Skip to main content
Use vessl.log in a training or testing loop to log a dictionary of metrics. Provide the step parameter for the loop unit – like the epoch value – and any metrics you want to log as a dictionary in the row parameter. You can also log images or audio types of objects. Provide a list of vessl.Image objects or vessl.Audio with data and captions as the payload parameter with any dictionary key. Note that only the first key will be logged.
ParameterDescription
stepUnit size of the loop
payloadDictionary of metrics or a list of vessl.Image objects or vessl.Audio objects

​
Logging metrics

# Logging loss values for each epoch in PyTorch

import vessl

for epoch in range(epochs):
    ...
    vessl.log(step=epoch, payload={'loss': loss.item})

​
Logging image objects

# Logging images in PyTorch

import vessl

def test(model, test_loader, ...):
    ...
    test_images = []
    with torch.no_grad():
        for data, target in test_loader:
            ...
            output = model(data)
            ...
            test_images.append(
                vessl.Image(
                    data[0], 
                    caption=f'Pred: {output[0].item()} Truth: {target[0]}'
                )
            )
    ...
    vessl.log(payload={"test-images": test_images})

​
Logging audio objects

# Logging audio

import vessl
import soundfile as sf

audio_path = "sample.wav"
data, sample_rate = sf.read(audio_path)

# Log audio with data
vessl.log(
  payload={
    "test-audio": [
      vessl.Audio(data, sample_rate=sample_rate, caption="audio with data example")
    ]
  }
)
⌘I