Trackio

[abidlabs]

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[julien-c]

love the url

[julien-c]

mention Parquet + effficient Hub storage thanks to Xet? (sorry for again pushing for this but i think this could be one of the best killer use cases of Xet storage, cc @lhoestq @kszucs

[kszucs]

Do we expect duplicated data in/between tracio databases?

[abidlabs]

Yes @znation is currently working on the parquet conversion: gradio-app/trackio#88, and we'll definitely include that in the blog post as well

[qgallouedec]

This is the first code snippet in the blog post, so it’s likely the one people will copy and paste the most. While I love the idea of a drop-in replacement, I don’t think it should be a long-term design choice. With that in mind, I’m suggesting a slightly different version:

Suggested change

	`trackio` is designed to be a drop-in replacement for experiment tracking libraries like `wandb`. The API is compatible with `wandb.init`, `wandb.log`, and `wandb.finish`, so you can simply import `trackio` as `wandb` in your code. Here is an example:
	```python
	import trackio as wandb
	import random
	import time
	runs = 3
	epochs = 8
	def simulate_multiple_runs():
	for run in range(runs):
	wandb.init(project="fake-training", config={
	"epochs": epochs,
	"learning_rate": 0.001,
	"batch_size": 64
	})
	for epoch in range(epochs):
	train_loss = random.uniform(0.2, 1.0)
	train_acc = random.uniform(0.6, 0.95)
	val_loss = train_loss - random.uniform(0.01, 0.1)
	val_acc = train_acc + random.uniform(0.01, 0.05)
	wandb.log({
	"epoch": epoch,
	"train_loss": train_loss,
	"train_accuracy": train_acc,
	"val_loss": val_loss,
	"val_accuracy": val_acc
	})
	time.sleep(0.2)
	wandb.finish()
	simulate_multiple_runs()
	`trackio` is designed to be a drop-in replacement for experiment tracking libraries like `wandb`. The API is compatible with `wandb.init`, `wandb.log`, and `wandb.finish`, so you can simply import `trackio` as `wandb` in your code.
	```diff
	- import wandb
	+ import trackio as wandb
	```
	Here is an example:
	```python
	import trackio
	import random
	import time
	runs = 3
	epochs = 8
	def simulate_multiple_runs():
	for run in range(runs):
	trackio.init(project="fake-training", config={
	"epochs": epochs,
	"learning_rate": 0.001,
	"batch_size": 64
	})
	for epoch in range(epochs):
	train_loss = random.uniform(0.2, 1.0)
	train_acc = random.uniform(0.6, 0.95)
	val_loss = train_loss - random.uniform(0.01, 0.1)
	val_acc = train_acc + random.uniform(0.01, 0.05)
	trackio.log({
	"epoch": epoch,
	"train_loss": train_loss,
	"train_accuracy": train_acc,
	"val_loss": val_loss,
	"val_accuracy": val_acc
	})
	time.sleep(0.2)
	trackio.finish()
	simulate_multiple_runs()

[merveenoyan]

love this! will we have this in model repos or is it going to be Spaces attached to model repos? 👀 i.e. when you push a model to Hub it spawns a Space with logs attached to model repo @abidlabs @julien-c
I think it would be nice to mention if we have even more plans