<jupyter_start><jupyter_text>Testing the performance of `dask.visualize` at various number of nodes  Assumptions made by me- 100 nodes of dask.array are the same as dask.dataframe or any other kind of Dask object- This test was conducted in a Python 3.8 Linux Environment and may/may not reflect Graphviz's performance reports elsewhere Device Specifications```Architecture:                    x86_64CPU op-mode(s):                  32-bit, 64-bitByte Order:                      Little EndianAddress sizes:                   39 bits physical, 48 bits virtualCPU(s):                          8On-line CPU(s) list:             0-7Thread(s) per core:              2Core(s) per socket:              4Socket(s):                       1NUMA node(s):                    1Vendor ID:                       GenuineIntelCPU family:                      6Model:                           142Model name:                      Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz``````<EMAIL>@dhokla ----------- OS: Pop!_OS 20.04 LTS x86_64 Host: HP Pavilion Laptop 15-cs1xxx Kernel: 5.11.0-7614-generic Uptime: 14 hours, 56 mins Packages: 2664 (dpkg), 12 (flatpak) Shell: zsh 5.8 Resolution: 1920x1080 DE: GNOME WM: Mutter WM Theme: Pop Theme: Nordic-darker [GTK2/3] Icons: Fluent [GTK2/3] Terminal: x-terminal-emul CPU: Intel i7-8565U (8) @ 4.600GHz GPU: NVIDIA GeForce GTX 1050 Mobile GPU: Intel UHD Graphics 620 Memory: 7018MiB / 15894MiB`````` active environment : dask-dev    active env location : /home/frey/miniconda3/envs/dask-dev            shell level : 2       user config file : /home/frey/.condarc populated config files : /home/frey/.condarc          conda version : 4.10.1    conda-build version : not installed         python version : 3.8.5.final.0       virtual packages : __cuda=11.2=0                          __linux=5.11.0=0                          __glibc=2.31=0                          __unix=0=0                          __archspec=1=x86_64       base environment : /home/frey/miniconda3  (writable)      conda av data dir : /home/frey/miniconda3/etc/conda  conda av metadata url : https://repo.anaconda.com/pkgs/main           channel URLs : https://repo.anaconda.com/pkgs/main/linux-64                          https://repo.anaconda.com/pkgs/main/noarch                          https://repo.anaconda.com/pkgs/r/linux-64                          https://repo.anaconda.com/pkgs/r/noarch          package cache : /home/frey/miniconda3/pkgs                          /home/frey/.conda/pkgs       envs directories : /home/frey/miniconda3/envs                          /home/frey/.conda/envs               platform : linux-64             user-agent : conda/4.10.1 requests/2.24.0 CPython/3.8.5 Linux/5.11.0-7614-generic pop/20.04 glibc/2.31                UID:GID : 1000:1000             netrc file : None           offline mode : False```<jupyter_code>import dask.array as da
# 10 nodes

ten = da.ones((1,10), chunks=(1,1))

%timeit ten.visualize()
# 100 nodes

hundred = da.ones((1,100), chunks=(1,1))

%timeit hundred.visualize()
# 500 nodes

five_hundred = da.ones((1,500), chunks=(1,1))

%timeit five_hundred.visualize()
# 1000 nodes

thousand = da.ones((1,1000), chunks=(1,1))

%timeit thousand.visualize()
# 2000 nodes
105226460
two_thousand = da.ones((1,2000), chunks=(1,1))

%timeit two_thousand.visualize()
# 5000 nodes

five_thousand = da.ones((1,5000), chunks=(1,1))

%timeit five_thousand.visualize()<jupyter_output>dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.0431152 to fit
{'fraction_non_alphanumeric': 0.10329562223315297, 'fraction_numerical': 0.07624200688637482, 'mean_word_length': 2.248402555910543, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 5, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '468815', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1498, 'n_tokens_neox': 1280, 'n_words': 378, 'path': 'testing-dask-visualize.ipynb', 'repo_name': 'freyam/my-dask', 'size': '4063'}