Logging Metrics

pip install trackio
# or
uv pip install trackio

import trackio

# Initialize a run
trackio.init(
    project="my-project",
    config={"learning_rate": 0.001, "epochs": 10}
)

# Log metrics during training
for epoch in range(10):
    loss = train_epoch()
    trackio.log({"loss": loss, "epoch": epoch})

# Finalize the run
trackio.finish()

trackio.init(
    project="my-project",           # Project name (groups runs together)
    name="run-name",                # Optional: name for this specific run
    config={...},                   # Hyperparameters and config to log
    space_id="username/trackio",    # Optional: sync to HF Space for remote dashboard
    private=True,                   # Optional: make an auto-created Space private.
                                    #   Default: PUBLIC (unless your org defaults to private)
    bucket_id="username/my-bucket", # Optional: pin the HF Bucket used for metric storage.
                                    #   Default: auto-derived from space_id
    group="experiment-group",       # Optional: group related runs
)

trackio.init(project="my-project")
# ... training ...
trackio.finish()

# Launch local dashboard
trackio.show()

trackio show --project my-project

trackio.init(
    project="my-project",
    space_id="username/trackio",  # Auto-creates Space if it doesn't exist
    private=True,                 # Spaces are PUBLIC by default; omit for a shareable dashboard
)

trackio.sync(project="my-project", space_id="username/my-experiments")

import trackio as wandb

wandb.init(project="my-project")
wandb.log({"loss": 0.5})
wandb.finish()

from trl import SFTConfig, SFTTrainer
import trackio

trackio.init(
    project="sft-training",
    space_id="username/trackio",
    private=True,  # Spaces are public by default; omit for a shareable dashboard
    config={"model": "Qwen/Qwen2.5-0.5B", "dataset": "trl-lib/Capybara"}
)

config = SFTConfig(
    output_dir="./output",
    report_to="trackio",  # Automatic metric logging
    # ... other config
)

trainer = SFTTrainer(model=model, args=config, ...)
trainer.train()
trackio.finish()

trackio.log({
    "train_loss": 0.5,
    "train_accuracy": 0.85,
    "val_loss": 0.4,
    "val_accuracy": 0.88,
    "epoch": 1
})

# Group by experiment type
trackio.init(project="my-project", name="baseline-v1", group="baseline")
trackio.init(project="my-project", name="augmented-v1", group="augmented")

# Group by hyperparameter
trackio.init(project="hyperparam-sweep", name="lr-0.001", group="lr_0.001")
trackio.init(project="hyperparam-sweep", name="lr-0.01", group="lr_0.01")

trackio.init(
    project="qwen-sft-capybara",
    name="baseline-lr2e5",
    config={
        "model": "Qwen/Qwen2.5-0.5B",
        "dataset": "trl-lib/Capybara",
        "learning_rate": 2e-5,
        "num_epochs": 3,
        "batch_size": 8,
    }
)

<iframe 
  src="https://username-trackio.hf.space/?project=my-project&metrics=train_loss,val_loss&sidebar=hidden" 
  style="width:1600px; height:500px; border:0;">
</iframe>