There is a command group 'az_ml_environment'. This group of commands could 'manage azure ml environments'. The group of commands has global parameter '--debug' which means 'Increase logging verbosity to show all debug logs.'. The group of commands has global parameter '--help -h' which means 'Show this help message and exit.'. The group of commands has global parameter '--only-show-errors' which means 'Only show errors, suppressing warnings.'. The group of commands has global parameter '--output -o' which means 'Output format.'. The group of commands has global parameter '--query' which means 'JMESPath query string. See <a href="http://jmespath.org/">http://jmespath.org/</a> for more information and examples.'. The group of commands has global parameter '--subscription' which means 'Name or ID of subscription. You can configure the default subscription using `az account set -s NAME_OR_ID`.'. The group of commands has global parameter '--verbose' which means 'Increase logging verbosity. Use --debug for full debug logs.'. This group contains a child command 'az_ml_environment_create'.The syntax is: az ml environment create [--build-context]
                         [--conda-file]
                         [--datastore]
                         [--description]
                         [--dockerfile-path]
                         [--file]
                         [--image]
                         [--name]
                         [--no-wait]
                         [--os-type]
                         [--registry-name]
                         [--resource-group]
                         [--set]
                         [--tags]
                         [--version]
                         [--workspace-name]. The 1th example of the child command would be to use it to 'create an environment from a yaml specification file', the syntax is: az ml environment create --file my_env.yml --resource-group my-resource-group --workspace-name my-workspace. The 2th example of the child command would be to use it to 'create an environment from a docker image', the syntax is: az ml environment create --name my-env --version 1 --file my_env.yml  --image pytorch/pytorch --resource-group my-resource-group --workspace-name my-workspace. The 3th example of the child command would be to use it to 'create an environment from a build context', the syntax is: az ml environment create --name my-env --version 1 --file my_env.yml  --build-context envs/context/ --dockerfile-path Dockerfile --resource-group my-resource-group --workspace-name my-workspace. The 4th example of the child command would be to use it to 'create an environment from a conda specification', the syntax is: az ml environment create --name my-env --version 1 --file my_env.yml  --conda-file conda_dep.yml --image mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04 --resource-group my-resource-group --workspace-name my-workspace. The 5th example of the child command would be to use it to 'create an environment in registry from a yaml specification file', the syntax is: az ml environment create --file my_env.yml --registry-name my-registry-name --resource-group my-resource-group. The parameter '--build-context -b' is optional. local path to the directory to use as a docker build context. --build-context/-b and --image/-i are mutually exclusive arguments.. The parameter '--conda-file -c' is optional. local path to a conda specification file. --image/-i must also be specified if this argument is used.. The parameter '--datastore' is optional. the datastore to upload the local artifact to.. The parameter '--description' is optional. description of the environment.. The parameter '--dockerfile-path -d' is optional. relative path to the dockerfile within the directory specified by --build-context/-b. if omitted, './dockerfile' is used.. The parameter '--file -f' is optional. local path to the yaml file containing the azure ml environment specification. the yaml reference docs for environment can be found at: https://aka.ms/ml-cli-v2-environment-yaml-reference.. The parameter '--image -i' is optional. docker image. --image/-i and --build-context/-b are mutually exclusive arguments.. The parameter '--name -n' is optional. name of the environment.. The parameter '--no-wait' is optional. do not wait for the long-running operation to finish.. The parameter '--os-type' is optional. type of operating system. allowed values: linux, windows. default: linux.. The parameter '--registry-name' is optional. if provided, the command will target the registry instead of a workspace. hence resource group and workspace won't be required.. The parameter '--resource-group -g' is optional. name of resource group. you can configure the default group using `az configure --defaults group=<name>`.. The parameter '--set' is optional. update an object by specifying a property path and value to set. example: --set property1.property2=<value>.. The parameter '--tags' is optional. space-separated key-value pairs for the tags of the object.. The parameter '--version -v' is optional. version of the environment.. The parameter '--workspace-name -w' is optional. name of the azure ml workspace. you can configure the default workspace using `az configure --defaults workspace=<name>`.. 