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Merge branch 'main' of https://github.com/allegroai/clearml-docs into example_images

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2
docs/apps/clearml_session.md
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@@ -56,7 +56,7 @@ error, you are good to go.
1. The session Task is enqueued in the selected queue, and a ClearML Agent pulls and executes it. The agent downloads the appropriate IDE(s) and
launches it.
1. Once the agent finishes the initial setup of the interactive Task, the local `cleaml-session` connects to the host
1. Once the agent finishes the initial setup of the interactive Task, the local `clearml-session` connects to the host
machine via SSH, and tunnels both SSH and IDE over the SSH connection. If a container is specified, the
IDE environment runs inside of it.

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docs/best_practices/data_scientist_best_practices.md
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@@ -47,7 +47,7 @@ that you need.
accessed, [compared](../webapp/webapp_exp_comparing.md) and [tracked](../webapp/webapp_exp_track_visual.md).
- [ClearML Agent](../clearml_agent.md) does the heavy lifting. It reproduces the execution environment, clones your code,
applies code patches, manages parameters (including overriding them on the fly), executes the code, and queues multiple tasks.
It can even [build](../../clearml_agent/clearml_agent_docker_exec#exporting-a-task-into-a-standalone-docker-container) the docker container for you!
It can even [build](../getting_started/clearml_agent_docker_exec.md#exporting-a-task-into-a-standalone-docker-container) the container for you!
- [ClearML Pipelines](../pipelines/pipelines.md) ensure that steps run in the same order,
programmatically chaining tasks together, while giving an overview of the execution pipeline's status.

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docs/best_practices/mlops_best_practices.md
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@@ -18,7 +18,7 @@ If you are afraid of clutter, use the archive option, and set up your own [clean
## Clone Tasks
Define a ClearML Task with one of the following options:
- Run the actual code with the `Task.init()` call. This will create and auto-populate the Task in CleaML (including Git Repo / Python Packages / Command line etc.).
- Run the actual code with the `Task.init()` call. This will create and auto-populate the Task in ClearML (including Git Repo / Python Packages / Command line etc.).
- Register local / remote code repository with `clearml-task`. See [details](../apps/clearml_task.md).
Once you have a Task in ClearML, you can clone and edit its definitions in the UI, then launch it on one of your nodes with [ClearML Agent](../clearml_agent.md).

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docs/clearml_agent/clearml_agent_dynamic_gpus.md
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@@ -1,8 +1,9 @@
---
title: Dynamic GPU Allocation
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Dynamic GPU allocation is available under the ClearML Enterprise plan.
:::
The ClearML Enterprise server supports dynamic allocation of GPUs based on queue properties.

4
docs/configs/clearml_conf.md
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@@ -414,7 +414,7 @@ These settings define which Docker image and arguments should be used unless [ex
* **`agent.default_docker.match_rules`** (*[dict]*)
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
The `match_rules` configuration option is available under the ClearML Enterprise plan.
:::
* Lookup table of rules that determine the default container and arguments when running a worker in Docker mode. The
@@ -1599,7 +1599,7 @@ sdk {
## Configuration Vault
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Configuration vaults are available under the ClearML Enterprise plan.
:::
The ClearML Enterprise Server includes the configuration vault. Users can add configuration sections to the vault and, once

2
docs/deploying_clearml/clearml_server_config.md
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@@ -422,7 +422,7 @@ options.
### Custom UI Context Menu Actions
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Custom UI context menu actions are available under the ClearML Enterprise plan.
:::
Create custom UI context menu actions to be performed on ClearML objects (projects, tasks, models, dataviews, or queues)

2
docs/deploying_clearml/clearml_server_es7_migration.md
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@@ -129,7 +129,7 @@ and ClearML Server needs to be installed.
1. Add the `clearml-server` repository to Helm client.
```
helm repo add allegroai https://allegroai.github.io/clearml-server-helm/
helm repo add clearml https://clearml.github.io/clearml-server-helm/
```
Confirm the `clearml-server` repository is now in the Helm client.

2
docs/deploying_clearml/clearml_server_linux_mac.md
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@@ -136,7 +136,7 @@ Deploying the server requires a minimum of 8 GB of memory, 16 GB is recommended.
2. Download the ClearML Server docker-compose YAML file.
```
sudo curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
sudo curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. For Linux only, configure the **ClearML Agent Services**:

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docs/deploying_clearml/clearml_server_win.md
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@@ -57,7 +57,7 @@ Deploying the server requires a minimum of 8 GB of memory, 16 GB is recommended.
1. Save the ClearML Server docker-compose YAML file.
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
```
1. Run `docker-compose`. In PowerShell, execute the following commands:

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docs/deploying_clearml/enterprise_deploy/app_install_ex_server.md
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@@ -2,6 +2,10 @@
title: Installing External Applications Server
---
:::important Enterprise Feature
UI application deployment is available under the ClearML Enterprise plan.
:::
ClearML supports applications, which are extensions that allow additional capabilities, such as cloud auto-scaling,
Hyperparameter Optimizations, etc. For more information, see [ClearML Applications](../../webapp/applications/apps_overview.md).

4
docs/deploying_clearml/enterprise_deploy/app_install_ubuntu_on_prem.md
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@@ -2,6 +2,10 @@
title: Application Installation on On-Prem and VPC Servers
---
:::important Enterprise Feature
UI application deployment is available under the ClearML Enterprise plan.
:::
ClearML Applications are like plugins that allow you to manage ML workloads and automatically run recurring workflows
without any coding. Applications are installed on top of the ClearML Server.

2
docs/deploying_clearml/enterprise_deploy/appgw.md
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@@ -3,7 +3,7 @@ title: AI Application Gateway
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
The AI Application Gateway is available under the ClearML Enterprise plan.
:::
Services running through a cluster orchestrator such as Kubernetes or cloud hyperscaler require meticulous configuration

8
docs/deploying_clearml/enterprise_deploy/appgw_install_compose.md
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@@ -1,4 +1,10 @@
# Docker-Compose Deployment
---
title: Docker-Compose Deployment
---
:::important Enterprise Feature
The Application Gateway is available under the ClearML Enterprise plan.
:::
## Requirements

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@@ -1,4 +1,10 @@
# Kubernetes Deployment
---
title: Kubernetes Deployment
---
:::important Enterprise Feature
The Application Gateway is available under the ClearML Enterprise plan.
:::
This guide details the installation of the ClearML AI Application Gateway, specifically the ClearML Task Router Component.
@@ -6,8 +12,8 @@ This guide details the installation of the ClearML AI Application Gateway, speci
* Kubernetes cluster: `>= 1.21.0-0 < 1.32.0-0`
* Helm installed and configured
* Helm token to access allegroai helm-chart repo
* Credentials for allegroai docker repo
* Helm token to access `allegroai` helm-chart repo
* Credentials for `allegroai` docker repo
* A valid ClearML Server installation
## Optional for HTTPS
@@ -21,7 +27,7 @@ This guide details the installation of the ClearML AI Application Gateway, speci
```
helm repo add allegroai-enterprise \
https://raw.githubusercontent.com/allegroai/clearml-enterprise-helm-charts/gh-pages \
https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages \
--username <GITHUB_TOKEN> \
--password <GITHUB_TOKEN>
```

2
docs/deploying_clearml/enterprise_deploy/change_artifact_links.md
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@@ -1,5 +1,5 @@
---
title: Changing CleaML Artifacts Links
title: Changing ClearML Artifacts Links
---
This guide describes how to update artifact references in the ClearML Enterprise server.

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docs/deploying_clearml/enterprise_deploy/custom_billing.md Normal file
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@@ -0,0 +1,122 @@
---
title: Custom Billing Events
---
:::important Enterprise Feature
Sending custom billing events is available under the ClearML Enterprise plan.
:::
ClearML supports sending custom events to selected Kafka topics. Event sending is triggered by API calls and
is available only for the companies with the `custom_events` settings set.
## Enabling Custom Events in ClearML Server
:::important Prerequisite
**Precondition**: Customer Kafka for custom events is installed and reachable from the `apiserver`.
:::
Set the following environment variables in the ClearML Enterprise helm chart under the `apiserver.extraEnv`:
* Enable custom events:
```
- name: CLEARML__services__custom_events__enabled
value: "true"
```
* Mount custom message template files into `/mnt/custom_events/templates` folder in the `apiserver` container and point
the `apiserver` into it:
```
- name: CLEARML__services__custom_events__template_folder
value: "/mnt/custom_events/templates"
```
* Configure the Kafka host for sending events:
```
- name: CLEARML__hosts__kafka__custom_events__host
value: "[<KAFKA host address:port>]"
```
Configure Kafka security parameters. Below is the example for SASL plaintext security:
```
- name: CLEARML__SECURE__KAFKA__CUSTOM_EVENTS__security_protocol
value: "SASL_PLAINTEXT"
- name: CLEARML__SECURE__KAFKA__CUSTOM_EVENTS__sasl_mechanism
value: "SCRAM-SHA-512"
- name: CLEARML__SECURE__KAFKA__CUSTOM_EVENTS__sasl_plain_username
value: "<username>"
- name: CLEARML__SECURE__KAFKA__CUSTOM_EVENTS__sasl_plain_password
value: "<password>"
```
* Define Kafka topics for lifecycle and inventory messages:
```
- name: CLEARML__services__custom_events__channels__main__topics__service_instance_lifecycle
value: "lifecycle"
- name: CLEARML__services__custom_events__channels__main__topics__service_instance_inventory
value: "inventory"
```
* For the desired companies set up the custom events properties required by the event message templates:
```
curl $APISERVER_URL/system.update_company_custom_events_config -H "Content-Type: application/json" -u $APISERVER_KEY:$APISERVER_SECRET -d'{
"company": "<company_id>",
"fields": {
"service_instance_id": "<value>",
"service_instance_name": "<value>",
"service_instance_customer_tenant_name": "<value>",
"service_instance_customer_space_name": "<value>",
"service_instance_customer_space_id": "<value>",
"parameters_connection_points": ["<value1>", "<value2>"]
}}'
```
## Sending Custom Events to the API Server
:::important Prerequisite
**Precondition:** Dedicated custom-events Redis instance installed and reachable from all the custom events deployments.
:::
Environment lifecycle events are sent directly by the `apiserver`. Other event types are emitted by the following helm charts:
* `clearml-pods-monitor-exporter` - Monitors running pods and sends container lifecycle events (should run one per cluster with a unique identifier, a UUID is required for the installation):
```
# -- Universal Unique string to identify Pods Monitor instances across worker clusters. It cannot be empty.
# Uniqueness is required across different cluster installations to preserve the reported data status.
podsMonitorUUID: "<Unique ID>"
# Interval
checkIntervalSeconds: 60
```
* `clearml-pods-inventory` - Periodically sends inventory events about running pods.
```
# Cron schedule - https://crontab.guru/
cronJob:
schedule: "@daily"
```
* `clearml-company-inventory` - Monitors Clearml companies and sends environment inventory events.
```
# Cron schedule - https://crontab.guru/
cronJob:
schedule: "@daily"
```
For every script chart add the below configuration to enable redis access and connection to the `apiserver`:
```
clearml:
apiServerUrlReference: "<APISERVER_URL>"
apiServerKey: "<APISERVER_KEY>"
apiServerSecret: "<APISERVER_SECRET>"
redisConnection:
host: "<REDIS_HOST>"
port: <REDIS_PORT>
password: "<REDIS_PWD>"
```
See all other available options to customize the `custom-events` charts by running:
```
helm show readme allegroai-enterprise/<CHART_NAME>
```

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docs/deploying_clearml/enterprise_deploy/import_projects.md
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@@ -1,5 +1,5 @@
---
title: Exporting and Importing ClearML Projects
title: Project Migration
---
When migrating from a ClearML Open Server to a ClearML Enterprise Server, you may need to transfer projects. This is done
@@ -235,6 +235,6 @@ Note that this is not required if the new file server is replacing the old file
exact address.
Once the projects' data has been copied to the target server, and the projects themselves were imported, see
[Changing CleaML Artifacts Links](change_artifact_links.md) for information on how to fix the URLs.
[Changing ClearML Artifacts Links](change_artifact_links.md) for information on how to fix the URLs.

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docs/deploying_clearml/upgrade_server_aws_ec2_ami.md
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@@ -2,14 +2,21 @@
title: AWS EC2 AMIs
---
:::note
For upgrade purposes, the terms **Trains Server** and **ClearML Server** are interchangeable.
:::
<Collapsible title="Important: Upgrading to v2.x from v1.16.0 or older" type="info">
MongoDB major version was upgraded from `v5.x` to `6.x`. Please note that if your current ClearML Server version is older than
`v1.17` (where MongoDB `v5.x` was first used), you'll need to first upgrade to ClearML Server v1.17.
First upgrade to ClearML Server v1.17 following the procedure below and using [this `docker-compose` file](https://github.com/clearml/clearml-server/blob/2976ce69cc91550a3614996e8a8d8cd799af2efd/upgrade/1_17_to_2_0/docker-compose.yml). Once successfully upgraded,
you can proceed to upgrade to v2.x.
</Collapsible>
The sections below contain the steps to upgrade ClearML Server on the [same AWS instance](#upgrading-on-the-same-aws-instance), and
to upgrade and migrate to a [new AWS instance](#upgrading-and-migrating-to-a-new-aws-instance).
### Upgrading on the Same AWS Instance
## Upgrading on the Same AWS Instance
This section contains the steps to upgrade ClearML Server on the same AWS instance.
@@ -42,7 +49,7 @@ If upgrading from Trains Server version 0.15 or older, a data migration is requi
1. Download the latest `docker-compose.yml` file. Execute the following command:
```
sudo curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
sudo curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build.
@@ -52,7 +59,7 @@ If upgrading from Trains Server version 0.15 or older, a data migration is requi
docker-compose -f docker-compose.yml up -d
```
### Upgrading and Migrating to a New AWS Instance
## Upgrading and Migrating to a New AWS Instance
This section contains the steps to upgrade ClearML Server on the new AWS instance.
@@ -67,8 +74,9 @@ This section contains the steps to upgrade ClearML Server on the new AWS instanc
1. On the old AWS instance, [backup your data](clearml_server_aws_ec2_ami.md#backing-up-and-restoring-data-and-configuration)
and, if your configuration folder is not empty, backup your configuration.
1. If upgrading from ClearML Server version older than 1.2, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
If upgrading from Trains Server version 0.15 or older, a data migration is required before continuing this upgrade. See instructions [here](clearml_server_es7_migration.md).
1. If upgrading from Trains Server version 0.15 or older, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_es7_migration.md).
1. If upgrading from ClearML Server version 1.1 or older, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. On the new AWS instance, [restore your data](clearml_server_aws_ec2_ami.md#backing-up-and-restoring-data-and-configuration) and, if the configuration folder is not empty, restore the
configuration.

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@@ -19,11 +19,13 @@ you can proceed to upgrade to v2.x.
```
docker-compose -f docker-compose.yml down
```
1. [Backing up data](clearml_server_gcp.md#backing-up-and-restoring-data-and-configuration) is recommended, and if the configuration folder is
not empty, backing up the configuration.
1. If upgrading from **Trains Server** version 0.15 or older to **ClearML Server**, do the following:
1. Follow these [data migration instructions](clearml_server_es7_migration.md),
and then continue this upgrade.
1. Follow these [data migration instructions](clearml_server_es7_migration.md).
1. Rename `/opt/trains` and its subdirectories to `/opt/clearml`:
@@ -31,14 +33,12 @@ you can proceed to upgrade to v2.x.
sudo mv /opt/trains /opt/clearml
```
1. If upgrading from ClearML Server version older than 1.2, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. [Backing up data](clearml_server_gcp.md#backing-up-and-restoring-data-and-configuration) is recommended, and if the configuration folder is
not empty, backing up the configuration.
1. If upgrading from ClearML Server version 1.1 or older, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. Download the latest `docker-compose.yml` file:
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build.

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docs/deploying_clearml/upgrade_server_kubernetes_helm.md
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@@ -7,13 +7,13 @@ title: Kubernetes
```bash
helm repo update
helm upgrade clearml allegroai/clearml
helm upgrade clearml clearml/clearml
```
**To change the values in an existing installation,** execute the following:
```bash
helm upgrade clearml allegroai/clearml --version <CURRENT CHART VERSION> -f custom_values.yaml
helm upgrade clearml clearml/clearml --version <CURRENT CHART VERSION> -f custom_values.yaml
```
See the [clearml-helm-charts repository](https://github.com/clearml/clearml-helm-charts/tree/main/charts/clearml#local-environment)

20
docs/deploying_clearml/upgrade_server_linux_mac.md
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@@ -40,24 +40,26 @@ For backwards compatibility, the environment variables ``TRAINS_HOST_IP``, ``TRA
```
docker-compose -f docker-compose.yml down
```
1. If upgrading from **Trains Server** version 0.15 or older, a data migration is required before continuing this upgrade. See instructions [here](clearml_server_es7_migration.md).
1. If upgrading from ClearML Server version older than 1.2, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. [Backing up data](clearml_server_linux_mac.md#backing-up-and-restoring-data-and-configuration) is recommended and, if the configuration folder is
not empty, backing up the configuration.
1. If upgrading from **Trains Server** version 0.15 or older to **ClearML Server**, do the following:
1. If upgrading from **Trains Server** to **ClearML Server**, rename `/opt/trains` and its subdirectories to `/opt/clearml`:
1. Follow these [data migration instructions](clearml_server_es7_migration.md).
1. Rename `/opt/trains` and its subdirectories to `/opt/clearml`:
```
sudo mv /opt/trains /opt/clearml
```
```
sudo mv /opt/trains /opt/clearml
```
1. If upgrading from ClearML Server version 1.1 or older, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. Download the latest `docker-compose.yml` file:
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose.yml -o /opt/clearml/docker-compose.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build:

15
docs/deploying_clearml/upgrade_server_win.md
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@@ -29,10 +29,7 @@ you can proceed to upgrade to v2.x.
```
docker-compose -f c:\opt\trains\docker-compose-win10.yml down
```
1. If upgrading from **Trains Server** version 0.15 or older, a data migration is required before continuing this upgrade. See instructions [here](clearml_server_es7_migration.md).
1. If upgrading from ClearML Server version older than 1.2, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. Backing up data is recommended, and if the configuration folder is not empty, backing up the configuration.
@@ -40,13 +37,19 @@ you can proceed to upgrade to v2.x.
For example, if the configuration is in ``c:\opt\clearml``, then backup ``c:\opt\clearml\config`` and ``c:\opt\clearml\data``.
Before restoring, remove the old artifacts in ``c:\opt\clearml\config`` and ``c:\opt\clearml\data``, and then restore.
:::
1. If upgrading from **Trains Server** to **ClearML Server**, rename `/opt/trains` and its subdirectories to `/opt/clearml`.
1. If upgrading from **Trains Server** version 0.15 or older to **ClearML Server**, do the following:
1. Follow these [data migration instructions](clearml_server_es7_migration.md).
1. Rename `/opt/trains` and its subdirectories to `/opt/clearml`.
1. If upgrading from ClearML Server version 1.1 or older, you need to migrate your data before upgrading your server. See instructions [here](clearml_server_mongo44_migration.md).
1. Download the latest `docker-compose.yml` file:
```
curl https://raw.githubusercontent.com/allegroai/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
curl https://raw.githubusercontent.com/clearml/clearml-server/master/docker/docker-compose-win10.yml -o c:\opt\clearml\docker-compose-win10.yml
```
1. Startup ClearML Server. This automatically pulls the latest ClearML Server build.

2
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@@ -1,5 +1,5 @@
---
title: Building Executable Task Containers
title: Building Executable Task Containers
---
## Exporting a Task into a Standalone Docker Container

2
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@@ -3,7 +3,7 @@ title: Managing Agent Work Schedules
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Agent work schedule management is available under the ClearML Enterprise plan.
:::
The Agent scheduler enables scheduling working hours for each Agent. During working hours, a worker will actively poll

6
docs/getting_started/main.md
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@@ -32,19 +32,19 @@ training, and deploying models at every scale on any AI infrastructure.
<tbody>
<tr>
<td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb"><b>Step 1</b></a> - Experiment Management</td>
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
</tr>
<tr>
<td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb"><b>Step 2</b></a> - Remote Execution Agent Setup</td>
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
</tr>
<tr>
<td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb"><b>Step 3</b></a> - Remotely Execute Tasks</td>
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/allegroai/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
<td className="align-center"><a className="no-ext-icon" target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
</tr>

2
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View File

@@ -49,7 +49,7 @@ Execution log at: https://app.clear.ml/projects/552d5399112d47029c146d5248570295
### Executing a Local Script
For this example, use a local version of [this script](https://github.com/clearml/events/blob/master/webinar-0620/keras_mnist.py).
1. Clone the [allegroai/events](https://github.com/clearml/events) repository
1. Clone the [clearml/events](https://github.com/clearml/events) repository
1. Go to the root folder of the cloned repository
1. Run the following command:

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@@ -16,7 +16,7 @@ and running, users can send Tasks to be executed on Google Colab's hardware.
## Steps
1. Open up [this Google Colab notebook](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/clearml_agent/clearml_colab_agent.ipynb).
1. Open up [this Google Colab notebook](https://colab.research.google.com/github/clearml/clearml/blob/master/examples/clearml_agent/clearml_colab_agent.ipynb).
1. Run the first cell, which installs all the necessary packages:
```

14
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@@ -3,7 +3,7 @@ title: Pipeline from Decorators
---
The [pipeline_from_decorator.py](https://github.com/clearml/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py)
example demonstrates the creation of a pipeline in ClearML using the [`PipelineDecorator`](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)
example demonstrates the creation of a pipeline in ClearML using the [`PipelineDecorator`](../../references/sdk/automation_controller_pipelinedecorator.md#class-automationcontrollerpipelinedecorator)
class.
This example creates a pipeline incorporating four tasks, each of which is created from a Python function using a custom decorator:
@@ -14,11 +14,11 @@ This example creates a pipeline incorporating four tasks, each of which is creat
* `step_four` - Uses data from `step_two` and the model from `step_three` to make a prediction.
The pipeline steps, defined in the `step_one`, `step_two`, `step_three`, and `step_four` functions, are each wrapped with the
[`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent)
[`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent)
decorator, which creates a ClearML pipeline step for each one when the pipeline is executed.
The logic that executes these steps and controls the interaction between them is implemented in the `executing_pipeline`
function. This function is wrapped with the [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
function. This function is wrapped with the [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
decorator which creates the ClearML pipeline task when it is executed.
The sections below describe in more detail what happens in the pipeline controller and steps.
@@ -28,7 +28,7 @@ The sections below describe in more detail what happens in the pipeline controll
In this example, the pipeline controller is implemented by the `executing_pipeline` function.
Using the `@PipelineDecorator.pipeline` decorator creates a ClearML Controller Task from the function when it is executed.
For detailed information, see [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline).
For detailed information, see [`@PipelineDecorator.pipeline`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline).
In the example script, the controller defines the interactions between the pipeline steps in the following way:
1. The controller function passes its argument, `pickle_url`, to the pipeline's first step (`step_one`)
@@ -39,13 +39,13 @@ In the example script, the controller defines the interactions between the pipel
:::info Local Execution
In this example, the pipeline is set to run in local mode by using
[`PipelineDecorator.run_locally()`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorrun_locally)
[`PipelineDecorator.run_locally()`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorrun_locally)
before calling the pipeline function. See pipeline execution options [here](../../pipelines/pipelines_sdk_function_decorators.md#running-the-pipeline).
:::
## Pipeline Steps
Using the `@PipelineDecorator.component` decorator will make the function a pipeline component that can be called from the
pipeline controller, which implements the pipeline's execution logic. For detailed information, see [`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent).
pipeline controller, which implements the pipeline's execution logic. For detailed information, see [`@PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent).
When the pipeline controller calls a pipeline step, a corresponding ClearML task will be created. Notice that all package
imports inside the function will be automatically logged as required packages for the pipeline execution step.
@@ -63,7 +63,7 @@ executing_pipeline(
```
By default, the pipeline controller and the pipeline steps are launched through ClearML [queues](../../fundamentals/agents_and_queues.md#what-is-a-queue).
Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
Use the [`PipelineDecorator.set_default_execution_queue`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorset_default_execution_queue)
method to specify the execution queue of all pipeline steps. The `execution_queue` parameter of the `@PipelineDecorator.component`
decorator overrides the default queue value for the specific step for which it was specified.

2
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@@ -22,7 +22,7 @@ The Slack API token and channel you create are required to configure the Slack a
1. In **Development Slack Workspace**, select a workspace.
1. Click **Create App**.
1. In **Basic Information**, under **Display Information**, complete the following:
- In **Short description**, enter "Allegro Train Bot".
- In **Short description**, enter "ClearML Train Bot".
- In **Background color**, enter "#202432".
1. Click **Save Changes**.
1. In **OAuth & Permissions**, under **Scopes**, click **Add an OAuth Scope**, and then select the following permissions

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@@ -4,14 +4,14 @@ title: PipelineDecorator
## Creating Pipelines Using Function Decorators
Use the [`PipelineDecorator`](../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)
class to create pipelines from your existing functions. Use [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent)
to denote functions that comprise the steps of your pipeline, and [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
Use the [`PipelineDecorator`](../references/sdk/automation_controller_pipelinedecorator.md#class-automationcontrollerpipelinedecorator)
class to create pipelines from your existing functions. Use [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent)
to denote functions that comprise the steps of your pipeline, and [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
for your main pipeline execution logic function.
## @PipelineDecorator.pipeline
Using the [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
Using the [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
decorator transforms the function which implements your pipeline's execution logic to a ClearML pipeline controller,
an independently executed task.
@@ -70,13 +70,13 @@ parameters. When launching a new pipeline run from the [UI](../webapp/pipelines/
![Pipeline new run](../img/pipelines_new_run.png)
## @PipelineDecorator.component
Using the [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent)
Using the [`@PipelineDecorator.component`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent)
decorator transforms a function into a ClearML pipeline step when called from a pipeline controller.
When the pipeline controller calls a pipeline step, a corresponding ClearML task is created.
:::tip Package Imports
In the case that the `skip_global_imports` parameter of [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorpipeline)
In the case that the `skip_global_imports` parameter of [`@PipelineDecorator.pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorpipeline)
is set to `False`, all global imports will be automatically imported at the beginning of each step's execution.
Otherwise, if set to `True`, make sure that each function which makes up a pipeline step contains package imports, which
are automatically logged as required packages for the pipeline execution step.
@@ -110,7 +110,7 @@ def step_one(pickle_data_url: str, extra: int = 43):
* `packages` - A list of required packages or a local requirements.txt file. Example: `["tqdm>=2.1", "scikit-learn"]` or
`"./requirements.txt"`. If not provided, packages are automatically added based on the imports used inside the function.
* `execution_queue` (optional) - Queue in which to enqueue the specific step. This overrides the queue set with the
[`PipelineDecorator.set_default_execution_queue method`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
[`PipelineDecorator.set_default_execution_queue method`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorset_default_execution_queue)
method.
* `continue_on_fail` - If `True`, a failed step does not cause the pipeline to stop (or marked as failed). Notice, that
steps that are connected (or indirectly connected) to the failed step are skipped (default `False`)
@@ -186,14 +186,14 @@ specify which frameworks to log. See `Task.init`'s [`auto_connect_framework` par
* `auto_connect_arg_parser` - Control automatic logging of argparse objects. See `Task.init`'s [`auto_connect_arg_parser` parameter](../references/sdk/task.md#taskinit)
You can also directly upload a model or an artifact from the step to the pipeline controller, using the
[`PipelineDecorator.upload_model`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_model)
and [`PipelineDecorator.upload_artifact`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_artifact)
[`PipelineDecorator.upload_model`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorupload_model)
and [`PipelineDecorator.upload_artifact`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorupload_artifact)
methods respectively.
## Controlling Pipeline Execution
### Default Execution Queue
The [`PipelineDecorator.set_default_execution_queue`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue)
The [`PipelineDecorator.set_default_execution_queue`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorset_default_execution_queue)
method lets you set a default queue through which all pipeline steps
will be executed. Once set, step-specific overrides can be specified through the `@PipelineDecorator.component` decorator.
@@ -226,7 +226,7 @@ You can run the pipeline logic locally, while keeping the pipeline components ex
#### Debugging Mode
In debugging mode, the pipeline controller and all components are treated as regular Python functions, with components
called synchronously. This mode is great to debug the components and design the pipeline as the entire pipeline is
executed on the developer machine with full ability to debug each function call. Call [`PipelineDecorator.debug_pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratordebug_pipeline)
executed on the developer machine with full ability to debug each function call. Call [`PipelineDecorator.debug_pipeline`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratordebug_pipeline)
before the main pipeline logic function call.
Example:
@@ -242,7 +242,7 @@ In local mode, the pipeline controller creates Tasks for each component, and com
into sub-processes running on the same machine. Notice that the data is passed between the components and the logic with
the exact same mechanism as in the remote mode (i.e. hyperparameters / artifacts), with the exception that the execution
itself is local. Notice that each subprocess is using the exact same Python environment as the main pipeline logic. Call
[`PipelineDecorator.run_locally`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorrun_locally)
[`PipelineDecorator.run_locally`](../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorrun_locally)
before the main pipeline logic function.
Example:

5
docs/references/sdk/automation_controller_pipelinedecorator.md Normal file
View File

@@ -0,0 +1,5 @@
---
title: PipelineDecorator
---
**AutoGenerated PlaceHolder**

2
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@@ -3,7 +3,7 @@ title: Identity Providers
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Identity provider integration is available under the ClearML Enterprise plan.
:::
Administrators can seamlessly connect ClearML with their identity service providers to easily implement single sign-on

15
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View File

@@ -319,17 +319,10 @@ to an IAM user, and create credentials keys for that user to configure in the au
"ssm:GetParameters",
"ssm:GetParameter"
],
"Resource": "arn:aws:ssm:*::parameter/aws/service/marketplace/*"
},
{
"Sid": "AllowUsingDeeplearningAMIAliases",
"Effect": "Allow",
"Action": [
"ssm:GetParametersByPath",
"ssm:GetParameters",
"ssm:GetParameter"
],
"Resource": "arn:aws:ssm:*::parameter/aws/service/deeplearning/*"
"Resource": [
"arn:aws:ssm:*::parameter/aws/service/marketplace/*",
"arn:aws:ssm:*::parameter/aws/service/deeplearning/*"
]
}
]
}

2
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View File

@@ -36,7 +36,7 @@ The pipeline run table contains the following columns:
| Column | Description | Type |
|---|---|---|
| **RUN** | Pipeline run identifier | String |
| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)'s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)'s `version` parameter | Version string |
| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)'s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinedecorator.md#class-automationcontrollerpipelinedecorator)'s `version` parameter | Version string |
| **TAGS** | Descriptive, user-defined, color-coded tags assigned to run. | Tag |
| **STATUS** | Pipeline run's status. See a list of the [task states and state transitions](../../fundamentals/task.md#task-states). For Running, Failed, and Aborted runs, you will also see a progress indicator next to the status. See [here](../../pipelines/pipelines.md#tracking-pipeline-progress). | String |
| **USER** | User who created the run. | String |

2
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View File

@@ -108,7 +108,7 @@ The details panel includes three tabs:
![console](../../img/webapp_pipeline_step_console_dark.png#dark-mode-only)
* **Code** - For pipeline steps generated from functions using either [`PipelineController.add_function_step`](../../references/sdk/automation_controller_pipelinecontroller.md#add_function_step)
or [`PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorcomponent),
or [`PipelineDecorator.component`](../../references/sdk/automation_controller_pipelinedecorator.md#pipelinedecoratorcomponent),
you can view the selected step's code.
![code](../../img/webapp_pipeline_step_code.png#light-mode-only)

2
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View File

@@ -3,7 +3,7 @@ title: Resource Policies
---
:::important ENTERPRISE FEATURE
This feature is available under the ClearML Enterprise plan.
Resource Policies are available under the ClearML Enterprise plan.
:::

2
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View File

@@ -3,7 +3,7 @@ title: Access Rules
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Access rules are available under the ClearML Enterprise plan.
:::
Workspace administrators can use the **Access Rules** page to manage workspace permissions, by specifying which users,

2
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View File

@@ -3,7 +3,7 @@ title: Administrator Vaults
---
:::info Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Administrator vaults are available under the ClearML Enterprise plan.
:::
Administrators can define multiple [configuration vaults](webapp_settings_profile.md#configuration-vault) which will each be applied to designated

2
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View File

@@ -3,7 +3,7 @@ title: Identity Providers
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Identity provider integration is available under the ClearML Enterprise plan.
:::
Administrators can connect identity service providers to the server: configure an identity connection, which allows

4
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View File

@@ -100,7 +100,7 @@ these credentials cannot be recovered.
### AI Application Gateway Tokens
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
The AI Application Gateway is available under the ClearML Enterprise plan.
:::
The AI Application Gateway enables external access to ClearML tasks and applications. The gateway is configured with an
@@ -146,7 +146,7 @@ in that workspace. You can rejoin the workspace only if you are re-invited.
### Configuration Vault
:::info Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Configuration vaults are available under the ClearML Enterprise plan.
:::
Use the configuration vault to store global ClearML configuration entries that can extend the ClearML [configuration file](../../configs/clearml_conf.md)

4
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View File

@@ -42,7 +42,7 @@ user can only rejoin your workspace when you re-invite them.
## Service Accounts
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
Service accounts are available under the ClearML Enterprise plan.
:::
Service accounts are ClearML users that provide services with access to the ClearML API, but not the
@@ -155,7 +155,7 @@ To delete a service account:
## User Groups
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan, as part of the [Access Rules](webapp_settings_access_rules.md)
User groups are available under the ClearML Enterprise plan, as part of the [Access Rules](webapp_settings_access_rules.md)
feature.
:::

14
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View File

@@ -93,7 +93,7 @@ using to set up an environment (`pip` or `conda`) are available. Select which re
### Container
The Container section list the following information:
* Image - a pre-configured container that ClearML Agent will use to remotely execute this task (see [Building Docker containers](../getting_started/clearml_agent_docker_exec.md))
* Image - a pre-configured container that ClearML Agent will use to remotely execute this task (see [Building Task Execution Environments in a Container](../getting_started/clearml_agent_base_docker.md))
* Arguments - add container arguments
* Setup shell script - a bash script to be executed inside the container before setting up the task's environment
@@ -230,13 +230,13 @@ The **INFO** tab shows extended task information:
* [Task description](#description)
* [Task details](#task-details)
### Latest Events Log
### Latest Events Log
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
:::info Hosted Service and Enterprise Feature
The latest events log is available only on the ClearML Hosted Service and under the ClearML Enterprise plan.
:::
The Enterprise Server also displays a detailed history of task activity:
The **INFO** tab includes a detailed history of task activity:
* Task action (e.g. status changes, project move, etc.)
* Action time
* Acting user
@@ -252,7 +252,7 @@ To download the task history as a CSV file, hover over the log and click <img sr
ClearML maintains a system-wide, large but strict limit for task history items. Once the limit is reached, the oldest entries are purged to make room for fresh entries.
:::
### Description
### Description
Add descriptive text to the task in the **Description** section. To modify the description, hover over the
description box and click **Edit**.
@@ -304,7 +304,7 @@ All scalars that ClearML automatically logs, as well as those explicitly reporte
Scalar series can be displayed in [graph view](#graph-view) (default) or in [metric values view](#metric-values-view):
#### Graph View
#### Graph View
Scalar graph view (<img src="/docs/latest/icons/ico-charts-view.svg" alt="Graph view" className="icon size-md space-sm" />)
shows scalar series plotted as a time series line chart. By default, a single plot is shown for each scalar metric,
with all variants overlaid within.

2
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View File

@@ -72,7 +72,7 @@ and/or Reset functions.
#### Default Container
Select a pre-configured container that the [ClearML Agent](../clearml_agent.md) will use to remotely execute this task (see [Building Docker containers](../getting_started/clearml_agent_docker_exec.md)).
Select a pre-configured container that the [ClearML Agent](../clearml_agent.md) will use to remotely execute this task (see [Building Task Execution Environments in a Container](../getting_started/clearml_agent_base_docker.md)).
**To add, change, or delete a default container:**

2
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View File

@@ -3,7 +3,7 @@ title: Orchestration Dashboard
---
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
The Orchestration Dashboard is available under the ClearML Enterprise plan.
:::
Use the orchestration dashboard to monitor all of your available and in-use compute resources:

8
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View File

@@ -424,22 +424,22 @@ To add an image, add an exclamation point, followed by the alt text enclosed by
image enclosed in parentheses:
```
![Logo](https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg)
![Logo](https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg)
```
The rendered output should look like this:
![Logo](https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg)
![Logo](https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg)
To add a title to the image, which you can see in a tooltip when hovering over the image, add the title after the image's
link:
```
![With title](https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg "ClearML logo")
![With title](https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg "ClearML logo")
```
The rendered output should look like this:
<img src="https://raw.githubusercontent.com/allegroai/clearml/master/docs/clearml-logo.svg" alt="Logo with Title" title="ClearML logo"/>
<img src="https://raw.githubusercontent.com/clearml/clearml/master/docs/clearml-logo.svg" alt="Logo with Title" title="ClearML logo"/>
Hover over the image to see its title.

4
docusaurus.config.js
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@@ -114,7 +114,7 @@ module.exports = {
{
label: 'References',
to: '/docs/references/sdk/task',
activeBaseRegex: '^/docs/latest/docs/(references/|webapp/.*|hyperdatasets/webapp/.*|clearml_agent/(clearml_agent_ref|clearml_agent_env_var)|configs/(clearml_conf|env_vars)|apps/(clearml_task|clearml_param_search))(/.*)?$',
activeBaseRegex: '^/docs/latest/docs/(references/.*|webapp/.*|hyperdatasets/webapp/.*|clearml_agent/(clearml_agent_ref|clearml_agent_env_var)|configs/(clearml_conf|env_vars)|apps/(clearml_task|clearml_param_search))(/.*)?$',
},
{
label: 'Best Practices',
@@ -127,7 +127,7 @@ module.exports = {
activeBaseRegex: '^/docs/latest/docs/guides',
},
{
label: 'Integrations',
label: 'Code Integrations',
to: '/docs/integrations',
activeBaseRegex: '^/docs/latest/docs/integrations(?!/storage)',
},

28
sidebars.js
View File

@@ -399,8 +399,10 @@ module.exports = {
'references/sdk/dataset',
{'Pipeline': [
'references/sdk/automation_controller_pipelinecontroller',
'references/sdk/automation_controller_pipelinedecorator',
'references/sdk/automation_job_clearmljob'
]},
]
},
'references/sdk/scheduler',
'references/sdk/trigger',
{'HyperParameter Optimization': [
@@ -635,11 +637,19 @@ module.exports = {
'getting_started/architecture',
]},*/
{
'Enterprise Server Deployment': [
'deploying_clearml/enterprise_deploy/multi_tenant_k8s',
'deploying_clearml/enterprise_deploy/vpc_aws',
'deploying_clearml/enterprise_deploy/on_prem_ubuntu',
]
'Enterprise Server': {
'Deployment Options': [
'deploying_clearml/enterprise_deploy/multi_tenant_k8s',
'deploying_clearml/enterprise_deploy/vpc_aws',
'deploying_clearml/enterprise_deploy/on_prem_ubuntu',
],
'Maintenance': [
'deploying_clearml/enterprise_deploy/import_projects',
'deploying_clearml/enterprise_deploy/change_artifact_links',
'deploying_clearml/enterprise_deploy/delete_tenant',
]
}
},
{
type: 'category',
@@ -651,11 +661,9 @@ module.exports = {
'deploying_clearml/enterprise_deploy/appgw_install_k8s',
]
},
'deploying_clearml/enterprise_deploy/delete_tenant',
'deploying_clearml/enterprise_deploy/import_projects',
'deploying_clearml/enterprise_deploy/change_artifact_links',
'deploying_clearml/enterprise_deploy/custom_billing',
{
'Enterprise Applications': [
'UI Applications': [
'deploying_clearml/enterprise_deploy/app_install_ubuntu_on_prem',
'deploying_clearml/enterprise_deploy/app_install_ex_server',
'deploying_clearml/enterprise_deploy/app_custom',