# concrete.ml.deployment.deploy\_to\_aws.md

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L0)

## module `concrete.ml.deployment.deploy_to_aws`

Methods to deploy a client/server to AWS.

It takes as input a folder with: - client.zip - server.zip - processing.json

It spawns a AWS EC2 instance with proper security groups. Then SSHs to it to rsync the files and update Python dependencies. It then launches the server.

### **Global Variables**

* **DATE\_FORMAT**
* **DEFAULT\_CML\_AMI\_ID**

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L97)

### function `create_instance`

```python
create_instance(
    instance_type: str = 'c5.large',
    open_port=5000,
    instance_name: Optional[str] = None,
    verbose: bool = False,
    region_name: Optional[str] = None,
    ami_id='ami-0d7427e883fa00ff3'
) → Dict[str, Any]
```

Create a EC2 instance.

**Arguments:**

* `instance_type` (str): the type of AWS EC2 instance.
* `open_port` (int): the port to open.
* `instance_name` (Optional\[str]): the name to use for AWS created objects
* `verbose` (bool): show logs or not
* `region_name` (Optional\[str]): AWS region
* `ami_id` (str): AMI to use

**Returns:**

* `Dict[str, Any]`: some information about the newly created instance. - ip - private\_key - instance\_id - key\_path - ip\_address - port

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L253)

### function `deploy_to_aws`

```python
deploy_to_aws(
    instance_metadata: Dict[str, Any],
    path_to_model: Path,
    number_of_ssh_retries: int = -1,
    wait_bar: bool = False,
    verbose: bool = False
)
```

Deploy a model to a EC2 AWS instance.

**Arguments:**

* `instance_metadata` (Dict\[str, Any]): the metadata of AWS EC2 instance created using AWSInstance or create\_instance
* `path_to_model` (Path): the path to the serialized model
* `number_of_ssh_retries` (int): the number of ssh retries (-1 is no limit)
* `wait_bar` (bool): whether to show a wait bar when waiting for ssh connection to be available
* `verbose` (bool): whether to show a logs

**Returns:** instance\_metadata (Dict\[str, Any])

**Raises:**

* `RuntimeError`: if launching the server crashed

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L422)

### function `wait_instance_termination`

```python
wait_instance_termination(instance_id: str, region_name: Optional[str] = None)
```

Wait for AWS EC2 instance termination.

**Arguments:**

* `instance_id` (str): the id of the AWS EC2 instance to terminate.
* `region_name` (Optional\[str]): AWS region (Optional)

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L434)

### function `terminate_instance`

```python
terminate_instance(instance_id: str, region_name: Optional[str] = None)
```

Terminate a AWS EC2 instance.

**Arguments:**

* `instance_id` (str): the id of the AWS EC2 instance to terminate.
* `region_name` (Optional\[str]): AWS region (Optional)

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L445)

### function `delete_security_group`

```python
delete_security_group(security_group_id: str, region_name: Optional[str] = None)
```

Terminate a AWS EC2 instance.

**Arguments:**

* `security_group_id` (str): the id of the AWS EC2 instance to terminate.
* `region_name` (Optional\[str]): AWS region (Optional)

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L456)

### function `main`

```python
main(
    path_to_model: Path,
    port: int = 5000,
    instance_type: str = 'c5.large',
    instance_name: Optional[str] = None,
    verbose: bool = False,
    wait_bar: bool = False,
    terminate_on_shutdown: bool = True
)
```

Deploy a model.

**Arguments:**

* `path_to_model` (Path): path to serialized model to serve.
* `port` (int): port to use.
* `instance_type` (str): type of AWS EC2 instance to use.
* `instance_name` (Optional\[str]): the name to use for AWS created objects
* `verbose` (bool): show logs or not
* `wait_bar` (bool): show progress bar when waiting for ssh connection
* `terminate_on_shutdown` (bool): terminate instance when script is over

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L33)

### class `AWSInstance`

AWSInstance.

Context manager for AWS instance that supports ssh and http over one port.

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/deployment/deploy_to_aws.py#L41)

#### method `__init__`

```python
__init__(
    instance_type: str = 'c5.large',
    open_port=5000,
    instance_name: Optional[str] = None,
    verbose: bool = False,
    terminate_on_shutdown: bool = True,
    region_name: Optional[str] = None,
    ami_id: str = 'ami-0d7427e883fa00ff3'
)
```


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