Once your code has trained a model or preprocessed a dataset, it is time to output something.

An output is a collection of files, which is you consider worth saving and are sent back to the data store by the Valohai platform.

You choose your output files by putting them in a special folder /valohai/outputs/{output_name}. Luckily valohai-utils makes this a bit easier.

Saving outputs with valohai-utils helps with these problems:

  • Differences between local and remote execution

  • Compressing outputs to a single archive

  • Live upload

Saving an Output

Saving an output with valohai-utils is easy. You ask for a correct path from the library and save the file there. The library creates the output folder automatically for you, if it already doesn’t exists.

For example a step that resizes a single image, would look like this:

import valohai

# Open the image
image ='image').path())

# Resize the image
new_image = image.resize((width, height))

# Query an output path for the filename "resized_image.png"
out_path = valohai.outputs().path('resized_image.png')

# Save the file to the output path

Outputs can also have names, which are basically subfolders. It is a good idea to use naming and not output everything into the output root folder.

out_path = valohai.outputs("my-output").path('resized_image.png')
print(out_path)  # my-output/resized_image.png

When building pipelines, you feed output(s) of one step as the input(s) of another, so clearly naming your outputs will make your pipelines more explicit and robust.

Compressing Outputs

It is often desired to archive the outputs. Not only do you save some space and get a quicker upload, but simply having a single archive instead of 200,000 separate files is a lot easier to manage.

Let’s say we have a step that resizes every .png file in the images input.

Here is an example of saving the resized images as a single archived output file (resized/

import valohai

for image_path in valohai.inputs('images').paths("*.png"):
    image =
    new_image = image.resize((width, height))
    out_path = valohai.outputs("resized").path(f"resized_{os.path.basename(image_path)}")

valohai.outputs('resized').compress("*.png", "")

By default, the original .png files are removed and just the archive is saved. If you want to save both the original files and the archive, you can set the remove_originals to false.

valohai.outputs('resized').compress("*.png", "", remove_originals=false)

Live Outputs

During a remote execution, Valohai waits for the execution to finish and only uploads the outputs afterwards.

That said, you often want to upload your outputs right away and not wait for the entire step to finish. In this case you can use the live uploading feature.

The way to request for a live upload is to call the live_upload() method. It sets the file as read-only, which signals to the Valohai platform that this file can be uploaded immediately.

# Query an output path for the filename "resized_image.png"
out_path = valohai.outputs().path("resized_image.png")

# Save the file to the output path

# Request for an immediate upload

# Carry on doing something else...

Local and Remote

When your code is executed remotely in the Valohai platform, the outputs are put into a special folder, which Valohai then sends them onward to the data store.

When your code is executed and debugged locally, you don’t want things to be sent anywhere, but you do want them to be saved to the hard disk.

When you call valohai.outputs("my-output"), the library knows whether the code is running locally or remotely and chooses the correct folder.

This is where the file will end up under the hood:

  • Local execution: .valohai/outputs/{hash}/my-output

  • Remote execution: /valohai/outputs/my-output

For each local run, a new {hash} is generated. The hash starts with a timestamp to make the latest outputs easier to find.


If you want to override the target folder for local or remote execution, set the VH_OUTPUTS_DIR environment variable.