vessl.log
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.Parameter | Description |
|---|---|
step | Unit size of the loop |
payload | Dictionary of metrics or a list of vessl.Image objects or vessl.Audio objects |
# Logging loss values for each epoch in PyTorch
import vessl
for epoch in range(epochs):
...
vessl.log(step=epoch, payload={'loss': loss.item})
# 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
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")
]
}
)
Last modified 1yr ago