# Storage

Training and evaluation data — metrics, checkpoints, rollouts, trajectories — is saved through a unified storage layer. By default, data is written to local disk. To use cloud storage (GCS, S3, Azure), just change the path to a URI.

For cloud support:

```
u pip install 'tinker-cookbook[cloud]'
```

## Training

Pass a cloud URI as `log_dir`:

```
# Local (default)
ml_logger = setup_logging(log_dir="/tmp/my_run", config=config)

# GCS
ml_logger = setup_logging(log_dir="gs://bucket/my_run", config=config)

# S3
ml_logger = setup_logging(log_dir="s3://bucket/my_run", config=config)
```

All training data — metrics, checkpoints, rollouts — is written to the cloud path automatically.

## Evaluation

Pass a cloud URI as `save_dir`:

```
# Local
config = BenchmarkConfig(save_dir="/tmp/evals/run1")

# GCS
config = BenchmarkConfig(save_dir="gs://bucket/evals/run1")

result = await run_benchmark("gsm8k", client, renderer, config)
```

## Reading Data Back

```
from tinker_cookbook.stores import TrainingRunStore, storage_from_uri

# Works with local paths and cloud URIs
store = TrainingRunStore(storage_from_uri("gs://bucket/my_run"))

config = store.read_config()
metrics = store.read_metrics()
rollouts = store.read_rollouts(0)
checkpoints = store.read_checkpoints()
```

## Supported Backends

| URI | Backend |
| --- | --- |
| `/local/path` | Local filesystem |
| `gs://bucket/prefix` | Google Cloud Storage |
| `s3://bucket/prefix` | Amazon S3 |
| `az://container/prefix` | Azure Blob Storage |

Any [fsspec-supported filesystem](https://filesystem-spec.readthedocs.io/) can be used.

Flush for cloud backends

Cloud writes are staged locally for performance. Call `flush()` at checkpoints to ensure data is uploaded:

```
storage = storage_from_uri("gs://bucket/run")
store = TrainingRunStore(storage)

for step in range(num_steps):
    store.write_metrics({"loss": loss}, step=step)
    if step % save_every == 0:
        storage.flush()
```

Or use a context manager for automatic flush:

```
with storage_from_uri("gs://bucket/run") as storage:
    store = TrainingRunStore(storage)
    # ... training ...
# auto-flush on exit
```