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Run docker containers

You can run arbitrary docker containers as jobs within your metapage workflows.

These steps need to be set up only once, then you can run any docker image.

caution

The public docker work queue service is currently for testing/demonstration purposes. Do not publicly share these workflows as others to run arbitrary docker containers on your infrastructure.

note

This service will soon be replaced with one secure for teams.

Run python via a docker container on your own computer​

note

Requires familiarity with the command line terminal

Host requirements:

Steps (from https://docker.mtfm.io/#?tab=6):

1. Generate a uuid for the job queue​

uuid

If your terminal does not have this program, go to this page and copy the uuid: https://www.uuidgenerator.net/version4

2. Take the uuid generated, and start the worker process​

Replace <uuid> with your generated one above

docker run --restart unless-stopped -tid -v /var/run/docker.sock:/var/run/docker.sock -v /tmp:/tmp metapage/metaframe-docker-worker:0.1.2 --cpus=2 --queue=<uuid>

Now your local machine will run docker images on this unguessable queue.

caution

Do not share this queue id, and do not publicly share workflows using this queue id.

3. Add a docker metaframe to your metapage​

Create a new metapage:​

Click “Edit” for edit mode​

The metapage will initially be empty

This is how you can add metaframe components.

Add the “Run Docker Container Jobs” metaframe​

Search for the docker metaframe, type “docker” in the search field, you should see something like:

Add the “Run Docker Container Jobs” metaframe, click on the “+ Add” button next to it

Switch to “View” mode​

You can directly configure the metaframe this way

Add your queue id​

Now time to add your queue id. Click on “Queue” the “Queue Key”

Enter in the uuid from above.

Congratulations! Now your local machine will run any docker containers you want.

4. Configure the docker container frame​

Set the docker image to python​

Set:

  • image: czentye/matplotlib-minimal:3.1.2
  • command:python3 /inputs/run.py

Add a python script​

Click the inputs tab

Click the + and name the new file run.py

Edit the python script​

Copy the script and save​

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)

ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]

plt.plot(x, ys, '-')
plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)

plt.title("Sample Visualization")
# plt.show() # save instead of show because we are headless 
plt.savefig('/outputs/plot.png')
print("Created image and sent!")

5. Add an image viewer to see the docker output​

Edit mode​

Search for “Image Viewer” and add​

Edit the Image Viewer​

Click “Show Image” and “Connect”

Connect all outputs to inputs​

* means all.

6. Congrats you have generated an image​

Do you want to save the image? See →

Files: upload, download, saving