> ## Documentation Index
> Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt
> Use this file to discover all available pages before exploring further.

# 파일 오퍼레이션

> CWSandbox 클라이언트를 사용하여 CoreWeave 샌드박스 인스턴스에서 파일을 읽고, 쓰고, 관리합니다

<Note>
  The examples on this page omit `auth`. To authenticate with your W\&B API key, install `cwsandbox[wandb]` and pass `auth=AuthStrategy.WANDB` to `Sandbox.run()`, `Sandbox()`, or `Sandbox.session()`. See [Get started](/products/sandboxes/serverless/get-started).
</Note>

This guide shows you how to read, write, and manage files inside a CoreWeave sandbox instance using the CWSandbox client. It's intended for developers who build workflows that move data into and out of sandboxes, including parallel uploads and downloads, error handling, and patterns for large files.

## Basic operations

The examples in this section cover the most common file operations: writing data to a path in the sandbox and reading data back. File operations return `OperationRef` objects. Use `.result()` to block for completion.

### Write files

```python theme={"system"}
from cwsandbox import Sandbox

with Sandbox.run() as sandbox:
    # Write bytes to a file
    sandbox.write_file("/app/data.txt", b"Hello, World!").result()

    # Write JSON
    import json
    config = {"key": "value", "count": 42}
    sandbox.write_file(
        "/app/config.json",
        json.dumps(config).encode()
    ).result()
```

### Read files

```python theme={"system"}
# Read file contents as bytes
content = sandbox.read_file("/app/data.txt").result()
print(content.decode())  # "Hello, World!"

# Read JSON
config_bytes = sandbox.read_file("/app/config.json").result()
config = json.loads(config_bytes.decode())
```

## Parallel operations

File operations return immediately, enabling natural parallelism. Running uploads or downloads in parallel can reduce total transfer time when you have multiple independent files.

### Parallel uploads

```python theme={"system"}
from cwsandbox import results

# Start all uploads simultaneously
write_refs = [
    sandbox.write_file("/app/config.json", config_bytes),
    sandbox.write_file("/app/data.csv", data_bytes),
    sandbox.write_file("/app/model.pkl", model_bytes),
]

# Wait for all to complete
results(write_refs)
```

### Parallel downloads

```python theme={"system"}
# Start all downloads simultaneously
read_refs = [
    sandbox.read_file("/app/output.json"),
    sandbox.read_file("/app/metrics.json"),
    sandbox.read_file("/app/logs.txt"),
]

# Get all results
output, metrics, logs = results(read_refs)
```

## Upload-process-download pattern

A common workflow: upload input files, run processing, download results.

```python theme={"system"}
from cwsandbox import Sandbox, results

with Sandbox.run() as sandbox:
    # 1. Parallel uploads
    results([
        sandbox.write_file("/app/config.json", config_bytes),
        sandbox.write_file("/app/input.csv", input_bytes),
    ])

    # 2. Sequential processing
    sandbox.exec(["pip", "install", "-r", "requirements.txt"]).result()
    sandbox.exec(["python", "/app/process.py"]).result()

    # 3. Parallel downloads
    output, metrics = results([
        sandbox.read_file("/app/output.json"),
        sandbox.read_file("/app/metrics.json"),
    ])
```

## Error handling

File operations can fail if a path doesn't exist or isn't writable. Catch `SandboxFileError` to handle these cases and inspect the affected path.

### File not found

```python theme={"system"}
from cwsandbox import SandboxFileError

try:
    content = sandbox.read_file("/nonexistent/file.txt").result()
except SandboxFileError as e:
    print(f"File error: {e.filepath}")
```

### Write errors

```python theme={"system"}
try:
    sandbox.write_file("/readonly/path.txt", b"data").result()
except SandboxFileError as e:
    print(f"Cannot write to: {e.filepath}")
```

## Binary files

File operations work with any binary content, including images and serialized Python objects:

```python theme={"system"}
# Images
with open("image.png", "rb") as f:
    sandbox.write_file("/app/image.png", f.read()).result()

# Pickle files (only unpickle data from trusted sources)
import pickle
model_bytes = pickle.dumps(my_model)
sandbox.write_file("/app/model.pkl", model_bytes).result()

# Download and unpickle
model_bytes = sandbox.read_file("/app/trained_model.pkl").result()
trained_model = pickle.loads(model_bytes)
```

<Warning>
  Only unpickle data from sources you trust. Loading a pickle file can execute arbitrary code.
</Warning>

## Text encoding

Files are transferred as bytes, so you must encode and decode text explicitly to avoid corruption with non-ASCII characters.

```python theme={"system"}
# Write text
text = "Hello, Unicode! "
sandbox.write_file("/app/text.txt", text.encode("utf-8")).result()

# Read text
content = sandbox.read_file("/app/text.txt").result()
text = content.decode("utf-8")
```

## Large file considerations

The `read_file` and `write_file` APIs are best suited for small to moderately sized files. For large files, consider the following:

* Account for bandwidth, because files are transferred through the API.
* Use streaming for large datasets when possible.
* Access object storage directly instead of transferring the data through the API.

For large temporary data that stays local to the sandbox, attach a volume instead. See [Volumes](../../volumes).