File size: 1,943 Bytes
76d9c4f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
title: vessl.log
version: EN
---
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 metrics
```python
# 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
```python
# 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
```python
# 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")
]
}
)
```
|