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2
docs/clearml_agent/clearml_agent_fractional_gpus.md
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@ -80,7 +80,7 @@ For either setup, you can set up in your Enterprise ClearML Agent Helm chart the
each queue. When a task is enqueued in ClearML, it translates into a Kubernetes pod running on the designated device
with the specified fractional resource as defined in the Agent Helm chart.
#### MIG-enabled GPUs
#### MIG-enabled GPUs
The **ClearML Dynamic MIG Operator** (CDMO) chart enables running AI workloads on K8s with optimized hardware utilization
and workload performance by facilitating MIG GPU partitioning. Make sure you have a [MIG capable GPU](https://docs.nvidia.com/datacenter/tesla/mig-user-guide/index.html#supported-gpus).

2
docs/clearml_sdk/clearml_sdk_setup.md
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@ -2,7 +2,7 @@
title: ClearML Python Package
---
This is step-by-step guide for installing the `clearml` Python package and connecting it to the ClearML Server. Once done,
This is a step-by-step guide for installing the `clearml` Python package and connecting it to the ClearML Server. Once done,
you can integrate `clearml` into your code.
## Install ClearML

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docs/clearml_sdk/hpo_sdk.md
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@ -71,8 +71,8 @@ optimization.
from clearml import Task
task = Task.init(
project_name='Hyper-Parameter Optimization',
task_name='Automatic Hyper-Parameter Optimization',
project_name='Hyperparameter Optimization',
task_name='Automatic Hyperparameter Optimization',
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False
)

1
docs/clearml_sdk/task_sdk.md
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@ -74,6 +74,7 @@ After invoking `Task.init` in a script, ClearML starts its automagical logging,
* [AutoKeras](../integrations/autokeras.md)
* [CatBoost](../integrations/catboost.md)
* [Fast.ai](../integrations/fastai.md)
* [Hugging Face Transformers](../integrations/transformers.md)
* [LightGBM](../integrations/lightgbm.md)
* [MegEngine](../integrations/megengine.md)
* [MONAI](../integrations/monai.md)

2
docs/deploying_clearml/clearml_server.md
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@ -4,7 +4,7 @@ title: ClearML Server
## What is ClearML Server?
The ClearML Server is the backend service infrastructure for ClearML. It allows multiple users to collaborate and
manage their tasks by working seamlessly with the ClearML Python package and [ClearML Agent](../clearml_agent.md).
manage their tasks by working seamlessly with the [ClearML Python package](../clearml_sdk/clearml_sdk_setup.md) and [ClearML Agent](../clearml_agent.md).
ClearML Server is composed of the following:
* Web server including the [ClearML Web UI](../webapp/webapp_overview.md), which is the user interface for tracking, comparing, and managing tasks.

2
docs/deploying_clearml/clearml_server_config.md
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@ -233,7 +233,7 @@ The following example, which is based on AWS load balancing, demonstrates the co
### Opening Elasticsearch, MongoDB, and Redis for External Access
### Opening Elasticsearch, MongoDB, and Redis for External Access
For improved security, the ports for ClearML Server Elasticsearch, MongoDB, and Redis servers are not exposed by default;
they are only open internally in the docker network. If external access is needed, open these ports (but make sure to

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docs/deploying_clearml/enterprise_deploy/app_custom.md
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@ -29,12 +29,12 @@ The `General` section is the root-level section of the configuration file, and c
* `id` - A unique id for the application
* `name` - The name to display in the web application
* `version` - The version of the application implementation. Recommended to have three numbers and to bump up when updating applications, so that older running instances can still be displayed
* `provider` - The person/team/group who is the owner of the application. This will appears in the UI
* `provider` - The person/team/group who is the owner of the application. This will appear in the UI
* `description` - Short description of the application to be displayed in the ClearML Web UI
* `icon` (*Optional*) - Small image to display in the ClearML web UI as an icon for the application. Can be a public web url or an image in the application’s assets directory (described below)
* `no_info_html` (*Optional*) - HTML content to display as a placeholder for the dashboard when no instance is available. Can be a public web url or a file in the application’s assets directory (described below)
* `default-queue` - The queue to which application instance will be sent when launching a new instance. This queue should have an appropriate agent servicing it. See details in the Custom Apps Agent section below.
* `badges` (*Optional*) - List of strings to display as a bacge/label in the UI
* `badges` (*Optional*) - List of strings to display as a badge/label in the UI
* `resumable` - Boolean indication whether a running application instance can be restarted if required. Default is false.
* `category` (*Optional*) - Way to separate apps into different tabs in the ClearML web UI
* `featured` (*Optional*) - Value affecting the order of applications. Lower values are displayed first. Defaults to 500
@ -61,7 +61,7 @@ The `task` section describes the task to run, containing the following fields:
* `branch` - The branch to use
* `entry_point` - The python file to run
* `working_dir` - The directory to run it from
* `hyperparams` (*Optional*) - A list of the task’s hyper-parameters used by the application, with their default values. There is no need to specify all the parameters here, but it enables summarizing of the parameters that will be targeted by the wizard entries described below, and allows to specify default values to optional parameters appearing in the wizard.
* `hyperparams` (*Optional*) - A list of the task’s hyperparameters used by the application, with their default values. There is no need to specify all the parameters here, but it enables summarizing of the parameters that will be targeted by the wizard entries described below, and allows to specify default values to optional parameters appearing in the wizard.
#### Example
The `task` section in the simple application example:
@ -264,7 +264,7 @@ The dashboard elements are organized into lines.
The section contains the following information:
* `lines` - The array of line elements, each containing:
* `style` - CSS definitions for the line e.g setting the line height
* `style` - CSS definitions for the line e.g. setting the line height
* `contents` - An array of dashboard elements to display in a given line. Each element may have several fields:
* `title` - Text to display at the top of the field
* `type` - one of the following:
@ -276,7 +276,7 @@ The section contains the following information:
* hyperparameter
* configuration
* html
* `text` - For HTML. You can refer to task elements such as hyper-parameters by using `${hyperparams.<section>.<parameter name>.value}`
* `text` - For HTML. You can refer to task elements such as hyperparameters by using `${hyperparams.<section>.<parameter name>.value}`
* `metric` - For plot, scalar-histogram, debug-images, scalar - Name of the metric
* `variant` - For plot, scalar-histogram, debug-images, scalar - List of variants to display
* `key` - For histograms, one of the following: `iter`, `timestamp` or, `iso_time`

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docs/deploying_clearml/enterprise_deploy/appgw.md
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@ -30,12 +30,12 @@ their instances:
* [Embedding Model Deployment](../../webapp/applications/apps_embed_model_deployment.md)
* [Llama.cpp Model Deployment](../../webapp/applications/apps_llama_deployment.md)
The AI Application Gateway is provided through an additional component to the ClearML Server deployment: The ClearML Task Traffic Router.
If your ClearML Deployment does not have the Task Traffic Router properly installed, these application instances may not be accessible.
The AI Application Gateway requires an additional component to the ClearML Server deployment: the **ClearML App Gateway Router**.
If your ClearML Deployment does not have the App Gateway Router properly installed, these application instances may not be accessible.
#### Installation
The Task Traffic Router supports two deployment options:
The App Gateway Router supports two deployment options:
* [Docker Compose](appgw_install_compose.md)
* [Kubernetes](appgw_install_k8s.md)

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docs/deploying_clearml/enterprise_deploy/appgw_install_compose.md
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@ -40,77 +40,72 @@ This is an example of the `docker-compose` file you will need:
```
version: '3.5'
services:
task_traffic_webserver:
image: allegroai/task-traffic-router-webserver:${TASK-TRAFFIC-ROUTER-WEBSERVER-TAG}
ports:
- "80:8080"
restart: unless-stopped
container_name: task_traffic_webserver
volumes:
- ./task_traffic_router/config/nginx:/etc/nginx/conf.d:ro
- ./task_traffic_router/config/lua:/usr/local/openresty/nginx/lua:ro
task_traffic_router:
image: allegroai/task-traffic-router:${TASK-TRAFFIC-ROUTER-TAG}
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
- CLEARML_API_HOST=${CLEARML_API_HOST:?err}
- CLEARML_API_ACCESS_KEY=${CLEARML_API_ACCESS_KEY:?err}
- CLEARML_API_SECRET_KEY=${CLEARML_API_SECRET_KEY:?err}
- ROUTER_URL=${ROUTER_URL:?err}
- ROUTER_NAME=${ROUTER_NAME:?err}
- AUTH_ENABLED=${AUTH_ENABLED:?err}
- SSL_VERIFY=${SSL_VERIFY:?err}
- AUTH_COOKIE_NAME=${AUTH_COOKIE_NAME:?err}
- AUTH_BASE64_JWKS_KEY=${AUTH_BASE64_JWKS_KEY:?err}
- LISTEN_QUEUE_NAME=${LISTEN_QUEUE_NAME}
- EXTRA_BASH_COMMAND=${EXTRA_BASH_COMMAND}
- TCP_ROUTER_ADDRESS=${TCP_ROUTER_ADDRESS}
- TCP_PORT_START=${TCP_PORT_START}
- TCP_PORT_END=${TCP_PORT_END}
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:
- ROUTER_NAME=${ROUTER_NAME:?err}
- ROUTER__WEBSERVER__SERVER_PORT=${ROUTER__WEBSERVER__SERVER_PORT:?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}
```
Create a *runtime.env* file containing the following entries:
Create a `runtime.env` file containing the following entries:
```
TASK-TRAFFIC-ROUTER-WEBSERVER-TAG=
TASK-TRAFFIC-ROUTER-TAG=
CLEARML_API_HOST=https://api.
PROXY_TAG=
ROUTER_TAG=
ROUTER_NAME=main-router
ROUTER__WEBSERVER__SERVER_PORT=8010
ROUTER_URL=
CLEARML_API_HOST=
CLEARML_API_ACCESS_KEY=
CLEARML_API_SECRET_KEY=
ROUTER_URL=
ROUTER_NAME=main-router
AUTH_ENABLED=true
SSL_VERIFY=true
AUTH_COOKIE_NAME=
AUTH_BASE64_JWKS_KEY=
LISTEN_QUEUE_NAME=
EXTRA_BASH_COMMAND=
AUTH_SECURE_ENABLED=true
TCP_ROUTER_ADDRESS=
TCP_PORT_START=
TCP_PORT_END=
```
Edit it according to the following guidelines:
* `CLEARML_API_HOST`: URL usually starting with `https://api.`
* `CLEARML_API_ACCESS_KEY`: ClearML server api key
* `CLEARML_API_SECRET_KEY`: ClearML server secret key
* `ROUTER_URL`: URL for this router that was previously configured in the load balancer starting with `https://`
* `ROUTER_NAME`: Unique name for this router
* `AUTH_ENABLED`: Enable or disable http calls authentication when the router is communicating with the ClearML server
* `SSL_VERIFY`: Enable or disable SSL certificate validation when the router is communicating with the ClearML server
* `AUTH_COOKIE_NAME`: Cookie name used by the ClearML server to store the ClearML authentication cookie. This can usually be found in the `value_prefix` key starting with `allegro_token` in `envoy.yaml` file in the ClearML server installation (`/opt/allegro/config/envoy/envoy.yaml`) (see below)
* `AUTH_SECURE_ENABLED`: Enable the Set-Cookie `secure` parameter
* `AUTH_BASE64_JWKS_KEY`: Value form `k` key in the `jwks.json` file in the ClearML server installation
* `LISTEN_QUEUE_NAME`: (*optional*) Name of queue to check for tasks (if none, every task is checked)
* `EXTRA_BASH_COMMAND`: Command to be launched before starting the router
**Configuration Options:**
* `PROXY_TAG`: AI Application Gateway proxy tag. The Docker image tag for the proxy component, which needs to be
specified during installation. This tag is provided by ClearML to ensure compatibility with the recommended version.
* `ROUTER_TAG`: App Gateway Router tag. The Docker image tag for the router component. It defines the specific version
to be installed and is provided by ClearML as part of the setup process.
* `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.
* `ROUTER__WEBSERVER__SERVER_PORT`: Webserver port. The default port is 8080, but it can be adjusted to meet specific network requirements.
* `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.
* `CLEARML_API_HOST`: ClearML API server URL starting with `https://api.`
* `CLEARML_API_ACCESS_KEY`: ClearML server API key.
* `CLEARML_API_SECRET_KEY`: ClearML server secret key.
* `AUTH_COOKIE_NAME`: Cookie used by the ClearML server to store the ClearML authentication cookie. This can usually be
found in the `envoy.yaml` file in the ClearML server installation (`/opt/allegro/config/envoy/envoy.yaml`), under the
`value_prefix` key starting with `allegro_token`
* `AUTH_SECURE_ENABLED`: Enable the Set-Cookie `secure` parameter. Set to `false` in case services are exposed with `http`.
* `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 Session feature
* `TCP_PORT_END`: End port for the TCP Session feature
@ -121,12 +116,42 @@ Run the following command to start the router:
sudo docker compose --env-file runtime.env up -d
```
:::note How to find my jwkskey
### Advanced Configuration
The *JSON Web Key Set* (*JWKS*) is a set of keys containing the public keys used to verify any JSON Web Token (JWT).
#### Using Open HTTP
In a `docker-compose` server installation, this can be found in the `CLEARML__secure__auth__token_secret` env var in the apiserver server component.
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/appgw_install_k8s.md
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@ -3,17 +3,26 @@ title: Kubernetes Deployment
---
:::important Enterprise Feature
The Application Gateway is available under the ClearML Enterprise plan.
The AI Application Gateway is available under the ClearML Enterprise plan.
:::
This guide details the installation of the ClearML App Gateway Router.
The App Gateway Router enables access to your AI workload applications (e.g. remote IDEs like VSCode and Jupyter, model API interface, etc.).
It acts as a proxy, identifying ClearML Tasks running within its [K8s namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/)
and making them available for network access.
:::important
The App Gateway Router must be installed in the same K8s namespace as a dedicated ClearML Agent.
It can only configure access for ClearML Tasks within its own namespace.
:::
This guide details the installation of the ClearML AI Application Gateway, specifically the ClearML Task Router Component.
## Requirements
* 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 `clearml` helm-chart repo
* Credentials for `clearml` docker repo
* A valid ClearML Server installation
## Optional for HTTPS
@ -26,62 +35,55 @@ This guide details the installation of the ClearML AI Application Gateway, speci
### Login
```
helm repo add allegroai-enterprise \
helm repo add clearml-enterprise \
https://raw.githubusercontent.com/clearml/clearml-enterprise-helm-charts/gh-pages \
--username <GITHUB_TOKEN> \
--password <GITHUB_TOKEN>
```
Replace `<GITHUB_TOKEN>` with your valid GitHub token that has access to the ClearML Enterprise Helm charts repository.
### Prepare Values
Before installing the TTR, create a `helm-override` files named `task-traffic-router.values-override.yaml`:
Before installing the App Gateway Router, create a Helm override file:
```
imageCredentials:
password: "<DOCKERHUB_TOKEN>"
password: ""
clearml:
apiServerKey: ""
apiServerSecret: ""
apiServerUrlReference: "https://api."
jwksKey: ""
authCookieName: ""
apiServerKey: ""
apiServerSecret: ""
apiServerUrlReference: ""
authCookieName: ""
sslVerify: true
ingress:
enabled: true
hostName: "task-router.dev"
enabled: true
hostName: ""
tcpSession:
routerAddress: ""
portRange:
start:
end:
routerAddress: ""
service:
type: LoadBalancer
portRange:
start:
end:
```
Edit it accordingly to these guidelines:
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 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.
* `clearml.jwksKey`: Value form `k` key in `jwks.json` file in ClearML server installation (see below)
* `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
:::note How to find my jwkskey
The *JSON Web Key Set* (*JWKS*) is a set of keys containing the public keys used to verify any JSON Web Token (JWT).
```
kubectl -n clearml get secret clearml-conf \
-o jsonpath='{.data.secure_auth_token_secret}' \
| base64 -d && echo
```
:::
* `imageCredentials.password`: ClearML DockerHub Access Token.
* `clearml.apiServerKey`: ClearML server API key.
* `clearml.apiServerSecret`: ClearML server secret key.
* `clearml.apiServerUrlReference`: ClearML API server URL starting with `https://api.`.
* `clearml.authCookieName`: Cookie used by the ClearML server to store the ClearML authentication cookie.
* `clearml.sslVerify`: Enable or disable SSL certificate validation on `apiserver` calls check.
* `ingress.hostName`: Hostname of router used by the ingress controller to access it.
* `tcpSession.routerAddress`: The external router address (can be an IP, hostname, or load balancer address) depending on your network setup. Ensure this address is accessible for TCP connections.
* `tcpSession.service.type`: Service type used to expose TCP functionality, default is `NodePort`.
* `tcpSession.portRange.start`: Start port for the TCP Session feature.
* `tcpSession.portRange.end`: End port for the TCP Session feature.
The whole list of supported configuration is available with the command:
The full list of supported configuration is available with the command:
```
helm show readme allegroai-enterprise/clearml-enterprise-task-traffic-router
@ -94,9 +96,22 @@ To install the TTR component via Helm use the following command:
```
helm upgrade --install \
<RELEASE_NAME> \
-n <NAME_SPACE> \
-n <WORKLOAD_NAMESPACE> \
allegroai-enterprise/clearml-enterprise-task-traffic-router \
--version <CURRENT CHART VERSION> \
-f task-traffic-router.values-override.yaml
--version <CHART_VERSION> \
-f override.yaml
```
Replace the placeholders with the following values:
* `<RELEASE_NAME>` - Unique name for the App Gateway Router within the K8s namespace. This is a required parameter in
Helm, which identifies a specific installation of the chart. The release name also defines the router’s name and
appears in the UI within AI workload application URLs (e.g. Remote IDE URLs). This can be customized to support multiple installations within the same
namespace by assigning different release names.
* `<WORKLOAD_NAMESPACE>` - [Kubernetes Namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/)
where workloads will be executed. This namespace must be shared between a dedicated ClearML Agent and an App
Gateway Router. The agent is responsible for monitoring its assigned task queues and spawning workloads within this
namespace. The router monitors the same namespace for AI workloads (e.g. remote IDE applications). The router has a
namespace-limited scope, meaning it can only detect and manage tasks within its
assigned namespace.
* `<CHART_VERSION>` - Version recommended by the ClearML Support Team.

11
docs/deploying_clearml/enterprise_deploy/multi_tenant_k8s.md
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@ -513,31 +513,30 @@ Create a `NetworkPolicy` in the tenant namespace with the following configuratio
- podSelector: {}
```
### Install Task Traffic Router Chart
### Install the App Gateway Router Chart
Install the [Task Traffic Router](appgw.md) in your Kubernetes cluster, allowing it to manage and route tasks:
Install the App Gateway Router in your Kubernetes cluster, allowing it to manage and route tasks:
1. Prepare the `overrides.yaml` file with the following content:
```
imageCredentials:
password: "<allegroaienterprise_DockerHub_TOKEN>"
password: "<clearmlenterprise_DockerHub_TOKEN>"
clearml:
apiServerUrlReference: "<http://clearml-enterprise-apiserver.clearml:8008>"
apiserverKey: "<TENANT_KEY>"
apiserverSecret: "<TENANT_SECRET>"
jwksKey: "<JWKS_KEY>"
ingress:
enabled: true
hostName: "<unique url in same domain as apiserver/webserver>"
```
2. Install Task Traffic Router in the specified tenant namespace:
2. Install App Gateway Router in the specified tenant namespace:
```
helm install -n <TENANT_NAMESPACE> \\
clearml-ttr \\
allegroai-enterprise/clearml-task-traffic-router \\
clearml-enterprise/clearml-task-traffic-router \\
--create-namespace \\
-f overrides.yaml
```

2
docs/deploying_clearml/enterprise_deploy/vpc_aws.md
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@ -38,7 +38,7 @@ It is recommended to use a VPC with IPv6 enabled for future usage expansion.
1. Create a security group for the main server (`clearml-main`):
* Ingress:
* TCP port 10000, from the load balancer's security group
* TCP port 10000 from the load balancer's security group
* TCP port 22 from trusted IP addresses.
* Egress: All addresses and ports

2
docs/getting_started/clearml_agent_scheduling.md
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@ -82,7 +82,7 @@ Currently, these runtime properties can only be set using an ClearML REST API ca
endpoint, as follows:
* The body of the request must contain the `worker-id`, and the runtime property to add.
* An expiry date is optional. Use the format `"expiry":<time>`. For example, `"expiry":86400` will set an expiry of 24 hours.
* An expiry date is optional. Use the format `"expiry":<time>`. For example, `"expiry":86400` will set an expiry of 24 hours.
* To delete the property, set the expiry date to zero, `"expiry":0`.
For example, to force a worker on for 24 hours:

4
docs/hyperdatasets/webapp/webapp_annotator.md
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@ -16,6 +16,10 @@ Use annotation tasks to efficiently organize the annotation of frames in Dataset
Click on an annotation task card to open the frame viewer, where you can view the task's frames and annotate them.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific annotation tasks. You can query by the task’s name, hyper-dataset version, and ID.
To search using regex, click the `.*` icon on the search bar.
## Annotation Task Actions
Click <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Menu" className="icon size-md space-sm" /> on the top right
of an annotation task card to open its context menu and access annotation task actions.

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@ -18,6 +18,10 @@ using the buttons on the top left of the page. Use the table view for a comparat
columns of interest. Use the details view to access a selected Dataview's details, while keeping the Dataview list in view.
Details view can also be accessed by double-clicking a specific Dataview in the table view to open its details view.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific dataviews. You can query by the dataview name, ID, description, hyper-datasets, and versions.
To search using regex, click the `.*` icon on the search bar.
You can archive Dataviews so the Dataview table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
table to open the archive and view all archived Dataviews. From the archive, you can restore
Dataviews to remove them from the archive. You can also permanently delete Dataviews.

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@ -8,6 +8,7 @@ ClearML seamlessly integrates with a wide range of popular machine learning fram
* [Keras](keras.md)
* [YOLO v5](yolov5.md)
* [YOLO v8](yolov8.md)
* [Hugging Face Accelerate](accelerate.md)
* [Hugging Face Transformers](transformers.md)
* [MMEngine](mmengine.md)
* [MMCV](mmcv.md)

2
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@ -47,7 +47,7 @@ title: Version 3.24
* Add UI pipeline DAG presentation with pipeline steps grouped into stages
* Add Hyper-Dataset version sort by update time in ascending and descending order
* Add opacity control to Hyper-Dataset frame ROIs
* Add search bar for UI Settings's Users and Groups
* Add search bar for UI Settings' Users and Groups
* Add number of frames display to UI DataView preview
* Remove legacy "Augmentation" sections from UI Dataview pages
* Add control to collapse and expand UI Hyper-Dataset version list

2
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@ -203,7 +203,7 @@ title: Version 1.1
- Add support for uploading artifacts with a list of files using `Task.upload_artifcats(name, [Path(), Path()])`
- Add missing *clearml-task* parameters `--docker_args`, `--docker_bash_setup_script` and `--output-uri`
- Change `CreateAndPopulate` will auto list packages imported but not installed locally
- Add `clearml.task.populate.create_task_from_function()` to create a Task from a function, wrapping function input arguments into hyper-parameter section as kwargs and storing function results as named artifacts
- Add `clearml.task.populate.create_task_from_function()` to create a Task from a function, wrapping function input arguments into hyperparameter section as kwargs and storing function results as named artifacts
- Add support for Task serialization (e.g. for pickle)
- Add `Task.get_configuration_object_as_dict()`
- Add `docker_image` argument to `Task.set_base_docker()` (deprecate `docker_cmd`)

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@ -13,7 +13,7 @@ running, it serves your embedding model through a secure, publicly accessible ne
endpoint activity and shuts down if the model remains inactive for a specified maximum idle time.
:::info AI Application Gateway
The Embedding Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
The Embedding Model Deployment app makes use of the App Gateway Router which implements a secure, authenticated
network endpoint for the model.
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.

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@ -16,7 +16,7 @@ The Gradio launcher monitors the Gradio app activity and shuts down if it is ina
<a id="traffic_router"/>
:::important AI Application Gateway
The Gradio Launcher relies on the ClearML Traffic Router which implements user authentication, and redirects requests
The Gradio Launcher relies on the ClearML App Gateway Router which implements user authentication, and redirects requests
to the IP/port served by the Gradio app.
If the ClearML AI application Gateway is not available, the Gradio app might not be accessible.

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@ -12,7 +12,7 @@ running, it serves your model through a secure, publicly accessible network endp
and shuts down if the model remains inactive for a specified maximum idle time.
:::important AI Application Gateway
The llama.cpp Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
The llama.cpp Model Deployment app makes use of the App Gateway Router which implements a secure, authenticated
network endpoint for the model.
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.

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@ -13,7 +13,7 @@ it serves your model through a secure, publicly accessible network endpoint. The
shuts down if the model remains inactive for a specified maximum idle time.
:::info AI Application Gateway
The vLLM Model Deployment app makes use of the ClearML Traffic Router which implements a secure, authenticated
The vLLM Model Deployment app makes use of the App Gateway Router which implements a secure, authenticated
network endpoint for the model.
If the ClearML AI application Gateway is not available, the model endpoint might not be accessible.

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@ -17,7 +17,7 @@ time.
<a id="traffic_router"/>
:::important AI Application Gateway
The Streamlit Launcher relies on the ClearML Traffic Router which implements user authentication, and redirects requests
The Streamlit Launcher relies on the ClearML App Gateway Router which implements user authentication, and redirects requests
to the IP/port served by the Streamlit app.
If the ClearML AI application Gateway is not available, the Streamlit app might not be accessible.

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@ -25,6 +25,10 @@ Filter the datasets to find the one you're looking for more easily. These filter
respectively. These options appear on the top of the tag list.
* Filter by the absence of a tag (logical "NOT") by clicking its checkbox twice. An X will appear in the tag's checkbox.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific datasets. You can query by the dataset name, ID, or description. To search using regex, click the `.*`
icon on the search bar.
![Dataset page](../../img/webapp_dataset_page.png#light-mode-only)
![Dataset page](../../img/webapp_dataset_page_dark.png#dark-mode-only)

6
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@ -12,9 +12,13 @@ or comparison view <img src="/docs/latest/icons/ico-charts-view.svg" alt="Compar
using the buttons on the top left of the page. Use the details view to access a selected run's details, while keeping
the run list in view. Details view can also be accessed by double-clicking a specific pipeline run in the table view to
open its details view. Use the [comparison view](#comparing-runs) to compare your pipeline run's scalar and plot results.
This view compares the scalars/plots of currently selected pipeline runs. If no runs are selected, The first 100 visible
This view compares the scalars/plots of currently selected pipeline runs. If no runs are selected, the first 100 visible
runs in the table are compared.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific pipeline runs. You can query by the run name, ID, and description.
To search using regex, click the `.*` icon on the search bar.
You can archive pipeline runs so the runs table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
table to open the archive and view all archived runs. From the archive, you can restore
runs to remove them from the archive. You can also permanently delete runs.

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@ -17,6 +17,11 @@ depth comparison, see [Comparing Tasks](webapp_exp_comparing.md)). This view com
the scalars/plots of currently selected tasks. If no tasks are selected, the first 100
visible tasks in the table are compared.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific tasks. You can query by the task name, ID, description and input and output models. In the Enterprise version,
you can also query by the task’s dataviews' hyper-datasets and versions. To search using regex, click the `.*`
icon on the search bar.
You can archive tasks so the table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
table to open the archive and view all archived tasks. From the archive, you can restore
tasks to remove them from the archive. You can also permanently delete tasks.

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@ -6,9 +6,9 @@ Tune tasks and edit their execution details, then execute the tuned tasks on loc
## To Tune a Task and Execute it Remotely:
1. Locate the task. Open the task's Project page from the Dashboard or the main Projects page.
1. Locate the task. Open the task's Project page from the Project Dashboard or the main Projects page.
* On the Dashboard,
* On the Project Dashboard,
* Click on a task from RECENT TASKS
* In RECENT PROJECTS **>** click on a project card **>** click task
* In RECENT PROJECTS **>** click **VIEW ALL** **>** click the project card **>** click task

33
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@ -1,36 +1,17 @@
---
title: Dashboard
title: Project Dashboard
---
The **Dashboard** provides the following options:
* Quickly access the summarized monitoring of recently updated projects and their experiments
* Create new projects
* Open the [**Orchestration**](webapp_workers_queues.md) tab to autoscale, monitor, and manage your resource usage and
worker queues.
The **project dashboard** provides a summary of your most recent projects, reports, and tasks. Click a project, report
or task to quickly access it.
![Dashboard](../img/webapp_dashboard.png#light-mode-only)
![Dashboard](../img/webapp_dashboard_dark.png#dark-mode-only)
**To select a project, experiment, or model:**
To access the [projects page](webapp_projects_page.md), click the `View All` button next to the recent projects summary.
Similarly, to access the [reports page](webapp_reports.md), click the `View All` button next to the recent reports summary.
* A project or all projects - to view activity for all experiments in a project.
* In **RECENT PROJECTS**, click a specific project's card or **VIEW ALL**
* An experiment - to view experiment results, edit an experiment, enqueue an experiment to execute, etc.
* In **RECENT EXPERIMENTS**, click the experiment.
* In **RECENT PROJECTS**, click a project or **VIEW ALL** **>** Click the experiment.
To create a new project, click the `+ New Project` button. Similarly, to create a new report, click the `+ New Report`.
* A model - to view a model's configuration, label enumeration, or other details.
* From an experiment, click **ARTIFACTS** **>** In **Input Model** or **Output Model**, click the model.
* In **RECENT PROJECTS**, click a project or **VIEW ALL** **>** **MODELS** tab **>** Click the model.
**To create a new project:**
1. Click **+ NEW PROJECT**
1. Enter the project name, and, optionally, the description and default output destination
1. Click **CREATE PROJECT**
**To autoscale, monitor, and manage your resource usage and workers queues:**
* Click **MANAGE WORKERS AND QUEUES** to go to the [**Orchestration**](webapp_workers_queues.md) page.
To access the [orchestration page](webapp_workers_queues.md), click `Manage Workers and Queues` (Open Source)/`Orchestration Control Center`.

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@ -11,6 +11,10 @@ using the buttons on the top left of the page. Use the table view for a comparat
columns of interest. Use the details view to access a selected model's details, while keeping the model list in view.
Details view can also be accessed by double-clicking a specific model in the table view to open its details view.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific models. You can query by the model name, ID and description. To search using regex, click the `.*`
icon on the search bar.
You can archive models so the model table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
table to open the archive and view all archived models. From the archive, you can restore
models to remove them from the archive, and permanently delete models.

85
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@ -3,11 +3,13 @@ title: WebApp
---
The **ClearML Web UI** is the graphical user interface for the ClearML platform, which includes:
* Task management
* Browsing
* Resource utilization monitoring
* Profile management
* Direct access to the ClearML community (Slack channel, YouTube, and GitHub).
* ML workload automation
* Resource utilization monitoring and management
* Live model endpoint monitoring
* ML experiment management and visualization
* Model and Dataset viewing and management
* Pipeline creation and monitoring
* User and administrator settings
![WebApp screenshots gif](../img/gif/webapp_screenshots.gif#light-mode-only)
![WebApp screenshots gif](../img/gif/webapp_screenshots_dark.gif#dark-mode-only)
@ -15,43 +17,46 @@ The **ClearML Web UI** is the graphical user interface for the ClearML platform,
## UI Modules
The WebApp's sidebar provides access to the following modules:
* <img src="/docs/latest/icons/ico-homepage.svg" alt="Homepage" className="icon size-md space-md" />[Dashboard](webapp_home.md) - The dashboard for recent activity and quick access to tasks and projects.
* <img src="/docs/latest/icons/ico-projects.svg" alt="Projects" className="icon size-md space-md" />[Projects](webapp_projects_page.md) - The main experimentation page. Access your tasks and models as they are organized into projects. The tasks and models are displayed in tables which let you:
* Track ongoing tasks and visualize their results
* Reproduce previously run tasks
* Tune tasks with no code change
* Compare tasks
* Share tasks and their models with other ClearML hosted service users
* <img src="/docs/latest/icons/ico-side-bar-datasets.svg" alt="Datasets" className="icon size-md space-md" />[Datasets](datasets/webapp_dataset_page.md) - View and manage your datasets.
* <img src="/docs/latest/icons/ico-pipelines.svg" alt="Pipelines" className="icon size-md space-md" />[Pipelines](pipelines/webapp_pipeline_page.md) - View and manage your pipelines.
* <img src="/docs/latest/icons/ico-model-endpoints.svg" alt="Model endpoints" className="icon size-md space-md" />[Model Endpoints](webapp_model_endpoints.md) - Monitor your live model endpoints.
* <img src="/docs/latest/icons/ico-reports.svg" alt="Reports" className="icon size-md space-md" />[Reports](webapp_reports.md) - View and manage your reports.
* <img src="/docs/latest/icons/ico-workers.svg" alt="Workers and Queues" className="icon size-md space-md" />[Orchestration](webapp_workers_queues.md) - Autoscale, monitor, and manage your resource usage and workers queues.
* <img src="/docs/latest/icons/ico-applications.svg" alt="ClearML Apps" className="icon size-md space-md" />[Applications](applications/apps_overview.md) - ClearML's GUI applications for no-code workflow execution (available in the ClearML Pro and Enterprise plans).
* <img src="/docs/latest/icons/ico-workers.svg" alt="Workers and Queues" className="icon size-md space-md" />[Orchestration](webapp_workers_queues.md) - Autoscaling, resource usage monitoring and allocation management.
* <img src="/docs/latest/icons/ico-model-endpoints.svg" alt="Model endpoints" className="icon size-md space-md" />[Model Endpoints](webapp_model_endpoints.md) - Monitor your live model endpoints.
* <img src="/docs/latest/icons/ico-side-bar-datasets.svg" alt="Datasets" className="icon size-md space-md" />[Datasets](datasets/webapp_dataset_page.md) - View and manage your datasets.
* <img src="/docs/latest/icons/ico-projects.svg" alt="Projects" className="icon size-md space-md" />[Projects](webapp_projects_page.md) - The main experimentation page. Access your tasks and models as they are organized into projects. The tasks and models are displayed in tables which let you:
* Track ongoing tasks and visualize their results
* Reproduce previous task runs
* Tune task parameter values with no code change
* Compare tasks and models
* Share tasks and models with other ClearML hosted service users
* Create and share rich content [Reports](webapp_reports.md)
* <img src="/docs/latest/icons/ico-pipelines.svg" alt="Pipelines" className="icon size-md space-md" />[Pipelines](pipelines/webapp_pipeline_page.md) - View and manage your pipelines.
## UI Top Bar
### Settings Menu
Click the profile menu button <img src="/docs/latest/icons/ico-me.svg" alt="Profile button" className="icon size-lg space-sm" />
in the top right corner of the web UI screen to access the following:
* **Settings** - Navigate to ClearML's user [Settings](settings/webapp_settings_profile.md) page:
* Set [WebApp preferences](settings/webapp_settings_profile.md)
* Manage [workspace API credentials](settings/webapp_settings_profile.md#clearml-api-credentials)
to access the following:
* **Settings** - Navigate to ClearML's [Settings](settings/webapp_settings_profile.md) page:
* Set personal [WebApp preferences](settings/webapp_settings_profile.md)
* Manage [workspace API credentials](settings/webapp_settings_profile.md#clearml-api-credentials)
* Manage [personal configuration vault](settings/webapp_settings_profile.md#configuration-vault) (Enterprise offering)
* Configure [cloud storage access credentials](settings/webapp_settings_profile.md#browser-cloud-storage-access) for the ClearML Web UI
* ClearML Hosted service specific options
* Administrator settings
* Manage [users and workspaces](settings/webapp_settings_users.md)
* Manage [resource access permissions](settings/webapp_settings_access_rules.md) (available in the ClearML Enterprise plan)
* View [usage and billing](settings/webapp_settings_usage_billing.md) information (Free Hosted Service)
* Manage [access rules](settings/webapp_settings_access_rules.md) (available in the ClearML Enterprise plan)
* Define [configuration vaults](settings/webapp_settings_admin_vaults.md) to apply to designated user groups (available in the ClearML Enterprise plan)
* Manage [server identity providers](settings/webapp_settings_id_providers.md) (available in the ClearML Enterprise plan)
* Define the [available resources](settings/webapp_settings_resource_configs.md) and the way in which they will be
allocated to different workloads (available in the ClearML Enterprise plan)
* View [billing and usage](settings/webapp_settings_usage_billing.md) information
* **Invite a User** to your workspace (supported in hosted service). Click **Invite a User** > input user's
email > click **ADD** > page redirects to the [Users & Groups](settings/webapp_settings_users.md#user-groups) section of
* Define the [resource access policies](settings/webapp_settings_resource_configs.md) (available in the ClearML Enterprise plan)
* Workspace Control (Free Hosted Service)
* **Invite a User** to your workspace (supported in hosted service). Click **Invite a User** > input user's
email > click **ADD** > page redirects to the [Users & Groups](settings/webapp_settings_users.md#user-groups) section of
the **Settings** page
* **Switch to Workspace** - Hosted service users can be members of multiple workspaces. These workspaces are listed here.
* **Switch to Workspace** - Hosted service users can be members of multiple workspaces. These workspaces are listed here.
Click a workspace to switch to.
* Appearance - Select the UI color scheme:
* Light: ClearML will be in a light theme.
* Dark: ClearML will be in a dark theme.
* System: ClearML will follow your device’s theme.
* **Logout** of ClearML
### Finding What You're Looking for
@ -60,26 +65,18 @@ to find your ClearML resources.
To search using regex, click the `.*` icon on the search bar.
Search results are returned for the different ClearML objects:
* Tasks - Searching a task table looks for matches in the tasks' name, ID, description and input and
output models. The enterprise version also includes task Dataviews' hyper-datasets and versions.
* Models - Searching a Model table looks for matches in the models' name, ID and description.
* Dataviews (Enterprise only) - Searching a Dataview table looks for matches in the Dataviews' name, ID, description,
hyper-datasets, and versions.
* Datasets - Searching the datasets page looks for matches in the datasets' name, ID, and description. In a dataset's
version table, a search looks for matches in the versions' name, ID, and description.
* Pipelines - Searching the pipelines page looks for matches in the datasets' name, ID, and description. In a pipeline
run's table, a search looks for matches in the runs' name and ID.
* Reports - Searching the reports page looks for matches in the reports' name, ID, tags, project, description, and
report content.
The search functionality is tailored to each page, returning results specific to the object type displayed on the page.
For example, searching a task table looks for matches in the tasks' name, ID, description and input and output models.
On the reports page, it matches reports by name, ID, tags, project, description, and content. Similarly, searches in
models, datasets, pipelines, dataviews, and annotations, focus on attributes relevant to their respective objects.
The search bar in the [Dashboard](webapp_home.md) page searches the whole WebApp for objects that match queries as
The search bar in the [Project Dashboard](webapp_home.md) page searches the whole WebApp for objects that match queries as
specified above and returns results divided by object type (projects, tasks, models, etc.).
:::tip Additional filtering
ClearML's object tables (e.g. [tasks](webapp_exp_table.md), [models](webapp_model_table.md), [pipelines](pipelines/webapp_pipeline_table.md),
and [datasets](datasets/webapp_dataset_page.md)) provide column filters to easily focus your search by object properties
(e.g status, creation/update time, metric values, etc.).
(e.g. status, creation/update time, metric values, etc.).
:::
### Helpful Resources

4
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@ -18,6 +18,10 @@ contents (i.e. tasks, models etc.) via the folder with the bracketed (`[ ]`) pro
If a project does not contain any subprojects, clicking on its folder will open its task table (or [Project Overview](webapp_project_overview.md)
page when relevant).
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific projects. You can query by the project’s name and ID.
To search using regex, click the `.*` icon on the search bar.
## Project Folders
Project folders display summarized project information:

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@ -175,6 +175,10 @@ or in List view <img src="/docs/latest/icons/ico-flat-view.svg" alt="List view"
view, all reports are shown side-by-side. In Project view, reports are organized according to their projects, and
top-level projects are displayed. Click on a project card to view the project's reports.
Use the search bar <img src="/docs/latest/icons/ico-search.svg" alt="Magnifying glass" className="icon size-md space-sm" />
to find specific reports. You can query by the report name, ID, tags, project, description, and report content.
To search using regex, click the `.*` icon on the search bar.
![Report page](../img/webapp_report_page.png#light-mode-only)
![Report page](../img/webapp_report_page_dark.png#dark-mode-only)

126
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@ -470,68 +470,6 @@ module.exports = {
label: 'WebApp',
link: {type: 'doc', id: 'webapp/webapp_overview'},
items: [
'webapp/webapp_home',
{'Projects': [
'webapp/webapp_projects_page',
'webapp/webapp_project_overview',
{'Tasks': [
'webapp/webapp_exp_table',
'webapp/webapp_exp_track_visual',
'webapp/webapp_exp_reproducing',
'webapp/webapp_exp_tuning',
'webapp/webapp_exp_comparing'
]},
{'Models': [
'webapp/webapp_model_table',
'webapp/webapp_model_viewing',
'webapp/webapp_model_comparing'
]},
{'Dataviews': [
'hyperdatasets/webapp/webapp_dataviews',
'hyperdatasets/webapp/webapp_exp_track_visual',
'hyperdatasets/webapp/webapp_exp_modifying',
'hyperdatasets/webapp/webapp_exp_comparing'
]},
'webapp/webapp_exp_sharing'
]},
{'Datasets': [
'webapp/datasets/webapp_dataset_page',
'webapp/datasets/webapp_dataset_viewing'
]},
{'Hyper-Datasets': [
'hyperdatasets/webapp/webapp_datasets',
'hyperdatasets/webapp/webapp_datasets_versioning',
'hyperdatasets/webapp/webapp_datasets_frames',
'hyperdatasets/webapp/webapp_annotator'
]},
{'Pipelines': [
'webapp/pipelines/webapp_pipeline_page',
'webapp/pipelines/webapp_pipeline_table',
'webapp/pipelines/webapp_pipeline_viewing'
]},
'webapp/webapp_model_endpoints',
'webapp/webapp_reports',
{
type: 'category',
collapsible: true,
collapsed: true,
label: 'Orchestration',
link: {type: 'doc', id: 'webapp/webapp_workers_queues'},
items: [
'webapp/webapp_orchestration_dash',
{
type: 'category',
collapsible: true,
collapsed: true,
label: 'Autoscalers',
items: [
'webapp/applications/apps_aws_autoscaler',
'webapp/applications/apps_gcp_autoscaler',
]
},
'webapp/resource_policies'
]
},
{
type: 'category',
collapsible: true,
@ -561,6 +499,70 @@ module.exports = {
]},
]
},
{
type: 'category',
collapsible: true,
collapsed: true,
label: 'Orchestration',
link: {type: 'doc', id: 'webapp/webapp_workers_queues'},
items: [
'webapp/webapp_orchestration_dash',
{
type: 'category',
collapsible: true,
collapsed: true,
label: 'Autoscalers',
items: [
'webapp/applications/apps_aws_autoscaler',
'webapp/applications/apps_gcp_autoscaler',
]
},
'webapp/resource_policies'
]
},
'webapp/webapp_model_endpoints',
{'Datasets': [
'webapp/datasets/webapp_dataset_page',
'webapp/datasets/webapp_dataset_viewing'
]
},
{'Hyper-Datasets': [
'hyperdatasets/webapp/webapp_datasets',
'hyperdatasets/webapp/webapp_datasets_versioning',
'hyperdatasets/webapp/webapp_datasets_frames',
'hyperdatasets/webapp/webapp_annotator'
]},
{'Projects': [
'webapp/webapp_home',
'webapp/webapp_projects_page',
'webapp/webapp_project_overview',
{'Tasks': [
'webapp/webapp_exp_table',
'webapp/webapp_exp_track_visual',
'webapp/webapp_exp_reproducing',
'webapp/webapp_exp_tuning',
'webapp/webapp_exp_comparing'
]},
{'Models': [
'webapp/webapp_model_table',
'webapp/webapp_model_viewing',
'webapp/webapp_model_comparing'
]},
{'Dataviews': [
'hyperdatasets/webapp/webapp_dataviews',
'hyperdatasets/webapp/webapp_exp_track_visual',
'hyperdatasets/webapp/webapp_exp_modifying',
'hyperdatasets/webapp/webapp_exp_comparing'
]},
'webapp/webapp_exp_sharing',
'webapp/webapp_reports',
]},
{'Pipelines': [
'webapp/pipelines/webapp_pipeline_page',
'webapp/pipelines/webapp_pipeline_table',
'webapp/pipelines/webapp_pipeline_viewing'
]},
{
type: 'category',
collapsible: true,