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An Env is a Runhouse primitive that represents a compute environment.
Builds an instance of Env
.
reqs (List[str]) – List of package names to install in this environment.
conda_env (Union[str, Dict], optional) – Dict representing conda env, Path to a conda env yaml file, or name of a local conda environment.
name (Optional[str], optional) – Name of the environment resource.
setup_cmds (Optional[List[str]]) – List of CLI commands to run for setup when the environment is being set up on a cluster.
env_vars (Dict or str) – Dictionary of environment variables, or relative path to .env file containing environment variables. (Default: {})
working_dir (str or Path) – Working directory of the environment, to be loaded onto the system. (Default: None)
compute (Dict) – Logical compute resources to be used by this environment, passed through to the
cluster scheduler (generally Ray). Only use this if you know what you’re doing.
Example: {"cpus": 1, "gpus": 1}
. (Default: {})
More info: https://docs.ray.io/en/latest/ray-core/scheduling/resources.html
load_from_den (bool) – Whether to try loading the Env resource from Den. (Default: True
)
dryrun (bool, optional) – Whether to run in dryrun mode. (Default: False
)
The resulting Env object.
Example
>>> # regular python env >>> env = rh.env(reqs=["torch", "pip"]) >>> env = rh.env(reqs=["reqs:./"], name="myenv") >>> >>> # conda env, see also rh.conda_env >>> conda_env_dict = >>> {"name": "new-conda-env", "channels": ["defaults"], "dependencies": "pip", {"pip": "diffusers"}) >>> conda_env = rh.env(conda_env=conda_env_dict) # from a dict >>> conda_env = rh.env(conda_env="conda_env.yaml") # from a yaml file >>> conda_env = rh.env(conda_env="local-conda-env-name") # from a existing local conda env >>> conda_env = rh.env(conda_env="conda_env.yaml", reqs=["pip:/accelerate"]) # with additional reqs
Builds an instance of CondaEnv
.
reqs (List[str]) – List of package names to install in this environment.
conda_env (Union[str, Dict], optional) – Dict representing conda env, Path to a conda env yaml file, or name of a local conda environment.
name (Optional[str], optional) – Name of the environment resource.
setup_cmds (Optional[List[str]]) – List of CLI commands to run for setup when the environment is being set up on a cluster.
env_vars (Dict or str) – Dictionary of environment variables, or relative path to .env file containing environment variables. (Default: {})
working_dir (str or Path) – Working directory of the environment, to be loaded onto the system. (Default: None)
compute (Dict) – Logical compute resources to be used by this environment, passed through to the
cluster scheduler (generally Ray). Only use this if you know what you’re doing.
Example: {"cpus": 1, "gpus": 1}
. (Default: {})
More info: https://docs.ray.io/en/latest/ray-core/scheduling/resources.html
dryrun (bool, optional) – Whether to run in dryrun mode. (Default: False
)
The resulting CondaEnv object.
Example
>>> rh.conda_env(reqs=["torch"]) >>> rh.conda_env(reqs=["torch"], name="resource_name") >>> rh.conda_env(reqs=["torch"], name="resource_name", conda_env={"name": "conda_env"})
- __init__(name: str | None = None, reqs: List[str | Package] = [], setup_cmds: List[str] = None, env_vars: Dict | str = {}, working_dir: str | Path | None = None, secrets: str | Secret | None = [], compute: Dict | None = {}, dryrun: bool = True, **kwargs)[source]
Runhouse Env object.
Note
To create an Env, please use the factory method
env()
.
Add an env var to the environment. Environment must be re-installed to propagate new environment variables if it already lives on a cluster.
Load or construct resource from config.
config (Dict) – Resource config.
dryrun (bool, optional) – Whether to construct resource or load as dryrun (Default: False
)
Locally install packages and run setup commands.
force (bool, optional) – Whether to setup the installation again if the env already exists
on the cluster. (Default: False
)
cluster (Clsuter, optional) – Cluster to install the env on. If not provided, env is installed
on the current cluster. (Default: None
)
Send environment to the system, and set it up if on a cluster.
system (str or Cluster) – Cluster or file system to send the env to.
node_idx (int, optional) – Node index of the cluster to send the env to. If not specified,
uses the head node. (Default: None
)
path (str, optional) – Path on the cluster to sync the env’s working dir to. Uses a default
path if not specified. (Default: None
)
force_install (bool, optional) – Whether to setup the installation again if the env already
exists on the cluster. (Default: False
)
Example
>>> env = rh.env(reqs=["numpy", "pip"]) >>> cluster_env = env.to(my_cluster) >>> s3_env = env.to("s3", path="s3_bucket/my_env")
- __init__(conda_yaml: str | Dict, name: str | None = None, reqs: List[str | Package] = [], setup_cmds: List[str] = None, env_vars: Dict | None = {}, working_dir: str | Path | None = './', secrets: List[str | Secret] = [], dryrun: bool = True, **kwargs)[source]
Runhouse CondaEnv object.
Note
To create a CondaEnv, please use the factory methods
env()
orconda_env()
.
Locally install packages and run setup commands.
force (bool, optional) – Whether to force re-install env if it has already been installed.
(default: False
)
cluster (bool, optional) – If None, installs env locally. Otherwise installs remotely
on the cluster using SSH. (default: None
)