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2
docs/deploying_clearml/clearml_server_gcp.md
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@ -125,7 +125,7 @@ If the data and the configuration need to be restored:
The following section contains a list of Custom Image URLs (exported in different formats) for each released ClearML Server version.
### Latest Version - v1.13.1
### Latest Version - v1.13.0
- [https://storage.googleapis.com/allegro-files/clearml-server/clearml-server.tar.gz](https://storage.googleapis.com/allegro-files/clearml-server/clearml-server.tar.gz)

2
docs/deploying_clearml/enterprise_deploy/appgw_install_compose.md
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@ -8,7 +8,7 @@ The Application Gateway is available under the ClearML Enterprise plan.
The AI Application Gateway enables external HTTP(S) or direct TCP access to ClearML tasks and applications running on
nodes. The gateway is configured with an endpoint or external address, making these services accessible from the user's
machine, outside the workload’ network.
machine, outside the workload's network.
This guide describes how to install and run the ClearML AI Application Gateway using docker-compose for environments
where you manage both the ClearML Server and the workload nodes.

3
docs/deploying_clearml/enterprise_deploy/extra_configs/dynamic_edit_task_pod_template.md
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@ -84,6 +84,8 @@ agentk8sglue:
# -- Custom Bash script for the Task Pods ran by Glue Agent
containerCustomBashScript: ""
```
:::
## Examples
@ -246,6 +248,7 @@ agentk8sglue:
- mountPath: "/tmp/task/"
name: task-pvc
```
:::
### Example: Required Role

8
docs/deploying_clearml/enterprise_deploy/extra_configs/self_signed_certificates.md
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@ -29,7 +29,7 @@ You have two configuration options:
- [**Append**](#append-extra-certificates-to-the-existing-ca-certificatescrt) extra certificates to the existing `ca-certificates.crt`
### Replace Entire `ca-certificates.crt` File
### Replace Entire ca-certificates.crt File
To replace the whole ca-bundle, provide a concatenated list of all trusted CA certificates in `pem` format as
they are stored in a standard `ca-certificates.crt`.
@ -51,7 +51,7 @@ customCertificates:
...
```
### Append Extra Certificates to the Existing `ca-certificates.crt`
### Append Extra Certificates to the Existing ca-certificates.crt
You can add certificates to the existing CA bundle. Each certificate must have a unique `alias`.
@ -104,7 +104,7 @@ You have two configuration options:
- [**Append**](#append-extra-certificates-to-the-existing-ca-certificatescrt-1) extra certificates to the existing `ca-certificates.crt`
### Replace Entire `ca-certificates.crt` File
### Replace Entire ca-certificates.crt File
To replace the whole ca-bundle, provide a concatenated list of all trusted CA certificates in `pem` format as
they are stored in a standard `ca-certificates.crt`.
@ -127,7 +127,7 @@ customCertificates:
...
```
### Append Extra Certificates to the Existing `ca-certificates.crt`
### Append Extra Certificates to the Existing ca-certificates.crt
You can add certificates to the existing CA bundle. Each certificate must have a unique `alias`.

40
docs/deploying_clearml/enterprise_deploy/fractional_gpus/cdmo.md
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@ -52,37 +52,37 @@ The ClearML Dynamic MIG Operator (CDMO) enables dynamic MIG (Multi-Instance GPU
### Installing CDMO
1. Create a `cdmo-values.override.yaml` file with the following content:
```yaml
imageCredentials:
password: "<CLEARML_DOCKERHUB_TOKEN>"
```
1. Create a `cdmo-values.override.yaml` file with the following content:
```yaml
imageCredentials:
password: "<CLEARML_DOCKERHUB_TOKEN>"
```
1. Install the CDMO Helm Chart using the previous override file:
```bash
helm install -n cdmo cdmo clearml-enterprise/clearml-dynamic-mig-operator --create-namespace -f cdmo-values.override.yaml
```
```bash
helm install -n cdmo cdmo clearml-enterprise/clearml-dynamic-mig-operator --create-namespace -f cdmo-values.override.yaml
```
1. Enable the NVIDIA MIG support on your cluster by running the following command on all nodes with a MIG-supported GPU
(run it for each GPU `<GPU_ID>` on the host):
```bash
nvidia-smi -mig 1
```
```bash
nvidia-smi -mig 1
```
:::note notes
* A node reboot may be required if the command output indicates so.
* For convenience, this command can be run from within the `nvidia-device-plugin-daemonset` pod running on the related node.
:::
:::note notes
* A node reboot may be required if the command output indicates so.
* For convenience, this command can be run from within the `nvidia-device-plugin-daemonset` pod running on the related node.
:::
1. Label all MIG-enabled GPU node `<NODE_NAME>` from the previous step:
```bash
kubectl label nodes <NODE_NAME> "cdmo.clear.ml/gpu-partitioning=mig"
```
```bash
kubectl label nodes <NODE_NAME> "cdmo.clear.ml/gpu-partitioning=mig"
```
## Disabling MIGs

152
docs/deploying_clearml/enterprise_deploy/fractional_gpus/cfgi.md
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@ -33,75 +33,75 @@ helm repo update
1. Create a Docker Registry secret named `clearml-dockerhub-access` in the `gpu-operator` namespace. Make sure to replace `<CLEARML_DOCKERHUB_TOKEN>` with your token.
```bash
kubectl create secret -n gpu-operator docker-registry clearml-dockerhub-access \
--docker-server=docker.io \
--docker-username=allegroaienterprise \
--docker-password="<CLEARML_DOCKERHUB_TOKEN>" \
--docker-email=""
```
```bash
kubectl create secret -n gpu-operator docker-registry clearml-dockerhub-access \
--docker-server=docker.io \
--docker-username=allegroaienterprise \
--docker-password="<CLEARML_DOCKERHUB_TOKEN>" \
--docker-email=""
```
1. Create a `gpu-operator.override.yaml` file as follows:
* Set `devicePlugin.repository` to `docker.io/clearml`
* Configure `devicePlugin.config.data.renamed-resources.sharing.timeSlicing.resources` for each GPU index on the host
* Use `nvidia.com/gpu-<INDEX>` format for the `rename` field, and set `replicas` to `8`.
* Set `devicePlugin.repository` to `docker.io/clearml`
* Configure `devicePlugin.config.data.renamed-resources.sharing.timeSlicing.resources` for each GPU index on the host
* Use `nvidia.com/gpu-<INDEX>` format for the `rename` field, and set `replicas` to `8`.
```yaml
gfd:
imagePullSecrets:
- "clearml-dockerhub-access"
toolkit:
env:
- name: ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED
value: "false"
- name: ACCEPT_NVIDIA_VISIBLE_DEVICES_AS_VOLUME_MOUNTS
value: "true"
devicePlugin:
repository: docker.io/clearml
image: k8s-device-plugin
version: v0.17.1-gpu-card-selection
imagePullPolicy: Always
imagePullSecrets:
- "clearml-dockerhub-access"
env:
- name: PASS_DEVICE_SPECS
value: "true"
- name: FAIL_ON_INIT_ERROR
value: "true"
- name: DEVICE_LIST_STRATEGY # Use volume-mounts
value: volume-mounts
- name: DEVICE_ID_STRATEGY
value: uuid
- name: NVIDIA_VISIBLE_DEVICES
value: all
- name: NVIDIA_DRIVER_CAPABILITIES
value: all
config:
name: device-plugin-config
create: true
default: "renamed-resources"
data:
renamed-resources: |-
version: v1
flags:
migStrategy: none
sharing:
timeSlicing:
renameByDefault: false
failRequestsGreaterThanOne: false
# Edit the following configuration as needed, adding as many GPU indices as many cards are installed on the Host.
resources:
- name: nvidia.com/gpu
rename: nvidia.com/gpu-0
devices:
- "0"
replicas: 8
- name: nvidia.com/gpu
rename: nvidia.com/gpu-1
devices:
- "1"
replicas: 8
```
```yaml
gfd:
imagePullSecrets:
- "clearml-dockerhub-access"
toolkit:
env:
- name: ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED
value: "false"
- name: ACCEPT_NVIDIA_VISIBLE_DEVICES_AS_VOLUME_MOUNTS
value: "true"
devicePlugin:
repository: docker.io/clearml
image: k8s-device-plugin
version: v0.17.1-gpu-card-selection
imagePullPolicy: Always
imagePullSecrets:
- "clearml-dockerhub-access"
env:
- name: PASS_DEVICE_SPECS
value: "true"
- name: FAIL_ON_INIT_ERROR
value: "true"
- name: DEVICE_LIST_STRATEGY # Use volume-mounts
value: volume-mounts
- name: DEVICE_ID_STRATEGY
value: uuid
- name: NVIDIA_VISIBLE_DEVICES
value: all
- name: NVIDIA_DRIVER_CAPABILITIES
value: all
config:
name: device-plugin-config
create: true
default: "renamed-resources"
data:
renamed-resources: |-
version: v1
flags:
migStrategy: none
sharing:
timeSlicing:
renameByDefault: false
failRequestsGreaterThanOne: false
# Edit the following configuration as needed, adding as many GPU indices as many cards are installed on the Host.
resources:
- name: nvidia.com/gpu
rename: nvidia.com/gpu-0
devices:
- "0"
replicas: 8
- name: nvidia.com/gpu
rename: nvidia.com/gpu-1
devices:
- "1"
replicas: 8
```
#### For CFGI version < 1.3.0 (Legacy)
@ -150,22 +150,22 @@ devicePlugin:
1. Install the NVIDIA `gpu-operator` using the previously created `gpu-operator.override.yaml` file:
```bash
helm install -n gpu-operator gpu-operator nvidia/gpu-operator --create-namespace -f gpu-operator.override.yaml
```
```bash
helm install -n gpu-operator gpu-operator nvidia/gpu-operator --create-namespace -f gpu-operator.override.yaml
```
1. Create a `cfgi-values.override.yaml` file with the following content:
```yaml
imageCredentials:
password: "<CLEARML_DOCKERHUB_TOKEN>"
```
```yaml
imageCredentials:
password: "<CLEARML_DOCKERHUB_TOKEN>"
```
1. Install the CFGI Helm Chart using the previous override file:
```bash
helm install -n cfgi cfgi clearml-enterprise/clearml-fractional-gpu-injector --create-namespace -f cfgi-values.override.yaml
```
```bash
helm install -n cfgi cfgi clearml-enterprise/clearml-fractional-gpu-injector --create-namespace -f cfgi-values.override.yaml
```
## Usage