[Hived]: Move Hived from OpenPAI to dedicated repo

.github/workflows/lint.yml

@@ -41,6 +41,5 @@ jobs:
         curl -L https://git.io/misspell | sudo bash -s -- -b /bin
     - name: Check spelling
       run: |
-        rm -rf ./subprojects/GOPATH/src/github.com/microsoft/hivedscheduler/vendor/
         rm -rf ./src/watchdog/GOPATH/src/github.com/microsoft/watchdog/vendor/
         misspell -error .

docs/hivedscheduler/devops.md

@@ -23,7 +23,7 @@ Update Scheduling Config, such as CRUD for virtual clusters and gpu types.
         virtualClusters: <your_virtual_clusters_config>
     ```
 
-    For how to config them, please check [Config HivedScheduler](../../subprojects/GOPATH/src/github.com/microsoft/hivedscheduler/doc/user-manual.md#Config)
+    For how to config them, please check [Config HivedScheduler](https://github.com/microsoft/hivedscheduler/blob/master/doc/user-manual.md#config)
 
 3. Push Config and Start Services
     ```bash

docs/system_architecture.md

@@ -22,7 +22,7 @@ The failure rules can be updated on-the-fly by the cluster operaters. Wheneven n
 
 OpenPAI provides comprehensive [monitoring tools](./grafana/README.md) to users and cluster admins for job and cluster monitoring. OpenPAI also monitors the status of key OpenPAI components in the cluster and is able to send [alerts](./alerting/README.md) (e.g., as in email) if pre-configed conditions have been triggered.
 
-OpenPAI is a modular platform, which is designed to enable various innovations. With the standard k8s scheduling API, OpenPAI introduces [HiveD](../subprojects/hivedscheduler/README.md), an optional but recommended scheduler designed for deep learning workloads in a multi-tenant GPU cluster. HiveD provides various advantages over standard k8s scheduler. For example, it introduces a notion of "virtual cluster", which allows a team of users to run workload in the virtual cluster as if they reserve a private, dedicated (smaller) GPU cluster.
+OpenPAI is a modular platform, which is designed to enable various innovations. With the standard k8s scheduling API, OpenPAI introduces [HiveD](https://github.com/microsoft/hivedscheduler), an optional but recommended scheduler designed for deep learning workloads in a multi-tenant GPU cluster. HiveD provides various advantages over standard k8s scheduler. For example, it introduces a notion of "virtual cluster", which allows a team of users to run workload in the virtual cluster as if they reserve a private, dedicated (smaller) GPU cluster.
 HiveD's virtual cluster reserves GPU resource not only in terms of quota (i.e., number of GPU), but also in terms of **topology**. For example, with HiveD a virtual cluster can reserve a GPU node, or a rack of GPU nodes within the same InfiniBand domain, instead of a set of GPUs randomly scatters across the cluster. This is important to preserve the training speed for jobs within the virtual cluster.
 With HiveD, OpenPAI also provides better topology-aware gang scheduling with no [resource starvation](https://en.wikipedia.org/wiki/Starvation_(computer_science)). HiveD also supports multi-priority jobs and job preemption.