Error on Image stylization with Docker image on examples

I am using Docker 1.12.3 (8488) on Windows 10. My launch script is: "docker run -it -p 6006:6006 -v d:/projects/data/magenta:/magenta-data -v d:/data/:/bigdata tensorflow/magenta".
I have followed the steps in https://github.com/tensorflow/magenta/blob/master/README.md#docker and got
/magenta-data# melody_rnn_generate \
   --config=lookback_rnn \
   --bundle_file=/magenta-models/lookback_rnn.mag \
   --output_dir=/magenta-data/lookback_rnn/generated \
   --num_outputs=10 \
   --num_steps=128 \
   --primer_melody="[60]"
INFO:tensorflow:Beam search yields melody with log-likelihood: -105.642350
INFO:tensorflow:Beam search yields melody with log-likelihood: -175.272188
INFO:tensorflow:Beam search yields melody with log-likelihood: -140.538503
INFO:tensorflow:Beam search yields melody with log-likelihood: -182.784687
INFO:tensorflow:Beam search yields melody with log-likelihood: -80.120105
INFO:tensorflow:Beam search yields melody with log-likelihood: -207.462564
INFO:tensorflow:Beam search yields melody with log-likelihood: -138.664485
INFO:tensorflow:Beam search yields melody with log-likelihood: -167.801560
INFO:tensorflow:Beam search yields melody with log-likelihood: -36.049449
INFO:tensorflow:Beam search yields melody with log-likelihood: -135.496597
INFO:tensorflow:Wrote 10 MIDI files to /magenta-data/lookback_rnn/generated

I want to use image_stylization and trying to run the examples on https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization#training-a-model.
I am receiving errors. I want to use image_stylization to:
1- Use a model with varying weights
2- Fine-tune my own model with my paintings
1- I am running a very slightly modified version of the example given in https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization#stylizing-an-image. Modification is limited to paths of the files only (so output is in shared folder).
image_stylization_transform
--num_styles=10
--checkpoint=/magenta-data/multistyle-pastiche-generator-monet.ckpt
--input_image=/magenta-data/photo.jpg
--which_styles="{0:0.1,1:0.1,2:0.1,3:0.1,4:0.1,5:0.1,6:0.1,7:0.1,8:0.1,9:0.1}"
--output_dir=/magenta-data/output
--output_basename="all_monet_styles"
Console output:
/magenta-data# image_stylization_transform \
       --num_styles=10 \
       --checkpoint=/magenta-data/multistyle-pastiche-generator-monet.ckpt \
       --input_image=/magenta-data/photo.jpg \
       --which_styles="{0:0.1,1:0.1,2:0.1,3:0.1,4:0.1,5:0.1,6:0.1,7:0.1,8:0.1,9:0.1}" \
       --output_dir=/magenta-data/output \
       --output_basename="all_monet_styles"
Traceback (most recent call last):
  File "/usr/local/bin/image_stylization_transform", line 11, in <module>
    sys.exit(console_entry_point())
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_transform.py", line 77, in console_entry_point
    tf.app.run(main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_transform.py", line 54, in main
    'labels': tf.constant(which_styles),
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 422, in make_tensor_proto
    tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/compat.py", line 64, in as_bytes
    (bytes_or_text,))
TypeError: Expected binary or unicode string, got {0: 0.1, 1: 0.1, 2: 0.1, 3: 0.1, 4: 0.1, 5: 0.1, 6: 0.1, 7: 0.1, 8: 0.1, 9: 0.1}

I can, however, run it without any weights. Below command generates a modified version of my input image.
image_stylization_transform
--num_styles=10
--checkpoint=/magenta-data/multistyle-pastiche-generator-monet.ckpt
--input_image=/magenta-data/photo.jpg
--output_dir=/magenta-data/output
--output_basename="all_monet_styles"
How can I use the different weights ?
2- I am following the guide here: https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization#training-a-model
Below command generates a 33 MB file as output from 7 images (1.26 MB total size).
image_stylization_create_dataset
--vgg_checkpoint=/magenta-data/train/vgg_16.ckpt
--style_files=/magenta-data/train/style/images/*.jpg
--output_file=/magenta-data/train/style_images.tfrecord
However, fine-tuning step returns an error on the model:
/magenta-data/train# image_stylization_finetune --checkpoint=/magenta-data/train/vgg_16.ckpt --train_dir=/magenta-data/train/traindir --style_dataset_file=/magenta-data/train/style_images.tfrecord --num_styles=7 --vgg_checkpoint=/magenta-data/train/vgg_16.ckpt --imagenet_data_dir=/bigdata/output
WARNING:tensorflow:tf.op_scope(values, name, default_name) is deprecated, use tf.name_scope(name, default_name, values)
WARNING:tensorflow:tf.op_scope(values, name, default_name) is deprecated, use tf.name_scope(name, default_name, values)
WARNING:tensorflow:tf.op_scope(values, name, default_name) is deprecated, use tf.name_scope(name, default_name, values)
WARNING:tensorflow:tf.op_scope(values, name, default_name) is deprecated, use tf.name_scope(name, default_name, values)
Traceback (most recent call last):
  File "/usr/local/bin/image_stylization_finetune", line 11, in <module>
    sys.exit(console_entry_point())
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 166, in console_entry_point
    tf.app.run(main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 162, in main
    save_interval_secs=FLAGS.save_interval_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/slim/python/slim/learning.py", line 753, in train
    master, start_standard_services=False, config=session_config) as sess:
  File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
    return self.gen.next()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 969, in managed_session
    self.stop(close_summary_writer=close_summary_writer)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 797, in stop
    stop_grace_period_secs=self._stop_grace_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/coordinator.py", line 386, in join
    six.reraise(*self._exc_info_to_raise)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 958, in managed_session
    start_standard_services=start_standard_services)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 715, in prepare_or_wait_for_session
    init_feed_dict=self._init_feed_dict, init_fn=self._init_fn)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 235, in prepare_session
    init_fn(sess)
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 145, in init_fn
    init_fn_n_styles(session)
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 141, in init_fn_n_styles
    saver_n_styles.restore(session, os.path.expanduser(FLAGS.checkpoint))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1345, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 717, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 915, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 965, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 985, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.NotFoundError: Tensor name "transformer/residual/residual5/conv2/weights" not found in checkpoint files /magenta-data/train/vgg_16.ckpt
         [[Node: save_1/restore_slice_15 = RestoreSlice[dt=DT_FLOAT, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/restore_slice_15/tensor_name, save_1/restore_slice_15/shape_and_slice)]]

Caused by op u'save_1/restore_slice_15', defined at:
  File "/usr/local/bin/image_stylization_finetune", line 11, in <module>
    sys.exit(console_entry_point())
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 166, in console_entry_point
    tf.app.run(main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 139, in main
    saver_n_styles = tf.train.Saver(other_vars)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 986, in __init__
    self.build()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1015, in build
    restore_sequentially=self._restore_sequentially)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 620, in build
    restore_sequentially, reshape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 357, in _AddRestoreOps
    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 270, in restore_op
    preferred_shard=preferred_shard))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/io_ops.py", line 204, in _restore_slice
    preferred_shard, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 359, in _restore_slice
    preferred_shard=preferred_shard, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2380, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1298, in __init__
    self._traceback = _extract_stack()

NotFoundError (see above for traceback): Tensor name "transformer/residual/residual5/conv2/weights" not found in checkpoint files /magenta-data/train/vgg_16.ckpt
         [[Node: save_1/restore_slice_15 = RestoreSlice[dt=DT_FLOAT, preferred_shard=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/restore_slice_15/tensor_name, save_1/restore_slice_15/shape_and_slice)]]

I have long since switched to VMWare and ran full version of Ubuntu 16.10. I will reinstall and test with the latest version of docker for windows and magenta image and let you know.

@iansimon  I did pull the latest and it solved the initial problem with the string.
Although it starts, training my own network still fails soon after with no obvious error. Below is the console output, almost entirely with warnings.
Maybe some kind of time-out functionality I am not aware of ?
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_utils.py:136 in imagenet_inputs.: image_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.image. Note that tf.summary.histogram uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, the max_images argument was renamed to max_outputs.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py:344 in init.: init (from tensorflow.python.training.summary_io) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.
Killed

It looks like you're running image_stylization_finetune, but passing the VGG checkpoint as the stylization checkpoint.  If you want to finetune you'll want to pass one of our pretrained model checkpoints (http://download.magenta.tensorflow.org/models/multistyle-pastiche-generator-monet.ckpt) or (http://download.magenta.tensorflow.org/models/multistyle-pastiche-generator-varied.ckpt).
If you want to train a model from scratch, use image_stylization_train instead of image_stylization_finetune.

I want to finetune..
 No imagenet parameter returns an error. No vgg execution started, I am not sure if it will work out in the end or not.
Could you also explain why I need imagenet data as a whole when I am just finetuning ? Maybe then I have a better understanding of the process..

root@92a258e36b5c:/magenta-data# image_stylization_finetune \
>       --checkpoint=/magenta-data/multistyle-pastiche-generator-monet.ckpt \
>       --train_dir=/magenta-data/train/style/images_4 \
>       --style_dataset_file=/magenta-data/train/style/style_images_4.tfrecord \
>       --num_styles=4 \
>       --imagenet_data_dir=/imagenet_data
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_utils.py:136 in imagenet_inputs.: image_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.image. Note that tf.summary.histogram uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, the max_images argument was renamed to max_outputs.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py:122 in main.: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py:344 in __init__.: __init__ (from tensorflow.python.training.summary_io) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.
Traceback (most recent call last):
  File "/usr/local/bin/image_stylization_finetune", line 11, in <module>
    sys.exit(console_entry_point())
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 166, in console_entry_point
    tf.app.run(main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 162, in main
    save_interval_secs=FLAGS.save_interval_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/slim/python/slim/learning.py", line 766, in train
    master, start_standard_services=False, config=session_config) as sess:
  File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
    return self.gen.next()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 974, in managed_session
    self.stop(close_summary_writer=close_summary_writer)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 802, in stop
    stop_grace_period_secs=self._stop_grace_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/coordinator.py", line 386, in join
    six.reraise(*self._exc_info_to_raise)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 963, in managed_session
    start_standard_services=start_standard_services)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 720, in prepare_or_wait_for_session
    init_feed_dict=self._init_feed_dict, init_fn=self._init_fn)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 235, in prepare_session
    init_fn(sess)
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 144, in init_fn
    init_fn_vgg(session)
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/image_stylization_finetune.py", line 134, in init_fn_vgg
    saver_vgg.restore(session, vgg.checkpoint_file())
  File "/usr/local/lib/python2.7/dist-packages/magenta/models/image_stylization/vgg.py", line 38, in checkpoint_file
    return os.path.expanduser(FLAGS.vgg_checkpoint)
  File "/usr/lib/python2.7/posixpath.py", line 261, in expanduser
    if not path.startswith('~'):
AttributeError: 'NoneType' object has no attribute 'startswith'

@iansimon Could you take a look ?

It looks like your most recent error is from not passing the VGG checkpoint.
Finetuning is very much like training, but instead of starting from scratch it starts with the weights of a pretrained model.  It still needs to use imagenet images as training data.

It looks like your most recent error is from not passing the VGG checkpoint.

When I pass VGG and everything, I got the initial error. Currently I cannot finetune (with or without passing vgg checkpoint) on docker image.


Problem running moxygen/moq-encoder-player out of the box

I followed the instructions to set up a local instance of moxygen/moq-encoder-player but was unable to get any video output on my player.
I started up the Moxygen relay using the following command (with certificates for running it locally):
./_build/bin/moqdateserver -port 4433 -cert ../moq-encoder-player/certs/certificate.pem -key ../moq-encoder-player/cert
s/certificate.key --logging DBG

Then I proceeded to start the moq-encoder-player using:
./start-http-server-cross-origin-isolated.py

Now when visiting the webinterfaces for the encoder and the player I can see that my camera gets captured in the encoder. I proceeded to copy and paste the trackname from the encoder to the player and pressed start on both. Nothing happened.
I looked back and my cli with Moxygen running and saw the following errors:
I1215 13:57:35.628149 39278 MoQServer.cpp:145] /moq
E1215 13:57:35.628404 39278 MoQServer.h:89] Got error=ApplicationError: 268,
I1215 13:57:35.628413 39278 MoQDateServer.cpp:252] terminateClientSession
V1215 13:57:35.628419 39278 MoQForwarder.h:134] subscribers_.size()=0

Now, I have been looking into what this application error could mean but I cannot seem to find it. I assume something goes wrong with the player part of the program and it never subscribes correctly (as the subscriber size states 0).
Is there anything that I am doing wrong or is this something that has happened before?

Thanks for your detailed explanation.
There a (known) current version incompatibility between moxygen and this repo. MoQ is moving fast recently and Moxygen is evolving at the same pace. We will try to fix that in following days
I will update this issue when fixed / tested
Details:

This repo is expecting (tested with) moxygen version 0xff070002, see link
Curren moxygen is in version 7  0xff000007, see link`

This is fixed now!
Steps I followwed to test it in my MACOS machine:
Start webserver for player (terminal 0)
cd ./moq-encoder-player
./create_self_signed_certs.sh
./start-http-server-cross-origin-isolated.py

Build relay (follow more instructions in the repo readme) with commit id: 86e54d31d3dbddd430aee8f0da50e5dcf05c4d18
cd moxygen
./build.sh

Start relay (terminal1 )
./_build/bin/moqrelayserver -port 4433 -cert ../moq-encoder-player/certs/certificate.pem -key ../moq-encoder-player/certs/certificate.key -endpoint "/moq" --logging DBG1

# Generate live data from ffmpeg to a FIFO (terminal 2)

rm ~/Movies/fifo.flv
mkfifo ~/Movies/fifo.flv
ffmpeg -y -f lavfi -re -i smptebars=duration=300:size=320x200:rate=30 -f lavfi -re -i sine=frequency=1000:duration=300:sample_rate=48000 -pix_fmt yuv420p -c:v libx264 -b:v 180k -g 60 -keyint_min 60 -profile:v baseline -preset veryfast -c:a aac -b:a 96k -vf "drawtext=fontfile=/usr/share/fonts/dejavu-sans-fonts/DejaVuSans.ttf: text='Local time %{localtime: %Y/%m/%d %H.%M.%S} (%{n})': x=10: y=10: fontsize=12: fontcolor=white: box=1: boxcolor=0x00000099" -f flv ~/Movies/fifo.flv

# Start FLV Streamer (terminal 3)

./_build/bin/moqflvstreamerclient -input_flv_file ~/Movies/fifo.flv --logging DBG4

# Open webplayer
- Open chrome with `http://localhost:8080/src-player/index.html?local&verbose=1&host=moq://localhost:4433/moq`
- Set Namespace to `flvstreamer`
- Remove trackname

TODO: Still testing

It seems this issue is solved, closing


Improvement of Phylogenetics Module

Features Added:

Improvement of ETE3PAML class
Added csvtolist function which turns a column of a csv file to a list using pandas
Added README skeletons for PAML, IQTREE, and PhyML
Completely reworked Phylip class
Improved readability of FilteredTree class

Addresses #35


global cached lookups /  java.sql.SQLException: No suitable driver found for jdbc

Hello,
as described on https://groups.google.com/forum/#!topic/druid-user/c0iQVkDFToE , I had a problem with druid (0.9.2) not loading lookups from a mysql database.
org.skife.jdbi.v2.exceptions.UnableToObtainConnectionException: java.sql.SQLException: No suitable driver found for jdbc:mysql://localhost:3306/xxxxxxx
        at org.skife.jdbi.v2.DBI.open(DBI.java:230) ~[jdbi-2.63.1.jar:2.63.1]
        at org.skife.jdbi.v2.DBI.withHandle(DBI.java:279) ~[jdbi-2.63.1.jar:2.63.1]
        at io.druid.server.lookup.namespace.JDBCExtractionNamespaceCacheFactory$2.call(JDBCExtractionNamespaceCacheFactory.java:84) ~[druid-lookups-cached-global-0.9.2.jar:0.9.2]
        at io.druid.server.lookup.namespace.JDBCExtractionNamespaceCacheFactory$2.call(JDBCExtractionNamespaceCacheFactory.java:73) ~[druid-lookups-cached-global-0.9.2.jar:0.9.2]
        at io.druid.server.lookup.namespace.cache.NamespaceExtractionCacheManager$3.run(NamespaceExtractionCacheManager.java:362) [druid-lookups-cached-global-0.9.2.jar:0.9.2]
        at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471) [?:1.7.0_111]
        at java.util.concurrent.FutureTask.run(FutureTask.java:262) [?:1.7.0_111]
        at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471) [?:1.7.0_111]
        at java.util.concurrent.FutureTask.run(FutureTask.java:262) [?:1.7.0_111]
        at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178) [?:1.7.0_111]
        at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292) [?:1.7.0_111]
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) [?:1.7.0_111]
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) [?:1.7.0_111]
        at java.lang.Thread.run(Thread.java:745) [?:1.7.0_111]
Caused by: java.sql.SQLException: No suitable driver found for jdbc:mysql://localhost:3306/xxxxxxx
        at java.sql.DriverManager.getConnection(DriverManager.java:596) ~[?:1.7.0_111]
        at java.sql.DriverManager.getConnection(DriverManager.java:215) ~[?:1.7.0_111]
        at org.skife.jdbi.v2.DBI$3.openConnection(DBI.java:140) ~[jdbi-2.63.1.jar:2.63.1]
        at org.skife.jdbi.v2.DBI.open(DBI.java:212) ~[jdbi-2.63.1.jar:2.63.1]
        ... 13 more

as a temporary fix, I copied mysql-connector-java-5.1.38.jar from the mysql-metadata-storage extension to lib directory.

Related to https://github.com/druid-io/druid/issues/2345
If adding the SQL driver jars to the application classpath fixes things, then the documentation should be updated to reflect this until a "proper" fix is in place.

I personnally fixed the issue with postgres here.
https://github.com/Igosuki/druid/commit/7435372ad2afce855a44a950060bf1a30ed5570f
I haven't tested it, but I bet I could remove the manual driver registration and simply implement the connection factory in the main thread.

Please update the documentation for lookups:regarding jdbc lookups!It would help others :)


Can't export a pdf. The html export works fine.

Environment data

VS Code version: Version: 1.80.1

Jupyter Extension version (available under the Extensions sidebar):
v2023.6.1101941928

Python Extension version (available under the Extensions sidebar):
2023.12.0.

OS (Windows | Mac | Linux distro) and version:
Ubuntu 20.04

Python and/or Anaconda version:
mamba 1.4.2
conda 23.1.0

Type of virtual environment used (N/A | venv | virtualenv | conda | ...):
conda

Jupyter server running:
Local

Expected behaviour
producing a pdf of the notebook
Actual behaviour
failure, here is the debug console
Visual Studio Code (1.80.1, undefined, desktop)
Jupyter Extension Version: 2023.6.1101941928.
Python Extension Version: 2023.12.0.
Platform: linux (x64).
No workspace folder opened.
12:34:32.593 [info] User belongs to experiment group 'FastKernelPicker'
12:34:32.864 [info] Start refreshing Kernel Picker<PHONE_NUMBER>864)
12:34:32.868 [info] Using Pylance
12:34:32.906 [info] End refreshing Kernel Picker<PHONE_NUMBER>864)
12:34:32.999 [info] Start refreshing Interpreter Kernel Picker
12:34:32.999 [info] Start refreshing Kernel Picker<PHONE_NUMBER>999)
12:34:34.792 [info] Starting Kernel startUsingPythonInterpreter, .jvsc74a57bd003807dcc214718de3fef1abbc62da4d562d2ddd650787426faadb17d03c3a4b6.~/anaconda3/python.~/anaconda3/python.-m#ipykernel_launcher  (Python Path: ~/anaconda3/bin/python, Conda, 3.10.9) for '~/Data/MAexperiment/test.ipynb' (disableUI=true)
12:34:37.072 [info] End refreshing Kernel Picker<PHONE_NUMBER>999)
12:34:37.160 [info] Process Execution: ~/anaconda3/bin/python -c "import ipykernel; print(ipykernel.__version__); print("5dc3a68c-e34e-4080-9c3e-2a532b2ccb4d"); print(ipykernel.__file__)"
12:34:37.184 [info] Process Execution: ~/anaconda3/bin/python -m ipykernel_launcher --ip=<IP_ADDRESS> --stdin=9003 --control=9001 --hb=9000 --Session.signature_scheme="hmac-sha256" --Session.key=b"15a163ba-4fdd-4bc7-97cf-615e13e5fd82" --shell=9002 --transport="tcp" --iopub=9004 --f=~/.local/share/jupyter/runtime/kernel-v2-21678gETypl8j7njb.json
    > cwd: ~/Data/MAexperiment
12:34:37.267 [info] Process Execution: ~/mambaforge/envs/jupyter/bin/python -m pip list
12:34:37.289 [info] Process Execution: ~/anaconda3/bin/python -m pip list
12:34:37.461 [info] ipykernel version & path 6.19.2, ~/anaconda3/lib/python3.10/site-packages/ipykernel/__init__.py for ~/anaconda3/bin/python
12:34:38.218 [info] Started Kernel anaconda3 (Python 3.10.9) (pid: 21864)
12:34:38.218 [info] Started new session 77784324-08d0-4543-9a87-a96afe5362c4
12:34:38.334 [info] Process Execution: ~/anaconda3/bin/python ~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/pythonFiles/printJupyterDataDir.py
12:34:44.507 [info] Process Execution: ~/anaconda3/bin/python -c "import nbconvert;print('6af208d0-cb9c-427f-b937-ff563e17efdf')"
12:34:45.204 [info] Process Execution: ~/anaconda3/bin/python -c "import jupyter;print('6af208d0-cb9c-427f-b937-ff563e17efdf')"
12:34:45.252 [info] Process Execution: ~/anaconda3/bin/python -m jupyter nbconvert /tmp/729403b1-bd85-41b1-96df-2cdb432c7f07/test.ipynb --to pdf --output tmp-21678JukQg5nhYv2s.pdf --output-dir /tmp --debug
12:34:47.166 [error] Export failed Error: [NbConvertApp] Searching ['~/anaconda3/etc/jupyter', '~/.jupyter', '~/.local/etc/jupyter', '/usr/local/etc/jupyter', '/etc/jupyter'] for config files
[NbConvertApp] Looking for jupyter_config in /etc/jupyter
[NbConvertApp] Looking for jupyter_config in /usr/local/etc/jupyter
[NbConvertApp] Looking for jupyter_config in ~/.local/etc/jupyter
[NbConvertApp] Looking for jupyter_config in ~/.jupyter
[NbConvertApp] Looking for jupyter_config in ~/anaconda3/etc/jupyter
[NbConvertApp] Looking for jupyter_nbconvert_config in /etc/jupyter
[NbConvertApp] Looking for jupyter_nbconvert_config in /usr/local/etc/jupyter
[NbConvertApp] Looking for jupyter_nbconvert_config in ~/.local/etc/jupyter
[NbConvertApp] Looking for jupyter_nbconvert_config in ~/.jupyter
[NbConvertApp] Looking for jupyter_nbconvert_config in ~/anaconda3/etc/jupyter
[NbConvertApp] Converting notebook /tmp/729403b1-bd85-41b1-96df-2cdb432c7f07/test.ipynb to pdf
[NbConvertApp] Notebook name is 'tmp-21678JukQg5nhYv2s'
[NbConvertApp] Template paths:
	~/anaconda3/share/jupyter/nbconvert/templates/latex
	~/anaconda3/share/jupyter/nbconvert/templates/base
	~/anaconda3/share/jupyter
	~/anaconda3/share/jupyter/nbconvert/templates
	~/anaconda3/share/jupyter/nbconvert/templates/compatibility
	~/.local/share/jupyter
	~/.local/share/jupyter/nbconvert/templates
	~/.local/share/jupyter/nbconvert/templates/compatibility
	/usr/local/share/jupyter
	/usr/local/share/jupyter/nbconvert/templates
	/usr/local/share/jupyter/nbconvert/templates/compatibility
	/usr/share/jupyter
	/usr/share/jupyter/nbconvert/templates
	/usr/share/jupyter/nbconvert/templates/compatibility
	~/anaconda3/share/jupyter/nbconvert/templates
[NbConvertApp] Applying preprocessor: TagRemovePreprocessor
[NbConvertApp] Applying preprocessor: RegexRemovePreprocessor
[NbConvertApp] Applying preprocessor: coalesce_streams
[NbConvertApp] Applying preprocessor: SVG2PDFPreprocessor
[NbConvertApp] Applying preprocessor: LatexPreprocessor
[NbConvertApp] Applying preprocessor: HighlightMagicsPreprocessor
[NbConvertApp] Applying preprocessor: ExtractOutputPreprocessor
[NbConvertApp] Attempting to load template index.tex.j2
[NbConvertApp]     template_paths: ~/anaconda3/share/jupyter/nbconvert/templates/latex:~/anaconda3/share/jupyter/nbconvert/templates/base:~/anaconda3/share/jupyter:~/anaconda3/share/jupyter/nbconvert/templates:~/anaconda3/share/jupyter/nbconvert/templates/compatibility:~/.local/share/jupyter:~/.local/share/jupyter/nbconvert/templates:~/.local/share/jupyter/nbconvert/templates/compatibility:/usr/local/share/jupyter:/usr/local/share/jupyter/nbconvert/templates:/usr/local/share/jupyter/nbconvert/templates/compatibility:/usr/share/jupyter:/usr/share/jupyter/nbconvert/templates:/usr/share/jupyter/nbconvert/templates/compatibility:~/anaconda3/share/jupyter/nbconvert/templates
[NbConvertApp] ERROR | Error while converting '/tmp/729403b1-bd85-41b1-96df-2cdb432c7f07/test.ipynb'
Traceback (most recent call last):
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/nbconvertapp.py", line 488, in export_single_notebook
    output, resources = self.exporter.from_filename(
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/exporters/exporter.py", line 189, in from_filename
    return self.from_file(f, resources=resources, **kw)
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/exporters/exporter.py", line 206, in from_file
    return self.from_notebook_node(
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/exporters/pdf.py", line 181, in from_notebook_node
    latex, resources = super().from_notebook_node(nb, resources=resources, **kw)
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/exporters/latex.py", line 74, in from_notebook_node
    return super().from_notebook_node(nb, resources, **kw)
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/exporters/templateexporter.py", line 413, in from_notebook_node
    output = self.template.render(nb=nb_copy, resources=resources)
  File "~/anaconda3/lib/python3.10/site-packages/jinja2/environment.py", line 1301, in render
    self.environment.handle_exception()
  File "~/anaconda3/lib/python3.10/site-packages/jinja2/environment.py", line 936, in handle_exception
    raise rewrite_traceback_stack(source=source)
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/index.tex.j2", line 8, in top-level template code
    ((* extends cell_style *))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/style_jupyter.tex.j2", line 176, in top-level template code
    \prompt{(((prompt)))}{(((prompt_color)))}{(((execution_count)))}{(((extra_space)))}
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/base.tex.j2", line 7, in top-level template code
    ((*- extends 'document_contents.tex.j2' -*))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/document_contents.tex.j2", line 51, in top-level template code
    ((*- block figure scoped -*))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/display_priority.j2", line 5, in top-level template code
    ((*- extends 'null.j2' -*))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/null.j2", line 30, in top-level template code
    ((*- block body -*))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/base.tex.j2", line 215, in block 'body'
    ((( super() )))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/null.j2", line 32, in block 'body'
    ((*- block any_cell scoped -*))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/null.j2", line 85, in block 'any_cell'
    ((*- block markdowncell scoped-*)) ((*- endblock markdowncell -*))
  File "~/anaconda3/share/jupyter/nbconvert/templates/latex/document_contents.tex.j2", line 68, in block 'markdowncell'
    ((( cell.source | citation2latex | strip_files_prefix | convert_pandoc('markdown+tex_math_double_backslash', 'json',extra_args=[]) | resolve_references | convert_pandoc('json','latex'))))
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/filters/pandoc.py", line 24, in convert_pandoc
    return pandoc(source, from_format, to_format, extra_args=extra_args)
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/utils/pandoc.py", line 51, in pandoc
    check_pandoc_version()
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/utils/pandoc.py", line 99, in check_pandoc_version
    v = get_pandoc_version()
  File "~/anaconda3/lib/python3.10/site-packages/nbconvert/utils/pandoc.py", line 76, in get_pandoc_version
    raise PandocMissing()
nbconvert.utils.pandoc.PandocMissing: Pandoc wasn't found.
Please check that pandoc is installed:
https://pandoc.org/installing.html
[NbConvertApp] Exiting application: jupyter-nbconvert

    at qw.executeCommand (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:325581)
    at Xw.export (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:329446)
    at aS.exportToFormat (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:333899)
    at aS.performNbConvertExport (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:333727)
    at aS.performExport (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:333402)
    at aS.exportImpl (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:333030)
    at aS.export (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:332786)
    at f_.export (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-linux-x64/out/extension.node.js:24:472378)
    at e.$vL.h (/usr/share/code/resources/app/out/vs/workbench/api/node/extensionHostProcess.js:99:64077)
12:34:47.167 [error] If you have not installed xelatex (TeX) you will need to do so before you can export to PDF, for further instructions please look https://nbconvert.readthedocs.io/en/latest/install.html#installing-tex.
To avoid installing xelatex (TeX) you might want to try exporting to HTML and using your browsers "Print to PDF" feature.

However, xelatex is installed because
which xelatex
/usr/bin/xelatex

Steps to reproduce:
Here is the notebook I made
mamba activate jupyter
visudo

then
a markdown block
this is a test
a bash block with the command
ls
and when I do "export to pdf" I have the error output above. The html export works without issues.
It seems the problem comes from latex but I have latex installed. any idea? Thanks a lot
Here is the link of the html

Thank you for filing this issue and sorry you are running into this.
Based on the logs, it looks lik eyou do not have pandoc installed, please install the necessary libraries.
If that does not work please feel free to ping here or feel free to create a new issue


body tracking is still slow, latent & inaccurate

Support for non-Nvidia GPUs + more performant body tracking was promised over 3 months ago yet we haven't seen any progress in the SDK. Can we expect this to happen before the year is over?
To Reproduce

Run Azure Kinect Body Tracking Viewer.
Stand in front of the sensor and make fast arm movements.
Observe the accuracy and latency of the joint positions with regard to the real arms.
Look at the colorized body-index map with regard to the real body.
Open up GPUz or any GPU monitor and check the % of GPU being used.

Expected behavior

30 fps or faster
No perceivable latency between real world movements and body tracking.
Body index map to match as precisely as possible the user's silhouette on the depth frame.
Body tracking to be less demanding (15% of GPU or less) in means of hardware and 3rd party software requirements.

These issues weren't present in previous generations and seriously need to be addressed. If there are no sizable efforts being made to fix them it would be appropriate to open source the body tracking SDK.

Thank you for your feedback. We are working on both improving performance and enabling support for non-NVIDIA GPUs (Windows only). We would also very much like to get this into the hands of developers before the end of the year.
When you state "15% of GPU or less" what GPU are you considering as a benchmark?

Also see #514, #1016, and #1343.

Would be optimal to have it run at 15% or below on i9 Intel Integrated Graphics. For AMD, maybe a RX 5300M or equivalent. With the current Nvidia limitation: GTX 1060.
Thanks for linking the topics, they are relevant but I'm asking more for an ETA... curious to know if this is being worked on full time or if the body tracking team has moved on to Hololens or some other project.

We have a small team actively working on the Body Tracking SDK. No ETA at the moment.


Plz how to cancel the defalut transparent style of dragging Image?

Same as the title.
I wannt no transparent style of dragging Image always.
Thanks very much.

This is not possible with the library. The half-transparent style is default of the browser, it is not done by the library.


Fix link

Fix typo inside link
BTW, Good project :+1:

@unn4m3d Do you think this PR is still needed? I mean, Is cppize working on Heroku yet?

@unn4m3d ping :sweat_smile:

Hi I'm cleaning my PR's and issues :sweat_smile:
I will close this, I hope you can take a look in the future, I like this project.
Also see: https://github.com/ysbaddaden/clang.cr


Create uczen.txt

@KarolinaMatusiewicz Please provide us with:

the school official website URL
a proof, which shows that the school recognizes the domain you are submitting as an official email domain for the students.

@KarolinaMatusiewicz You might miss my previous comment. Please react.

@KarolinaMatusiewicz I am sorry, this request remains unanswered for a while. I have to close it without merging. Please reopen and comment if you have an update regarding the aforementioned issue. Thank you for the understanding!


Amplify Configure WSL 2 does not create IAM User

Describe the bug
When using amplify configure and creating an IAM user in WSL 2, the link that automatically opens in browser does not create the IAM user. However, the link displayed in the terminal is correct and when pasted into the browser does create the IAM user
Amplify CLI Version
4.30.0
To Reproduce

Get windows subsystem for linux https://docs.microsoft.com/en-us/windows/wsl/install-win10
Install amplify cli
Run amplify configure

Expected behavior
Ideally, the link that automatically opens in the browser should automatically create the IAM user. Worst case no link should open in the browser, because a wrong link opening is quite confusing.
Enviornment:

OS: Windows using WSL 2
Node Version : v12.18.1
WSL 2 Ubuntu 18.04
ZSH (Oh my zsh) zsh 5.4.2 (x86_64-ubuntu-linux-gnu)
Browser: Firefox 83.0b2

Complete Enviorment info

```
npx envinfo --system --binaries --browsers --npmPackages --npmGlobalPackages
  System:
    OS: Linux 4.19 Ubuntu 18.04.2 LTS (Bionic Beaver)
    CPU: (12) x64 Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
    Memory: 5.80 GB / 12.27 GB
    Container: Yes
    Shell: 5.4.2 - /usr/bin/zsh
  Binaries:
    Node: 12.18.1 - ~/.nvm/versions/node/v12.18.1/bin/node
    Yarn: 1.22.4 - /usr/bin/yarn
    npm: 6.14.5 - ~/.nvm/versions/node/v12.18.1/bin/npm
  npmPackages:
    @babel/cli: ^7.0.0 => 7.12.0
    @babel/core: ^7.0.0 => 7.12.0
    @babel/preset-env: ^7.0.0 => 7.12.0
    @babel/preset-react: ^7.0.0 => 7.10.4
    @types/jest: ^24.0.18 => 24.9.1
    @types/node: ^8.9.5 => 8.10.64
    @types/puppeteer: 1.3.0 => 1.3.0
    babel-loader: ^8.0.0 => 8.1.0
    codecov: ^3.6.5 => 3.8.0
    compression-webpack-plugin: ^1.1.3 => 1.1.12
    cypress: ^3.2.0 => 3.8.3
    husky: ^3.0.5 => 3.1.0
    jest: ^24.x.x => 24.9.0
    jest-config: 24.8.0 => 24.8.0
    json-loader: ^0.5.7 => 0.5.7
    lerna: ^3.13.1 => 3.22.1
    prettier: ^1.19.0 => 1.19.1
    pretty-quick: ^1.11.1 => 1.11.1
    rimraf: ^2.6.2 => 2.7.1
    rollup: ^0.67.4 => 0.67.4
    rollup-plugin-commonjs: ^9.2.0 => 9.3.4
    rollup-plugin-json: ^3.1.0 => 3.1.0
    rollup-plugin-node-resolve: ^4.0.0 => 4.2.4
    rollup-plugin-sourcemaps: ^0.4.2 => 0.4.2
    rollup-plugin-typescript: ^1.0.0 => 1.0.1
    source-map-loader: ^0.2.1 => 0.2.4
    ts-jest: ^24.x.x => 24.3.0
    tslint: ^5.7.0 => 5.20.1
    tslint-config-airbnb: ^5.8.0 => 5.11.2
    typedoc: ^0.16.9 => 0.16.11
    typescript: ~3.6.4 => 3.6.5
    uglifyjs-webpack-plugin: ^0.4.6 => 0.4.6
    uuid-validate: ^0.0.3 => 0.0.3
    webpack: ^4.32.0 => 4.44.2
    webpack-bundle-analyzer: ^3.3.2 => 3.9.0
    webpack-cli: ^3.1.0 => 3.3.12
    winston: ^3.2.1 => 3.3.3
  npmGlobalPackages:
    @aws-amplify/cli: 4.30.0
    @cloudflare/wrangler: 1.11.0
    expo-cli: 3.28.1
    gatsby: 2.24.3
    gitmoji-cli: 3.2.12
    http-server: 0.12.3
    jest: 26.1.0
    md-file-tree: 0.2.0
    netlify-cli: 2.59.1
    npm: 6.14.5
    typedoc: 0.17.8
    typescript: 3.9.5```

Additional context
The link opened in the browser is to the IAM dashboard instead of an intent to create the user
Edit: More context and digging
I was exploring my network tab to see the link before it redirects to the IAM dashboard(I didn't get any 300x Requests) and there is a slight difference between the links

Link in the browser before any requests (first requested domain)
https://console.aws.amazon.com/iam/home?region=us-east-1#/users=final&accessKey&userNames=amplify-test&permissionType=policies&policies=arn:aws:iam::aws:policy%2FAdministratorAccess

Link in printed in console (This one works)
https://console.aws.amazon.com/iam/home?region=us-east-1#/users$new?step=final&accessKey&userNames=amplify-test&permissionType=policies&policies=arn:aws:iam::aws:policy%2FAdministratorAccess

The users$new?step= became users=
Edit 2: Bug in dependency sindresorhus/open#202

@CryogenicPlanet thanks for tracking it down, good job!

@CryogenicPlanet The PR is merged should be available in the next release


{AKS} az aks nodepool upgrade: Align command implementation

This checklist is used to make sure that common guidelines for a pull request are followed.
{AKS} az aks nodepool upgrade: Align command implementation.

For new extensions:

[ ] My extension description/summary conforms to the Extension Summary Guidelines.

About Extension Publish
There is a pipeline to automatically build, upload and publish extension wheels.
Once your pull request is merged into main branch, a new pull request will be created to update src/index.json automatically.
The precondition is to put your code inside this repository and upgrade the version in the pull request but do not modify src/index.json.

AKS

Inviting @paulgmiller to help take a look


[WIP][SQL][SPARK-6632]: Read schema from each input split in the ReadSupport hook, reconciling with the metastore schema at that time

Hey @liancheng,
How about this approach for schema reconciliation, where we use the metastore schema, and reconcile within the ReadSupport init function. This way, we handle each input file in the map task, and no need to read schema from all part files and merging before initiating the tasks.
I have not removed the schema merging code/ test cases for now. Let me know your thoughts on this one.

Can one of the admins verify this patch?

ok to test

Test FAILed.
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Test FAILed.

Ah, I'm also considering similar optimizations for Spark 1.4 :)
The tricky part here is that, when scanning the Parquet table, Spark needs to call ParquetInputFormat.getSplits to compute (Spark) partition information. This getSplits call can be super expensive as it needs to read footers of all Parquet part-files to compute the Parquet splits. And that's why ParquetRelation2 caches those footers at the very beginning and inject them into an extended Parquet input format. With all these footers cached, ParquetRelation2.readSchma() is actually quite lightweight. So the real bottleneck is reading all those footers.
Fortunately, Parquet is also trying to avoid reading footers entirely at the driver side (see https://github.com/apache/incubator-parquet-mr/pull/91 and https://github.com/apache/incubator-parquet-mr/pull/45). After upgrading to Parquet 1.6, which is expected to be released next week, we can do this properly for better performance.
So ideally, we don't read footers on driver side, and when we have a central arbitrative schema at hand, either from metastore or data source DDL, we don't do schema merging at driver side either. I haven't got time to walk through all related Parquet code path and PRs yet, so the above statements may be inaccurate. Please correct me if you find any mistakes.

hmm i see. Would definitely go through these PRs. Anyways fixed the whitespace problem here.

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add to whitelist

Test build #29958 has started for   PR 5298 at commit 89efac5.

Test build #29958 has finished for   PR 5298 at commit 89efac5.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.
This patch does not change any dependencies.

Test FAILed.
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Test build #30118 has started for   PR 5298 at commit 866aa93.

Test build #30120 has started for   PR 5298 at commit 90d7782.

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Test build #30120 has finished for   PR 5298 at commit 90d7782.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.
This patch does not change any dependencies.

Test PASSed.
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Test build #30118 has finished for   PR 5298 at commit 866aa93.

This patch passes all tests.
This patch does not merge cleanly.
This patch adds no public classes.
This patch does not change any dependencies.

Test PASSed.
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hey @liancheng , this change now reconciles schema within the tasks. do suggest. After that I will remove the merge schema functions that are no longer needed

@saucam Right now I feel kinda hesitant to have this. As explained in my previous comment, the major bottleneck for Parquet metadata handling happens when reading footers. Without eliminating this, moving schema merging to task side doesn't bring performance benefits (although I haven't done any benchmark for this PR yet). Plus, there are risks of introducing regressions.
However, this PR is still very valuable as it proves this approach is doable. Eventually, we would like to have this after upgrading Parquet to 1.6.0 and add the ability to avoid reading footers on driver side whenever a global arbitrative schema is available. I've opened [SPARK-6795] 1 to track this issue. I will probably start working on SPARK-6795 later this month. Would you mind me revisiting this at that time?

ok @liancheng
Thanks for the comments. In the meantime let me try to address your suggestions. Can we keep this open in WIP state for now ?

Yeah, sure.

Hi @saucam, thanks for working on this.  I think that a lot of this has been implemented in Spark 1.5.  Can we close this issue?


Error: Cannot find module './util/toSubscriber'

Upon initialisation, "The Code Spell Checker server crashed 5 times in the last 3 minutes. The server will not be restarted" appears and the error log says:
Error: Cannot find module './util/toSubscriber'
    at Function.Module._resolveFilename (module.js:470:15)
    at Function.Module._load (module.js:418:25)
    at Module.require (module.js:498:17)
    at require (internal/module.js:20:19)
    at Object.<anonymous> (/home/chuckles/.vscode/extensions/streetsidesoftware.code-spell-checker-1.6.10/server/node_modules/rxjs/internal/Observable.js:3:22)
    at Object.<anonymous> (/home/chuckles/.vscode/extensions/streetsidesoftware.code-spell-checker-1.6.10/server/node_modules/rxjs/internal/Observable.js:299:3)
    at Module._compile (module.js:571:32)
    at Object.Module._extensions..js (module.js:580:10)
    at Module.load (module.js:488:32)
    at tryModuleLoad (module.js:447:12)
[Error - 12:47:48] Connection to server got closed. Server will not be restarted.
I'm running manjaro linux, but it works on my other machine so it may be my fault, I'm not sure. This is after a fresh install.

Thank you.
There seems to be an issue with VS Code on some installs. VS Code doesn't seem to correctly restart after an extension is added/updated.


How to implement something like an OptionFlag?

How to implement something like an OptionFlag
I'm looking to implement a Flag which can have a few predefined values.
E.g.
mycli --output json
mycli --output xml

Is there a way to implement this using the existing flags while also informing the user of the cli what are the available options?
If not would it make sense to add this OptionFlag? I would be happy to make a PR for that with some guidance.

To rephrase the question - are if asking if there's a builtin way to implement a flag that gives users a set of choices from a predefined list? Something like Choices: []string{'json', 'xml'}?

@lynncyrin yes that is exactly what I meant.

Makes sense! There's no obvious builtin way to implement this, but I've implemented a few "manual" versions of it. I don't happen to have that code on-hand, though.

@lynncyrin would be great if you could dig it up and share a snippet here with the community. I'm happy to take that snippet of your further to integrate it here.

Ping @lynncyrin

This was previously requested in #620

Not stale

Here is how I implemented it using GenericFlag and a custom value
`type choiceValue struct {
choices []string // the choices that this value can take
value   *string  // the actual value
}
func (v *choiceValue) Set(value string) error {
for _, choice := range v.choices {
if strings.Compare(choice, value) == 0 {
*v.value = value
return nil
}
}
return fmt.Errorf("%s is not a valid option. need %+v", value, v.choices)
}
func (v *choiceValue) String() string {
if v.value == nil {
return ""
}
return *v.value
}`
Then
 v c string &cli.GenericFlag{ 			Name: "foo", 			Usage: "the foo option", 			Value: &choiceValue{ 				choices: []string{"bar", "zzzz", "some", "thing"}, 				value:   &c, 			},

There is a PR for this feature. https://github.com/urfave/cli/pull/1234

Wy Wy Wy! should we open it again?

There are 2 ways to approach this. One is with PR #1234 . The other is using validations as in PR #1261 . Its upto the maintainers to choose the way forward.

This is a duplicate of #786 .


Implement decoding the binary form

Fix https://github.com/rustwasm/wasm-webidl-bindings/pull/20.

How do you “extract” the resulting WebidlBindings struct with on_parse? The following code does not compile:
let mut bindings;
config.on_parse(|module, ids| { bindings = decode(ids, &mut data); Ok(()) });

I use on_parse because IndicesToIds is required for decode, and this is the only place where an instance of IndicesToIds exists.


Add File Records functions

Add both File Records functions (0x14 Read and 0x15 Write) for Client and Server roles. Also, add a little tests and example.
Resolves: #1 and #24

Thanks so much!  I wanted to implement these FCs since forever


BUG: check_additivity fails when running torch on gpu

Issue Description
Unfortunately https://github.com/shap/shap/pull/3265 just got merged the moment I found a bug.
When running DeepExplainer with torch on gpu, the model outputs need to be transferred to cpu prior running _checkaddivity.
This bug was introduced to the master due to merge of https://github.com/shap/shap/pull/3265 and is NOT released yet (0.42.1 does not contain this bug).
test_deep.py is not testing torch with gpu device currently, so this bug slipped through unfortunately. I also made an issue regarding tests on cuda: https://github.com/shap/shap/issues/3279
Minimal Reproducible Example
-

Traceback
91 def shap_values(self, X, ranked_outputs=None, output_rank_order='max', check_additivity=True):
     92     """ Return approximate SHAP values for the model applied to the data given by X.
     93
     94     Parameters
   (...)
    123         were chosen as "top".
    124     """
--> 125     return self.explainer.shap_values(X, ranked_outputs, output_rank_order, check_additivity=check_additivity)

File ~/mambaforge/envs/mlvapto/lib/python3.10/site-packages/shap/explainers/_deep/deep_pytorch.py:218, in PyTorchDeep.shap_values(self, X, ranked_outputs, output_rank_order, check_additivity)
    215         with torch.no_grad():
    216             model_output_values = self.model(*X)
--> 218     _check_additivity(self, model_output_values, output_phis)
    220 if not self.multi_output:
    221     return output_phis[0]

File ~/mambaforge/envs/mlvapto/lib/python3.10/site-packages/shap/explainers/_deep/deep_utils.py:11, in _check_additivity(explainer, model_output_values, output_phis)
      9 for l in range(len(explainer.expected_value)):
     10     if not explainer.multi_input:
---> 11         diffs = model_output_values[:, l] - explainer.expected_value[l] - output_phis[l].sum(axis=tuple(range(1, output_phis[l].ndim)))
     12     else:
     13         diffs = model_output_values[:, l] - explainer.expected_value[l]

File ~/mambaforge/envs/mlvapto/lib/python3.10/site-packages/torch/_tensor.py:970, in Tensor.__array__(self, dtype)
    968     return handle_torch_function(Tensor.__array__, (self,), self, dtype=dtype)
    969 if dtype is None:
--> 970     return self.numpy()
    971 else:
    972     return self.numpy().astype(dtype, copy=False)

TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Expected Behavior
No response
Bug report checklist

[X] I have checked that this issue has not already been reported.
[X] I have confirmed this bug exists on the latest release of shap.
[X] I have confirmed this bug exists on the master branch of shap.
[X] I'd be interested in making a PR to fix this bug

Installed Versions
master - 6e807f8f567ac5745103924311433256b2ec69bf

Bug should be fixed with https://github.com/shap/shap/pull/3281


Fixed input bug with Dialog letting click events getting to listeners with touchfocus

Please consider this example
TextButton testBtn = new TextButton("Test", skin);
testBtn.addListener(new ClickAdapter(() -> {

	if(testBtn.isDisabled())
		return;

	testBtn.setDisabled(true);

	new Thread(() -> {
		try
		{
			Thread.sleep(200); //simulate async call
		}
		catch(InterruptedException ignored) {}

		Gdx.app.postRunnable(() -> {
			Dialog diag1 = new Dialog("", skin);

			diag1.getContentTable().pad(20);
			diag1.getButtonTable().padBottom(10);

			Label label = new Label("test", diag1.getSkin());
			label.setWrap(true);
			diag1.getContentTable().add(label).prefWidth(500);

			Button ok = new Button(diag1.getSkin());
			ok.add(new Label("Ok", diag1.getSkin()));
			ok.padLeft(30).padRight(30);

			ok.addListener(new ClickAdapter(() -> {
				diag1.hide();
			}));

			diag1.getButtonTable().add(ok);
			diag1.show(stage);
			testBtn.setDisabled(false);
		});
	}).start();
}));
testBtn.setBounds(1420, 220, 160, 40);

stage.addActor(testBtn);

This is a button getting disabled during async work and undisabled when the work is done. The problem is that it is possible to click the button right after it being disabled but before the modal Dialog shows up.  All you have to do to produce this bug is to click the button once and then let it be pressed until the Dialog shows up and then release. The second release will trigger a second dialog no matter how long you waited.
ClickAdapter is simple class extending ClickListener and does nothing special but accepting a Runnable and running it in the clicked() method.
The problem is caused by the touchfocus array present in the stage. If this array is not cleared when the Dialog is shown, the existing touchfocus will match with the touchup events getting fired and call the listener.
The fix was simple: clearing the touchfocus array when showing the dialog.
Note: This should be applied to any modal Window for it to be really modal. However, it would require adding a show method to Window since Window doesn't have any.

Cheers!


Data Science with Python : CatBoost Algorithm

Welcome to 'DSWP' Team, good to see you here.
This issue will helps readers in acquiring all the knowledge to learn about getting involved with CatBoost Algorithm. Tutorial to CatBoost Algorithm and how it's applied using sample code.
To get assigned to this issue, add your serial numbers mentioned in the spreadsheet of "Data Science with Python", the approach one would follow and choice you prefer (Documentation, Audio, Video). You can go with all three or any number of options you're interested to work on.
This issue is created for beginners and who aren't assigned to any issues yet.
If you had referred any resources, add them up in "DS Resources". Similarly if you had used datasets, include them in "DS Datasets".
Domain : Machine Learning
Mentors Assigned : Yash Goel / Dhruv Bajaj
Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Changes should be made inside the Datascience_With_Python/ directory & Datascience_With_Python branch.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'GWOC' contributors of 'DSWP' domain.
All the best. Enjoy your open source journey ahead. 😎

ds-with-python-batch4
Name: Mohit Kumar
Topic: CatBoost Algorithm
@paramsiddharth
Preference: Documentation
Github profile: https://github.com/m0hit-kumar
Approaches- What is CatBoost
Advantages of CatBoost library
CatBoost in comparison to other boosting algorithms,
Handling Categorical features
Solving various ML challenges using CatBoost
using an appropriate dataset

Issue assigned to @m0hit-kumar for documentation

@prathimacode-hub , Mam
I am M. Ashish Reddy. I would like to do video on this issue.
Batch number: 5
serial number:174.
Approach: A detailed explanation with examples and real life uses

Issue assigned to @ashish-reddy-20-08 for video contribution

Hello, I am Deepthi M with serial number:172, Batch-5. I would like to do audio on this issue.
My approach: a detailed explanation

Issue assigned to @deepthi1107 for audio contribution


CSS class container collides with bootstrap

Is it possible to change the name of the CSS class container?
I want to use bootstrap which uses the same name for its container class.

Are you using version 2.0.5? You should be able to change the container css class by adjusting your viewConfig. Something like this should work
viewConfig : Selectize.ViewConfig a
viewConfig =
    Selectize.viewConfig
        { container =
            [ Attributes.class "some-other-container-class-name" ]
        ...
        }


Better documentation for individual settings

New VSCode users typically end up frustrated trying to get the editor to behave as they'd expect it to. A lot of times there is just a misunderstanding as to what the consequences might be when a setting is modified from its default state.
One way to improve this situation is to add more comprehensive descriptions to settings (even if it's a hidden description that needs to be expanded with a "more" button).
It also doesn't help that custom descriptions (via comments) currently have their own limitations.
Below is an example of what my own comments look like for just a few of the settings in my settings.json file. This is a good snapshot of the problem that new users are facing.
{
  // This only applies when quick suggestions are not active. Tab always completes the selected
  // quick suggestion. Setting this to `onlySnippets` allows snippets to be tab completed faster (no
  // need to wait for quick suggestions to appear).
  "editor.tabCompletion": "onlySnippets",

 // Setting `snippetSuggestions` to `top` allows you to keep quick suggestions on, while also
  // guaranteeing that you can enter a snippet prefix and immediately tab complete it. In other
  // words, if quick suggestions appear before you've had a chance to tab, that snippet will be the
  // first suggestion (as opposed to some local variable, for example), so tab will still insert the
  // snippet. With this set to `top`, snippet prefixes should be kept short (3 characters or less),
  // so that auto completing other useful suggestions like local variables does not require typing
  // more than 4 characters.
  "editor.snippetSuggestions": "top",

 // This only applies when quick suggestions are not active. Tab always completes quick
  // suggestions, regardless of whether or not it's an Emmet abbreviation. Setting this to `true` is
  // required for expanding / auto completing React components. Note that if setting this to `true`,
  // Emmet will expand all words as custom tags when quick suggestions is not active (not just React
  // component names). This does not appear to negatively affect having `editor.tabCompletion` set
  // to `onlySnippets`. In other words, snippet expansion and Emmet abbreviation expansion appear to
  // correctly take precedence over each other according to context.
  "emmet.triggerExpansionOnTab": true,

  // Emmet works with .jsx files out of the box. If using React with .js files, setting
  // "javascript": "javascriptreact" is required for consistent tab expansion, and for Emmet
  // abbreviation suggestions (e.g. getting `div` suggestion after typing `di`).
  "emmet.includeLanguages": {
    "javascript": "javascriptreact"
  },
}

@ramya-rao-a I removed you from the auto-assign bot
@jabacchetta Much of this seems specific to your use cases and your workflow and preferences. We try to explain what the settings do, and some of that is in the description for an individual enum value, but I'm not going to explain "why" you should use a setting, or how to use it with specific technologies like React.

Yeah, I was really only presenting my comments as a snapshot of how users could easily become confused on what the consequences of changing a setting would be.
Focusing on the first setting (non-opinionated, not tech-specific), editor.tabCompletion for example, I think the comment explains exactly what most users probably wouldn't be aware of.
There's a lot of synergy happening between different settings, and the advantages/disadvantages to the different options usually aren't clear until you've either stumbled across them or spent a lot of time experimenting with them.
Whether this information is added to the descriptions in the editor, in the documentation, or somewhere else, it would without a doubt be helpful to new users.
Of course, feel free to close this issue if you disagree. At this point, I've already personally spent the many hours necessary to figure it all out, and am just throwing the idea out there as a way to potentially smooth over the transition to VSCode for others.


refactor: pcall nvim_buf_attach

I'm erroring out on the attach function call multiple times a day when starting an nvim instance. It's not always happening, though.
Error executing vim.schedule lua callback: ...l/share/nvim/lazy/bookmarks.nvim/lua/bookmarks/async.lua:45: The coroutine failed with this message: .../.local/share/nvim/lazy/bookmarks.nvim/lua/bookmarks.lua:42: Invalid buffer id: 1
stack traceback:
	[C]: in function 'nvim_buf_attach'
	.../.local/share/nvim/lazy/bookmarks.nvim/lua/bookmarks.lua:42: in function 'attach'
	.../.local/share/nvim/lazy/bookmarks.nvim/lua/bookmarks.lua:73: in function 'fn'
	.../.local/share/nvim/lazy/bookmarks.nvim/lua/bookmarks.lua:56: in function 'on_or_after_vimenter'
	.../.local/share/nvim/lazy/bookmarks.nvim/lua/bookmarks.lua:71: in function <.../.local/share/nvim/lazy/bookmarks.nvim/lua/bookmarks.lua:65>
stack traceback:
	[C]: in function 'error'
	...l/share/nvim/lazy/bookmarks.nvim/lua/bookmarks/async.lua:45: in function <...l/share/nvim/lazy/bookmarks.nvim/lua/bookmarks/async.lua:40>

Since I couldn't find an fixable issue I figured it is okay to just pcall the function so we still have a working plugin.
The error led me to move to another bookmarks plugin. But well, I'd like to stay with yours, as it's a really made plugin after all. Thanks for the work that went into this 🙂👍.
I don't know if this workaround can be called a fix, so I went with refactor. Feel free to squash and rename.

try to add this line  at lua/bookmarks.lua:42,
if not api.nvim_buf_is_loaded(bufnr) then return end

Awesome, I'd say this can be called a real fix now 😊.
Tyvm!


Update to membrane_core 1.0

I wanted to finally learn and build something with Membrane. So I started playing around with the examples but got blocked because the plugin is not compatible with membrane_core ~> 1.0 yet. This is my attempt to fix the incompatibilities. Guess what better way to learn a new framework than by fixing tests? 😅
WARNING: Huge Membrane newbie here!
I don't really know what I did besides following the migration guides and trying to get the tests green. I'm especially unsure about the last commit where I had to change the :chunk_size option. Is this test now giving a false-positive?

Hi @ream88, since we have multiple plugins, we try updating them all in a relatively short period, the process is partially automated, and the file plugin was updated as a part of it. That said, thanks for trying to help with this! Should you have any problems with incompatible plugins, don't hesitate to reach out ;)

Looking forward to comparing your changes with mine 😊


[iOS] Native component for "RCTVideo" does not exist

After following the installation instructions, I am running into this warning: "Native component for "RCTVideo" does not exist" when I try to run my app on the iOS simulator. When I navigate to the view, I get the error _"undefined is not an object (evaluating 'reactNative.NativeMOdules.UIManager.RCTVideo.Constants)
I have tried: deleting my node_modules and clearing the cache, then running "rnpm link react-native-video", but nothing seems to work.
When I looked in my Libraries folder in XCode I didn't see a RCTVideo.xcodeproj folder, but when I tried adding it manually and linking libRCTVideo.a, I get a build error saying "RCTVIewManager.h
file not found. "
I've tried following what other people have done to solve their problems that were similar to mine, however nothing seems to be working. Any help would be appreciated!

I did get this but react-native link react-native-video followed by killing the package manager, followed by react-native run-ios did the trick. Sorry that probably doesn't help.

@fu24d @pennyandsean I tried the solution you mentioned but I doesn't seem to work. I am on 0.32.0

I got it to work on my IOS Simulator at last. The require makes it work rather than using uri={{myvideo}}
<Video
        repeat
        resizeMode='cover'
        source={require('./assets/myvide.mp4')}
        style={styles.backgroundVideo}
      />

Is there a way to use require to set the source to a web url?

-1 when using react-native link it shows an error like this
-2 and when i want add libRCTvidoe.a, it doesn't apear in linkages so let start hacking :)
i cloned project and use libRCTvideo.a in project , droped to my project and walla.. now librctvideo apears and i added to my project and it worked !

You just need to remove the Podfile.lock and then cd ios and run pod install . This solved the issue for me


Please download a ready-made apk and we will modify it without android studio

Please download a ready-made apk and we will modify it without android studio

Please download a ready-made apk and we will modify it without android studio

As you wish the new version of teardroid you don't need android studio anymore

how do we modify the output apk or how do we bind it with other apk ?


[resotocore][feat] Allow configuration of workflow schedules

Description

Allow configuration of workflow schedules.
To-Dos

[x] Add test coverage for new or updated functionality
[x] Lint and test with tox

Code of Conduct
By submitting this pull request, I agree to follow the code of conduct.

@lloesche this is not validation, but yaml syntax: *xxx is used by yaml for alias handling (https://www.educative.io/blog/advanced-yaml-syntax-cheatsheet).
You need to wrap the schedule in quotes, so it can be read by the yaml parser.

Can we make it clear in the error message that this is a config file syntax issue and what needs to be done to resolve it? Or wrap the schedule from the start?


Firefox - [ReferenceError: Quagga is not defined]

Hello.
I'm using Firefox 46.0.1 on OS X 10.11.4 and I'm getting this error, both in my personal project and the official website example as shown in the screenshot.
I've also checked it with the same Firefox version on Windows and I get the same error:

Thanks!

That's an odd error. I've just tried to reproduce this error, but I cannot. I'm using OS X 10.11.4 and Firefox 46.0.1 and it looks like this:

Can you try to open a new tab, open the console and type: typeof mozRTCPeerConnection. What's the output?

Hello @serratus, I have checked it and I get "undefined" and had a search on Google and fixed it enabling mozRTCPeerConnection as it was disabled.
Thank you!

I'm glad it worked out. Just out of curiosity: was it disabled by default? Also on mobile?

No, I was using Firefox at work so they probably disabled it for some reason, but luckily it's not disabled by default

Alright, thanks for the info. Good to know 👍


Stream::merge does not end prematurely if one stream is delayed

r? @yoshuawuyts
The bug was that if left returned Poll::Pending, the right could have returnrf Poll::Ready(None), and we would consider the stream as finished, although we haven't really polled the left part thoroughly enough.
I also don't understand why we need cx.waker().wake_by_ref(); here: we are returning Poll::Ready, and calling wake_by_ref is only required if you return Pending?

Question (unrelated to PR mostly): is it a contract of Stream trait that, after returning Poll::Ready(None) once, all subsequent calls to poll_next should return Poll::Ready(None) as well? I think both initial and this version of merge assume this behavior, but it is not documented. I think merge doesn't neccaary have to require this contract from substreams, if we change it's internal state to
enum MergeState {
    Both(L, R),
    Left(L),
    Right(R),
}

Hm, judging by the StreamExt::fuse method, we don't actually have this guarantee. so we should call .fuse ourselves int the Merge ctor

Hm, judging by the StreamExt::fuse method, we don't actually have this guarantee. so we should call .fuse ourselves int the Merge ctor

Took some time to think about this, and you're entirely right. Phew, you just caught a whole lot of bugs here. Thanks so much for this patch!

@yoshuawuyts i’ve even found one (debatable though) in std::iter::Chain: https://github.com/rust-lang/rust/issues/66031 as a result of yesterday’s thinking about cancellation. Still, haven’t figured our cancellation itself :D

added fusing as well


[FEATURE REQUEST] Variant: Lenovo Legion

Hey folks,
Would it be possible for Universal Blue via hwe to support Lenovo Legion devices by adding LenovoLegionLinux please ?
Links

https://github.com/johnfanv2/LenovoLegionLinux
https://copr.fedorainfracloud.org/coprs/mrduarte/LenovoLegionLinux/

@johnfanv2 ⬆

This is supported in Bazzite thanks to the fsync kernel.

Comment from @KyleGospo :

This is supported in Bazzite ⋯

Do you mean "already added" or "could be added" in Bazzite?

⋯ thanks to the fsync kernel.

Does Bluefin and Aurora not use the same kernel?
Would it be impossible to be included otherwise?

Bluefin/Aurora does not use the fsync kernel currently. However, this may be something considered in the future since it includes the surface/Asus patches as well.
That said, you are able to swap the kernel in a custom image. Fsync has all of the akmods built in ublue-os/akmods as well.

Looking into the fsync kernel, it seems very interesting for the HWE variants
What would be the tradeoff to consider to make Bluefin and Aurora switch to the fsync kernel, or at least only for the HWE variant of it?

Hello again @m2Giles !

Comment from @m2Giles :

⋯ this may be something considered in the future ⋯

Are there any open issue about that I can track ?
If not, should I open one ?

Latest is already using fsync kernel for Aurora/bluefin

@m2Giles nice to hear
I didn't found the commit / PR related to that
Is it planned for stable and gts ?

Since the fsync kernel has been dropped (learned that in https://universal-blue.discourse.group/t/stable-fsync-kernel/4767), may it be possible to reconsider this issue ?

Currently working on an image that replaces surface and Asus with a single image that contains all of the hardware enablement patches.

Very nice to hear !
Would you also consider adding support for Lenovo Legion too ?

@m2Giles ⤴

It is using an fsync like kernel. Basically what was the old fsync option for bluefin. This is happening in bluefin repo.


Publishing .whl next to .tar.gz in the same version breaks hash matching.

Issue description
This is not an issue related to the orderedmultidict but I will use it as the example.
I had my pipfile locked on orderedmultidict 1.0 (please note there used to be only one hash until 3 days ago)
"orderedmultidict":
{
             "hashes": [ "sha256:b89895ba6438038d0bdf88020ceff876cf3eae0d5c66a69b526fab31125db2c5"],
             "version": "==1.0"
}

Everything worked as expected, until a new .whl was added to the "orderedmultidict" in PyPi.
Now, when trying to install it on a new environment (CI), it will fail due to hash mismatch:
11:52:47         Expected sha256 b89895ba6438038d0bdf88020ceff876cf3eae0d5c66a69b526fab31125db2c5
11:52:47              Got        24e3b730cf84e4a6a68be5cc760864905cf66abc89851e724bd5b4e849eaa96b

A previously absent .whl is preferred, even though the known tar.gz remains untouched.
Expected result
Any "additive" changes on https://pypi.org/project/orderedmultidict/1.0/#files (added .whl on August 31, 2018, 3 days ago) should not affect the hash matching as no code changes were released.
By an "additive" change I mean republishing a package in a new format, like adding a .whl next to .tar.gz.
I understand that wheels are the new, preferred packaging method, so alternatively, pipenv should inform about it's .whl preference and inform about new files (new hashes) being available instead of failing or forcing me to install a re-packaged version.
It should be possible to lock on a specific file format (if initially installed from a tar.gz it should not be obligatory to suddenly switch to the .whl version).
Actual result
Pipenv tries to install a newly added wheel and returns an error with a hash conflict, because a newly added file has obviously a different hash.
Steps to replicate

Publish 'your_package' on pypi, without .whl (it will have its own SHA256 hash)
Install the package with (pipenv install your_package)
Re-publish 'your_package' on pypi just add a .whl (it add its own SHA256 hash)

Try to install the dependency in a new environment
pipenv install should fail due to hash mismatch
Work around
Reinstall the dependency
pipenv install orderedmultidict
it will add the hash of the new .whl file and install without any issues.

This is essentially the same problem described in #2618. Closing this so the feedback can be centralised. The feature is working as intended in a sense, but we would very much welcome any suggestions to improve the ergonomics.


[Android] API method: setAllowedOrientation not found.

When trying to call supersonic.ui.screen.setAllowedRotations() on a Crosswalk AMD build (and the Native Android build as well), I am getting the following error in my log:
supersonic.ui.screen.setAllowedRotations rejected: API method: setAllowedOrientation not found.

Perhaps the method name is different for Android?

The method is not implemented for Android due to the fact that allowed orientations cannot be easily changed during runtime. Sorry – the documentation doesn't mention that this is not available on Android.


Change user dropdown menu styles

The purpose of this PR is to apply some new styles to the user dropdown menu on the navbar.
Styles added:

Color change on hover
Logout icon
Divider between reefs and logout button
Border color
Change menu items padding

Designs

before
after

Importance of border

Looks great overall! I am not a huge fan of the black border though, maybe try a more subtle color? Like grey or blue?

Updated border color. Applied the same color as the dividing horizontal line.

While we're at it, how does it look on mobile?


Add-SPOFile doesnt generate .aspx when Masterpage is uploaded

Add-SPOFile doesnt generate .aspx when Masterpage is uploaded to a sub directory underneath _catalogs/masterpage/mycompany/masterpages. It works fine for display templates and Page layouts works with Add-SPOHtmlPublishingPageLayout.

I've tested this using both Add-SPOFile and Add-SPOMasterPage both to _catalogs/masterpage and to a subfolder. I could repro the issue in every case.
The crux of the issue appears to be that the PnP library doesn't support uploading HTML master pages, only HTML page layouts.

thx @arangas for confirming this - appreciated. We do not really support html template model, like with design packages in the engine. This is actually by design. What we suggest you to do for these scenarios is to use the html model in development, but use only master page in production. This will reduce also possible future challenges around this.
We absolutely welcome contributions on this area as pull requests or we can have more detailed discussion around the approach in PnP Yammer group at http://aka.ms/OfficeDevPnPYammer.


Initial implementation of candles

Partially addresses #75

Creates Candle object
Creates piping for candle subscriptions in Basic Client and Basic Multi Client
Adds candle support for Binance, Binance Jersery, Binance US, and Kraken clients

@bmancini55  is there a way to subscribe to 1m candle and get only the candle which is finalized and not each 2 second get the unfinished candle ... ???

@bmancini55  is there a way to subscribe to 1m candle and get only the candle which is finalized and not each 2 second get the unfinished candle ... ???


Optimizer: 2 sided face rendering material

When combining materials, there are some cases where user would like to have 2 side face rendering (more common in mtoon), we need to have an option box to support this when using the optimizer

option box added, keep open?

Closing as completed :) should be working now


Fix Rubocop code offenses

Do you agree with these changes, @pvcarrera ?  I am not sure I do.
The only one that I agree with is the one in anagram.rb.

@pvcarrera What Rubocop settings are you using? None of these things generate rubocop warnings on my system.

@Insti You are right, I was running a different version run version: 0.42.0. When I run the project version bundle exec rubocop I only get:
lib/alphametics_cases.rb:1:1: C: Missing frozen string literal comment.
lib/generator.rb:1:1: C: Missing frozen string literal comment.
Should we close this PR then? I can open a new one with only the changes related to this version.

I am in the process of disabling all cops until they can be discussed.  The one change is good, but very minor.  Please look at #449 and #443 for further discussion as to what is happening.  Looking forward to your input as well.

As to opening a new PR, no need to do that, just force push the changes that you would really like to see happen, @pvcarrera then the reasoning and discussion stays intact.

Sorry at the end this PR only removes a withe space 😄

Definitely worthwhile!


Common base class for exceptions; differentiating transient and fatal exceptions

First of all its useful to differentiate snakebite exceptions from other using common base class - so all snakebite exceptions could be caught without need to support error-prone list of exception classes in user code.
Secondly, its helpful to differentiate transient errors (e.g. network-related problems), so user code could apply custom retry policy instead of failing entire peace of work altogether.
I have to confess I was't sure about SASL code, so exceptions in rpc_sasl.py left unchanged.
And I should note that differentiating exceptions has to be done in new and edited code on a constant basis for it to work.

Hi! Thanks for this. I just merged some other stuff, so you might want to rebase on master. Also, one of the tests failed. @ravwojdyla can you have a look at this before we merge?

Done. Btw, looks like previously failed test is erratic.

hi @zline, good idea!

@garthcn @bolkedebruin - do you have opinion on the kerberos fatal/transient error handling?

I don't think there are any transient kerberos errors. I cannot think of a case where kerberos throws an error but you still can continue.


Test for the "Test if a Value is of a Specific Data Structure Type" is failing when it should pass

Challenge Name

https://beta.freecodecamp.org/en/challenges/quality-assurance-and-testing-with-chai/test-if-a-value-is-of-a-specific-data-structure-type
Issue Description

This issue  is very similar to https://github.com/freeCodeCamp/freeCodeCamp/issues/17091
Test is failing when it should pass, because of a typo in the index

Browser Information

Browser Name, Version:
Operating System:
Mobile, Desktop, or Tablet:

Your Code

Screenshot

https://github.com/freeCodeCamp/freeCodeCamp/pull/17096

I'm new to this project and contributions on GitHub. I'd like to take a stab at this if nobody else is working on it.

I'd like to get on the waitlist if @sharad-vm for some reason changes his mind.