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ClearML is able to interface with the most popular storage solutions in the market for storing model checkpoints, artifacts and charts.

Supported storage mediums include:



Once uploading an object to a storage medium, each machine that uses the object must have access to it.

Configuring Storage#

Configuration for storage is done by editing the clearml.conf.

Configuring AWS S3#

Modify these parts of the clearml.conf file and add the key, secret, and region of the s3 bucket. It's possible to also give access to specific s3 buckets.

aws {
s3 {
# S3 credentials, used for read/write access by various SDK elements
# default, used for any bucket not specified below
key: ""
secret: ""
region: ""
credentials: [
# specifies key/secret credentials to use when handling s3 urls (read or write)
# {
# bucket: "my-bucket-name"
# key: "my-access-key"
# secret: "my-secret-key"
# },
# {
boto3 {
pool_connections: 512
max_multipart_concurrency: 16

ClearML also supports MinIO by adding this configuration:

# host: "my-minio-host:9000"
# key: "12345678"
# secret: "12345678"
# multipart: false
# secure: false
# }

Configuring Azure#

To configure Azure blob storage specify the account name and key. {
# containers: [
# {
# account_name: "clearml"
# account_key: "secret"
# # container_name:
# }
# ]

Configuring Google Storage#

To configure Google Storage, specify the project and the path to the credentials json file. It's also possible to specify credentials for a specific bucket. {
# # Default project and credentials file
# # Will be used when no bucket configuration is found
# project: "clearml"
# credentials_json: "/path/to/credentials.json"
# # Specific credentials per bucket and sub directory
# credentials = [
# {
# bucket: "my-bucket"
# subdir: "path/in/bucket" # Not required
# project: "clearml"
# credentials_json: "/path/to/credentials.json"
# },
# ]

Storage Manager#

ClearML Offers a package to manage downloading, uploading and caching of content directly from code.

Uploading files#

To upload a file using storage manager, just run the following line specifying the path to a local file or folder, and the remote destination.

from clearml import StorageManager

Downloading files#

To download files into cache, run the following line, specifying the remote destination's URL.


Zip and tar.gz files will be automatically extracted to cache. This can be controlled with theextract_archive flag.

Controling cache file limit#

It's possible to control the maximum cache size by limiting the number of files it stores. This is done by calling the StorageManager.set_cache_file_limit() method.


ClearML also manages a cache of all downloaded content so nothing is duplicated, and code won't need to download the same piece twice!

Configure cache location by modifying the clearml.conf file:

storage {
cache {
# Defaults to system temp folder / cache
default_base_dir: "~/.clearml/cache"
direct_access: [
# Objects matching are considered to be available for direct access, i.e. they will not be downloaded
# or cached, and any download request will return a direct reference.
# Objects are specified in glob format, available for url and content_type.
{ url: "file://*" } # file-urls are always directly referenced

Direct Access#

By default, all artifacts (Models \ Artifacts \ Datasets) are automatically downloaded to the cache before they're used.
Some storage mediums (NFS \ Local storage) allows for direct access, which means that the code would work with the object where it's originally stored and not downloaded to cache first.
To enable direct access, specify the urls to access directly.