Ex: unit tests, manual tests, workflow run on the fork git repo.)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this.)

@sokui I think this PR in your fork was opened accidentally.

@sokui I think this PR in your fork was opened accidentally.

I intentionally opened this to check how CI works. Will close afterwards.

I intentionally opened this to check how CI works.

It's great that you check CI status.  But opening a PR is not necessary to get CI feedback.  CI checks are run for both push and pull_request events.  Two workflow runs for the same commit:
push: https://github.com/sokui/ozone/actions/runs/12309831979
PR: https://github.com/sokui/ozone/actions/runs/12309832197


Refactor preview doesn't show preview with clangd

Coming from https://github.com/clangd/vscode-clangd/issues/376

VS Code Version: 1.71.2
OS Version: MacOS

Steps to Reproduce:

opened a dummy t.cc (auto S = 1;) in VSCode with vscode-clangd extension, waiting for clangd to warm up.
Move the cursor to auto, click the 💡 and get a CodeAction menu
Move the cursor to the Expand auto type Code ACtion, and press Command + Enter

Expected result: a preview pane will be shown
Actual result: we apply the code action without showing the preview
A special bit about clangd which may be related: the CodeAction returned from clangd doesn't have the edit field propagated, instead we use the applyTweak command to apply a workspace edit.
The detailed logs in case it is useful:
V[] <<< {"id":9,"jsonrpc":"2.0","method":"textDocument/codeAction","params":{"context":{"diagnostics":[],"triggerKind":2},"range":{"end":{"character":2,"line":0},"start":{"character":2,"line":0}},"textDocument":{"uri":"file:///tmp/t3.cpp"}}}
I[] <-- textDocument/codeAction(9)
I[] --> reply:textDocument/codeAction(9) 0 ms
V[] >>> {"id":9,"jsonrpc":"2.0","result":[{"command":{"arguments":[{"file":"file:///tmp/t3.cpp","selection":{"end":{"character":2,"line":0},"start":{"character":2,"line":0}},"tweakID":"ExpandAutoType"}],"command":"clangd.applyTweak","title":"Expand auto type"},"kind":"refactor","title":"Expand auto type"}]}

V[] <<< {"id":10,"jsonrpc":"2.0","method":"workspace/executeCommand","params":{"arguments":[{"file":"file:///tmp/t3.cpp","selection":{"end":{"character":2,"line":0},"start":{"character":2,"line":0}},"tweakID":"ExpandAutoType"}],"command":"clangd.applyTweak"}}
I[] <-- workspace/executeCommand(10)
I[] --> workspace/applyEdit(1)
V[] >>> {"id":1,"jsonrpc":"2.0","method":"workspace/applyEdit","params":{"edit":{"changes":{"file:///tmp/t3.cpp":[{"newText":"int","range":{"end":{"character":4,"line":0},"start":{"character":0,"line":0}}},{"newText":"\n","range":{"end":{"character":0,"line":2},"start":{"character":11,"line":0}}}]}}}}

The refactor preview only supports previewing code actions that have an edit. This example shows that the code action is instead using a command, which we are not able to preview (since running the command may have other side effects)

The refactor preview only supports previewing code actions that have an edit.

This makes sense, but this seems more like a UX issue in VSCode -- it'd be better for VSCode to disable the preview feature if the code action that don't have edit.

Can vscode-clangd investigate switching use using edits instead?

In clangd, we want to avoid paying the cost of generating edits for each code action (as code action is triggered when evert the cursor moves in VSCode).
I think we could support the newly-added codeAction/resolve in clangd, but it is not our priority at the moment.


Cannot modify "Terminal: Find Next" keybinding

VS Code version: Code 1.49.2 (e5e9e69aed6e1984f7499b7af85b3d05f9a6883a, 2020-09-24T16:26:09.944Z)
OS version: Linux x64 5.8.7-arch1-1

System Info

Item
Value

CPUs
Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz (6 x 4300)

GPU Status
2d_canvas: enabledflash_3d: enabledflash_stage3d: enabledflash_stage3d_baseline: enabledgpu_compositing: enabledmultiple_raster_threads: enabled_onoop_rasterization: disabled_offopengl: enabled_onprotected_video_decode: unavailable_offrasterization: disabled_softwareskia_renderer: enabled_onvideo_decode: unavailable_offvulkan: disabled_offwebgl: enabledwebgl2: enabled

Load (avg)
2, 2, 2

Memory (System)
61.77GB (3.51GB free)

Process Argv
--disable-extensions --no-sandbox

Screen Reader
no

VM
0%

DESKTOP_SESSION
undefined

XDG_CURRENT_DESKTOP
undefined

XDG_SESSION_DESKTOP
undefined

XDG_SESSION_TYPE
tty

Steps to Reproduce:

Remove Keybinding -> No effect.
Change Keybinding -> Duplicate binding created.
Change When Expression -> Duplicate binding created.

Does this issue occur when all extensions are disabled?:  I try to have a Byobu terminal in the VSCode integrated terminal, but cannot make the F3 (next window in Byobu) key work. I added
  {
    "key": "f3",
    "command": "-workbench.action.terminal.findNext",
    "when": "terminalFindFocused && terminalProcessSupported || terminalFocus && terminalProcessSupported"
  },

in my "keybindings.json" file (well, VSCOde added it when I clicked on "Remove keybinding") but F3 is still triggering the "Find" panel in terminal.

This seems to be fixed in 1.50.0, presumably by 2441447b026ac0.
Nice. :+1:


[WIP] Avoid duplicated Computation of integral images for Self Guided Filte…

…r RDO
Avoid a duplicated computation of integral images for
RDO of Self Guided Filter in Loop Restoration Filter.

Looks nice :) it needs a rebase though.

#1697 has resolved this issue in a better shape, so I am happily closing this! Thanks, @xiphmont !


Escape search string

search function accept regex argument or converts it to regex if string is provider. If you tried search for \ it threw exception. This change fixes it.
https://sentry.io/organizations/oacore/issues/2049656723/?project=2091955&query=is%3Aunresolved

@viktor-yakubiv Maybe it would be better if we have this in design because @oacore/search is affect to. I can imagine to have a prop like escape=Bool for select?

The prop you suggest is very ambiguous: you can escape any chars, " for CSV, \ for regex, & for HTML, %` and non-ASCII for URLs and so on. If I hear this first time, I would rather think about the last because I tend to think that input should be URL-encoded on submit.
Also, this is a big side-effect. I would duplicate this for the time being.


sampling efficiency from prior model

Sampling a large number of samples from variables in data model takes much.
@inf.probmodel
def linear_reg(d):
    w0 = inf.Normal(0, 1, name="w0")
    w = inf.Normal(tf.zeros([d,1]), 1, name="w")

    with inf.datamodel():
        x = inf.Normal(tf.ones([d]), 2, name="x")
        y = inf.Normal(w0 + x @ w, 1.0, name="y")

m = linear_reg(d=2)
m.prior(["x", "y"], data={"w0":0, "w":[[2],[1]]}).sample(1000)

Could we improve the efficiency if we just replicate variables in the data model and not those
outside? Or maybe make possible both behaviours?

We are generating the samples in just one session.run. The efficiency issue comes from compact the result into a dict in the form var_name: samples, because the result of the session.run is a list of var_name: samples, where the size of the samples (of each element in the list) is the plate size (1 for not expanded models).

Improved efficiency by adding a parameter size_datamodel at probmodel.prior().
See commit: a133d224aacad5ae766a9bb604b8b95175e5e468


Numpy version

Hi, thank you for your great work.
I can't use your repository using latest Numpy versions and I can't downgrade my Numpy version for some reasons.
would you please solve this problem that cause many errors on some devices?
thank you.

I upgraded your code,  now I can use it with Numpy version 1.26.x.


Publish to Heroku

My goal is to find a good continuous deployment solution in Azure as per #3 , but while this doesn't happen, it'd be good to have this published to Heroku at least.

Live on http://rawrjs.herokuapp.com/ .


Make install command more precise

to work around the naming clash with another "clp" package in homebrew (the COIN-OR Linear Programming Solver).

Thank you!


Dependencies update 20.06

Coverage remained the same at 98.627% when pulling 57dd4fc9b69b00326fd91ef6cc5d7bae38b5612a on dependencies-update-20.06 into 74489f07a3d3e81ee97c662336970d5f61a31a21 on master.


Implement id parsing in conjuction with $ref fixes

This commit adds support for the id keyword and rewrites part of the code that deals with schema references. In conjuction with https://github.com/xeipuuv/gojsonreference/pull/8 this fixes issues #134, #143, #168, #170, which were all related to schema references in one way or another.

Thank you! I am not the maintainer, but I tried this out and it works for me.
It solves the problem I encountered where I wanted to avoid accessing the network to resolve referenced JSON schemas and instead provide them as strings. There may be an even better way to do it, but with this PR, I was able to use code like (simplifed):
type jsonLoader struct {
	gojsonschema.JSONLoader
}

func (l jsonLoader) LoaderFactory() gojsonschema.JSONLoaderFactory {
	return &jsonLoaderFactory{}
}

type jsonLoaderFactory struct {
	schemas map[string]string
}

func (f jsonLoaderFactory) New(source string) gojsonschema.JSONLoader {
	switch source {
	case "https://sourcegraph.com/v1/settings.schema.json":
		return gojsonschema.NewStringLoader(schema.SettingsSchemaJSON)
	case "https://sourcegraph.com/v1/site.schema.json":
		return gojsonschema.NewStringLoader(schema.SiteSchemaJSON)
	}
	return nil
}

And then use it in Validate like so:
	return gojsonschema.Validate(
		jsonLoader{gojsonschema.NewBytesLoader(schema)},
		gojsonschema.NewBytesLoader(input),
	)

The code above works with this PR and not on master.


DS-977

Added docs folder containing code to generate docs for readthedocs.io here is a sample that I put up to test that it would compile successfully on readthedocs (will get rid of it once we finish the pr). I used apparate as a basis as suggested.
-changed the changelogs and versions from 0.3.1 -> 0.3.2
-new foder 'docs' includes files to generate the hosted docs
-Makefile and make.bat were generated as was conf.py. I put you as the author in line 24 of the conf.py file.
-contrib.rst is based on the conduct.md and contributing.md files
-index.rst contains the general creevey info

Please PR into WIP

I think the version should go to 0.4.0 because you're adding a new feature.

How do the generated files get generated? Do they need to be tracked by git?

contrib.rst is based on the conduct.md and contributing.md files

Should we delete the .md files?

Files were almost all generated by sphinx as defaults (Makefile and make.bat are default generated). conf.py, contrib.rst, and index.rst are the only ones that should get changed since there is creevey info in those. I guess we can track them?
As far as deleting the md files, apparate has the md files  in the gitrepo and the info in the hosted docs so I think it is alright to leave them.
I'll go open a new PR into WIP


fix: Missing provider in litellm Router initialization

Aims to solve the following issue, mentioned in #419 and #588.
Traceback (most recent call last):
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 240, in run_asgi
    result = await self.app(self.scope, self.asgi_receive, self.asgi_send)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 69, in __call__
    return await self.app(scope, receive, send)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/fastapi/applications.py", line 1054, in __call__
    await super().__call__(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/applications.py", line 123, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/middleware/errors.py", line 151, in __call__
    await self.app(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/middleware/cors.py", line 77, in __call__
    await self.app(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
    await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/routing.py", line 756, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/routing.py", line 776, in app
    await route.handle(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/routing.py", line 373, in handle
    await self.app(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/routing.py", line 96, in app
    await wrap_app_handling_exceptions(app, session)(scope, receive, send)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
    raise exc
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
    await app(scope, receive, sender)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/starlette/routing.py", line 94, in app
    await func(session)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/fastapi/routing.py", line 348, in app
    await dependant.call(**values)
  File "opendevin/server/listen.py", line 25, in websocket_endpoint
    await session.start_listening()
  File "/Users/yoni/OpenDevin/opendevin/server/session.py", line 88, in start_listening
    await self.create_controller(data)
  File "/Users/yoni/OpenDevin/opendevin/server/session.py", line 134, in create_controller
    llm = LLM(model=model, api_key=api_key, base_url=api_base)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/yoni/OpenDevin/opendevin/llm/llm.py", line 42, in __init__
    self._router = Router(
                   ^^^^^^^
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/litellm/router.py", line 198, in __init__
    self.set_model_list(model_list)
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/litellm/router.py", line 2075, in set_model_list
    ) = litellm.get_llm_provider(
        ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/litellm/utils.py", line 5733, in get_llm_provider
    raise e
  File "/Users/yoni/.local/share/virtualenvs/OpenDevin-CrrnA4bA/lib/python3.11/site-packages/litellm/utils.py", line 5720, in get_llm_provider
    raise litellm.exceptions.BadRequestError(  # type: ignore
litellm.exceptions.BadRequestError: LLM Provider NOT provided. Pass in the LLM provider you are trying to call. You passed model=llama2
 Pass model as E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/starcoder',..)` Learn more: https://docs.litellm.ai/docs/providers

This seems to solve the Router initialization problem, however when we reach the following code in codeact_agent.py:
        response = self.llm.completion(
            messages=self.messages,
            stop=["</execute>"],
            temperature=0.0,
            seed=42,
        )
        action_str: str = parse_response(response)
        self.messages.append({"role": "assistant", "content": action_str})

The value in messages is:
[{'role': 'system', 'content': 'You are a helpful assistant. You will be provided access (as root) to a bash shell to...close the shell and end the conversation.\n'}, {'role': 'user', 'content': 'create a simple html page'}]
However, it seems like the model only answer the "system" role message, getting the following action_str:
Great! I'm excited to help you with your tasks. Please provide me with the first task you would like me to assist you with, and I will do my best to help you.

@johnnyaug Thank you for this. I apologize, somehow I missed your PR. You might have been on to something here. Although, doesn't litellm read the provider from env?
At the moment, we've put the use of Router in the back-burner. We've encountered other issues and we've refactored it out of the code for now, at least until we figure how to use it reliably! If you want to play with making it work, please feel free to.


How to extend other database plugin

Please answer these questions before submitting your issue.

Why do you submit this issue?
[ ] Question or discussion
[ ] Bug
[ ] Requirement
[ ] Feature or performance improvement

Question

What do you want to know?
How to extend other database plugin, so that skywalking can monitor it, I don't know how to start, thanks.

Bug

Which version of SkyWalking, OS and JRE?
8.1.0

Which company or project?

What happened?
If possible, provide a way to reproduce the error. e.g. demo application, component version.

Requirement or improvement

Please describe your requirements or improvement suggestions.

We could develop plugins for your database client driver. We have provided plugin development doc already.

We could develop plugins for your database client driver. We have provided plugin development doc already.

Can Kingbase be supported？

What do you mean can? Please explain.

What do you mean can? Please explain.

My database is Kingbase, can you develop plugins for Kingbase ? thanks.

As an open-source community, this is not how we work. From my check, Kingbase is a commercial database, and not open sourced. So, you need to contact your vendor and ask them to do this in an open source or private way.

As an open-source community, this is not how we work. From my check, Kingbase is a commercial database, and not open sourced. So, you need to contact your vendor and ask them to do this in an open source or private way.

I see,thanks a lot.


Bit Shifting Contribution: BitViz

Community Contribution
Link to live project, video, or image:
https://unsuitable001.github.io/BitViz/
BitViz is a bit wise operation visualizer. It can visualize bit shifting, basic mathematical operations and logical operations.
Sharing

[x] I would be happy to have my project shared on The Coding Train's social media!

Twitter: @unsuitable001

This is awesome!


Fetch attached sensors from connected bodies in RobotResource::GetAttachedSensors

This PR seeks to address the issue of the controller client user hitting an error when calling mujinclient::utils::GetSensorData/GetSensorTransform with the sensorname argument referring to a connected body camera of the robot referred to with the bodyname argument.
The primary change this MR makes is adding a section in RobotResource::GetAttachedSensors for gathering attached sensor pointers from the robot resource's connected bodies before merging them into the attached sensors array that the method later iterates on.

LGTM

Wait, Jeronimo should take a look at this, vm is not supposed to use this function.

thanks~


A11y: Cross selling slider element count wrong

In a slider with 4 elements the screenreader claims to be displaying the elements 6 and 7.
!image-20241021-160118.png|width=1476,height=891,alt="image-20241021-160118.png"!

[public] Shopware automated response: This issue is linked to the internal issue: NEXT-39190
[created from NEXT-39190, comment 505893]


Update SDWebImage to the latest version

@DylanVann , @MiguelGrability , is this good to merge?

Please, merge my pull request.

Tested. Works. Please merge.

Please merge @DylanVann

@DylanVann, @AngelTrinidad, @taschik, could you please accept this change?


Refactoring fails on file

Issue Type: Bug
I have a code file (in this project on github - this file in particular https://github.com/omartin2010/jetson_object_detection/blob/master/src/detector.py) that when I try to refactor, it fails. However if I delete many lines, like from line ~93 to ~574 (I tried deleting function by function until it works), then it starts working. If I only comment these lines out, it doesn't work. I try for instance to rename one member function and it fails and when I delete the lines, it just works. The same function, that is. Any idea on how to enable more tracing for that?
In particular, I get this exception from the notification area :
Refactor failed. Rename refactoring should be performed on resolvable python identifiers. [<FrameSummary file refactor.py, line 382 in watch>, <FrameSummary file refactor.py, line 352 in _process_request>, <FrameSummary file refactor.py, line 260 in _rename>, <FrameSummary file refactor.py, line 129 in refactor>, <FrameSummary file refactor.py, line 156 in onRefactor>, <FrameSummary file /home/omartin/.local/lib/python3.6/site-packages/rope/refactor/rename.py, line 28 in __init__>]

Extension version: 2020.3.71659
VS Code version: Code - Insiders 1.45.0-insider (4f05de82a75569601bc2dc894023383b4ce1c2c0, 2020-04-13T17:53:50.995Z)
OS version: Windows_NT x64 10.0.18363
Remote OS version: Linux arm64 4.9.140-tegra

System Info

Item
Value

CPUs
Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz (8 x 2112)

GPU Status
2d_canvas: enabledflash_3d: enabledflash_stage3d: enabledflash_stage3d_baseline: enabledgpu_compositing: enabledmultiple_raster_threads: enabled_onoop_rasterization: disabled_offprotected_video_decode: enabledrasterization: enabledskia_renderer: disabled_off_okvideo_decode: enabledviz_display_compositor: enabled_onviz_hit_test_surface_layer: disabled_off_okwebgl: enabledwebgl2: enabled

Load (avg)
undefined

Memory (System)
15.93GB (5.13GB free)

Process Argv

Screen Reader
no

VM
0%

Item
Value

Remote
SSH: jetson-wired

OS
Linux arm64 4.9.140-tegra

CPUs
unknown (6 x 345)

Memory (System)
7.68GB (1.40GB free)

VM
0%

@omartin2010 we use https://github.com/python-rope/rope to do python refactoring. You may have to file this bug on rope.


Web search/ modality support?

Hi!. Any chance of adding web search?

Hi. For this purpose we need to implement something like agents from autogpt. Maybe someday I will implement it.

yeah it would be a welcome addition


Adjust broken link location (GCP)

No Results Found
The page you requested could not be found. Try refining your search, or use the navigation above to locate the post.

https://ctid.mitre-engenuity.org/our-work/security-stack-mappings-gcp/ → https://ctid.mitre-engenuity.org/our-work/gcp/

Thank you @TechSolomon for reporting this, and extra thanks for submitting it as a PR! 🙏


Chrome extension is not rebuilt when files are changed

Run "npm run-script build" and it'll build and watch over your files :)


iOS 16 STT breaks TTS

on iOS 16 (verified with iOS 16.0.2) after using the speech practice feature, the speech playback is lowered in volume. But oddly, the quited speech is unaffected by the mute switch, which normally completely mutes.

Fixed in 14.98


Add "Scroll to Top" button in Readme.md

Description
Add the "Scroll to top" button in the Readme.md file.Which will act as a automatic scroller when clicked.And scrolls to the TOP of the File in just one click.

@EternoSeeker I would like to add this feature in your Readme.md file.Can i work on it?

yes you can.


server_functional_requirements.md needs to be modified with current addition of HMAC in the payload

TL;DR

https://github.com/google/exposure-notifications-server/blob/master/docs/server_functional_requirements.md
Sample payload is missing HMAC in the request.
{
  "temporaryExposureKeys": [
    {"key": "base64 KEY1", "rollingStartNumber": 12345, "rollingPeriod": 144, "transmissionRisk": 5},
    {"key": "base64 KEY2", "rollingStartNumber": 12489, "rollingPeriod": 10, "transmissionRisk": 6},
    {"key": "base64 KEYN", "rollingStartNumber": 12499, "rollingPeriod": 100, "transmissionRisk": 7}],
  "regions": ["US", "CA", "MX"],
  "appPackageName": "com.foo.app",
  "verificationPayload": "signature /code from  of verifying authority",
  "padding": "random string data..."
}

Expected behavior

Should have HMAC added in the sample payload

Hi @xyz23 - can you provide more information? What kind of HMAC are you expecting?

/assign
The HMAC value isn't actually part of the request, the HMAC key is.
I'll get it updated and link to the PHA verification certificate design


Update fs2-core, fs2-io to 2.5.1

Updates

co.fs2:fs2-core
co.fs2:fs2-io

from 2.4.6 to 2.5.1.
GitHub Release Notes - Changelog - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "co.fs2" } ]

labels: library-update, semver-minor

Superseded by #223.