Remove gpuCI scripts. (#5208)

Since migrating to GitHub Actions, the gpuCI scripts are no longer needed. This PR removes those outdated gpuCI scripts.

Authors:
  - Bradley Dice (https://github.com/bdice)
  - AJ Schmidt (https://github.com/ajschmidt8)

Approvers:
  - AJ Schmidt (https://github.com/ajschmidt8)
  - Dante Gama Dessavre (https://github.com/dantegd)

URL: #5208

CONTRIBUTING.md

@@ -38,11 +38,6 @@ into three categories:
 8. Wait for other developers to review your code and update code as needed.
 9. Once reviewed and approved, a RAPIDS developer will merge your pull request.
 
-### A note related to our CI process
-After you have started a PR (refer to step 6 in the previous section), every time you do a `git push <yourRemote> <pr-branch>`, it triggers a new CI run on all the commits thus far. Even though GPUCI has mechanisms to deal with this to a certain extent, if you keep `push`ing too frequently, it might just clog our GPUCI servers and slow down every PR and conda package generation! So, please be mindful of this and try not to do many frequent pushes.
-
-To quantify this, the average check in our CI takes between 80 and 90 minutes on our servers. The GPUCI infrastructure has limited resources, so if the servers get overwhelmed, every current active PR will not be able to correctly schedule CI.
-
 Remember, if you are unsure about anything, don't hesitate to comment on issues and ask for clarifications!
 
 ### Managing PR labels
@@ -71,35 +66,30 @@ implementation of the issue, ask them in the issue instead of the PR.
 
 ### Branches and Versions
 
-The cuML repository has two main branches: 
+The cuML repository has two main branches:
 
-1. `main` branch: it contains the last released version. Only hotfixes are targeted and merged into it.  
+1. `main` branch: it contains the last released version. Only hotfixes are targeted and merged into it.
 2. `branch-x.y`: it is the development branch which contains the upcoming release. All the new features should be based on this branch and Merge/Pull request should target this branch (with the exception of hotfixes).
-    
+
 ### Additional details
 
-For every new version `x.y` of cuML there is a corresponding branch called `branch-x.y`, from where new feature development starts and PRs will be targeted and merged before its release. The exceptions to this are the 'hotfixes' that target the `main` branch, which target critical issues raised by Github users and are directly merged to `main` branch, and create a new subversion of the project. While trying to patch an issue which requires a 'hotfix', please state the intent in the PR. 
+For every new version `x.y` of cuML there is a corresponding branch called `branch-x.y`, from where new feature development starts and PRs will be targeted and merged before its release. The exceptions to this are the 'hotfixes' that target the `main` branch, which target critical issues raised by Github users and are directly merged to `main` branch, and create a new subversion of the project. While trying to patch an issue which requires a 'hotfix', please state the intent in the PR.
 
-For all development, your changes should be pushed into a branch (created using the naming instructions below) in your own fork of cuML and then create a pull request when the code is ready. 
+For all development, your changes should be pushed into a branch (created using the naming instructions below) in your own fork of cuML and then create a pull request when the code is ready.
 
 A few days before releasing version `x.y` the code of the current development branch (`branch-x.y`) will be frozen and a new branch, 'branch-x+1.y' will be created to continue development.
 
 ### Branch naming
 
 Branches used to create PRs should have a name of the form `<type>-<name>`
 which conforms to the following conventions:
-- Type: 
+- Type:
     - fea - For if the branch is for a new feature(s)
     - enh - For if the branch is an enhancement of an existing feature(s)
     - bug - For if the branch is for fixing a bug(s) or regression(s)
-- Name: 
+- Name:
     - A name to convey what is being worked on
     - Please use dashes or underscores between words as opposed to spaces.
 
-### Building and Testing on a gpuCI image locally
-
-Before submitting a pull request, you can do a local build and test on your machine that mimics our gpuCI environment using the `ci/local/build.sh` script.
-For detailed information on usage of this script, see [here](ci/local/README.md).
-
 ## Attribution
 Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md

README.md

@@ -1,18 +1,15 @@
 # <div align="left"><img src="img/rapids_logo.png" width="90px"/>&nbsp;cuML - GPU Machine Learning Algorithms</div>
 
-[![Build Status](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cuml/job/branches/job/cuml-branch-pipeline/badge/icon)](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cuml/job/branches/job/cuml-branch-pipeline/)
-
 cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other [RAPIDS](https://rapids.ai/) projects.
 
 cuML enables data scientists, researchers, and software engineers to run
 traditional tabular ML tasks on GPUs without going into the details of CUDA
 programming. In most cases, cuML's Python API matches the API from
 [scikit-learn](https://scikit-learn.org).
 
-
 For large datasets, these GPU-based implementations can complete 10-50x faster
 than their CPU equivalents. For details on performance, see the [cuML Benchmarks
-Notebook](https://github.com/rapidsai/cuml/tree/branch-0.14/notebooks/tools).
+Notebook](https://github.com/rapidsai/cuml/tree/branch-23.04/notebooks/tools).
 
 As an example, the following Python snippet loads input and computes DBSCAN clusters, all on GPU, using cuDF:
 ```python
@@ -77,7 +74,7 @@ neighbors = nn.kneighbors(df)
 For additional examples, browse our complete [API
 documentation](https://docs.rapids.ai/api/cuml/stable/), or check out our
 example [walkthrough
-notebooks](https://github.com/rapidsai/cuml/tree/branch-0.15/notebooks). Finally, you
+notebooks](https://github.com/rapidsai/cuml/tree/branch-23.04/notebooks). Finally, you
 can find complete end-to-end examples in the [notebooks-contrib
 repo](https://github.com/rapidsai/notebooks-contrib).
 
@@ -112,9 +109,9 @@ repo](https://github.com/rapidsai/notebooks-contrib).
 | **Preprocessing** | Standardization, or mean removal and variance scaling / Normalization / Encoding categorical features / Discretization / Imputation of missing values / Polynomial features generation / and coming soon custom transformers and non-linear transformation | Based on Scikit-Learn preprocessing
 | **Time Series** | Holt-Winters Exponential Smoothing | |
 |  | Auto-regressive Integrated Moving Average (ARIMA) | Supports seasonality (SARIMA) |
-| **Model Explanation**                                 | SHAP Kernel Explainer                                                                                                               
+| **Model Explanation**                                 | SHAP Kernel Explainer
 | [Based on SHAP](https://shap.readthedocs.io/en/latest/)                                                                                                                                              |
-|                                                       | SHAP Permutation Explainer                       
+|                                                       | SHAP Permutation Explainer
 | [Based on SHAP](https://shap.readthedocs.io/en/latest/)                                                                                                                                               |
 | **Execution device interoperability** | | Run estimators interchangeably from host/cpu or device/gpu with minimal code change [demo](https://docs.rapids.ai/api/cuml/stable/execution_device_interoperability.html) |
 | **Other**                                             | K-Nearest Neighbors (KNN) Search                                                                                                          | Multi-node multi-GPU via Dask+[UCX](https://github.com/rapidsai/ucx-py), uses [Faiss](https://github.com/facebookresearch/faiss) for Nearest Neighbors Query. |