Brian Naughton // Sat 23 March 2019 // Filed under datascience // Tags datascience cloud gcp aws

These days most everything is on the cloud. However, probably the most common mode of working is to develop locally on a laptop, then deploy to the cloud when necessary. Instead, I like to try to run everything remotely on an instance on the cloud.


  • All your files are together in one place.
  • You can backup your cloud instance very easily (especially on GCP), and even spawn clone machines as necessary with more CPU etc.
  • You can work with large datasets. Laptops usually max out at 16GB RAM, but on a cloud instance you can get 50GB+. You can also expand the disk size on your cloud instance as necessary.
  • You can do a lot of computational work without making your laptop fans explode.
  • Your laptop becomes more like a dumb terminal. When your laptop dies, or is being repaired, it's easy to continue work without interruption.

The big caveats here are that this requires having an always-on cloud instance, which is relatively expensive, and you cannot work without an internet connection. As someone who spends a lot of time in jupyter notebooks munging large dataframe, I find the trade-offs are worth it.

Here are some tools I use that help make working on the cloud easier.


Mosh is the most important tool here for working on the cloud. Instead of sshing into a machine once or more a day, now my laptop is continuously connected to a cloud instance, essentially until the instance needs to reboot (yearly?). Mosh also works better than regular ssh on weak connections, so it's handy for working on the train, etc. It makes working on a remote machine feel like working locally. If you use ssh a lot, try mosh instead.


Since I just have one mosh connection at a time, I need to have some tabs. Most people probably already use screen or tmux anyway. I have a basic tmux setup, with ten or so tabs, each with a two-letter name just to keep things a bit neater.

tmux resurrect is the only tmux add-on I use. It works ok: if your server needs to restart, tmux resurrect will at least rememeber the names of your tabs.


Mosh works amazingly well for a primary ssh connection, but to run everything on the cloud I also need a few ssh tunnels. Mosh cannot do this, so instead I need to use autossh. Like mosh, autossh tries to keep a connection open indefinitely. It seems to be slightly less reliable and fiddlier to set up than mosh, but has been working great for me recently.

Here's the command I use, based on this article and others. It took a while to get working via trial and error, so there may well be better ways. The ssh part of this autossh command comes from running gcloud compute ssh --dry-run.

autossh -M 0 -o "ServerAliveInterval 30" -o "ServerAliveCountMax 3" -f -t -i $HOME/.ssh/google_compute_engine -o CheckHostIP=no -o HostKeyAlias=compute.1234567890 -o IdentitiesOnly=yes -o StrictHostKeyChecking=yes -o UserKnownHostsFile=/Users/briann/.ssh/google_compute_known_hosts brian@12.345.67.890 -N -L 2288:localhost:8888 -L 2280:localhost:8880 -L 8385:localhost:8384 -L 2222:localhost:22 -L 8443:localhost:8443

The tunnels I set up:

  • 2288->8888 : to access jupyter running on my cloud instance (I keep 8888 for local jupyter)
  • 2280->8880 : to access a remote webserver (e.g., if i run python -m http.server on my cloud instance)
  • 8385->8384 : syncthing (see below)
  • 2222->22 : sshfs (see below)
  • 8443->8443 : coder (see below)

So to access jupyter, I just run jupyter notebook in a tmux tab on my cloud box, and go to https://localhost:2288.

To view a file on my cloud box I run python -m http.server on my cloud box, and go to https://localhost:2280.


Syncthing is a dropbox-like tool that syncs files across a group of computers. Unlike dropbox, the connections between machines are direct (i.e., there is no centralized server). It's pretty simple: you run syncthing on your laptop and on your cloud instance, they find each other and start syncing. Since 8384 is the default syncthing port, I can see syncthing's local and remote dashboards on https://localhost:8384 and https://localhost:8385 respectively. In my experience, syncthing works pretty well, but I recently stopped using it because I've found it unnecessary to have files synced to my laptop.


sshfs is a tool that lets you mount a filesystem over ssh. Like syncthing, I also don't use sshfs much any more since it's pretty slow and can fail on occasion. It is handy if you want to browse PDFs or similar files stored on your cloud instance though.


I recently started using Coder, which is Visual Studio Code (my preferred editor), but running in a browser. Amazingly, it's almost impossible to tell the difference between "native" VS Code (an Electron app) and the browser version, especially if it's running in full-screen mode.

It's very fast to get started. You run this on your cloud instance: docker run -t -p -v "${PWD}:/root/project" codercom/code-server code-server --allow-http --no-auth then go to http://localhost:8443 and that's it!

Coder is new and has had some glitches and limitations for me. For example, I don't know how you are supposed to install extensions without also updating the Docker image, which is less than ideal, and the documentation is minimal. Still, the VS Code team seems to execute very quickly, so I am sticking with it for now. It think it will improve and stabilize soon.


One annoyance with having everything on the cloud is viewing files. X11 is the typical way to solve this problem, but I've never had much success with X11. Even at its best, it's ugly and slow. Most of my graphing, etc. happens in jupyter, so this is usually not a big issue.

However, for infrequent file viewing, this python code has come in handy.

def view(filename):
    from pathlib import Path
    from flask import Flask, send_file
    app = Flask(__name__)
    def get_view_func(_filename):
        def fn(): return send_file(filename_or_fp=str(Path(_filename).resolve()))
        return fn
    print(f'python -m webbrowser -t "http://localhost:2280"')
    app.add_url_rule(rule='/', view_func=get_view_func(filename))"", FLASK_PORT_LOCAL, debug=False)

Appendix: GCP activation script

This is the bash script I use to set up the above tools from my mac for my GCP instance. People using GCP might find something useful in here.

_gcp_activate () {
    # example full command: _gcp_activate myuserid my-instance my-gcp-project us-central1-c $HOME/gcp/

    printf "#\n#    [[ gcp_activate script ]]   \n#\n"
    printf "# mosh: on mac, 'brew install mosh'\n"
    printf "# autossh: on mac, 'brew install autossh'\n"
    printf "# sshfs: on mac, download osxfuse and sshfs from\n"
    printf "#\n"
    printf "#     sshfs may need a new entry in $HOME/.ssh/known_hosts if logging in to a new host\n"
    printf "#     The error is \"remote host has disconnected\"\n"
    printf "#     to achieve that, delete the localhost:2222 entry from $HOME/.ssh/known_hosts\n"
    printf "#\n"

    [ $# -eq 0 ] && printf "No arguments supplied\n" && return 1


    printf "#\n# 1. set gcp project, if it's not set \n#\n"

    # automatically set project
    echo gcloud config set account ${account};
    echo gcloud config set project ${gcpproject};

    # unmount sshfs
    printf "#\n# 2. unmount sshfs (this command fails if it's already unmounted, which is ok)\n#\n"
    echo umount -f ${mountpoint};

    # commands
    ssh_cmd=$(gcloud compute ssh ${user}@${instance} --zone=${zone} --dry-run) && \
    external_ip=$(printf "${ssh_cmd}" | sed -E 's/.+@([0-9\.]+)/\1/') && \
    autossh_cmd=$(printf "${ssh_cmd}" | sed s/'\/usr\/bin\/ssh'/'autossh -M 0 -o "ServerAliveInterval 30" -o "ServerAliveCountMax 3" -f'/) && \
    fullssh_cmd=$(printf "${autossh_cmd} -N -L 2288:localhost:8888 -L 2222:localhost:22 -L 2280:localhost:8000 -L 8385:localhost:8384 -L 8443:localhost:8443") && \
    printf "#\n# 3. run autossh to set up ssh tunnels for jupyter (2288), web (2280), and sshfs (2222)\n#\n" && \
    echo "${fullssh_cmd}" && \
    printf "#\n# 4. run sshfs to mount to ${mountpoint}\n#\n" && \
    echo sshfs -p 2222 -o reconnect,compression=yes,transform_symlinks,defer_permissions,IdentityFile=$HOME/.ssh/google_compute_engine,ServerAliveInterval=30,ServerAliveCountMax=0 -f \
    ${user}@localhost:. ${mountpoint} && \
    printf "#\n# 5. run mosh\n#\n" && \
    echo mosh -p 60000 --ssh=\"ssh -i $HOME/.ssh/google_compute_engine\" ${user}@${external_ip} -- tmux a
    printf "#\n# 6. if mosh fails run this\n#\n"
    echo gcloud compute ssh ${user}@${instance} -- killall mosh-server

gcp_activate () {
  _gcp_activate myuserid my-instance my-project us-central1-c $HOME/gcp/
Brian Naughton // Sun 20 January 2019 // Filed under deeplearning // Tags deeplearning datascience neuralnet

Jake Dangerback is an instagram celebrity and influencer (of sorts). He's unusual in that he does not exist; he was created using millions of matrix multiplications. This post is a look at some of the freely available state-of-the-art neural networks I used to create him.


Neural nets, specifically GANs, are getting really good at hallucinating realistic faces. Notably, NVIDIA had a paper in December 2018 that showed some pretty amazing results.


Since these faces do not belong to anyone, they are perfect for use as celebrities. They don't need to be paid, never complain, and can be tailored to appeal to any niche demographic.

Step One: Making A Face

I used this awesome tool on kaggle (github) to create a face. I did not know that kaggle supported this colab-like interface but it's quite advanced. The tool has 21 levers to pull so you can create the perfect face for your audience.


Using this tool I created country music star Jake Dangerback's face (his fans call him jdb). He is handsome but rugged and most importantly he has no legal personhood so I can use his likeness to endorse and sell products of all kinds.


Photoshopping jdb

It's fairly easy to create new images using this face. There are several sources of royalty-free images (e.g., pexels) where I can photoshop jdb's face in. jdb_cowboy_hat

Photoshopping a face is not that hard — at least at this quality — but it would be easier if a neural net did the photoshopping for me. Luckily there are many papers and github repos that do face swapping since it produces funny pictures. Most face swapping tools are mobile apps, but I did find Reflect face swap online. It seems to do a good job generally, but the result below looks a bit weird. It seems to be trying to mix the photos for realism rather than just replace the face.

reagan reagan faceswap
Reagan wearing a cowboy hat; jdb wearing a cowboy hat


If we had enough photos, we might consider automatically optimizing the image for likes using some kind of selfie-rating neural net like @karpathy's. I think the photoshopped images would have to be autogenerated to make this worthwhile.

Image filters, which are popular on instagram, can also use neural nets. The most famous example is neural style, which maps the style of one image — usually a painting — onto the content of another. The website does this as a service. These neural style filters are very cool but not that useful for instagram content.

jdb deepart jdb deepart2

What About DeepFake?

DeepFake is a powerful technique for swapping faces in video that can produce very realistic results. There's even a tool called FakeApp that automates some of the steps. There's a nice blogpost showing how to swap Jimmy Fallon's face for John Oliver's. It looks pretty convincing.

jdb deepfake

DeepFake creates video, which I do not really need, and to train it you need video of the subject, which I do not have. I suppose theoretically you could create a 3D model and use that to generate the source video...

Step Two: The Third Dimension

It is limiting if every photo has to have jdb's face looking head-on. Luckily, there is a very cool 3D face reconstruction neural net that works based on a single photo.

jdb in 3D

The results are great and it takes less than a minute to work. You can even load it into Blender. jdb in blender

Taking it a step further, the free MakeHuman software will create a mesh of a body with various parameters. jdb in blender

This could probably be made to work well but it's way beyond my Blender skills. jdb in blender

There's also an interesting iOS app called mug life that will animate photos based on an inferred 3D mesh. jdb muglife The results are impressive, if creepy. jdb looks so alive! I don't think you can download the mesh though.


Sometimes the resulting photoshopped image can be pretty blurry, partially because the 3D model's texture resolution is not that high. Luckily there is another deep net to help here, called neural-enhance. It really does enhance a photo by doubling the resolution, which is pretty slick. The author includes a docker container, which makes running it very simple.

The results are very impressive in general, and it only takes a few minutes even on a CPU. Since it's trained on real photos, I am guessing it might also remove artifacts and rogue pixels due to photoshopping.

jdb singing jdb singing enhanced
From blurry (left) to enhanced (right). The shirt buttons are the most obvious improvement.

Step Three: Captioning

Instagram posts have captions, which I assume are important for engagement. There have been many attempts to caption or describe images using neural nets. The oldest one I remember is an influential Stanford paper from 2015. There are a few tools online too. I first tried Microsoft's, assuming it used a neural net. The results were very meh, and it turns out it's not a neural net and is famously vague/bad.

Google's Cloud Vision API does much better though it's still not super-engaging content. For now, computers have not solved the instagram captioning problem.

jdb beach walk
Captionbot: I think it's a person standing on a beach and he seems 😐.
Google Cloud Vision: couple looking at each other on beach

Step Four: Songs

jdb is a country music celebrity, so he may need some songs. Thankfully there's a neural net for everything, including country music lyrics!

Some example lyrics:

No one with the danger in the world
I love my black fire as I know
But the short knees just around me
Fun the heart couldnes fall to back

It's not terrible ("couldnes"?), but it's also pretty dark for instagram... You can also generate music using RNNs but I did not find an easy way to generate country music.


Creating a speaking/singing voice from lyrics was not as easy as I thought it would be. I tried a few iOS apps, including LyreBird, but got strange results. Macs also have the say command (just type say hello into the terminal), which works ok.

I ended up using Google Cloud text-to-speech, which uses WaveNet, to turn lyrics into speech. It works via a simple json upload. Sadly, none of the available voices sounded particularly country.

To produce actual singing, there are several autotune apps, e.g., Voloco. However, the ones I tried sounded pretty autotuned, so perhaps more suitable for another genre.

I replaced the RNN lyrics with wikipedia-derived specs for a truck (a product jdb could endorse and a scalable lyric-generation system), added some royalty-free music, and the result is really something.

The Future

One of the most difficult aspects here is finding working neural nets and getting them to run. Even if the code is on github there are often missing files or onerous installation steps. Things will be easier when more neural nets get converted to javascript/tensorflow.js or appear on colab.

I don't know how many instagram celebrities are computer-generated today, though computer-generated celebrities are not a new thing, especially in Japan. Lil Miquela has >1M followers on instagram, though it's clear she is computer-generated.

It's pretty obvious there will be a lot of this kind of thing in the future. We can evolve new celebrities as ecological niches emerge, catering to new audiences. As people lose interest in one celebrity, we can just create others. They could even inhabit the same world and date each other. Eventually they will outnumber us, then maybe skynet us.

In the meantime, jdb is available to endorse products of all kinds.


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