I am trying to commit and push my change to the branch, I cannot load the difference. I haven't changed many cells and each cells doesn't exceed the 500 lines in the notebook file.
I am wondering why this happens and how to solve it?
As per official documentation, The maximum size for a notebook cell, both contents and output, is 16MB.
Graphing tools like plotly and matplotlib can generate large sets of results that display as large images. You can reduce the notebook size by hiding these large results and images.
Reference - https://learn.microsoft.com/en-us/azure/databricks/kb/notebooks/notebook-autosave
Related
I have a large graph with many nodes and edges. The problem I am facing with the Graphviz python package is that rendering the file takes a lot of time.
There are other alternatives mentioned here and here. But the problem I am facing is that all of them work with the dot file, and these methods generate image files that do not look good; I mean, the formatting intended is not quite visible.
I want a pdf file to be generated. The large image files being generated are crashing my Linux. The default image viewer in Linux cannot handle them, or Mozilla Firefox, though it can open it, takes a tremendous amount of time for a portion of the image to become apparent.
Please can anyone help me generate a pdf file very fast which can be quickly viewed in usual pdf viewers or if an image, so can be easily viewed using usual image viewers?
I want the graphs generated to look something like this, this, and this. [These are the graphs rendered to pdf by python for a subgraph of the input].
For the entire graph, the situation of the dot file is like this, and the command:
$sfdp -x -Goverlap=scale -Tpng syscall > data.png
sfdp: graph is too large for cairo-renderer bitmaps. Scaling by 0.487931 to fit
tcmalloc: large alloc 3142361088 bytes == 0x558a701ce000 # 0x7f45c7679001 0x7f45c39101fa 0x7f45c39102ad 0x7f45c4a9b6df 0x7f45c4f92261 0x7f45c740f468 0x7f45c7411d53 0x558a6ee01092 0x7f45c6dc4c87 0x558a6ee0112a
It is returning the following data.png file, which I cannot view correctly on any image viewer on my Linux system. And also, it is not of the same format (the look of the graph, I mean) as generated by Graphviz render.
And for this dot file, even sfdp is taking considerable time...
Unsure why your device takes so much time other than its running around in circles like a headless chicken before falling over.
Your error feedback should give you a clue by reporting the file is too large for an image:
sfdp: graph is too large for cairo-renderer bitmaps. Scaling by 0.531958 to fit
sfdp: failure to create cairo surface: out of memory
Here it is as SVG, note the size is roughly 600 in square that's roughly 61,598 pixels x 51,767 pixels = roughly 3GB (your error says 3142361088 bytes cannot be Memory ALLOCated)
A large file by any standard, but as SVG its only 1.63 MB
sfdp -Goverlap=scale -x -Tsvg syscall -o data.svg
File: data.svg
File Size: 1.63 MB (1,707,939 Bytes)
Number of Pages: 1
Page Size: 641.64 x 539.24 in
You can open the svg in a browser and print to PDF HOWEVER even at 10% scale on A0 Landscape that requires 2 PAGES and you cant see lettering, thus at full scale it would be more than 100 of those poster pages
Add this to your input file: graph [nslimit=2 nslimit1=2 maxiter=5000] (values somewhat arbitrary)
And use this commandline dot -v -Tsvg ... (if svg works, then try pdf)
I think dot has the best chance of producing a graph you will like
I created a macro that updates around 55 PowerPoint slides, ranging from populating tables to updating line and bar charts. The macro works well, however, for some reason the PowerPoint file size has increased significantly. While working on the macro the size was around 80,000 KB, after making very few minor changes it suddenly close to doubled to 150,000 KB. To find out which slides cause this huge size, I published the slides to see the individual slide sizes and am able to narrow down the problem. Due to the large variety of charts I will focus on one kind.
I have 2 regular line charts on one slide and the size is 5000+ KB! Whenever I delete one of the two, the size is reduced to roughly half the size.
I have taken the following steps to try to find the problem:
1) Removed and deleted all cells that the chart references to (inside the PowerPoint) -- No change in file size.
2) Removed all chart features, such as axis title, legends, etc -- No change in file size.
3) Slide is not macro enabled and has therefore no macro included in the file.
4) Made sure there are no hidden objects.
All that is left is an empty 'Chart Placeholder' with no data in the XL file and yet the size is very large.
The PowerPoint slide contains no images either. A regular PowerPoint slide with a line chart should only have a size of around 50-100 KB and I am not sure how the chart has such a massive size.
First time posting my question here! Hopefully someone can help out.
Thanks!
UPDATE:
I finally was able to find the problem. For some reason, all charts had the maximum number of rows open (1+ million rows) making the file size that large!
I added: wb.worksheets(1).UsedRangeto the end of each procedure and now the entire file size is around 4000 KB!
Thank you.
I have just begun to use TensorFlow in python. I want to build a binary image classifier using CNN.
I found an example code on the internet: https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
The explanation is given here: https://www.tensorflow.org/get_started/mnist/pros
This code builds a small Neural Network and uses the MNIST dataset to train and test it.
I roughly understood the working of the CNN but I didn't understand the code line by line.
I want to use the same code with my own Dataset of Images (for both training and testing). In the example the input images are converted into mx784, where m is the number of training/testing examples and 784 comes from flattened images of size 28x28 each. I have converted all my images into an array of size mx1024 using a python script and Similarly converted the ground truth into an array of size mx1. I have stored them into text file as X.txt and y.txt.
Now in the code I have changed the dimensions according to my image dimensions. However, I am confused how to feed the images into the network. Is there a way out other than going through the code line by line? I will be very grateful if you could help me out.
This Get Started is just great to understand line by line and to go "deeper" into neural networks step by step.
https://www.tensorflow.org/get_started/
Try to understand it, it will really help you :)
I'm currently working on my thesis on the neural networks. I'm using the CIFAR10 as a reference dataset. Now I would like to show some example results in my paper. The problem is, that the images in the dataset are 32x32 pixels so it's really hard to recognize something on them when printed on paper.
Is there any way to get hold of the original images with higher resolution?
UPDATE: I'm not asking for image processing algorithm, but for the original images presented in CIFAR-10. I need some higher resolution samples to put in my paper.
I now have the same problem and I just found your question.
It seems that CIFAR was built from labeling the tinyimages dataset, and are kind enough to share the indexing from CIFAR to tinyimages. Now tinyimages contain metadata file with URL of the original images and a toolbox for getting for any image you wish (e.g. those included in the CIFAR index).
So one may write a mat file which does this and share the results...
They're just small:
The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset.
You could use Google reverse image search if you're curious.
I've been trying to plot a dataset containing about 500,000 values using gnuplot. Although the plotting went well, the SVG file it produced was too large (about 25 MB) and takes ages to render. Is there some way I can improve the file size?
I have vague understanding of the SVG file format and I realize that this is because SVG is a vector format and thus have to store 500,000 points individually.
I also tried Scour and re-printing the SVG without any success.
The time it takes to render you SVG file is proportional to the amount of information in it. Thus, the only way to speed up rendering is to reduce the amount of data
I think it is a little tedious to fiddle with an already generated SVG file. I would suggest to reduce the amount of data for gnuplot to plot.
Maybe every or some other reduction of data can help like splitting the data into multiple plots...
I would recommend keeping it in vector graphic format and then choosing a resolution for the document that you put it in later.
Main reason for doing this is that you might one day use that image in a poster (for example) and print it at hundreds of times the current resolution.
I normally convert my final pdf into djvu format.
pdf2djvu --dpi=600 -o my_file_600.djvu my_file.pdf
This lets me specify the resolution of the document as a whole (including the text), rather than different resolutions scattered throughout.
On the downside it does mean having a large pdf for the original document. However, this can be mitigated against if you are latex to make your original pdf - for you can use the draft option until you have finished, so that images are not imported in your day-to-day editing of the text (where rendering large images would be annoying).
Did you try printing to PDF and then convert to SVG?
In Linux, you can do that with imagemagick, which you even be able to use to reduce the size of your original SVG file.
Or there are online converters, such as http://image.online-convert.com/convert-to-svg