# Outputs: Save Models and Files

When your execution ends, Valohai needs to know which files to keep. This guide shows how to save your trained models, datasets, and other files.

> 💡 **Already saving files locally?** Just change the path to `/valohai/outputs/` and Valohai handles the rest.

## How Outputs Work

1. **Save to a special directory**: `/valohai/outputs/`
2. **Valohai uploads automatically** to your cloud storage (AWS S3, Azure Blob, Google Cloud Storage, MinIO, etc.)
3. **Every file is versioned**, no accidental overwrites

The `/valohai/outputs/` directory already exists in every execution. Just save your files there.

## Update Your Code

### Before (Local)

```python
# Saving locally
model.save("model.h5")
df.to_csv("results.csv")
```

### After (Valohai)

```python
# Save to Valohai outputs
model.save("/valohai/outputs/model.h5")
df.to_csv("/valohai/outputs/results.csv")
```

That's the only change needed.

## Common Patterns

### Save Multiple Files

```python
# Models
model.save("/valohai/outputs/model.h5")

# Metrics
with open("/valohai/outputs/metrics.json", "w") as f:
    json.dump(metrics, f)

# Plots
plt.savefig("/valohai/outputs/loss_curve.png")
```

### Preserve Directory Structure

Your folder structure is maintained:

```python
# This structure...
"""
/valohai/outputs/
├── models/
│   └── best_model.h5
├── logs/
│   └── training.log
└── visualizations/
    ├── loss.png
    └── accuracy.png
"""
# ...stays exactly the same in cloud storage
```

### Save During Training

Don't wait until the end, save checkpoints as you go. Files that are marked as read only under the `/valohai/outputs/` directory will be uploaded immediately to the data store.

```python
import os
from stat import S_IREAD, S_IRGRP, S_IROTH

# Some code

for epoch in range(epochs):
    # Training code...

    if epoch % 10 == 0:
        filename = f"/valohai/outputs/checkpoint_epoch_{epoch}.h5"
        model.save(filename)
        os.chmod(filename, S_IREAD | S_IRGRP | S_IROTH)
```

<details>

<summary>Optional: Use the <code>valohai-utils</code> Python helper tool</summary>

The `valohai-utils` library offers convenience methods:

```python
import valohai

# Generate output path
output_path = valohai.outputs().path("model.h5")
model.save(output_path)

# Live logging
valohai.outputs().live_upload("training.log")
```

This is optional, direct paths work just fine.

</details>

## File Types and Sizes

Save any file type your code can create:

* **Models**: `.h5`, `.pkl`, `.pt`, `.onnx`, `.joblib`
* **Data**: `.csv`, `.parquet`, `.json`, `.npz`
* **Images**: `.png`, `.jpg`, `.pdf`
* **Archives**: `.tar`, `.zip`, `.gz`
* **R files**: `.rdata`, `.rds`

No size limits, save what you need.

## Using Aliases (Human-Friendly Names)

Each job is isolated and versioned on its own. This means that if job #1 outputs a file called `model.h5` it will be versioned on its own, and when job #2 outputs a `model.h5` Valohai won't override the first file but create a separate file for it.

We recommend looking into the [Valohai Aliases](/data/data-versioning/metadata-overview/aliases.md) if you're looking for friendly names for a specific version of a file:

* `latest-model-project-b`
* `production-model-project-a`

Now you can reference this model as `datum://best-model` in future jobs instead of a long cloud storage URL.

## Where Do Outputs Go?

Valohai automatically uploads your outputs to:

* **Cloud**: Your configured AWS S3, Azure Blob Storage, Google Cloud Storage, OCI
* **On-premise**: MinIO, NetApp, or other S3-compatible storage

You don't need to write upload code. Save locally, Valohai handles the rest.

## Quick Reference

### Essential Pattern

```python
# Just prepend /valohai/outputs/ to your existing save paths
model.save("/valohai/outputs/model.h5")
```

### Supported Storage

* AWS S3
* Azure Blob Storage
* Google Cloud Storage
* MinIO
* NetApp
* Any S3-compatible storage

### Good to Know

* Directory structure is preserved
* Every execution's outputs are versioned separately
* No file size or count limits
* Files appear in UI immediately after execution

***


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.valohai.com/migration-strategy/migrate-job-outputs.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.