Hello, i install mxboard and run the demo py, but raise errors:

No handlers could be found for logger "mxboard.event_file_writer"

my conda list is as follows: mxboard 0.1.0 mxnet 1.2.0 tensorboard 1.12.0 tensorflow 1.12.0

how can i solve this, thank u very much!

The documentation states that sw.add_embedding() can take a list of elements, convertible to string as an argument to the labels parameter. However, the code prevents this both in add_embedding() and in _make_metadata_tsv() by throwing an error if labels is a List and not NDArray or NPArray.

Issues, if available: #35

Description of changes: one line changed in utils/make_image_grid to make sure the produced sprite image is precisely square

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

After pip install mxboard, when I import mxboard, it errors:

`import mxboard as mb TypeError Traceback (most recent call last) in () ----> 1 import mxboard as mb

/home/users/nn/code/py/mxnet/python/mxboard/init.py in () 19 20 from future import absolute_import ---> 21 from .writer import SummaryWriter 22 23 version = '0.1.0'

/home/users/nn/code/py/mxnet/python/mxboard/writer.py in () 26 import os 27 import logging ---> 28 from .proto import event_pb2 29 from .proto import summary_pb2 30 from .event_file_writer import EventFileWriter

/home/users/nn/code/py/mxnet/python/mxboard/proto/event_pb2.py in () 14 15 ---> 16 from mxboard.proto import summary_pb2 as mxboard_dot_proto_dot_summary__pb2 17 18

/home/users/nn/code/py/mxnet/python/mxboard/proto/summary_pb2.py in () 14 15 ---> 16 from mxboard.proto import tensor_pb2 as mxboard_dot_proto_dot_tensor__pb2 17 18

/home/users/nn/code/py/mxnet/python/mxboard/proto/tensor_pb2.py in () 14 15 ---> 16 from mxboard.proto import resource_handle_pb2 as mxboard_dot_proto_dot_resource__handle__pb2 17 from mxboard.proto import tensor_shape_pb2 as mxboard_dot_proto_dot_tensor__shape__pb2 18 from mxboard.proto import types_pb2 as mxboard_dot_proto_dot_types__pb2

/home/users/nn/code/py/mxnet/python/mxboard/proto/resource_handle_pb2.py in () 39 message_type=None, enum_type=None, containing_type=None, 40 is_extension=False, extension_scope=None, ---> 41 options=None, file=DESCRIPTOR), 42 _descriptor.FieldDescriptor( 43 name='container', full_name='tensorboard.ResourceHandleProto.container', index=1,

TypeError: new() got an unexpected keyword argument 'file'`

Is it possible to configure the logger of mxboard? If one uses frequent statements as such

with SummarWriter(logdir=.) as sw:
  sw.add_scalar()

the log of the application becomes quite cluttered with outputs like:

[INFO:root] Successfully opened events file: events.out.tfevents.1522834055.186590d665ad.ant.amazon.com
[INFO:root] Wrote 1 event to disk
[INFO:root] Wrote 1 event to disk
[INFO:root] Wrote 6 events to disk

I followed this gluon's document classification tutorial and tried to visualise my training with mxboard. So far I can visualise my model architecture, training loss, and accuracy.

However, my problem is when I want to record histograms of my model. Python throws this error:

Runtime Error: "Cannot get gradient array for Parameter [my layer name] because grad_req='null'"

then I checked my model initialise block and found these lines:

model.embedding_layer.weight.set_data(vocab.embedding.idx_to_vec)
model.embedding_layer.collect_params().setattr('grad_req', 'null')

and mxnet didn't run the gradient for this layer because I want to use my pre-trained embedding.

So, my question is how do I get gradients for the other layers except for the embedding layer?

This pr adds support for multi labels per data point for the tensorboard embeddings. This means you can now pass a either a 1D or 2D list/numpy.ndarray/mx.ndarray for the labels to the add_embedding for use in tensorboard. The file format for the 2D label input is a TSV with column headers based on the tensorboard documentation https://www.tensorflow.org/guide/embedding#metadata

I tired to cover my bases with the input checking, any suggestions for improvements are appreciated.

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

root@gpu05:/opt/package# pip install mxboard
Requirement already satisfied: mxboard in /usr/local/anaconda2/lib/python2.7/site-packages (0.1.0)
Requirement already satisfied: six in /usr/local/anaconda2/lib/python2.7/site-packages (from mxboard) (1.11.0)
Requirement already satisfied: numpy in /usr/local/anaconda2/lib/python2.7/site-packages (from mxboard) (1.13.3)
Requirement already satisfied: Pillow in /usr/local/anaconda2/lib/python2.7/site-packages (from mxboard) (4.2.1)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/anaconda2/lib/python2.7/site-packages (from mxboard) (3.5.2)
Requirement already satisfied: olefile in /usr/local/anaconda2/lib/python2.7/site-packages (from Pillow->mxboard) (0.44)
Requirement already satisfied: setuptools in /usr/local/anaconda2/lib/python2.7/site-packages (from protobuf>=3.0.0->mxboard) (36.5.0.post20170921)
root@gpu05:/opt/package# python mxboard.py
Traceback (most recent call last):
  File "mxboard.py", line 2, in <module>
    from mxboard import SummaryWriter
  File "/opt/package/mxboard.py", line 2, in <module>
    from mxboard import SummaryWriter
ImportError: cannot import name SummaryWriter
root@gpu05:/opt/package#

This problem has been bothering me for a long time

if I only import mxboard, it does not have error. when I run the mxboard demo, it report this error.

Issue #, if available:

Description of changes: Tensorboard no longer requires TensorFlow, so remove tensorflow from the installation instruction for tensorboard.

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

@reminisce

This reverts commit 2be9b6ef174ec35165321e70636e0f73232bf384.

Issue #, if available:

Description of changes:

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

Hi,

I just found a ting bug in how the current mxboard generating the sprite image for embedding projector. This bug may result in wrong embedding-thumbnail association. A simple example borrowed from here is shown as below.

import numpy as np
import mxnet as mx
from mxnet import gluon
from mxboard import SummaryWriter


batch_size = 65


def transformer(data, label):
    data = data.reshape((-1,)).astype(np.float32)/255
    return data, label

train_data = gluon.data.DataLoader(
    gluon.data.vision.MNIST('./data', train=True, transform=transformer),
    batch_size=batch_size, shuffle=False, last_batch='discard')

initialized = False
embedding = None
labels = None
images = None

for i, (data, label) in enumerate(train_data):
    if i >= 1:
        # only fetch the first batche of images, i.e. 65 images -> 8*9 sprite image
        break
    if initialized:
        embedding = mx.nd.concat(*(embedding, data), dim=0)
        labels = mx.nd.concat(*(labels, label), dim=0)
        images = mx.nd.concat(*(images, data.reshape(batch_size, 1, 28, 28)), dim=0)
    else:
        embedding = data
        labels = label
        images = data.reshape(batch_size, 1, 28, 28)
        initialized = True

with SummaryWriter(logdir='./logs') as sw:
    sw.add_embedding(tag='mnist', embedding=embedding, labels=labels, images=images)

The above code takes 65 images of handwritten digits from the MNIST dataset and log them as embedding vectors with labels and original images. The generated sprite image is of grid 8 * 9, which is not square:

But, the current TensorBoard requires a square sprite image for the embedding projector working correctly (see here). So, the above non-square sprite image will make the thumbnails misaligned with their embeddings:

(e.g. embedding at the center of label 2 is displayed with thumbnail '9')

This issue can be easily solved by changing just one line to constrain the produced sprite image to be precisely squared:

I tested it locally and would like to make a pull request. ;)

This PR supports use case below:

from mxboard import SummaryWriter
with SummaryWriter(logdir='hdfs://hdfs-nn-proxy-ns1/department/ai/user/xyz/logs') as sw:
    for i in range(10):
        # create a normal distribution with fixed mean and decreasing std
        data = mx.nd.normal(loc=0, scale=10.0/(i+1), shape=(10, 3, 8, 8))
        sw.add_histogram(tag='norml_dist', values=data, bins=200, global_step=i)

TypeError: Conflict register for file "mxboard/proto/resource_handle.proto": tensorboard.ResourceHandleProto is already defined in file "tensorboard/compat/proto/resource_handle.proto". Please fix the conflict by adding package name on the proto file, or use different name for the duplication. ... File "C:\Users\selcu\anaconda3\lib\site-packages\mxboard_init_.py", line 21, in from .writer import SummaryWriter File "C:\Users\selcu\anaconda3\lib\site-packages\mxboard\writer.py", line 28, in from .proto import event_pb2 File "C:\Users\selcu\anaconda3\lib\site-packages\mxboard\proto\event_pb2.py", line 16, in from mxboard.proto import summary_pb2 as mxboard_dot_proto_dot_summary__pb2 File "C:\Users\selcu\anaconda3\lib\site-packages\mxboard\proto\summary_pb2.py", line 16, in from mxboard.proto import tensor_pb2 as mxboard_dot_proto_dot_tensor__pb2 File "C:\Users\selcu\anaconda3\lib\site-packages\mxboard\proto\tensor_pb2.py", line 16, in from mxboard.proto import resource_handle_pb2 as mxboard_dot_proto_dot_resource__handle__pb2 File "C:\Users\selcu\anaconda3\lib\site-packages\mxboard\proto\resource_handle_pb2.py", line 94, in _sym_db.RegisterMessage(ResourceHandleProto) File "C:\Users\selcu\anaconda3\lib\site-packages\google\protobuf\symbol_database.py", line 84, in RegisterMessage self.RegisterMessageDescriptor(desc) File "C:\Users\selcu\anaconda3\lib\site-packages\google\protobuf\symbol_database.py", line 95, in RegisterMessageDescriptor self.pool._AddDescriptor(message_descriptor) File "C:\Users\selcu\anaconda3\lib\site-packages\google\protobuf\descriptor_pool.py", line 238, in _AddDescriptor self._CheckConflictRegister(desc, desc.full_name, desc.file.name) File "C:\Users\selcu\anaconda3\lib\site-packages\google\protobuf\descriptor_pool.py", line 191, in _CheckConflictRegister raise TypeError(error_msg) TypeError: Conflict register for file "mxboard/proto/resource_handle.proto": tensorboard.ResourceHandleProto is already defined in file "tensorboard/compat/proto/resource_handle.proto". Please fix the conflict by adding package name on the proto file, or use different name for the duplication.

Process finished with exit code 1

I have set the i.grad != "null" which I found in here but now I receive error conv0_weight' was not initialized on context cpu(0). It was only initialized on [gpu(0), gpu(1)], but if I run it on just one gpu I receive the assertion error len(grads) == len(param_names) this occurs on all nets with a BatchNorm ones I have written and one in gluon's model zoo tried Inception, Densenet121 error:

grads = [i.grad() for i in net.collect_params().values() if i.grad_req != 'null'] 50 assert len(grads) == len(param_names) 51

in (.0) 47 sw.add_graph(net) 48 ---> 49 grads = [i.grad() for i in net.collect_params().values() if i.grad_req != 'null'] 50 assert len(grads) == len(param_names) 51

/usr/local/lib/python3.6/dist-packages/mxnet/gluon/parameter.py in grad(self, ctx) 570 "Cannot get gradient array for Parameter '%s' "
571 "because grad_req='null'"%(self.name)) --> 572 return self._check_and_get(self._grad, ctx) 573 574 def list_grad(self):

/usr/local/lib/python3.6/dist-packages/mxnet/gluon/parameter.py in _check_and_get(self, arr_list, ctx) 225 "Parameter '%s' was not initialized on context %s. " 226 "It was only initialized on %s."%( --> 227 self.name, str(ctx), str(self._ctx_list))) 228 if self._deferred_init: 229 raise DeferredInitializationError(

RuntimeError: Parameter 'densenet0_conv0_weight' was not initialized on context cpu(0). It was only initialized on [gpu(0), gpu(1)].

Is there a way to make SummaryWriter append to old event file, instead of creating new one each time it's called?

Or is there any other method to prepare log files, so that if I interrupt and then continue training later, get a desired Tensorboard output (and not just graph with duplicated values)?.