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9
docs/clearml_agent.md
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@ -100,7 +100,8 @@ Install ClearML Agent as a system Python package and not in a Python virtual env
Verifying credentials ...
Credentials verified!
1. Enter your Git username and password. Leave blank for SSH key authentication or when only using public repositories.<br/>
1. Enter your Git username and password. Leave blank for SSH key authentication or when only using public repositories.
This is needed for cloning repositories by the agent.
Enter git username for repository cloning (leave blank for SSH key authentication): []
@ -110,7 +111,8 @@ Install ClearML Agent as a system Python package and not in a Python virtual env
Git repository cloning will be using user=<username> password=<password>
1. Enter an additional artifact repository, or press **Enter** if not required.<br/>
1. Enter an additional artifact repository, or press **Enter** if not required.
This is needed for installing Python packages not found in pypi.
Enter additional artifact repository (extra-index-url) to use when installing python packages (leave blank if not required):
@ -580,7 +582,8 @@ It's possible to add the Docker container as the base Docker image to a Task (ex
## Google Colab
ClearML Agent can run on a [google colab](https://colab.research.google.com/) instance. This helps users to leverage
compute resources provided by google colab and send experiments for execution on it. <br/>
compute resources provided by google colab and send experiments for execution on it.
Check out [this](guides/ide/google_colab.md) tutorial on how to run a ClearML Agent on Google Colab!

3
docs/guides/ide/google_colab.md
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@ -3,7 +3,8 @@ title: ClearML Agent on Google Colab
---
[Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It offers a convenient IDE as well as
compute provided by google.<br/>
compute provided by google.
Users can transform a Google Colab instance into an available resource in ClearML using [Clearml Agent](../../clearml_agent.md).
In this tutorial, we will go over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up

11
docs/integrations/storage.md
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@ -39,7 +39,7 @@ aws {
# key: "my-access-key"
# secret: "my-secret-key"
# },
# {
]
}
boto3 {
@ -51,6 +51,7 @@ aws {
ClearML also supports [MinIO](https://github.com/minio/minio) by adding this configuration:
```
# {
# host: "my-minio-host:9000"
# key: "12345678"
# secret: "12345678"
@ -128,8 +129,10 @@ storage {
```
### Direct Access
By default, all artifacts (Models \ Artifacts \ Datasets) are automatically downloaded to the cache before they're used.<br/>
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.<br/>
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.