File operations - CoreWeave Docs

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

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

# 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

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

# 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.

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

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

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:

# 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)

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

Text encoding

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

# 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.
Mount S3 or object storage for large data.

# For large data, use S3 mount instead
with Sandbox.run(
    s3_mount={
        "bucket": "my-data-bucket",
        "mount_path": "/data",
        "read_only": False,
    }
) as sandbox:
    # Files in /data are backed by S3
    sandbox.exec(["ls", "/data"]).result()

​Basic operations

​Write files

​Read files

​Parallel operations

​Parallel uploads

​Parallel downloads

​Upload-process-download pattern

​Error handling

​File not found

​Write errors

​Binary files

​Text encoding

​Large file considerations

Basic operations

Write files

Read files

Parallel operations

Parallel uploads

Parallel downloads

Upload-process-download pattern

Error handling

File not found

Write errors

Binary files

Text encoding

Large file considerations