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docs/apps/clearml_session.md
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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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The following are some recommendations for using ClearML Data.
![Dataset UI gif](../img/gif/dataset.gif)
![Dataset UI gif](../img/gif/dataset.gif#light-mode-only)
![Dataset UI gif](../img/gif/dataset_dark.gif#dark-mode-only)
## Versioning Datasets

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## 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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---
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.

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* **`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

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### 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)

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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).

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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.

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---
:::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

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# 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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# 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.

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---
title: Changing ClearML Artifacts Links
---
This guide describes how to update artifact references in the ClearML Enterprise server.
By default, artifacts are stored on the file server; however, an external storage such as AWS S3, Minio, Google Cloud
Storage, etc. may be used to store artifacts. References to these artifacts may exist in ClearML databases: MongoDB and ElasticSearch.
This procedure should be used if external storage is being migrated to a different location or URL.
:::important
This procedure does not deal with the actual migration of the data--only with changing the references in ClearML that
point to the data.
:::
## Preparation
### Version Confirmation
To change the links, use the `fix_fileserver_urls.py` script, located inside the `allegro-apiserver`
Docker container. This script will be executed from within the `apiserver` container. Make sure the `apiserver` version
is 3.20 or higher.
### Backup
It is highly recommended to back up the ClearML MongoDB and ElasticSearch databases before running the script, as the
script changes the values in the databases, and can't be undone.
## Fixing MongoDB links
1. Access the `apiserver` Docker container:
* In `docker-compose:`
```commandline
sudo docker exec -it allegro-apiserver /bin/bash
```
* In Kubernetes:
```commandline
kubectl exec -it -n clearml <clearml-apiserver-pod-name> -- bash
```
1. Navigate to the script location in the `upgrade` folder:
```commandline
cd /opt/seematics/apiserver/server/upgrade
```
1. Run the following command:
:::important
Before running the script, verify that this is indeed the correct version (`apiserver` v3.20 or higher,
or that the script provided by ClearML was copied into the container).
::::
```commandline
python3 fix_fileserver_urls.py \
--mongo-host mongodb://mongo:27017 \
--elastic-host elasticsearch:9200 \
--host-source "<old fileserver host and/or port, as in artifact links>" \
--host-target "<new fileserver host and/or port>" --datasets
```
:::note Notes
* If MongoDB or ElasticSearch services are accessed from the `apiserver` container using custom addresses, then
`--mongo-host` and `--elastic-host` arguments should be updated accordingly.
* If ElasticSearch is set up to require authentication then the following arguments should be used to pass the user
and password: `--elastic-user <es_user> --elastic-password <es_pass>`
:::
The script fixes the links in MongoDB, and outputs `cURL` commands for updating the links in ElasticSearch.
## Fixing the ElasticSearch Links
Copy the `cURL` commands printed by the script run in the previous stage, and run them one after the other. Make sure to
inspect that a "success" result was returned from each command. Depending on the amount of the data in the ElasticSearch,
running these commands may take some time.

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---
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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---
title: Project Migration
---
When migrating from a ClearML Open Server to a ClearML Enterprise Server, you may need to transfer projects. This is done
using the `data_tool.py` script. This utility is available in the `apiserver` Docker image, and can be used for
exporting and importing ClearML project data for both open source and Enterprise versions.
This guide covers the following:
* Exporting data from Open Source and Enterprise servers
* Importing data into an Enterprise server
* Handling the artifacts stored on the file server.
:::note
Export instructions differ for ClearML open and Enterprise servers. Make sure you follow the guidelines that match your
server type.
:::
## Exporting Data
The export process is done by running the ***data_tool*** script that generates a zip file containing project and task
data. This file should then be copied to the server on which the import will run.
Note that artifacts stored in the ClearML ***file server*** should be copied manually if required (see [Handling Artifacts](#handling-artifacts)).
### Exporting Data from ClearML Open Servers
#### Preparation
* Make sure the `apiserver` is at least Open Source server version 1.12.0.
* Note that any `pending` or `running` tasks will not be exported. If you wish to export them, make sure to stop/dequeue
them before exporting.
#### Running the Data Tool
Execute the data tool within the `apiserver` container.
Open a bash session inside the `apiserver` container of the server:
* In docker-compose:
```commandline
sudo docker exec -it clearml-apiserver /bin/bash
```
* In Kubernetes:
```commandline
kubectl exec -it -n <clearml-namespace> <clearml-apiserver-pod-name> -- bash
```
#### Export Commands
**To export specific projects:**
```commandline
python3 -m apiserver.data_tool export --projects <project_id1> <project_id2>
--statuses created stopped published failed completed --output <output-file-name>.zip
```
As a result, you should get a `<output-file-name>.zip` file that contains all the data from the specified projects and
their children.
**To export all the projects:**
```commandline
python3 -m apiserver.data_tool export \
--all \
--statuses created stopped published failed completed \
--output <output-file-name>.zip
```
#### Optional Parameters
* `--experiments <list of experiment IDs>` - If not specified then all experiments from the specified projects are exported
* `--statuses <list of task statuses>` - Export tasks of specific statuses. If the parameter
is omitted, only `published` tasks are exported
* `--no-events` - Do not export task events, i.e. logs and metrics (scalar, plots, debug samples).
Make sure to copy the generated zip file containing the exported data.
### Exporting Data from ClearML Enterprise Servers
#### Preparation
* Make sure the `apiserver` is at least Enterprise Server version 3.18.0.
* Note that any `pending` or `running` tasks will not be exported. If you wish to export them, make sure to stop/dequeue
before exporting.
#### Running the Data Tool
Execute the data tool from within the `apiserver` docker container.
Open a bash session inside the `apiserver` container of the server:
* In `docker-compose`:
```commandline
sudo docker exec -it allegro-apiserver /bin/bash
```
* In Kubernetes:
```commandline
kubectl exec -it -n <clearml-namespace> <clearml-apiserver-pod-name> -- bash
```
#### Export Commands
**To export specific projects:**
```commandline
PYTHONPATH=/opt/seematics/apiserver/trains-server-repo python3 data_tool.py \
export \
--projects <project_id1> <project_id2> \
--statuses created stopped published failed completed \
--output <output-file-name>.zip
```
As a result, you should get `<output-file-name>.zip` file that contains all the data from the specified projects and
their children.
**To export all the projects:**
```commandline
PYTHONPATH=/opt/seematics/apiserver/trains-server-repo python3 data_tool.py \
export \
--all \
--statuses created stopped published failed completed \
--output <output-file-name>.zip
```
#### Optional Parameters
* `--experiments <list of experiment IDs>` - If not specified then all experiments from the specified projects are exported
* `--statuses <list of task statuses>` - Can be used to allow exporting tasks of specific statuses. If the parameter is
omitted, only `published` tasks are exported.
* `--no-events` - Do not export task events, i.e. logs, and metrics (scalar, plots, debug samples).
Make sure to copy the generated zip file containing the exported data.
## Importing Data
This section explains how to import the exported data into a ClearML Enterprise server.
### Preparation
* It is highly recommended to back up the ClearML databases before importing data, as import injects data into the
databases, and can't be undone.
* Make sure you are working with `apiserver` version 3.22.3 or higher.
* Make the zip file accessible from within the `apiserver` container by copying the exported data to the
`apiserver` container or to a folder on the host, which the `apiserver` is mounted to.
### Usage
The data tool should be executed from within the `apiserver` docker container.
1. Open a bash session inside the `apiserver` container of the server:
* In `docker-compose`:
```commandline
sudo docker exec -it allegro-apiserver /bin/bash
```
* In Kubernetes:
```commandline
kubectl exec -it -n <clearml-namespace> <clearml-apiserver-pod-name> -- bash
```
1. Run the data tool script in *import* mode:
```commandline
PYTHONPATH=/opt/seematics/apiserver/trains-server-repo python3 data_tool.py \
import \
<path to zip file> \
--company <company_id> \
--user <user_id>
```
* `company_id`- The default company ID used in the target deployment. Inside the `apiserver` container you can
usually get it from the environment variable `CLEARML__APISERVER__DEFAULT_COMPANY`.
If you do not specify the `--company` parameter then all the data will be imported as `Examples` (read-only)
* `user_id` - The ID of the user in the target deployment who will become the owner of the imported data
## Handling Artifacts
***Artifacts*** refers to any content which the ClearML server holds references to. This can include:
* Dataset or Hyper-Dataset frame URLs
* ClearML artifact URLs
* Model snapshots
* Debug samples
Artifacts may be stored in any external storage (e.g., AWS S3, minio, Google Cloud Storage) or in the ClearML file server.
* If the artifacts are **not** stored in the ClearML file server, they do not need to be moved during the export/import process,
as the URLs registered in ClearML entities pointing to these artifacts will not change.
* If the artifacts are stored in the ClearML file server, then the file server content must also be moved, and the URLs
in the ClearML databases must point to the new location. See instructions [below](#exporting-file-server-data-for-clearml-open-server).
### Exporting File Server Data for ClearML Open Server
Data in the file server is organized by project. For each project, all data references by entities in that project is
stored in a folder bearing the name of the project. This folder can be located in:
```
/opt/clearml/data/fileserver/<project name>
```
The entire projects' folders content should be copied to the target server (see [Importing Fileserver Data](#importing-file-server-data)).
### Exporting File Server Data for ClearML Enterprise Server
Data in the file server is organized by tenant and project. For each project, all data references by entities in that
project is stored in a folder bearing the name of the project. This folder can be located in:
```
/opt/allegro/data/fileserver/<company_id>/<project name>
```
The entire projects' folders content should be copied to the target server (see [Importing Fileserver Data](#importing-file-server-data)).
## Importing File Server Data
### Copying the Data
Place the exported projects' folder(s) content into the target file server's storage in the following folder:
```
/opt/allegro/data/fileserver/<company_id>/<project name>
```
### Fixing Registered URLs
Since URLs pointing to the file server contain the file server's address, these need to be changed to the address of the
new file server.
Note that this is not required if the new file server is replacing the old file server and can be accessed using the same
exact address.
Once the projects' data has been copied to the target server, and the projects themselves were imported, see
[Changing ClearML Artifacts Links](change_artifact_links.md) for information on how to fix the URLs.

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---
title: Multi-Tenant Login Mode
---
In a multi-tenant setup, each external tenant can be represented by an SSO client defined in the customer Identity provider
(Keycloak). Each ClearML tenant can be associated with a particular external tenant. Currently, only one
ClearML tenant can be associated with a particular external tenant
## Setup IdP/SSO Client in Identity Provider
1. Add the following URL to "Valid redirect URIs": `<clearml_webapp_address>/callback_<client_id>`
2. Add the following URLs to "Valid post logout redirect URIs":
```
<clearml_webapp_address>/login
<clearml_webapp_address>/login/<external tenant ID>
```
3. Make sure the external tenant ID and groups are returned as claims for a each user
## Configure ClearML to use Multi-Tenant Mode
Set the following environment variables in the ClearML enterprise helm chart under the `apiserver` section:
* To turn on the multi-tenant login mode:
```
- name: CLEARML__services__login__sso__tenant_login
value: "true"
```
* To hide any global IdP/SSO configuration that's not associated with a specific ClearML tenant:
```
- name: CLEARML__services__login__sso__allow_settings_providers
value: "false"
```
Enable `onlyPasswordLogin` by setting the following environment variable in the helm chart under the `webserver` section:
```
- name: WEBSERVER__onlyPasswordLogin`
value: “true”`
```
## Setup IdP for a ClearML Tenant
To set an IdP client for a ClearML tenant, you’ll need to set the ClearML tenant settings and define an identity provider:
1. Call the following API to set the ClearML tenant settings:
```
curl $APISERVER_URL/system.update_company_sso_config -H "Content-Type: application/json" -u $APISERVER_KEY:$APISERVER_SECRET -d'{
"company": "<company_id>",
"sso": {
"tenant": "<external tenant ID>",
"group_mapping": {
"IDP group name1": "Clearml group name1",
"IDP group name2": "Clearml group name2"
},
"admin_groups": ["IDP admin group name1", "IDP admin group name2"]
}}'
```
2. Call the following API to define the ClearML tenant identity provider:
```
curl $APISERVER_URL/sso.save_provider_configuration -H "Content-Type: application/json" -u $APISERVER_KEY:$APISERVER_SECRET -d'{
"provider": "keycloak",
"company": "<company_id>",
"configuration": {
"id": "<some unique id here, you can use company_id>",
"display_name": "<The text that you want to see on the login button>",
"client_id": "<client_id from IDP>",
"client_secret": "<client secret from IDP>",
"authorization_endpoint": "<authorization_endpoint from IDP OpenID configuration>",
"token_endpoint": "<token_endpoint from IDP OpenID configuration>",
"revocation_endpoint": "<revocation_endpoint from IDP OpenID configuration>",
"end_session_endpoint": "<end_session_endpoint from IDP OpenID configuration>",
"logout_from_provider": true,
"claim_tenant": "tenant_key",
"claim_name": "name",
"group_enabled": true,
"claim_groups": "ad_groups_trusted",
"group_prohibit_user_login_if_not_in_group": true
}}'
```
The above configuration assumes the following:
* On logout from ClearML, the user is also logged out from the Identity Provider
* External tenant ID for the user is returned under the `tenant_key` claim
* User display name is returned under the `name` claim
* User groups list is returned under the `ad_groups_trusted` claim
* Group integration is turned on and a user will be allowed to log in if any of the groups s/he belongs to in the
IdP exists under the corresponding ClearML tenant (this is after group name translation is done according to the ClearML tenant settings)
## Webapp Login
When running in multi-tenant login mode, a user belonging to some external tenant should use the following link to log in:
```
<clearml_webapp_address>/login/<external tenant ID>
```

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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.
@@ -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.

10
docs/deploying_clearml/upgrade_server_gcp.md
View File

@@ -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`:
@@ -31,9 +33,7 @@ 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:

18
docs/deploying_clearml/upgrade_server_linux_mac.md
View File

@@ -40,19 +40,21 @@ 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:

13
docs/deploying_clearml/upgrade_server_win.md
View File

@@ -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.
@@ -40,9 +37,15 @@ 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:
```

2
docs/getting_started/clearml_agent_scheduling.md
View File

@@ -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

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3
docs/integrations/autokeras.md
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@@ -95,7 +95,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/catboost.md
View File

@@ -93,7 +93,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/fastai.md
View File

@@ -92,7 +92,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/keras.md
View File

@@ -105,7 +105,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/lightgbm.md
View File

@@ -94,7 +94,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/megengine.md
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@@ -90,7 +90,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/pytorch.md
View File

@@ -114,7 +114,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/pytorch_lightning.md
View File

@@ -120,7 +120,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/scikit_learn.md
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@@ -96,7 +96,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/tao.md
View File

@@ -113,7 +113,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/tensorflow.md
View File

@@ -107,7 +107,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/transformers.md
View File

@@ -78,7 +78,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/xgboost.md
View File

@@ -120,7 +120,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/yolov5.md
View File

@@ -169,7 +169,8 @@ and shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:

3
docs/integrations/yolov8.md
View File

@@ -166,4 +166,5 @@ with the new configuration on a remote machine:
The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent.md).
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)

2
docs/user_management/identity_providers.md
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 seamlessly connect ClearML with their identity service providers to easily implement single sign-on

15
docs/webapp/applications/apps_aws_autoscaler.md
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
docs/webapp/resource_policies.md
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
docs/webapp/settings/webapp_settings_access_rules.md
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
docs/webapp/settings/webapp_settings_admin_vaults.md
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
docs/webapp/settings/webapp_settings_id_providers.md
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
docs/webapp/settings/webapp_settings_profile.md
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
docs/webapp/settings/webapp_settings_users.md
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.
:::

2
docs/webapp/webapp_exp_track_visual.md
View File

@@ -233,7 +233,7 @@ The **INFO** tab shows extended task information:
### Latest Events Log
:::important Enterprise Feature
This feature is available under the ClearML Enterprise plan.
The latest events log is available under the ClearML Enterprise plan.
:::
The Enterprise Server also displays a detailed history of task activity:

2
docs/webapp/webapp_orchestration_dash.md
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:

2
docusaurus.config.js
View File

@@ -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)',
},

2
package-lock.json generated
View File

@@ -15,7 +15,7 @@
"@docusaurus/plugin-google-analytics": "^3.6.1",
"@docusaurus/plugin-google-gtag": "^3.6.1",
"@docusaurus/preset-classic": "^3.6.1",
"@easyops-cn/docusaurus-search-local": "^0.48.0",
"@easyops-cn/docusaurus-search-local": "^0.48.5",
"@mdx-js/react": "^3.0.0",
"clsx": "^1.1.1",
"joi": "^17.4.0",

2
package.json
View File

@@ -23,7 +23,7 @@
"@docusaurus/plugin-google-analytics": "^3.6.1",
"@docusaurus/plugin-google-gtag": "^3.6.1",
"@docusaurus/preset-classic": "^3.6.1",
"@easyops-cn/docusaurus-search-local": "^0.48.0",
"@easyops-cn/docusaurus-search-local": "^0.48.5",
"@mdx-js/react": "^3.0.0",
"clsx": "^1.1.1",
"medium-zoom": "^1.0.6",

23
sidebars.js
View File

@@ -635,11 +635,19 @@ 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',
@@ -651,9 +659,9 @@ module.exports = {
'deploying_clearml/enterprise_deploy/appgw_install_k8s',
]
},
'deploying_clearml/enterprise_deploy/delete_tenant',
'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',
@@ -671,6 +679,7 @@ module.exports = {
label: 'Identity Provider Integration',
link: {type: 'doc', id: 'user_management/identity_providers'},
items: [
'deploying_clearml/enterprise_deploy/sso_multi_tenant_login',
'deploying_clearml/enterprise_deploy/sso_saml_k8s',
'deploying_clearml/enterprise_deploy/sso_keycloak',
'deploying_clearml/enterprise_deploy/sso_active_directory'