def client(env: str):
"""
Initialize a client based on the specified environment.
Args:
env: Name of the environment to use.
Returns:
Cortex client that can be used to deploy and manage APIs in the specified environment.
"""
environments = env_list()
found = False
for environment in environments:
if environment["name"] == env:
found = True
break
if not found:
raise NotFound(
f"can't find environment {env}, create one by calling `cortex.cluster_client()`"
)
return Client(environment)
def client(env: Optional[str] = None) -> Client:
"""
Initialize a client based on the specified environment.
Args:
env: Name of the environment to use.
Returns:
Cortex client that can be used to deploy and manage APIs in the specified environment.
"""
environments = env_list()
if env is None:
if not environments["default_environment"]:
raise NotFound("no default environment configured")
env = environments["default_environment"]
found = False
for environment in environments["environments"]:
if environment["name"] == env:
found = True
break
if not found:
raise NotFound(
f"can't find environment {env}, create one by calling `cortex.new_client()`"
)
return Client(environment)
def local_client(
aws_access_key_id: str,
aws_secret_access_key: str,
aws_region: str,
) -> Client:
"""
Initialize a client to deploy and manage APIs locally.
The specified AWS credentials will be used by the CLI to download models
from S3 and authenticate to ECR, and will be set in your Predictor.
Args:
aws_access_key_id: AWS access key ID.
aws_secret_access_key: AWS secret access key.
aws_region: AWS region.
Returns:
Cortex client that can be used to deploy and manage APIs locally.
"""
args = [
"env",
"configure",
"--provider",
"local",
"--aws-region",
aws_region,
"--aws-access-key-id",
aws_access_key_id,
"--aws-secret-access-key",
aws_secret_access_key,
]
run_cli(args, hide_output=True)
return Client("local")
def client(env: str):
"""
Initialize a client based on the specified environment.
To deploy and manage APIs on a new cluster:
1. Spin up a cluster using the CLI command `cortex cluster up`.
An environment named "aws" will be created once the cluster is ready.
2. Initialize your client:
```python
import cortex
c = cortex.client("aws")
c.deploy("./cortex.yaml")
```
To deploy and manage APIs on an existing cluster:
1. Use the command `cortex cluster info` to get the Operator Endpoint.
2. Configure a client to your cluster:
```python
import cortex
c = cortex.cluster_client("aws", operator_endpoint, aws_access_key_id, aws_secret_access_key)
c.deploy("./cortex.yaml")
```
To deploy and manage APIs locally:
```python
import cortex
c = cortex.client("local")
c.deploy("./cortex.yaml")
```
Args:
env: Name of the environment to use.
Returns:
Cortex client that can be used to deploy and manage APIs in the specified environment.
"""
environments = env_list()
found = False
for environment in environments:
if environment["name"] == env:
found = True
if not found:
raise NotFound(
f"can't find environment {env}, create one by calling `cortex.cluster_client()`"
)
return Client(env)
def cluster_client(
name: str,
provider: str,
operator_endpoint: str,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
) -> Client:
"""
Create a new environment to connect to an existing Cortex Cluster, and initialize a client to deploy and manage APIs on that cluster.
Args:
name: Name of the environment to create.
provider: The provider of your Cortex cluster. Can be "aws" or "gcp".
operator_endpoint: The endpoint for the operator of your Cortex Cluster. You can get this endpoint by running the CLI command `cortex cluster info` for an AWS provider or `cortex cluster-gcp info` for a GCP provider.
aws_access_key_id: AWS access key ID. Required when `provider` is set to "aws".
aws_secret_access_key: AWS secret access key. Required when `provider` is set to "aws".
Returns:
Cortex client that can be used to deploy and manage APIs on a Cortex Cluster.
"""
cli_args = [
"env",
"configure",
name,
"--provider",
provider,
"--operator-endpoint",
operator_endpoint,
]
if provider == "aws":
cli_args += [
"--aws-access-key-id",
aws_access_key_id,
"--aws-secret-access-key",
aws_secret_access_key,
]
run_cli(cli_args, hide_output=True)
return Client(name)
def cluster_client(
name: str,
operator_endpoint: str,
aws_access_key_id: str,
aws_secret_access_key: str,
) -> Client:
"""
Create a new environment to connect to an existing Cortex Cluster, and initialize a client to deploy and manage APIs on that cluster.
Args:
name: Name of the environment to create.
operator_endpoint: The endpoint for the operator of your Cortex Cluster. You can get this endpoint by running the CLI command `cortex cluster info`.
aws_access_key_id: AWS access key ID.
aws_secret_access_key: AWS secret access key.
Returns:
Cortex client that can be used to deploy and manage APIs on a Cortex Cluster.
"""
run_cli(
[
"env",
"configure",
name,
"--provider",
"aws",
"--operator-endpoint",
operator_endpoint,
"--aws-access-key-id",
aws_access_key_id,
"--aws-secret-access-key",
aws_secret_access_key,
],
hide_output=True,
)
return Client(name)
def client():
return Client(host=_HOST,
port=_PORT,
sample=_SAMPLE,
sample_format=_SAMPLE_FORMAT)