This guide explains how to copy data between AI Object Storage buckets using s5cmd and Rclone. You can also use s3cmd or Cyberduck to copy data to CoreWeave AI Object Storage. The same approaches can also be used to transfer data from any S3-compatible storage service, such as AWS S3, Azure Blob Storage, or Google Cloud Storage.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.
Choose a migration tool
| Tool | Best for | Notes |
|---|---|---|
| s5cmd | High-performance parallel transfers; transfers involving Local Storage | Recommended for most use cases. High default concurrency (256 workers). |
| Rclone | Complex sync operations; transfers between diverse cloud providers | Do not use with Local Storage due to known kernel panic issues. Safe for AI Object Storage, Distributed File Storage, and external systems. |
Prerequisites
Before you start, ensure you have the following:-
The primary endpoint for AI Object Storage,
https://cwobject.com, requires TLS v1.3. Ensure your S3-compatible tools and OpenSSL support TLS v1.3. - An organization access policy configured.
-
An access key and secret key for each bucket.
For CoreWeave AI Object Storage, use either of these methods:
- Create keys with Workload Identity Federation
- Create keys with a Cloud Console token.
Migrate data with s5cmd
s5cmd is a high-performance, parallel S3 and local filesystem execution tool. CoreWeave maintains a fork of s5cmd that defaults to virtual-style addressing forcwobject.com and cwlota.com, which is required for AI Object Storage.
Install s5cmd
- Download the latest release binary for your platform from the CoreWeave s5cmd releases page. The CoreWeave fork is based on the latest upstream s5cmd and the only difference is that it defaults to virtual-style addressing for AI Object Storage. Other S3-compatible backends are unaffected, so this fork can safely replace any existing s5cmd installation.
-
After downloading, make the binary executable and move it to a directory in your
PATH: - Verify the installation:
Configure credentials
s5cmd reads credentials from environment variables or the AWS shared credentials file. Set the following environment variables with your AI Object Storage access keys: Replace[ACCESS-KEY-ID] and [SECRET-ACCESS-KEY] with your AI Object Storage access key credentials.
Copy objects between buckets
Copy objects from a source bucket to a target bucket: Replace[SOURCE-BUCKET] and [TARGET-BUCKET] with the names of your source and target buckets.
Optimize s5cmd performance
s5cmd provides two main options for tuning parallelism:--numworkers: Sets the size of the global worker pool (default: 256). This controls how many files are transferred concurrently.--concurrency: Acpcommand option that sets the number of parts uploaded or downloaded in parallel for a single file (default: 5). This is useful for large files using multipart transfers.
--numworkers to maximize parallel file transfers:
--numworkers low and increase --concurrency to maximize multipart upload parallelism:
Additional s5cmd operations
s5cmd supports many S3 operations beyond copying. Some useful commands include:s5cmd --help.
Migrate data with Rclone
Rclone is a versatile tool for managing files across cloud storage providers. It supports complex sync operations and works with a wide variety of storage backends.Install Rclone
Follow the Rclone installation guide to install Rclone. This guide uses Rclone versionv1.69.
Configure Rclone
-
Create source and destination profiles in your Rclone config. To locate the config file, run:
If the config file doesn’t exist, Rclone reports the default location where the config file should be created:If an active config file exists, Rclone reports its actual location with a similar message.
-
Edit the config file with your preferred text editor and add the following profiles. Replace
[ACCESS-KEY-ID]and[SECRET-ACCESS-KEY]with your AI Object Storage access key credentials:~/.config/rclone/rclone.conf - Save and close the file.
Copy objects between buckets
Copy objects from the source to the target: Replace[SOURCE-BUCKET] and [TARGET-BUCKET] with the names of your source and target buckets.
- from the source bucket on the
sourceprofile - to the target bucket on the
targetprofile
--progress --stats 15s print a progress bar with estimated time to completion and detailed transfer statistics every 15 seconds.
When working with versioned buckets, Rclone copies the latest version of each object by default. See Use Rclone with Versioned Buckets and Objects for more details, including specific guidance about how to work with specific versions and delete markers.
Optimize Rclone performance
To optimize Rclone throughput with AI Object Storage, use flags to fine-tune parallelism and chunking for large files. The most significant flags are:--transfers: Sets the number of concurrent file transfers. Adjusting this helps fully utilize the available network bandwidth between the customer and CoreWeave, or between CoreWeave regions.--checkers: Controls the number of concurrent file checks for equality. This is useful to adjust when transferring many small files.--s3-chunk-size: Defines the chunk size used for uploading files larger than the upload_cutoff or files with unknown sizes. Larger chunks reduce HTTP requests but increase memory usage.--s3-upload-concurrency: Sets the level of concurrency for multipart uploads.
rclone help flags. Rclone has many flags, so grep is useful to filter the output.
Example output
Optimization guidelines
Use these guidelines to optimize Rclone throughput when copying data to and from CoreWeave AI Object Storage:-
To maximize migration throughput, consider the combined effects of
--transfersand--s3-upload-concurrencyas multiplicative: Total streams ≈--transfers×--s3-upload-concurrency-
For transfers dominated with many small or medium-sized files (KBs to MBs), increase
--transfersto a value between 8 and 32 to move multiple files in parallel, but keep--s3-upload-concurrencyat a lower value, between 1 and 4, because small files don’t benefit from multipart uploads. -
For transfers with a few large files (hundreds of GB), do the opposite: set
--transfersto 1 or 2 to avoid initiating too many multipart uploads, while increasing--s3-upload-concurrencyto a large value, between 8 and 16, to upload multiple parts of each large file in parallel and saturate available bandwidth.
-
For transfers dominated with many small or medium-sized files (KBs to MBs), increase
-
Increase
--s3-chunk-sizeto 50MB for best performance. The default is 5MB. -
Monitor memory use. Estimate Rclone’s RAM needs with this calculation:
RAM ≈
--transfers× (--s3-upload-concurrency× (--s3-chunk-size+--buffer-size))- Each active stream uses
--buffer-size(the default 16MB) - Each multipart chunk consumes
--s3-chunk-size.
- Each active stream uses
-
Increase one flag at a time while monitoring with
rclone --progress --stats 15s. - Stop tuning when throughput plateaus or retries increase. This method ensures you maximize the available bandwidth without overloading local I/O, system memory, or remote service limits.
Usage example
The following command copies data from the source bucket to the target bucket, using the recommended flags. Use--s3-chunk-size 50M for best performance, while adjusting the other flags based on your data size, number of files, and available bandwidth.
Check bucket usage
If you have S3cmd installed, you can check the usage of the target bucket with:Example output