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2
docs/clearml_serving/clearml_serving_tutorial.md
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@ -7,7 +7,7 @@ In this tutorial, you will go over the model lifecycle -- from training to servi
* Serving the model using **ClearML Serving**
* Spinning the inference container
The tutorial also covers the following`clearml-serving` features:
The tutorial also covers the following `clearml-serving` features:
* Automatic model deployment
* Canary endpoints
* Model performance monitoring

3
docs/deploying_clearml/enterprise_deploy/appgw.md
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@ -35,9 +35,10 @@ If your ClearML Deployment does not have the App Gateway Router properly install
#### Installation
The App Gateway Router supports two deployment options:
The App Gateway Router supports the following deployment options:
* [Docker Compose](appgw_install_compose.md)
* [Docker Compose for hosted servers](appgw_install_compose_hosted.md)
* [Kubernetes](appgw_install_k8s.md)
The deployment configuration specifies the external and internal address and port mappings for routing requests.

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docs/deploying_clearml/enterprise_deploy/appgw_install_compose_hosted.md Normal file
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@ -0,0 +1,166 @@
---
title: Docker-Compose - Hosted Server
---
:::important Enterprise Feature
The AI Application Gateway is available under the ClearML Enterprise plan.
:::
The AI Application Gateway enables external access to ClearML tasks, and applications running on workload nodes that
require HTTP or TCP access. The gateway is configured with an endpoint or external address, making these services
accessible from the user's machine, outside the workload nodes’ network.
This guide details the installation of the App Gateway Router for ClearML users who use ClearML's hosted control
plane while hosting their own workload nodes.
## Requirements
* Linux OS (x86) machine with root access
* The machine needs to be reachable from your user network
* The machine needs to have network reachability to workload nodes
* Credentials for the ClearML docker repository
* A valid ClearML Server installation
Additionally, for a secure connection, it is recommended to have a DNS entry and a valid SSL Certificate assigned to the machine IP.
## Host Configuration
### Docker Installation
Installing `docker` and `docker-compose` might vary depending on the specific operating system you're using. Here is an
example for AmazonLinux:
```
sudo dnf -y install docker
DOCKER_CONFIG="/usr/local/lib/docker"
sudo mkdir -p $DOCKER_CONFIG/cli-plugins
sudo curl -SL https://github.com/docker/compose/releases/download/v2.17.3/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
sudo chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose
sudo systemctl enable docker
sudo systemctl start docker
sudo docker login
```
Use the ClearML docker hub credentials when prompted by `docker` login.
### Docker-compose File
This is an example of the `docker-compose` file you will need to create:
```
version: '3.5'
services:
task_traffic_webserver:
image: clearml/ai-gateway-proxy:${PROXY_TAG:?err}
network_mode: "host"
restart: unless-stopped
container_name: task_traffic_webserver
volumes:
- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:ro
- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:ro
task_traffic_router:
image: clearml/ai-gateway-router:${ROUTER_TAG:?err}
restart: unless-stopped
container_name: task_traffic_router
volumes:
- /var/run/docker.sock:/var/run/docker.sock
- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:rw
- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:rw
environment:
- LOGGER_LEVEL=INFO
- ROUTER__WEBSERVER__SERVER_PORT="8010"
- ROUTER_NAME=${ROUTER_NAME:?err}
- ROUTER_URL=${ROUTER_URL:?err}
- CLEARML_API_HOST=${CLEARML_API_HOST:?err}
- CLEARML_API_ACCESS_KEY=${CLEARML_API_ACCESS_KEY:?err}
- CLEARML_API_SECRET_KEY=${CLEARML_API_SECRET_KEY:?err}
- AUTH_COOKIE_NAME=${AUTH_COOKIE_NAME:?err}
- AUTH_SECURE_ENABLED=${AUTH_SECURE_ENABLED}
- TCP_ROUTER_ADDRESS=${TCP_ROUTER_ADDRESS}
- TCP_PORT_START=${TCP_PORT_START}
- TCP_PORT_END=${TCP_PORT_END}
```
### Configuration File
You will be provided with a prefilled `runtime.env` file containing the following entries:
```
# PREFILLED SECTION, PROVIDED BY CLEARML
PROXY_TAG=
ROUTER_TAG=
CLEARML_API_HOST=https://api.
AUTH_COOKIE_NAME=
# TO BE FILLED BY USER
ROUTER_NAME=main-router
ROUTER_URL=http://<ROUTER-HOST-PUBLIC-IP>:8010
CLEARML_API_ACCESS_KEY=
CLEARML_API_SECRET_KEY=
AUTH_SECURE_ENABLED=true
TCP_ROUTER_ADDRESS=<ROUTER-HOST-PUBLIC-IP>
TCP_PORT_START=
TCP_PORT_END=
```
**Configuration Options:**
* `ROUTER_NAME`: In the case of [multiple routers on the same tenant](#multiple-router-in-the-same-tenant), each router
needs to have a unique name.
* `CLEARML_API_ACCESS_KEY, CLEARML_API_SECRET_KEY:` API credentials for Admin user or Service Account with admin privileges
created in the ClearML web UI. Make sure to label these credentials clearly, so that they will not be revoked by mistake.
* `ROUTER_URL`: External address to access the router. This can be the IP address or DNS of the node where the router
is running, or the address of a load balancer if the router operates behind a proxy/load balancer. This URL is used
to access AI workload applications (e.g. remote IDE, model deployment, etc.), so it must be reachable and resolvable for them.
* `TCP_ROUTER_ADDRESS`: Router external address, can be an IP or the host machine or a load balancer hostname, depends on network configuration.
* `TCP_PORT_START`: Start port for the TCP Tasks, chosen by the customer. Ensure that ports are open and can be allocated on the host.
* `TCP_PORT_END`: End port for the TCP Tasks, chosen by the customer. Ensure that ports are open and can be allocated on the host.
### Installation
Run the following command to start the router:
```
sudo docker compose --env-file runtime.env up -d
```
### Advanced Configuration
#### Using Open HTTP
To deploy the App Gateway Router on open HTTP (without a certificate), set the `AUTH_SECURE_ENABLED` entry
to `false` in the `runtime.env` file.
#### Multiple Router in the Same Tenant
If you have workloads running in separate networks that cannot communicate with each other, you need to deploy multiple
routers, one for each isolated environment. Each router will only process tasks from designated queues, ensuring that
tasks are correctly routed to agents within the same network.
For example:
* If Agent A and Agent B are in separate networks, each must have its own router to receive tasks.
* Router A will handle tasks from Agent A’s queues. Router B will handle tasks from Agent B’s queues.
To achieve this, each router must be configured with:
* A unique `ROUTER_NAME`
* A distinct set of queues defined in `LISTEN_QUEUE_NAME`.
##### Example Configuration
Each router's `runtime.env` file should include:
* Router A:
```
ROUTER_NAME=router-a
LISTEN_QUEUE_NAME=queue1,queue2
```
* Router B:
```
ROUTER_NAME=router-b
LISTEN_QUEUE_NAME=queue3,queue4
```
Make sure `LISTEN_QUEUE_NAME` is set in the [`docker-compose` environment variables](#docker-compose-file) for each router instance.

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docs/deploying_clearml/enterprise_deploy/change_artifact_links.md
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@ -29,18 +29,19 @@ 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 `docker-compose`:
```commandline
sudo docker exec -it allegro-apiserver /bin/bash
```
* In Kubernetes:
```commandline
kubectl exec -it -n clearml <clearml-apiserver-pod-name> -- 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

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docs/deploying_clearml/enterprise_deploy/k8s.md Normal file
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@ -0,0 +1,448 @@
---
title: Kubernetes
---
This guide provides step-by-step instructions for installing the ClearML Enterprise setup in a Kubernetes cluster.
## Prerequisites
* A Kubernetes cluster
* An ingress controller (e.g. `nginx-ingress`) and the ability to create LoadBalancer services (e.g. MetalLB) if needed
to expose ClearML
* Credentials for ClearML Enterprise GitHub Helm chart repository
* Credentials for ClearML Enterprise DockerHub repository
* URL for downloading the ClearML Enterprise applications configuration
## Control Plane Installation
The following steps cover installing the control plane (server and required charts) and will
require some or all of the tokens/deliverables mentioned above.
### Requirements
* Add the ClearML Enterprise repository:
```
helm repo add clearml-enterprise https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages --username <clearmlenterprise_GitHub_TOKEN> --password <clearmlenterprise_GitHub_TOKEN>
```
* Update the repository locally:
```
helm repo update
```
### Install ClearML Enterprise Chart
#### Configuration
The Helm Chart must be installed with an `overrides.yaml` overriding values as follows:
:::note
In the following configuration, replace `<BASE_DOMAIN>` with a valid domain
that will have records pointing to the cluster’s ingress controller (see ingress details in the values below).
:::
```
imageCredentials:
password: "<clearml_enterprise_DockerHub_TOKEN>"
clearml:
cookieDomain: "<BASE_DOMAIN>"
# Set values for improved security
apiserverKey: "<GENERATED_API_SERVER_KEY>"
apiserverSecret: "<GENERATED_API_SERVER_SECRET>"
fileserverKey: "<GENERATED_FILE_SERVER_KEY>"
fileserverSecret: "<GENERATED_FILE_SERVER_SECRET>"
secureAuthTokenSecret: "<GENERATED_AUTH_TOKEN_SECRET>"
testUserKey: "<GENERATED_TEST_USER_KEY>"
testUserSecret: "<GENERATED_TEST_USER_SECRET>"
apiserver:
ingress:
enabled: true
hostName: "api.<BASE_DOMAIN>"
service:
type: ClusterIP
extraEnvs:
- name: CLEARML__services__organization__features__user_management_advanced
value: "true"
- name: CLEARML__services__auth__ui_features_per_role__user__show_datasets
value: "false"
- name: CLEARML__services__auth__ui_features_per_role__user__show_orchestration
value: "false"
- name: CLEARML__services__workers__resource_usages__supervisor_company
value: "<SUPERVISOR_TENANT_ID>"
- name: CLEARML__secure__credentials__supervisor__role
value: "system"
- name: CLEARML__secure__credentials__supervisor__allow_login
value: "true"
- name: CLEARML__secure__credentials__supervisor__user_key
value: "<SUPERVISOR_USER_KEY>"
- name: CLEARML__secure__credentials__supervisor__user_secret
value: "<SUPERVISOR_USER_SECRET>"
- name: CLEARML__secure__credentials__supervisor__sec_groups
value: "[\"users\", \"admins\", \"queue_admins\"]"
- name: CLEARML__secure__credentials__supervisor__email
value: "\"<SUPERVISOR_USER_EMAIL>\""
- name: CLEARML__apiserver__company__unique_names
value: "true"
fileserver:
ingress:
enabled: true
hostName: "file.<BASE_DOMAIN>"
service:
type: ClusterIP
webserver:
ingress:
enabled: true
hostName: "app.<BASE_DOMAIN>"
service:
type: ClusterIP
clearmlApplications:
enabled: true
```
The credentials specified in `<SUPERVISOR_USER_KEY>` and `<SUPERVISOR_USER_SECRET>` can be used to login as the
supervisor user from the ClearML Web UI accessible using the URL `app.<BASE_DOMAIN>`.
Note that the `<SUPERVISOR_USER_EMAIL>` value must be explicitly quoted. To do so, put `\"` around the quoted value.
For example `"\"email@example.com\""`.
#### Additional Configuration Options
##### Fixed Users (Simple Login)
Enable static login with username and password in `overrides.yaml`.
This is an optional step in case SSO (Identity provider) configuration will not be performed.
```
apiserver:
additionalConfigs:
apiserver.conf: |
auth {
fixed_users {
enabled: true
pass_hashed: false
users: [
{
username: "my_user"
password: "my_password"
name: "My User"
admin: true
},
]
}
}
```
##### SSO (Identity Provider)
The following examples (Auth0 and Keycloak) show how to configure an identity provider on the ClearML server.
Add the following values configuring `extraEnvs` for `apiserver` in the `clearml-enterprise` values `override.yaml` file.
Substitute all `<PLACEHOLDER>`s with the correct value for your configuration.
##### Auth0 Identity Provider
```
apiserver:
extraEnvs:
- name: CLEARML__secure__login__sso__oauth_client__auth0__client_id
value: "<SSO_CLIENT_ID>"
- name: CLEARML__secure__login__sso__oauth_client__auth0__client_secret
value: "<SSO_CLIENT_SECRET>"
- name: CLEARML__services__login__sso__oauth_client__auth0__base_url
value: "<SSO_CLIENT_URL>"
- name: CLEARML__services__login__sso__oauth_client__auth0__authorize_url
value: "<SSO_CLIENT_AUTHORIZE_URL>"
- name: CLEARML__services__login__sso__oauth_client__auth0__access_token_url
value: "<SSO_CLIENT_ACCESS_TOKEN_URL>"
- name: CLEARML__services__login__sso__oauth_client__auth0__audience
value: "<SSO_CLIENT_AUDIENCE>"
```
##### Keycloak Identity Provider
```
apiserver:
extraEnvs:
- name: CLEARML__secure__login__sso__oauth_client__keycloak__client_id
value: "<KC_CLIENT_ID>"
- name: CLEARML__secure__login__sso__oauth_client__keycloak__client_secret
value: "<KC_SECRET_ID>"
- name: CLEARML__services__login__sso__oauth_client__keycloak__base_url
value: "<KC_URL>/realms/<REALM_NAME>/"
- name: CLEARML__services__login__sso__oauth_client__keycloak__authorize_url
value: "<KC_URL>/realms/<REALM_NAME>/protocol/openid-connect/auth"
- name: CLEARML__services__login__sso__oauth_client__keycloak__access_token_url
value: "<KC_URL>/realms/<REALM_NAME>/protocol/openid-connect/token"
- name: CLEARML__services__login__sso__oauth_client__keycloak__idp_logout
value: "true"
```
#### Installing the Chart
```
helm install -n clearml \
clearml \
clearml-enterprise/clearml-enterprise \
--create-namespace \
-f overrides.yaml
```
### Install ClearML Agent Chart
#### Configuration
To configure the agent you will need to choose a Redis password and use that when setting up Redis as well
(see [Shared Redis installation](multi_tenant_k8s.md#shared-redis-installation)).
The Helm Chart must be installed with `overrides.yaml`:
```
imageCredentials:
password: "<CLEARML_DOCKERHUB_TOKEN>"
clearml:
agentk8sglueKey: "<ACCESS_KEY>"
agentk8sglueSecret: "<SECRET_KEY>"
agentk8sglue:
apiServerUrlReference: "https://api.<BASE_DOMAIN>"
fileServerUrlReference: "https://files.<BASE_DOMAIN>"
webServerUrlReference: "https://app.<BASE_DOMAIN>"
defaultContainerImage: "python:3.9"
```
#### Installing the Chart
```
helm install -n <WORKLOAD_NAMESPACE> \
clearml-agent \
clearml-enterprise/clearml-enterprise-agent \
--create-namespace \
-f overrides.yaml
```
To create a queue by API:
```
curl $APISERVER_URL/queues.create \
-H "Content-Type: application/json" \
-H "X-Clearml-Impersonate-As:<USER_ID>" \
-u $APISERVER_KEY:$APISERVER_SECRET \
-d '{"name":"default"}'
```
## ClearML AI Application Gateway Installation
### Configuring Chart
The Helm Chart must be installed with `overrides.yaml`:
```
imageCredentials:
password: "<DOCKERHUB_TOKEN>"
clearml:
apiServerKey: ""
apiServerSecret: ""
apiServerUrlReference: "https://api."
authCookieName: ""
ingress:
enabled: true
hostName: "task-router.dev"
tcpSession:
routerAddress: "<NODE_IP OR EXTERNAL_NAME>"
portRange:
start: <START_PORT>
end: <END_PORT>
```
**Configuration options:**
* **`clearml.apiServerUrlReference`:** URL usually starting with `https://api.`
* **`clearml.apiServerKey`:** ClearML server API key
* **`clearml.apiServerSecret`:** ClearML server secret key
* **`ingress.hostName`:** URL of the router we configured previously for load balancer starting with `https://`
* **`clearml.sslVerify`:** Enable or disable SSL certificate validation on apiserver calls check
* **`clearml.authCookieName`:** Value from `value_prefix` key starting with `allegro_token` in `envoy.yaml` file in ClearML server installation.
* **`tcpSession.routerAddress`**: Router external address can be an IP or the host machine or a load balancer hostname, depends on the network configuration
* **`tcpSession.portRange.start`**: Start port for the TCP Session feature
* **`tcpSession.portRange.end`**: End port for the TCP Session feature
### Installing the Chart
```
helm install -n <WORKLOAD_NAMESPACE> \
clearml-ttr \
clearml-enterprise/clearml-enterprise-task-traffic-router \
--create-namespace \
-f overrides.yaml
```
## Applications Installation
To install the ClearML Applications on the newly installed ClearML Enterprise control-plane, download the applications
package using the URL provided by the ClearML staff.
### Download and Extract
```
wget -O apps.zip "<ClearML enterprise applications configuration download url>"
unzip apps.zip
```
### Adjust Application Docker Images Location (Air-Gapped Systems)
ClearML applications use pre-built docker images provided by ClearML on the ClearML DockerHub
repository. If you are using an air-gapped system, these images must be available as part of your internal docker
registry, and the correct docker images location must be specified before installing the applications.
Use the following script to adjust the applications packages accordingly before installing the applications:
```
python convert_image_registry.py \
--apps-dir /path/to/apps/ \
--repo local_registry/clearml-apps
```
The script will change the application zip files to point to the new registry, and will output the list of containers
that need to be copied to the local registry. For example:
```
make sure allegroai/clearml-apps:hpo-1.10.0-1062 was added to local_registry/clearml-apps
```
### Install Applications
Use the `upload_apps.py` script to upload the application packages to the ClearML server:
```
python upload_apps.py \
--host $APISERVER_ADDRESS \
--user $APISERVER_USER --password $APISERVER_PASSWORD \
--dir apps -ml
```
## Configuring Shared Memory for Large Model Deployment
Deploying large models may fail due to shared memory size limitations. This issue commonly arises when the allocated
`/dev/shm` space is insufficient.:
```
> 3d3e22c3066f:168:168 [0] misc/shmutils.cc:72 NCCL WARN Error: failed to extend /dev/shm/nccl-UbzKZ9 to 9637892 bytes
> 3d3e22c3066f:168:168 [0] misc/shmutils.cc:113 NCCL WARN Error while creating shared memory segment /dev/shm/nccl-UbzKZ9 (size 9637888)
> 3d3e22c3066f:168:168 [0] NCCL INFO transport/shm.cc:114 -> 2
> 3d3e22c3066f:168:168 [0] NCCL INFO transport.cc:33 -> 2
> 3d3e22c3066f:168:168 [0] NCCL INFO transport.cc:113 -> 2
> 3d3e22c3066f:168:168 [0] NCCL INFO init.cc:1263 -> 2
> 3d3e22c3066f:168:168 [0] NCCL INFO init.cc:1548 -> 2
> 3d3e22c3066f:168:168 [0] NCCL INFO init.cc:1799 -> 2
```
To configure a proper SHM size you can use the following configuration in the agent `overrides.yaml`.
Replace `<SIZE>` with the desired memory allocation in GiB, based on your model requirements.
This example configures a specific queue, but you can include this setting in the `basePodTemplate` if you need to
apply it to all tasks.
```
agentk8sglue:
queues:
GPUshm:
templateOverrides:
env:
- name: VLLM_SKIP_P2P_CHECK
value: "1"
volumeMounts:
- name: dshm
mountPath: /dev/shm
volumes:
- name: dshm
emptyDir:
medium: Memory
sizeLimit: <SIZE>Gi
```

58
docs/deploying_clearml/enterprise_deploy/multi_tenant_k8s.md
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@ -268,7 +268,7 @@ metadata:
spec:
podSelector: {}
policyTypes:
- Ingress
- Ingress
ingress:
- from:
- podSelector: {}
@ -337,7 +337,7 @@ must be substituted with valid domain names or values from responses.
APISERVER_SECRET="<APISERVER_SECRET>"
```
2. Create a *Tenant* (company):
2. Create a **Tenant** (company):
```
curl $APISERVER_URL/system.create_company \\
@ -352,7 +352,7 @@ must be substituted with valid domain names or values from responses.
curl -u $APISERVER_KEY:$APISERVER_SECRET $APISERVER_URL/system.get_companies
```
3. Create an *Admin User*:
3. Create an **Admin User**:
```
curl $APISERVER_URL/auth.create_user \\
@ -363,7 +363,7 @@ must be substituted with valid domain names or values from responses.
This returns the new User ID (`<USER_ID>`).
4. Generate *Credentials* for the new Admin User:
4. Generate **Credentials** for the new Admin User:
```
curl $APISERVER_URL/auth.create_credentials \\
@ -374,7 +374,7 @@ must be substituted with valid domain names or values from responses.
This returns a set of key and secret credentials associated with the new Admin User.
5. Create an SSO Domain *Whitelist*. The `<USERS_EMAIL_DOMAIN>` is the email domain setup for users to access through SSO.
5. Create an SSO Domain **Whitelist**. The `<USERS_EMAIL_DOMAIN>` is the email domain setup for users to access through SSO.
```
curl $APISERVER_URL/login.set_domains \\
@ -541,3 +541,51 @@ Install the App Gateway Router in your Kubernetes cluster, allowing it to manage
-f overrides.yaml
```
## Configuring Options per Tenant
### Override Options When Creating a New Tenant
When creating a new tenant company, you can specify several tenant options. These include:
* `features` - Add features to a company
* `exclude_features` - Exclude features from a company.
* `allowed_users` - Set the maximum number of users for a company.
#### Example: Create a New Tenant with a Specific Feature Set
```
curl $APISERVER_URL/system.create_company \
-H "Content-Type: application/json" \
-u $APISERVER_KEY:$APISERVER_SECRET \
-d '{"name":"<TENANT_NAME>", "defaults": { "allowed_users": "10", "features": ["experiments"], "exclude_features": ["app_management", "applications", "user_management"] }}'
```
**Note**: make sure to replace the `<TENANT_NAME>` placeholder.
### Limit Features for all Users
This Helm Chart value in the `overrides.yaml` will have priority over all tenants, and will limit the features
available to any user in the system. This means that even if the feature is enabled for the tenant, if it's not in this
list, the user will not see it.
Example: all users will only have the `applications` feature enabled.
```
apiserver:
extraEnvs:
- name: CLEARML__services__auth__default_groups__users__features
value: "[\"applications\"]"
```
**Available Features**:
* `applications` - Viewing and running applications
* `data_management` - Working with hyper-datasets and dataviews
* `experiments` - Viewing experiment table and launching experiments
* `queues` - Viewing the queues screen
* `queue_management` - Creating and deleting queues
* `pipelines` - Viewing/managing pipelines in the system
* `reports` - Viewing and managing reports in the system
* `show_dashboard` - Show the dashboard screen
* `show_projects` - Show the projects menu option
* `resource_dashboard` - Display the resource dashboard in the orchestration page

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@ -30,12 +30,12 @@ To configure groups that should automatically become admins in ClearML set the f
CLEARML__services__login__sso__saml_client__microsoft_ad__groups__admins=[<admin_group_name1>, <admin_group_name2>, ...]
```
To change the the default Group Claim set the following environment variable:
To change the default Group Claim, set the following environment variable:
```
CLEARML__services__login__sso__saml_client__microsoft_ad__groups__claim=...
```
To make group matching case insensitive set the following environment variable:
To make group matching case-insensitive, set the following environment variable:
```
CLEARML__services__login__sso__saml_client__microsoft_ad__groups__case_sensitive=false
```

2
docs/deploying_clearml/enterprise_deploy/sso_keycloak.md
View File

@ -10,7 +10,7 @@ browser).
In the following sections, you will be instructed to set up different environment variables for the ClearML Server. If
using a `docker-compose` deployment, these should be defined in your `docker-compose.override.yaml` file, under the
`apiserver` service’ environment variables, as follows:
`apiserver` service’s environment variables, as follows:
```
services:

2
docs/deploying_clearml/enterprise_deploy/sso_multi_tenant_login.md
View File

@ -15,7 +15,7 @@ ClearML tenant can be associated with a particular external tenant
<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
3. Make sure the external tenant ID and groups are returned as claims for each user
## Configure ClearML to use Multi-Tenant Mode

2
docs/getting_started/data_management.md
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@ -49,7 +49,7 @@ Your goal is to create an immutable copy of the data to be used by further steps
The second step is to preprocess the data. First access the data, then modify it,
and lastly create a new version of the data.
1. Create a task for you data preprocessing (not required):
1. Create a task for your data preprocessing (not required):
```python
from clearml import Task, Dataset

2
docs/getting_started/video_tutorials/hands-on_mlops_tutorials/ml_ci_cd_using_github_actions_and_clearml.md
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@ -202,7 +202,7 @@ you'll get is the best performance here because our checks already run, so you s
open the PR, so basically the dummy task here was found to be the best performance, and it has been tagged but that
means that every single time I open a PR or I update a PR, it will search ClearML, and get this dummy task. It will get
this one, and then we say if we find the best task, if not we'll just add the best performance anyway because you're the
first task in the list, you'll always be getting best performance, but if you're not then we'll get the best latest
first task in the list, you'll always be getting the best performance, but if you're not then we'll get the best latest
metric. For example `get_reported_scalars().get('Performance Metric').get('Series 1').get('y')`, so the `y` value there
so this could basically be the best or the highest map from a task or the highest F1 score from a task, or any some
such. Then you have the best metric. We do the same thing for the current task as well, and then it's fairly easy. We

2
docs/guides/advanced/execute_remotely.md
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@ -28,7 +28,7 @@ moved to be executed by a stronger machine.
During the execution of the example script, the code does the following:
* Uses ClearML's automatic and explicit logging.
* Creates an task named `Remote_execution PyTorch MNIST train` in the `examples` project.
* Creates a task named `Remote_execution PyTorch MNIST train` in the `examples` project.
## Scalars

2
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@ -9,7 +9,7 @@ The example script does the following:
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
dataset
* Creates a task named `pytorch mnist train with abseil` in the `examples` project
* ClearML automatically logs the absl.flags, and the models (and their snapshots) created by PyTorch
* ClearML automatically logs the `absl.flags`, and the models (and their snapshots) created by PyTorch
* Additional metrics are logged by calling [`Logger.report_scalar()`](../../../references/sdk/logger.md#report_scalar)
## Scalars

2
docs/guides/frameworks/tensorflow/tensorflow_mnist.md
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@ -4,7 +4,7 @@ title: TensorFlow MNIST
The [tensorflow_mnist.py](https://github.com/clearml/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py)
example demonstrates the integration of ClearML into code that uses TensorFlow and Keras to train a neural network on
the Keras built-in [MNIST](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist) handwritten digits dataset.
the Keras built-in [MNIST](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist) handwritten digit dataset.
When the script runs, it creates a task named `Tensorflow v2 mnist with summaries` in the `examples` project.

2
docs/hyperdatasets/webapp/webapp_exp_track_visual.md
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@ -9,7 +9,7 @@ Dataviews are available under the ClearML Enterprise plan.
While a task is running, and any time after it finishes, results are tracked and can be visualized in the ClearML
Enterprise WebApp (UI).
In addition to all of ClearML's offerings, ClearML Enterprise keeps track of the Dataviews associated with an
In addition to all of ClearML's offerings, ClearML Enterprise keeps track of the Dataviews associated with a
task, which can be viewed and [modified](webapp_exp_modifying.md) in the WebApp.
## Viewing a Task's Dataviews

4
docs/pipelines/pipelines_sdk_function_decorators.md
View File

@ -167,8 +167,8 @@ Additionally, you can enable automatic logging of a step's metrics / artifacts /
following arguments:
* `monitor_metrics` (optional) - Automatically log the step's reported metrics also on the pipeline Task. The expected
format is one of the following:
* List of pairs metric (title, series) to log: [(step_metric_title, step_metric_series), ]. Example: `[('test', 'accuracy'), ]`
* List of tuple pairs, to specify a different target metric to use on the pipeline Task: [((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ].
* List of pairs metric (title, series) to log: `[(step_metric_title, step_metric_series), ]`. Example: `[('test', 'accuracy'), ]`
* List of tuple pairs, to specify a different target metric to use on the pipeline Task: `[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]`.
Example: `[[('test', 'accuracy'), ('model', 'accuracy')], ]`
* `monitor_artifacts` (optional) - Automatically log the step's artifacts on the pipeline Task.
* Provided a list of

4
docs/pipelines/pipelines_sdk_tasks.md
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@ -221,8 +221,8 @@ You can enable automatic logging of a step's metrics /artifacts / models to the
* `monitor_metrics` (optional) - Automatically log the step's reported metrics also on the pipeline Task. The expected
format is one of the following:
* List of pairs metric (title, series) to log: [(step_metric_title, step_metric_series), ]. Example: `[('test', 'accuracy'), ]`
* List of tuple pairs, to specify a different target metric to use on the pipeline Task: [((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ].
* List of pairs metric (title, series) to log: `[(step_metric_title, step_metric_series), ]`. Example: `[('test', 'accuracy'), ]`
* List of tuple pairs, to specify a different target metric to use on the pipeline Task: `[((step_metric_title, step_metric_series), (target_metric_title, target_metric_series)), ]`.
Example: `[[('test', 'accuracy'), ('model', 'accuracy')], ]`
* `monitor_artifacts` (optional) - Automatically log the step's artifacts on the pipeline Task.
* Provided a list of artifact names created by the step function, these artifacts will be logged automatically also

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

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

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

11
docs/release_notes/clearml_server/enterprise/ver_3_24.md
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@ -3,6 +3,12 @@ title: Version 3.24
---
### Enterprise Server 3.24.3
**New Features**
* Add option to limit UI application instance endpoint access to the application instance creator only
* Add custom user properties to multi-tenant usage reports
### Enterprise Server 3.24.2
**New Features**
@ -54,7 +60,8 @@ title: Version 3.24
**Bug Fixes**
* Fix ctrl-f does not open a search bar in UI editor modals ([ClearML Web GitHub issue #99](https://github.com/clearml/clearml-web/issues/99))
* Fix UI Incorrect project statistics in project page
* Fix webserver configuration environment variables don't load with single-quoted strings ([ClearML Server GitHub issue #271](https://github.com/clearml/clearml-server/issues/271))
* Fix UI incorrect project statistics in project page
* Fix UI Hyper-Dataset version's "Publish" function is sometimes unnecessarily disabled
* Fix UI Task manual refresh function does not work in full screen mode
* Fix links to tasks are broken in the Orchestration's Queues’ task lists
@ -73,5 +80,5 @@ title: Version 3.24
* Fix UI global search results display aborted tasks as completed
* Fix UI breadcrumbs sometimes don't display project name of newly cloned task
* Fix scroll sometimes doesn't work in UI global search results
* Fix Hyper-Dataset FrameGroup Details and FrameGroup Metadata sections are not expanding
* Fix Hyper-Dataset FrameGroup Details and Metadata sections are not expanding
* Fix unsaved content is not discarded in UI Hyper-Dataset frame viewer when moving to another frame source

22
docs/release_notes/clearml_server/open_source/ver_2_0.md
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@ -2,6 +2,26 @@
title: Version 2.0
---
### ClearML Server 2.0.1
**New Features**
* New UI task creation options
* Support bash as well as python scripts
* Support file upload
**Bug Fixes**
* Fix ctrl-f does not open a search bar in UI editor modals ([ClearML Web GitHub issue #99](https://github.com/clearml/clearml-web/issues/99))
* Fix UI smoothed plots are dimmer than original plots in dark mode ([ClearML Server GitHub issue #270](https://github.com/clearml/clearml-server/issues/270))
* Fix webserver configuration environment variables don't load with single-quoted strings ([ClearML Server GitHub issue #271](https://github.com/clearml/clearml-server/issues/271))
* Fix image plots sometimes not rendered in UI
* Fix "All" tag filter not working in UI model selection modal in comparison pages
* Fix manual refresh function sometimes does not work in UI task
* Fix UI embedded plot colors do not change upon UI theme change
* Fix deleting a parameter in the UI task creation modal incorrectly removes another parameter
* Fix UI global search displays aborted tasks as completed
* Fix can't show/hide specific UI plot variants
* Fix UI breadcrumbs sometimes does not display project name
### ClearML Server 2.0.0
**Breaking Changes**
@ -18,7 +38,7 @@ Upgrading to ClearML Server v1.17 from a previous version:
* New UI task creation options
* Support bash as well as Python scripts
* Support file upload
* New UI setting for configuring cloud storage credentials with which ClearML can clean up cloud storage artifacts on task deletion.
* New UI setting for configuring cloud storage credentials with which ClearML can clean up cloud storage artifacts on task deletion ([ClearML Server GitHub issue #144](https://github.com/clearml/clearml-server/issues/144)).
* Add UI scalar plots presentation of plots in sections grouped by metrics.
* Add UI batch export plot embed codes for all metric plots in a single click.
* Add UI pipeline presentation of steps grouped into stages

17
sidebars.js
View File

@ -456,14 +456,17 @@ module.exports = {
{'Server API': [
'references/api/index',
'references/api/definitions',
'references/api/login',
'references/api/debug',
'references/api/events',
'references/api/login',
'references/api/models',
'references/api/pipelines',
'references/api/projects',
'references/api/queues',
'references/api/workers',
'references/api/events',
'references/api/models',
'references/api/reports',
'references/api/serving',
'references/api/tasks',
'references/api/workers',
]},
{
type: 'category',
@ -645,7 +648,8 @@ module.exports = {
label: 'Enterprise Server',
items: [
{'Deployment Options': [
'deploying_clearml/enterprise_deploy/multi_tenant_k8s',
'deploying_clearml/enterprise_deploy/k8s',
'deploying_clearml/enterprise_deploy/multi_tenant_k8s',
'deploying_clearml/enterprise_deploy/vpc_aws',
'deploying_clearml/enterprise_deploy/on_prem_ubuntu',
]
@ -658,7 +662,8 @@ module.exports = {
},
{'ClearML Application Gateway': [
'deploying_clearml/enterprise_deploy/appgw_install_compose',
'deploying_clearml/enterprise_deploy/appgw_install_k8s',
'deploying_clearml/enterprise_deploy/appgw_install_compose_hosted',
'deploying_clearml/enterprise_deploy/appgw_install_k8s',
]
},
'deploying_clearml/enterprise_deploy/custom_billing',