clearml-docs/docs/integrations/storage.md

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---
title: Storage
---
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ClearML is able to interface with the most popular storage solutions in the market for storing model checkpoints, artifacts
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and charts.
Supported storage mediums include:
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![Supported storage](../../static/icons/ClearML_Supported_Storage--on-light.png)
To use cloud storage with ClearML, [install](#installation) the `clearml` package for your cloud storage type, and then
[configure](#configuring-storage) your storage credentials.
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:::note
Once uploading an object to a storage medium, each machine that uses the object must have access to it.
:::
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## Installation
For cloud storage, install the ClearML package for your cloud storage type:
* AWS S3 - `pip install clearml[s3]`
* Azure - `pip install clearml[azure]`
* Google Storage - `pip install clearml[gs]`
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## Configuring Storage
Configuration for storage is done by editing the [clearml.conf](../configs/clearml_conf.md).
The ClearML configuration file uses [HOCON](https://github.com/lightbend/config/blob/main/HOCON.md) format, which supports runtime environment variable access.
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### Configuring AWS S3
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Modify these parts of the clearml.conf file and add the key, secret, and region of the S3 bucket.
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It's possible to also give access to specific S3 buckets in the `aws.s3.credentials` section. The default configuration
provided in the `aws.s3` section is applied to any bucket without a bucket-specific configuration.
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You can also enable using a credentials chain to let Boto3
pick the right credentials. This includes picking credentials from environment variables, a credential file, and metadata service
with an IAM role configured. See [Boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials).
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You can specify additional [ExtraArgs](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-uploading-files.html#the-extraargs-parameter)
to pass to boto3 when uploading files. You can set this on a per-bucket basis.
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```
aws {
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s3 {
# S3 credentials, used for read/write access by various SDK elements
# default, used for any bucket not specified below
key: ""
secret: ""
region: ""
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use_credentials_chain: false
extra_args: {}
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credentials: [
# specifies key/secret credentials to use when handling s3 urls (read or write)
{
bucket: "my-bucket-name"
key: ""
secret: ""
verify: "/path/to/ca/bundle.crt" OR false to not verify
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use_credentials_chain: false
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},
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]
}
boto3 {
pool_connections: 512
max_multipart_concurrency: 16
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}
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}
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```
AWS's S3 access parameters can be specified by referencing the standard environment variables if already defined.
For example:
```
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aws {
s3 {
# default, used for any bucket not specified below
key: ${AWS_ACCESS_KEY_ID}
secret: ${AWS_SECRET_ACCESS_KEY}
region: ${AWS_DEFAULT_REGION}
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}
}
```
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ClearML also supports [MinIO](https://github.com/minio/minio) by adding this configuration:
```
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aws {
s3 {
# default, used for any bucket not specified below
key: ""
secret: ""
region: ""
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credentials: [
{
# This will apply to all buckets in this host (unless key/value is specifically provided for a given bucket)
host: "my-minio-host:9000"
key: ""
secret: ""
multipart: false
secure: false
}
]
}
}
```
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:::info non-AWS Endpoints
To force usage of a non-AWS endpoint (like the MinIO example above), port declaration is *always* needed, even if standard.
To enable TLS, pass `secure: true`.
:::
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### Configuring Azure
To configure Azure blob storage specify the account name and key.
```
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azure.storage {
containers: [
{
account_name: ""
account_key: ""
# container_name:
}
]
}
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```
Azure's storage access parameters can be specified by referencing the standard environment variables if already defined.
For example:
```
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azure.storage {
containers: [
{
account_name: ${AZURE_STORAGE_ACCOUNT}
account_key: ${AZURE_STORAGE_KEY}
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# container_name:
}
]
}
```
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### Configuring Google Storage
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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 in the `google.storage.credentials` section. The default
configuration provided in the `google.storage` section is applied to any bucket without a bucket-specific configuration.
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```
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google.storage {
# 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: ""
subdir: "path/in/bucket" # Not required
project: ""
credentials_json: "/path/to/credentials.json"
},
]
}
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```
GCP's storage access parameters can be specified by referencing the standard environment variables if already defined.
```
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google.storage {
credentials = [
{
bucket: ""
subdir: "path/in/bucket" # Not required
project: ""
credentials_json: ${GOOGLE_APPLICATION_CREDENTIALS}
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},
]
}
```
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## Storage Manager
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ClearML provides the [StorageManager](../references/sdk/storage.md) class to manage downloading, uploading, and caching of
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content directly from code.
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See [Storage Examples](../guides/storage/examples_storagehelper.md).
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### Path Substitution
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The ClearML StorageManager supports local path substitution when fetching files.
This is especially useful when managing data using [`clearml-data`](../clearml_data/clearml_data_cli.md)! If different data consumers have the data physically stored in different locations, path
substitution allows for registering the data into `clearml-data` once, and then storing and accessing it in multiple locations.
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To enable path substitution, modify the clearml.conf file and configure:
```bash
sdk{
storage{
path_substitution = [
# Replace registered links with local prefixes,
# Solve mapping issues, and allow for external resource caching.
# {
# registered_prefix = "s3://bucket/research"
# local_prefix = "file:///mnt/shared/bucket/research
# },
# {
# registered_prefix = "file:///mnt/shared/folder/"
# local_prefix = "file:///home/user/shared/folder"
# }
]
}
}
```
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## Caching
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ClearML also manages a cache of all downloaded content so nothing is duplicated, and code won't need to download the same
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piece twice!
Configure cache location by modifying the [clearml.conf](../configs/clearml_conf.md) file:
```
storage {
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cache {
# Defaults to system temp folder / cache
default_base_dir: "~/.clearml/cache"
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}
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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
]
}
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```
### Direct Access
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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.
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To enable direct access, specify the urls to access directly.