Tensorlake Documentation

File operations use sandbox-specific ingress, commonly seen as https://<sandbox-id-or-name>.sandbox.tensorlake.ai, derived from the sandbox’s ingress_endpoint.Named sandboxes can use the sandbox name in place of the ID in the proxy hostname. The file APIs documented here run on the management URL on port 9501, which always requires authentication. Unauthenticated proxy access applies only to exposed user ports.

Copy Files

CLI
Python
TypeScript
HTTP

# Copy a local file into the sandbox
tl sbx cp data.csv <sandbox-id>:/workspace/data.csv

# Copy a file from the sandbox to local
tl sbx cp <sandbox-id>:/workspace/data.csv ./data.csv

from tensorlake.sandbox import Sandbox

sandbox = Sandbox.create()

sandbox.write_file("/workspace/data.csv", b"name,score\nAlice,95\nBob,87")

content = sandbox.read_file("/workspace/data.csv")
print(bytes(content).decode("utf-8"))

await sandbox.writeFile(
  "/workspace/data.csv",
  new TextEncoder().encode("name,score\nAlice,95\nBob,87"),
);

const content = await sandbox.readFile("/workspace/data.csv");
console.log(new TextDecoder().decode(content));

curl -X PUT "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/data.csv" \
  -H "Authorization: Bearer $TL_API_KEY" \
  -H "Content-Type: application/octet-stream" \
  --data-binary "name,score\nAlice,95\nBob,87"

curl "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/data.csv" \
  -H "Authorization: Bearer $TL_API_KEY"

tl sbx cp is file-only today. Directory copy workflows should use the Python SDK, TypeScript SDK, or the raw file API.

Read Files

CLI
Python
TypeScript
HTTP

tl sbx cp <sandbox-id>:/workspace/data.csv ./data.csv
tl sbx exec <sandbox-id> cat /workspace/data.csv

content = sandbox.read_file("/workspace/data.csv")
print(bytes(content).decode("utf-8"))

image_bytes = sandbox.read_file("/workspace/chart.png")
with open("chart.png", "wb") as f:
    f.write(image_bytes)

const content = await sandbox.readFile("/workspace/data.csv");
console.log(new TextDecoder().decode(content));

const bytes = await sandbox.readFile("/workspace/chart.png");
console.log(`Read ${bytes.byteLength} bytes`);

curl "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/data.csv" \
  -H "Authorization: Bearer $TL_API_KEY"

curl "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/chart.png" \
  -H "Authorization: Bearer $TL_API_KEY" \
  -o chart.png

Write Files

CLI
Python
TypeScript
HTTP

tl sbx cp config.json <sandbox-id>:/workspace/config.json

sandbox.write_file("/workspace/config.json", b'{"debug": true, "port": 8080}')

with open("model.pkl", "rb") as f:
    sandbox.write_file("/workspace/model.pkl", f.read())

import { readFile } from "node:fs/promises";

await sandbox.writeFile(
  "/workspace/config.json",
  new TextEncoder().encode('{"debug": true, "port": 8080}'),
);

const modelBytes = await readFile("model.pkl");
await sandbox.writeFile("/workspace/model.pkl", modelBytes);

curl -X PUT "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/config.json" \
  -H "Authorization: Bearer $TL_API_KEY" \
  -H "Content-Type: application/octet-stream" \
  --data-binary '{"debug": true, "port": 8080}'

curl -X PUT "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/model.pkl" \
  -H "Authorization: Bearer $TL_API_KEY" \
  -H "Content-Type: application/octet-stream" \
  --data-binary @model.pkl

List Directory Contents

CLI
Python
TypeScript
HTTP

tl sbx exec <sandbox-id> ls -la /workspace

entries = sandbox.list_directory("/workspace")
for entry in entries.entries:
    print(f"{entry.name} ({entry.size} bytes)")

const listing = await sandbox.listDirectory("/workspace");
for (const entry of listing.entries) {
  console.log(`${entry.name} (${entry.size ?? 0} bytes)`);
}

curl "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files/list?path=/workspace" \
  -H "Authorization: Bearer $TL_API_KEY"

Delete Files

CLI
Python
TypeScript
HTTP

tl sbx exec <sandbox-id> rm -rf /workspace/temp

sandbox.delete_file("/workspace/temp")

await sandbox.deleteFile("/workspace/temp");

curl -X DELETE "https://<sandbox-id>.sandbox.tensorlake.ai/api/v1/files?path=/workspace/temp" \
  -H "Authorization: Bearer $TL_API_KEY"

Organize Files

CLI
Python
TypeScript
HTTP

tl sbx exec <sandbox-id> mkdir -p /workspace/src/components
tl sbx exec <sandbox-id> mv /workspace/old.txt /workspace/new.txt

sandbox.run("mkdir", ["-p", "/workspace/src/components"])
sandbox.run("mv", ["/workspace/old.txt", "/workspace/new.txt"])

await sandbox.run("mkdir", {
  args: ["-p", "/workspace/src/components"],
});
await sandbox.run("mv", {
  args: ["/workspace/old.txt", "/workspace/new.txt"],
});

Best Practices

Use /workspace as the default directory for application files.
Use absolute paths to avoid ambiguity.
Use write_file / read_file for programmatic access.
Use tl sbx cp for single-file transfers.
Use the Python SDK, TypeScript SDK, or raw file API for directory-oriented workflows.

Learn More

Commands

Execute commands in sandboxes.

Snapshots

Save and restore sandbox filesystem, memory, and running processes.

Lifecycle

Sandbox states, resources, and timeouts.

Overview

Guides

Use Cases

File Operations

Copy Files

Read Files

Write Files

List Directory Contents

Delete Files

Organize Files

Best Practices

Learn More

Commands

Snapshots

Lifecycle

​Copy Files

​Read Files

​Write Files

​List Directory Contents

​Delete Files

​Organize Files

​Best Practices

​Learn More

Commands

Snapshots

Lifecycle

Copy Files

Read Files

Write Files

List Directory Contents

Delete Files

Organize Files

Best Practices

Learn More