Updating MLCube trademark to R.

sergey-serebryakov · Jul 26, 2023 · 30bceae · 30bceae
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diff --git a/README.md b/README.md
@@ -1,16 +1,16 @@
-# MLCube™
+# MLCube
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) 
 
 [![PyPI MLCube](https://img.shields.io/pypi/v/mlcube.svg?label="pypi%20(MLCube)")](https://pypi.org/project/mlcube)
 [![PyPI MLCube Docker Runner](https://img.shields.io/pypi/v/mlcube-docker.svg?label="pypi%20(Docker%20Runner)")](https://pypi.org/project/mlcube-docker)
 [![PyPI MLCube Singularity Runner](https://img.shields.io/pypi/v/mlcube-singularity.svg?label="pypi%20(Singularity%20Runner)")](https://pypi.org/project/mlcube-singularity)
 
-MLCube brings the concept of interchangeable parts to the world of machine learning models. It is the shipping container that enables researchers and developers to easily share the software that powers machine learning. 
+MLCube&reg; brings the concept of interchangeable parts to the world of machine learning models. It is the shipping container that enables researchers and developers to easily share the software that powers machine learning. 
 
 MLCube is a set of common conventions for creating ML software that can just "plug-and-play" on many systems. MLCube makes it easier for researchers to share innovative ML models, for a developer to experiment with many models, and for software companies to create infrastructure for models. It creates opportunities by putting ML in the hands of more people.
 
-MLCube isn’t a new framework or service; MLCube is a consistent interface to machine learning models in containers like Docker. Models published with the MLCube interface can be run on local machines, on a variety of major clouds, or in Kubernetes clusters - all using the same code. MLCommons provides open source “runners” for each of these environments that make training a model in an MLCube™ a single command. 
+MLCube isn’t a new framework or service; MLCube is a consistent interface to machine learning models in containers like Docker. Models published with the MLCube interface can be run on local machines, on a variety of major clouds, or in Kubernetes clusters - all using the same code. MLCommons provides open source “runners” for each of these environments that make training a model in an MLCube a single command. 
 
 *Note: This project is still in the very early stages and under active development, some parts may have unexpected/inconsistent behaviours.*
 

diff --git a/docs/getting-started/concepts.md b/docs/getting-started/concepts.md
@@ -1,7 +1,7 @@
 # MLCube concepts
 
 ### Command Line Arguments
-MLCube runtime and MLCube runners accept multiple command line arguments. They can be classified into two categories:
+MLCube&reg; runtime and MLCube runners accept multiple command line arguments. They can be classified into two categories:
 
 - Fixed command-specific parameters such as `--mlcube`, `--platform` and `--task` for the MLCube's `run` command, or
  `create_platform` and `rename_platform` for the `config` command.

diff --git a/docs/getting-started/hello-world.md b/docs/getting-started/hello-world.md
@@ -1,5 +1,5 @@
 # Hello World
-Hello World MLCube is an example of a Docker-based MLCube. 
+Hello World MLCube&reg; is an example of a Docker-based MLCube. 
 
 
 ## QuickStart

diff --git a/docs/getting-started/index.md b/docs/getting-started/index.md
@@ -1,6 +1,6 @@
 # Installation
 
-Here is the step-by-step guide to install MLCube library and run simple MLCube cubes.
+Here is the step-by-step guide to install MLCube&reg; library and run simple MLCube cubes.
 
 ## Create a python environment
 

diff --git a/docs/getting-started/mlcube-configuration.md b/docs/getting-started/mlcube-configuration.md
@@ -1,6 +1,6 @@
 # MLCube Configuration
 
-MLCube configuration provides information about MLCube's authors, requirements and 
+MLCube&reg; configuration provides information about MLCube's authors, requirements and 
 [tasks](https://mlcommons.github.io/mlcube/getting-started/concepts/#task). This is example configuration for
 the [MNIST MLCube](https://github.com/mlcommons/mlcube_examples/tree/master/mnist):
 

diff --git a/docs/getting-started/mnist.md b/docs/getting-started/mnist.md
@@ -1,6 +1,6 @@
 # MNIST
 The [MNIST dataset](http://yann.lecun.com/exdb/mnist/) is a collection of 60,000 handwritten digits widely used for
-training statistical, Machine Learning (ML) and Deep Learning (DL) models. The MNIST MLCube example demonstrates
+training statistical, Machine Learning (ML) and Deep Learning (DL) models. The MNIST MLCube&reg; example demonstrates
 how data scientists, ML and DL researchers and developers can distribute their ML projects (including training,
 validation and inference code) as MLCube cubes. MLCube establishes a standard to package user workloads,
 and provides unified command line interface. In addition, MLCube provides a number of reference 

diff --git a/docs/getting-started/system-settings.md b/docs/getting-started/system-settings.md
@@ -1,7 +1,7 @@
 # MLCube System Settings
-MLCube system settings configure MLCube and MLCube runners at a system level. The term `system level` here implies that
-these settings are not tied to particular MLCubes (MLCube compliant ML projects). Instead, these settings are used by
-MLCube runners on every machine where MLCube runtime is configured to use these settings.
+MLCube&reg; system settings configure MLCube and MLCube runners at a system level. The term `system level` here implies 
+that these settings are not tied to particular MLCubes (MLCube compliant ML projects). Instead, these settings are used 
+by MLCube runners on every machine where MLCube runtime is configured to use these settings.
 
 ## Introduction
 When MLCube runners run MLCubes, they need to know not only the content of MLCubes (tasks that MLCubes provide), but 

diff --git a/docs/index.md b/docs/index.md
@@ -1,6 +1,6 @@
 # MLCube
 
-MLCube is a project that reduces friction for machine learning by ensuring that models are easily portable and 
+MLCube&reg; is a project that reduces friction for machine learning by ensuring that models are easily portable and 
 reproducible, e.g., between different stacks such as different clouds, between cloud and on-prem, etc.
 
 Interested in getting started with MLCube? Follow the 

diff --git a/docs/runners/docker-runner.md b/docs/runners/docker-runner.md
@@ -1,5 +1,5 @@
 # Docker Runner
-Docker runner uses docker/nvidia-docker/podman to run MLCube cubes. It supports two mandatory commands - `configure` and
+Docker runner uses docker/nvidia-docker/podman to run MLCube&reg; cubes. It supports two mandatory commands - `configure` and
 `run` with standard arguments - `mlcube`, `platform` and `task`. Users can configure docker runner in MLCube 
 configuration file, system setting file, and override parameters on a command line.
 

diff --git a/docs/runners/gcp-runner.md b/docs/runners/gcp-runner.md
@@ -1,7 +1,7 @@
 # Google Compute Platform (GCP) Runner
 
 !!! attention
- MLCube is under active development. Allocating and using instances in clouds are associated with costs. Users of 
+ MLCube&reg; is under active development. Allocating and using instances in clouds are associated with costs. Users of 
  GCP runners should be aware about it, especially, taking into account capability of GCP runners to automatically 
  create and start remote instances. GCP RUNNERS in current implementation DO NOT stop/destroy remote instances. 
  Users are encouraged to visit web consoles to identify what virtual instances exist and run.

diff --git a/docs/runners/index.md b/docs/runners/index.md
@@ -1,5 +1,5 @@
 # Runners
-MLCube runners run MLCube cubes on one or multiple platforms. Examples of platforms are Docker and Singularity 
+MLCube&reg; runners run MLCube cubes on one or multiple platforms. Examples of platforms are Docker and Singularity 
 containers, Kubernetes, remote hosts, virtual machines in the cloud, etc. Every runner has a fixed set of configuration
 parameters that users can change to configure MLCubes and runners for their environments. Concretely, runners can take
 information from three different sources:

diff --git a/docs/runners/kubeflow.md b/docs/runners/kubeflow.md
@@ -1,7 +1,7 @@
 # Kubeflow Runner
 
 !!! warning
- Work in progress. Some functionality described below may not be available.
+ MLCube&reg; Kubeflow runner is work in progress. Some functionality described below may not be available.
 
 Kubeflow supports two mandatory commands - `configure` and `run` with standard arguments - `mlcube`, `platform` and 
 `task`. Users can configure SSH runner in system setting file, and override parameters on a command line.

diff --git a/docs/runners/kubernetes.md b/docs/runners/kubernetes.md
@@ -3,7 +3,7 @@
 !!! warning
  Work in progress. Some functionality described below may not be available.
 
-The Kubernetes Runner runs a MLCube on a Kubernetes cluster.
+The Kubernetes Runner runs a MLCube&reg; on a Kubernetes cluster.
 
 ## Why Kubernetes?
 

diff --git a/docs/runners/singularity-runner.md b/docs/runners/singularity-runner.md
@@ -1,5 +1,5 @@
 # Singularity Runner
-Singularity runner uses singularity to run MLCube cubes. It supports two mandatory commands - `configure` and
+Singularity runner uses singularity to run MLCube&reg; cubes. It supports two mandatory commands - `configure` and
 `run` with standard arguments - `mlcube`, `platform` and `task`. Users can configure Singularity runner in MLCube 
 configuration file, system setting file, and override parameters on a command line.
 

diff --git a/docs/runners/ssh-runner.md b/docs/runners/ssh-runner.md
@@ -3,7 +3,7 @@
 !!! warning
  Work in progress. Some functionality described below may not be available.
 
-SSH runner uses other runners to run MLCube cubes on remote hosts. It uses `ssh` and `rsync` internally. It
+SSH runner uses other runners to run MLCube&reg; cubes on remote hosts. It uses `ssh` and `rsync` internally. It
 supports two mandatory commands - `configure` and `run` with standard arguments - `mlcube`, `platform` and `task`. Users 
 can configure SSH runner in system setting file, and override parameters on a command line.
 

diff --git a/docs/tutorials/create-mlcube.md b/docs/tutorials/create-mlcube.md
@@ -1,5 +1,5 @@
 # Tutorial: Create an MLCube 
-Interested in getting started with MLCube? Follow the instructions in this tutorial. 
+Interested in getting started with MLCube&reg;? Follow the instructions in this tutorial. 
 ## Step 1: Setup 
 Get MLCube, MLCube examples and MLCube Templates, and CREATE a Python environment.
 ```shell

diff --git a/mlcube/mlcube/__main__.py b/mlcube/mlcube/__main__.py
@@ -129,7 +129,7 @@ def parser_process(value: str, state: click.parser.ParsingState):
 @Options.loglevel
 @Options.help
 def cli(log_level: t.Optional[str]):
- """MLCube 📦 is a tool for packaging, distributing and running Machine Learning (ML) projects and models.
+ """MLCube® is a tool for packaging, distributing and running Machine Learning (ML) projects and models.
 
  \b
  - GitHub: https://github.com/mlcommons/mlcube

diff --git a/release_tests/README.md b/release_tests/README.md
@@ -1,6 +1,6 @@
 # MLCube Release process
 
-To run MLCube and MLCuber runners unitests, run the following command in the root directory of the project:
+To run MLCube&reg; and MLCube runners unittests, run the following command in the root directory of the project:
 ```shell
 conda create -n mlcube python=3.6
 conda activate mlcube

diff --git a/runners/mlcube_docker/README.md b/runners/mlcube_docker/README.md
@@ -1,5 +1,5 @@
 # MLCube Docker Runner
-MLCube Docker Runner runs cubes (packaged Machine Learning (ML) workloads) in the docker environment. 
+MLCube&reg; Docker Runner runs cubes (packaged Machine Learning (ML) workloads) in the docker environment. 
 
 1. Create MLCube system settings file. It should be located in a user home directory: `${HOME}/mlcube.yaml`. If this 
  is not possible or not convenient, this file can be placed in any location given that environment variable

diff --git a/runners/mlcube_k8s/README.md b/runners/mlcube_k8s/README.md
@@ -1,6 +1,6 @@
 # Kubernetes Runner
 
-1. Create MLCube system settings file. It should be located in a user home directory: `${HOME}/mlcube.yaml`. If this 
+1. Create MLCube&reg; system settings file. It should be located in a user home directory: `${HOME}/mlcube.yaml`. If this 
  is not possible or not convenient, this file can be placed in any location given that environment variable
  `MLCUBE_SYSTEM_SETTINGS` points to this file. 
 2. Put the following in this file:

diff --git a/runners/mlcube_kubeflow/README.md b/runners/mlcube_kubeflow/README.md
@@ -1,6 +1,6 @@
 # Kubeflow Runner
 
-1. Create MLCube system settings file. It should be located in a user home directory: `${HOME}/mlcube.yaml`. If this 
+1. Create MLCube&reg; system settings file. It should be located in a user home directory: `${HOME}/mlcube.yaml`. If this 
  is not possible or not convenient, this file can be placed in any location given that environment variable
  `MLCUBE_SYSTEM_SETTINGS` points to this file. 
 2. Put the following in this file:

diff --git a/runners/mlcube_singularity/README.md b/runners/mlcube_singularity/README.md
@@ -1,4 +1,4 @@
 # MLCube Singularity Runner
-MLCube Singularity Runner runs cubes (packaged Machine Learning (ML) workloads) in the singularity environment.
+MLCube&reg; Singularity Runner runs cubes (packaged Machine Learning (ML) workloads) in the singularity environment.
 Read Singularity Runner documentation [here](../../docs/runners/singularity-runner.md).
 
diff --git a/runners/mlcube_ssh/README.md b/runners/mlcube_ssh/README.md
@@ -1,3 +1,3 @@
 # MLCube SSH Runner
-MLCube SSH Runner runs cubes (packaged Machine Learning (ML) workloads) in the remote environment. Read
+MLCube&reg; SSH Runner runs cubes (packaged Machine Learning (ML) workloads) in the remote environment. Read
 SSH Runner documentation [here](../../docs/runners/ssh-runner.md).