Simplismart

https://docs.simplismart.ai
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### Runtime configuration example

Source: https://docs.simplismart.ai/model-suite/adding-a-custom-model

Defines the execution environment, dependencies, and setup scripts for a custom model.

```yaml
python_version: "3.10"

environment_variables: {}

requirements:
  - accelerate==0.20.3
  - bitsandbytes==0.39.1
  - peft==0.3.0
  - protobuf==4.23.3
  - sentencepiece==0.1.99
  - torch==2.0.1
  - transformers==4.30.2

system_packages:
  - wget
  - curl

custom_setup_script: "script.sh"
```

--------------------------------

### Get Model Repository Example

Source: https://docs.simplismart.ai/sdk/cli/model-repos

Example command to fetch details for a specific model repository by ID.

```bash
simplismart model-repos get --model-id <MODEL_REPO_ID>
```

--------------------------------

### List Model Deployments Example

Source: https://docs.simplismart.ai/sdk/cli/deployments

Example of listing model deployments sorted in descending order.

```bash
simplismart deployments list-model --order-type desc
```

--------------------------------

### Deployment List Examples

Source: https://docs.simplismart.ai/sdk/cli/deployments

Examples of listing all deployments, filtering by status, and filtering by model repository ID.

```bash
# List all deployments
simplismart deployments list --offset 0 --count 20

# Filter by status
simplismart deployments list --status DEPLOYED --offset 0 --count 20

# Filter by model repo
simplismart deployments list --model-repo-id <MODEL_REPO_ID> --status FAILED
```

--------------------------------

### Start Deployment

Source: https://docs.simplismart.ai/sdk/cli/deployments

Initiate a previously stopped deployment.

```bash
simplismart deployments start [options]
```

```bash
simplismart deployments start --deployment-id <DEPLOYMENT_ID>
```

--------------------------------

### simplismart deployments start

Source: https://docs.simplismart.ai/sdk/cli/deployments

Starts a deployment that is currently in a stopped state.

```APIDOC
## simplismart deployments start

### Description
Starts a stopped deployment.

### Options
- **--deployment-id** (UUID) - Required - Deployment UUID

### Example
```bash
simplismart deployments start --deployment-id <DEPLOYMENT_ID>
```
```

--------------------------------

### JSONL File Example

Source: https://docs.simplismart.ai/training-suite/llms/grpo-llm

Example content for the train.jsonl file.

```json
{"prompt": "...","answer": "..."}
{"prompt": "...","answer": "..."}
{"prompt": "...","answer": "..."}
{"prompt": "...","answer": "..."}
```

--------------------------------

### Install the Simplismart SDK

Source: https://docs.simplismart.ai/sdk/cli/overview

Install the SDK via pip to make the simplismart command available in your terminal.

```bash
pip install simplismart-sdk
```

--------------------------------

### Start a deployment

Source: https://docs.simplismart.ai/sdk/python/deployments

Initiates a previously stopped deployment.

```python
result = client.start_deployment(deployment_id=os.getenv("DEPLOYMENT_ID", "deployment-uuid"))
```

--------------------------------

### Install Dependencies and Run Script

Source: https://docs.simplismart.ai/quickstart/inference

Commands to install the required OpenAI client and execute the inference script.

```shell
pip install openai
```

```shell
python inference.py
```

--------------------------------

### Install LiveKit agents and dependencies

Source: https://docs.simplismart.ai/guides/livekit

Install the LiveKit agents framework along with the Simplismart plugin and required voice activity detection components.

```bash
pip install livekit-plugins-simplismart 'livekit-agents[silero]' python-dotenv
```

--------------------------------

### Install eksctl

Source: https://docs.simplismart.ai/model-suite/clusters/import-cluster/import-aws-cluster

Installation commands for the eksctl CLI tool on macOS and Linux platforms.

```bash
brew tap weaveworks/tap
brew install weaveworks/tap/eksctl
```

```bash
curl --silent --location "https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_$(uname -s)_amd64.tar.gz" | tar xz -C /tmp
sudo mv /tmp/eksctl /usr/local/bin
```

--------------------------------

### start_deployment

Source: https://docs.simplismart.ai/sdk/python/deployments

Starts a deployment that is currently in a stopped state.

```APIDOC
## start_deployment(deployment_id)

### Description
Starts a stopped deployment.

### Parameters
- **deployment_id** (string) - Required - The unique identifier of the deployment.
```

--------------------------------

### Complete Dataset Entry Example

Source: https://docs.simplismart.ai/training-suite/llms/grpo-llm

A full example of a prompt and answer pair in the training dataset.

```json
train_dataset = [
  {
    "prompt": [
      {
        "role": "user",
        "content": "When a spring does work on an object, … provide your reasoning between <think> and </think> and then your final answer between <answer> and (put a float here) </answer>"
      }],

    "answer": "1.2",
  },
]
```

--------------------------------

### Depth ControlNet Example

Source: https://docs.simplismart.ai/guides/multi-control-net

Example parameters for the Depth estimation model.

```text
"depth_control_image" → The input image processed with the `Depth estimation` model.
"depth_weightage" → Determines how strongly the depth control image impacts the generation.
```

--------------------------------

### List Model Repositories Examples

Source: https://docs.simplismart.ai/sdk/cli/model-repos

Common usage patterns for listing and filtering model repositories.

```bash
# List all model repos
simplismart model-repos list --offset 0 --count 5

# Filter by status and name
simplismart model-repos list --status SUCCESS --name vision

# Get specific model repo
simplismart model-repos list --model-id <MODEL_REPO_ID>
```

--------------------------------

### ShareGPT JSONL example

Source: https://docs.simplismart.ai/training-suite/llms/sft-llm

Example of multiple training entries in a JSONL file using the ShareGPT format.

```json
{"system": "...", "conversation": ["...."]}
{"system": "...", "conversation": ["...."]}
{"system": "...", "conversation": ["...."]}
```

--------------------------------

### Example Dataset Entry

Source: https://docs.simplismart.ai/training-suite/vlms/sft-vlm

A concrete example of a multi-turn conversation entry with associated images.

```json
{
  "messages": [
    {"role": "user", "content": "<image>Which of these states is farthest north?"},
    {"role": "assistant", "content": "The answer is: A. West Virginia"},
    {"role": "user", "content": "<image>Which of these states is farthest south?"},
    {"role": "assistant", "content": "The answer is: C. Texas"}
  ],
  "images": ["/home/datasets/images/000000.png","/home/datasets/images/000001.png"]
}
```

--------------------------------

### Scaling Configuration Examples

Source: https://docs.simplismart.ai/sdk/cli/deployments

Various configurations for scaling deployments, including traffic-based, cron-based, and metric-based scaling.

```bash
# Plain min/max replicas
simplismart deployments scale \
  --deployment-id <DEPLOYMENT_ID> \
  --min-replicas 1 \
  --max-replicas 3
```

```bash
# Traffic-based scale-to-zero with a cooldown
simplismart deployments scale \
  --deployment-id <DEPLOYMENT_ID> \
  --min-replicas 1 --max-replicas 3 \
  --scale-to-zero --cooldown-period 30
```

```bash
# Cron window — min-replicas 0, run only weekdays 8am–6pm UTC
simplismart deployments scale \
  --deployment-id <DEPLOYMENT_ID> \
  --min-replicas 0 --max-replicas 2 \
  --cron-scaling '[{"timezone":"UTC","start":"0 8 * * 1-5","end":"0 18 * * 1-5","desiredReplicas":1}]'
```

```bash
# Latency metric target + both HPA behaviors (fast up, gentle down)
simplismart deployments scale \
  --deployment-id <DEPLOYMENT_ID> \
  --min-replicas 1 --max-replicas 8 \
  --targets '[{"metric":"latency","target":200,"percentile":95}]' \
  --scale-up-behavior '{"stabilizationWindowSeconds":0,"selectPolicy":"Max","policies":[{"type":"Pods","value":4,"periodSeconds":15}]}' \
  --scale-down-behavior '{"stabilizationWindowSeconds":300,"selectPolicy":"Min","policies":[{"type":"Percent","value":25,"periodSeconds":60}]}'
```

```bash
# Large configs can be read from a file with @
simplismart deployments scale \
  --deployment-id <DEPLOYMENT_ID> \
  --min-replicas 1 --max-replicas 8 --scale-up-behavior @scale_up.json
```

--------------------------------

### OpenAI SFT JSONL example

Source: https://docs.simplismart.ai/training-suite/llms/sft-llm

Example of multiple training entries in a JSONL file using the OpenAI SFT format.

```json
{"messages": [{"role": "...", "content": "..."},]}
{"messages": [{"role": "...", "content": "..."},]}
{"messages": [{"role": "...", "content": "..."},]}
```

--------------------------------

### Dataset Configuration Example

Source: https://docs.simplismart.ai/api-reference/training/llm/start-a-new-llm-vlm-training-job

JSON structure for defining the dataset source, type, and authentication details.

```json
{
  "dataset_name": "dataset-name",
  "dataset_path": "s3://training-dev-datasets/ds/sharegpt_ds_half.jsonl",
  "dataset_description": "",
  "dataset_type": "jsonl",
  "dataset_format": "sharegpt",
  "source_type": "s3",
  "ownership": "private",
  "secret_id": "<your-secret-key>",
  "region": "us-west-2"
}
```

--------------------------------

### Minimal Prompt Example

Source: https://docs.simplismart.ai/training-suite/llms/grpo-llm

A single-turn prompt entry containing reasoning and answer tags.

```json
[
  {
    "role": "user",
    "content":"When a spring does work on an object, we cannot find the work by simply multiplying the spring force by the object's displacement. … Provide your reasoning between <think> and </think> and then your final answer between <answer> and (put a float here) </answer>."
 }
]
```

--------------------------------

### Reward Functions Implementation

Source: https://docs.simplismart.ai/training-suite/llms/grpo-llm

Example implementation of formatting and correctness reward functions in reward_models.py.

```python
from your_tags import reasoning_start, reasoning_end, solution_start, solution_end
import re
from typing import Callable, List

def formatting_reward_func(completions, **kwargs) -> List[float]:
    """
    Rewards the presence of both reasoning and answer tags in the model's output.
    """
    thinking_pattern = f'{reasoning_start}(.*?){reasoning_end}'
    answer_pattern   = f'{solution_start}(.*?){solution_end}'
    scores = []

    for completion in completions:
        text    = completion[0]['content']
        score   = 0.0
        if len(re.findall(thinking_pattern, text, re.DOTALL)) == 1:
            score += 1.0
        if len(re.findall(answer_pattern, text, re.DOTALL)) == 1:
            score += 1.0
        scores.append(score)

    return scores


def correctness_reward_func(prompts, completions, answer, **kwargs) -> List[float]:
    """
    Rewards exact match of the numeric answer within the <answer> tags.
    """
    answer_pattern = f'{solution_start}(.*?){solution_end}'
    responses = [
        re.findall(answer_pattern, comp[0]['content'], re.DOTALL)
        for comp in completions
    ]

    # Example debug print
    q     = prompts[0][-1]['content']
    a     = answer[0]
    resp0 = completions[0][0]['content']
    print("-"*20, f"Q: {q}\nA: {a}\nR: {resp0}")

    return [
        2.0 if len(r)==1 and r[0].strip()==str(a) else 0.0
        for r in responses
    ]

reward_functions: List[Callable] = [formatting_reward_func, correctness_reward_func]
```

--------------------------------

### Deployment JSON Response Structure

Source: https://docs.simplismart.ai/sdk/cli/deployments

Example of the JSON object returned when fetching deployment details.

```json
{
  "uuid": "<DEPLOYMENT_UUID>",
  "avatar": {
    ...
  },
  "created_by": {
    ...
  },
  "persistent_volume_claims": [],
  "autoscale_config": {
    "targets": [
      {
        "unit": "%",
        "metric": "cpu",
        "target": 80,
        "description": "Average CPU usage across all pods"
      }
    ]
  },
  "deployment_custom_configuration": {
    "command": []
  },
  "ports": {
    "http": {
      "port": 8000
    }
  },
  "healthcheck": {
    "path": "/health",
    "port": 8000,
    "initialDelaySeconds": 30,
    "periodSeconds": 10,
    "timeoutSeconds": 5
  },
  "env_variables": {},
  "metrics_path": [],
  "metrics_http_method_overrides": {},
  "created_at": "2026-03-02T11:53:05.744416Z",
  "updated_at": "2026-03-02T12:05:17.018284Z",
  "is_public": false,
  "name": "whisper-nemo-diarization",
  "display_name": "whisper-nemo-diarization",
  "proxy_name": "",
  "status": "DEPLOYED",
  "slug": "fi0pjr7jaf",
  "autoscaling": true,
  "min_pod_replicas": 1,
  "max_pod_replicas": 8,
  "scale_to_zero_enabled": false,
  "model_cloud": "",
  "accelerator_type": "nvidia-h100",
  "accelerator_request": 1.0,
  "accelerator_limit": 1.0,
  "cpu_request": 23.0,
  "cpu_limit": 23.0,
  "memory_request": 190.0,
  "memory_limit": 190.0,
  "node_affinity": "none",
  "ended_at": null,
  "image_url": null,
  "tags": null,
  "proxy_url": "<PROXY_URL>",
  "api_details": {},
  "input_configuration": {},
  "deleted_at": null,
  "deployment_details": {},
  "advanced_configuration": {},
  "extra_details": {
    "deployment_tag": "production",
    "deletion_retry_count": 0
  },
  "external_deployment_id": null,
  "is_deployable": true,
  "model_output_type": "",
  "paused_at": null,
  "resumed_at": null,
  "async_deployment_type": "SYNC",
  "has_custom_proxy": false,
  "is_private_request_tracking_enabled": false,
  "is_privately_deployed": true,
  "status_description": null,
  "image_prepuller": false,
  "is_warmpool_deployment": false,
  "additional_tags": {},
  "cluster_selection_logic": [
    ...
  ],
  "org": "<ORG_ID>",
  "updated_by": null,
  "model_repo": "<MODEL_REPO_ID>",
  "cluster": "<CLUSTER_ID>",
  "pricing_catalog": null,
  "additional_usage_details": null,
  "input_volume_mounts": null,
  "output_volume_mounts": null,
  "source_secret": null,
  "deployment_config": null,
  "pricing_config": null,
  "node_groups": [
    "<NODE_GROUP_ID>"
  ]
}
```

--------------------------------

### Get Model Repository Output Format

Source: https://docs.simplismart.ai/sdk/cli/model-repos

Example JSON response structure for a model repository get request.

```json
{
  "uuid": "<MODEL_REPO_ID>",
  "name": "whisper-nemo-diarization",
  "source_type": "docker_hub",
  "source_url": "simplismart/MODEL_NAME:latest",
  "is_byom": true,
  "accelerator": null,
  "runtime_gpus": 1,
  "byom": {
      "image": "simplismart/MODEL-NAME:latest",
      "registry": "simplismart/REGISTRY-NAME",
    "tag": "latest"
  },
  "secrets": {
    "source_secret": {
      "uuid": "<SECRET_ID>",
      "name": "<SECRET_NAME>"
    }
  },
  "status": "SUCCESS",
  "model_type": "byom",
  "env": {},
  "created_at": "2026-03-02T11:52:16.925151Z",
  "updated_at": "2026-03-02T11:52:16.925162Z",
  "org_id": "<ORG_ID>",
  "healthcheck": {
    "path": "/health",
    "port": 8000,
    "periodSeconds": 10,
    "timeoutSeconds": 5,
    "initialDelaySeconds": 30
  },
  "ports": {
    "http": {
      "port": 8000
    }
  },
  "metrics_path": [],
  "deployment_custom_configuration": {
    "command": []
  }
}
```

--------------------------------

### Retrieve Flux training jobs across multiple languages

Source: https://docs.simplismart.ai/api-reference/training/flux/retrieve-list-of-flux-jobs-by-org-id

Examples of how to perform a GET request to the training job list endpoint using different programming languages and libraries.

```bash
curl --request GET \
  --url https://training-suite.simplismart.ai/api/flux/training_job/list/ \
  --header 'Authorization: Bearer <token>'
```

```python
import requests  
  
url = "https://training-suite.simplismart.ai/api/flux/training_job/list/"  
  
headers = {"Authorization": "Bearer <token>"}  
  
response = requests.get(url, headers=headers)  
  
print(response.text)
```

```javascript
const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};  
  
fetch('https://training-suite.simplismart.ai/api/flux/training_job/list/', options)  
  .then(res => res.json())  
  .then(res => console.log(res))  
  .catch(err => console.error(err));
```

```php
<?php  
  
$curl = curl_init();  
  
curl_setopt_array($curl, [  
  CURLOPT_URL => "https://training-suite.simplismart.ai/api/flux/training_job/list/",  
  CURLOPT_RETURNTRANSFER => true,  
  CURLOPT_ENCODING => "",  
  CURLOPT_MAXREDIRS => 10,  
  CURLOPT_TIMEOUT => 30,  
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,  
  CURLOPT_CUSTOMREQUEST => "GET",  
  CURLOPT_HTTPHEADER => [  
    "Authorization: Bearer <token>"  
  ],  
]);  
  
$response = curl_exec($curl);  
$err = curl_error($curl);  
  
curl_close($curl);  
  
if ($err) {  
  echo "cURL Error #:" . $err;  
} else {  
  echo $response;  
}
```

```go
package main  
  
import (  
	"fmt"  
	"net/http"  
	"io"  
)  
  
func main() {  
  
	url := "https://training-suite.simplismart.ai/api/flux/training_job/list/"  
  
	req, _ := http.NewRequest("GET", url, nil)  
  
	req.Header.Add("Authorization", "Bearer <token>")  
  
	res, _ := http.DefaultClient.Do(req)  
  
	defer res.Body.Close()  
	body, _ := io.ReadAll(res.Body)  
  
	fmt.Println(string(body))  
  
}
```

```java
HttpResponse<String> response = Unirest.get("https://training-suite.simplismart.ai/api/flux/training_job/list/")  
  .header("Authorization", "Bearer <token>")  
  .asString();
```

```ruby
require 'uri'  
require 'net/http'  
  
url = URI("https://training-suite.simplismart.ai/api/flux/training_job/list/")  
  
http = Net::HTTP.new(url.host, url.port)  
http.use_ssl = true  
  
request = Net::HTTP::Get.new(url)  
request["Authorization"] = 'Bearer <token>'  
  
response = http.request(request)  
puts response.read_body
```

--------------------------------

### Canny ControlNet Example

Source: https://docs.simplismart.ai/guides/multi-control-net

Example parameters for the Canny edge detection model.

```text
"canny_control_image" - The input image processed with the **Canny edge detection** model.
"canny_weightage" - Defines the influence of the **Canny edge map** on the final image generation.
```

--------------------------------

### Infrastructure Configuration Example

Source: https://docs.simplismart.ai/api-reference/training/llm/start-a-new-llm-vlm-training-job

JSON structure for specifying hardware requirements such as GPU type and node count.

```json
{
  "gpu_type": "h100",
  "gpu_count": 2,
  "infra_type": "simplismart",
  "node_count": 2
}
```

--------------------------------

### Initialize Simplismart Client

Source: https://docs.simplismart.ai/sdk/python/overview

Initialize the client using environment variables for the token, base URL, and timeout settings.

```python
import os
from dotenv import load_dotenv
load_dotenv()

from simplismart import Simplismart

# Token and optional settings from env: SIMPLISMART_PG_TOKEN, SIMPLISMART_BASE_URL, SIMPLISMART_TIMEOUT
client = Simplismart(
    pg_token=os.getenv("SIMPLISMART_PG_TOKEN"),
    base_url=os.getenv("SIMPLISMART_BASE_URL", "https://api.app.simplismart.ai"),
    timeout=int(os.getenv("SIMPLISMART_TIMEOUT", "300")),
)
```

--------------------------------

### Entrypoint Script for SSH Initialization

Source: https://docs.simplismart.ai/model-suite/deployments/ssh-access

This script initializes the SSH environment, configures user resolution via nss-wrapper, generates host keys, and starts the SSH daemon.

```bash
#!/bin/bash
set -euo pipefail

# When not the designated ssh sidecar, skip sshd and idle as the main container.
if [ "${SSH_CONTAINER_ROLE:-}" != "sidecar" ]; then
    exec /bin/sleep infinity
fi

RUNTIME="${SSH_RUNTIME_DIR:-/ssh-runtime}"
SSH_UID="${PUID:-1000}"
SSH_GID="${PGID:-1000}"

if [ ! -d "${RUNTIME}" ]; then
    echo "ERROR: SSH runtime dir ${RUNTIME} is missing; ensure the ssh-runtime volume is mounted here" >&2
    exit 1
fi

# SSH_USERNAMES: comma-separated list of all usernames that SSHPiper may forward.
# Each gets a passwd entry so sshd can resolve them (all map to uid/gid 1000).
IFS=',' read -ra USERS <<< "${SSH_USERNAMES:-${SSH_USERNAME:-deployment-user}}"

: > "${RUNTIME}/nss-passwd"
: > "${RUNTIME}/nss-group"

for USER in "${USERS[@]}"; do
    USER="${USER// /}"  # trim spaces
    [ -n "${USER}" ] || continue
    echo "${USER}:x:${SSH_UID}:${SSH_GID}:SSH User:${RUNTIME}/home:/bin/bash" >> "${RUNTIME}/nss-passwd"
done

echo "${USERS[0]}:x:${SSH_GID}:" >> "${RUNTIME}/nss-group"

NSS_WRAPPER_LIB=$(find /usr/lib -name "libnss_wrapper.so*" -type f 2>/dev/null | head -1)
if [ -z "${NSS_WRAPPER_LIB}" ]; then
    echo "ERROR: libnss_wrapper.so not found — cannot resolve SSH username" >&2
    exit 1
fi

export NSS_WRAPPER_PASSWD="${RUNTIME}/nss-passwd"
export NSS_WRAPPER_GROUP="${RUNTIME}/nss-group"
export LD_PRELOAD="${NSS_WRAPPER_LIB}"

if [ ! -f "${RUNTIME}/sshd_config" ]; then
    if [ -x /ssh-init/05-ssh-init.sh ]; then
        /bin/bash /ssh-init/05-ssh-init.sh
    else
        # main_container mode: no init container pre-runs; self-initialise.
        ssh-keygen -t ed25519 -f "${RUNTIME}/ssh_host_ed25519_key" -N "" -q
        ssh-keygen -t rsa -b 4096 -f "${RUNTIME}/ssh_host_rsa_key" -N "" -q
        chmod 600 "${RUNTIME}/ssh_host_"*

        AUTH_KEYS="${SSH_AUTHORIZED_KEYS_FILE:-${RUNTIME}/home/.ssh/authorized_keys}"
        mkdir -p "$(dirname "${AUTH_KEYS}")"
        [ -f "${AUTH_KEYS}" ] || touch "${AUTH_KEYS}"
        chmod 600 "${AUTH_KEYS}"

        if [ "${SSH_AUDIT_LOGGING:-true}" = "true" ]; then
            cat > "${RUNTIME}/ssh-audit-shell" << 'AUDIT_EOF'
#!/bin/bash
SESSION_ID=$(tr -dc 'a-f0-9' < /dev/urandom 2>/dev/null | head -c8 || printf '%x' "$(date +%s)")
SRC_IP="${SSH_CLIENT%% *}"
TS="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
printf '%s SSH_AUDIT connect user=%s src=%s session=%s\n' "$TS" "$USER" "$SRC_IP" "$SESSION_ID" >&2
if [ -n "${SSH_ORIGINAL_COMMAND:-}" ]; then
  printf '%s SSH_AUDIT exec user=%s src=%s session=%s cmd=%s\n' "$TS" "$USER" "$SRC_IP" "$SESSION_ID" "$(printf '%q' "$SSH_ORIGINAL_COMMAND")" >&2
  exec /bin/bash -c "$SSH_ORIGINAL_COMMAND"
else
  exec /bin/bash -l
fi
AUDIT_EOF
            chmod +x "${RUNTIME}/ssh-audit-shell"
        fi

        SSH_PORT_VAL="${SSH_PORT:-2222}"
        cat > "${RUNTIME}/sshd_config" << EOF
Port ${SSH_PORT_VAL}
HostKey ${RUNTIME}/ssh_host_ed25519_key
HostKey ${RUNTIME}/ssh_host_rsa_key
PidFile ${RUNTIME}/sshd.pid
AuthorizedKeysFile ${AUTH_KEYS}

UsePAM no
PasswordAuthentication no
ChallengeResponseAuthentication no
KbdInteractiveAuthentication no

PermitRootLogin no
AllowTcpForwarding no
AllowAgentForwarding no
GatewayPorts no
X11Forwarding no
MaxAuthTries 3
MaxSessions 2
LoginGraceTime 30
ClientAliveInterval 120
ClientAliveCountMax 2
PermitEmptyPasswords no
PermitUserEnvironment no
PermitUserRC no
PermitTunnel no
PrintLastLog yes
EOF
        if [ "${SSH_AUDIT_LOGGING:-true}" = "true" ]; then
            echo "ForceCommand ${RUNTIME}/ssh-audit-shell" >> "${RUNTIME}/sshd_config"
        fi
    fi
fi

exec /usr/sbin/sshd -D -e -f "${RUNTIME}/sshd_config"
```

--------------------------------

### Configure Prompt Template

Source: https://docs.simplismart.ai/configurations/llm-training-configuration

Configuration example using a prompt template to combine multiple dataset columns into a single input feature.

```json
{
  "prompt": {
    "template": "{system_prompt}\n\n {question}\n\n Response:\n"
  },
  "input_features": [
    {
      "name": "prompt",
      "type": "text",
      "preprocessing": {
        "max_sequence_length": 4096
      }
    }
  ],
  "output_features": [
    {
      "name": "response",
      "type": "text",
      "preprocessing": {
        "max_sequence_length": 4096
      }
    }
  ],
  "quantization": {
    "bits": 4,
    "llm_int8_threshold": 6,
    "llm_int8_has_fp16_weight": false,
    "bnb_4bit_compute_dtype": "float16",
    "bnb_4bit_use_double_quant": true,
    "bnb_4bit_quant_type": "nf4"
  },
  "trainer": {
    "type": "finetune",
    "learning_rate_scheduler": {
      "warmup_fraction": 0.01,
      "decay": "linear"
    }
  },
  "preprocessing": {
    "sample_ratio": 1
  },
  "backend": {
    "type": "local"
  }
}
```

--------------------------------

### Create a basic voice agent

Source: https://docs.simplismart.ai/guides/livekit

Initialize the voice agent with Simplismart STT and LLM configurations.

```python
import logging
import os
from dotenv import load_dotenv
from livekit import agents, api
from livekit.agents import AgentSession, Agent
from livekit.plugins import openai, silero, simplismart

load_dotenv()

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("voice-agent")

# Load Simplismart credentials
SIMPLISMART_API_KEY = os.getenv("SIMPLISMART_API_KEY")
SIMPLISMART_BASE_URL = "https://api.simplismart.live"

# Load LiveKit credentials
LIVEKIT_API_KEY = os.getenv("LIVEKIT_API_KEY")
LIVEKIT_API_SECRET = os.getenv("LIVEKIT_API_SECRET")
LIVEKIT_URL = os.getenv("LIVEKIT_URL")

class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant.")

# Initialize Simplismart STT (Speech-to-Text) model
stt = simplismart.STT(
    base_url=f"{SIMPLISMART_BASE_URL}/predict",
    api_key=SIMPLISMART_API_KEY,
    model="openai/whisper-large-v3-turbo"
)

# Initialize Simplismart LLM
llm = openai.LLM(
    model="google/gemma-3-4b-it",
    api_key=SIMPLISMART_API_KEY,
    base_url=SIMPLISMART_BASE_URL,
)
```

--------------------------------

### Define Scaling Strategies

Source: https://docs.simplismart.ai/troubleshooting-faq/model-deployment-troubleshooting

Configuration examples for aggressive scaling based on GPU utilization and latency-sensitive scaling requirements.

```html
# Aggressive Scaling (Variable Traffic)
Min Pods: 1  
Max Pods: 20
Metric: GPU Utilisation
Threshold: 60%   

# Latency-Sensitive Scaling
Min Pods: 3
Max Pods: 15
Metric: Latency
Threshold: 300ms
```

--------------------------------

### Answer Format Example

Source: https://docs.simplismart.ai/training-suite/llms/grpo-llm

The expected format for the ground-truth answer.

```text
1.2
```

--------------------------------

### Create and activate a virtual environment

Source: https://docs.simplismart.ai/guides/livekit

Set up an isolated Python environment to manage project dependencies.

```bash
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
```

--------------------------------

### GET /api/flux/training_job/list/

Source: https://docs.simplismart.ai/api-reference/training/flux/retrieve-list-of-flux-jobs-by-org-id

Retrieves a list of all Flux training jobs.

```APIDOC
## GET /api/flux/training_job/list/

### Description
Retrieves a list of all Flux training jobs.

### Method
GET

### Endpoint
https://training-suite.simplismart.ai/api/flux/training_job/list/

### Response
#### Success Response (200)
- **jobs** (array) - A list of training job objects.
  - **lora_id** (string) - The ID of the LoRA.
  - **name** (string) - The name of the job.
  - **created_at** (string) - The timestamp when the job was created.
  - **trigger_word** (string) - The trigger word for the job.
  - **steps** (string) - The number of steps for the job.

#### Response Example
{
  "jobs": [
    {
      "lora_id": "<string>",
      "name": "<string>",
      "created_at": "2023-11-07T05:31:56Z",
      "trigger_word": "<string>",
      "steps": "<string>"
    }
  ]
}
```

--------------------------------

### Initialize Simplismart TTS and Agent Session

Source: https://docs.simplismart.ai/guides/livekit

Configures the TTS model and defines the entrypoint for the LiveKit agent session.

```python
# Initialize Simplismart TTS (Text-to-Speech) model
tts = simplismart.TTS(
    base_url=f"{SIMPLISMART_BASE_URL}/tts",
    api_key=SIMPLISMART_API_KEY,
    model="canopylabs/orpheus-3b-0.1-ft"
)

async def entrypoint(ctx: agents.JobContext):
    logger.info(f"Starting agent in room {ctx.room.name}")
    
    session = AgentSession(
        stt=stt,
        llm=llm,
        tts=tts,
        vad=silero.VAD.load(),
    )
    
    await session.start(
        room=ctx.room,
        agent=Assistant(),
    )
    
    await session.generate_reply(
        instructions="Greet the user and offer your assistance."
    )

if __name__ == "__main__":
    # Generate and display token
    if not LIVEKIT_API_KEY or not LIVEKIT_API_SECRET:
        print("Missing LIVEKIT_API_KEY or LIVEKIT_API_SECRET in .env file")
        print("Get these from your LiveKit Cloud dashboard: https://cloud.livekit.io/")
    else:
        token = api.AccessToken(LIVEKIT_API_KEY, LIVEKIT_API_SECRET) \
            .with_identity("test_user") \
            .with_grants(api.VideoGrants(
                room_join=True,
                room="test_room",
            ))
        
        jwt_token = token.to_jwt()
        
        print("\n\nLiveKit Agent Ready to Connect!\033[0m\n\033[94m" + "="*50 + "\033[0m")
        print(f"\033[94mConnect at: https://agents-playground.livekit.io/\033[0m\n\033[94m")
        print(f"\033[94mURL: {LIVEKIT_URL}\033[0m")
        print(f"\033[94mToken: {jwt_token}\033[0m\n" + "="*50 + "\033[0m")
    
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))
```

--------------------------------

### GRPOTrainer Initialization

Source: https://docs.simplismart.ai/training-suite/llms/grpo-llm

How to pass the reward functions list to the GRPOTrainer.

```python
GRPOTrainer(reward_functions=reward_functions, **kwargs)
```

--------------------------------

### Delete Deployment Command

Source: https://docs.simplismart.ai/sdk/cli/deployments

Command structure and example for deleting a deployment.

```bash
simplismart deployments delete [options]
```

```bash
simplismart deployments delete --deployment-id <DEPLOYMENT_ID>
```

--------------------------------

### simplismart secrets get

Source: https://docs.simplismart.ai/sdk/cli/secrets

Retrieves details of a specific secret by its UUID.

```APIDOC
## simplismart secrets get

### Description
Gets details of a specific secret. Secret values are never returned.

### Usage
`simplismart secrets get [options]`

### Options
- **--secret-id** (UUID) - Required - Secret UUID

### Example
```bash
simplismart secrets get --secret-id <SECRET_ID>
```
```

--------------------------------

### Get Secret Details

Source: https://docs.simplismart.ai/sdk/cli/secrets

Retrieves metadata for a specific secret by its UUID.

```bash
simplismart secrets get [options]
```

```bash
simplismart secrets get --secret-id <SECRET_ID>
```

--------------------------------

### GET /api/flux/training_job/get/

Source: https://docs.simplismart.ai/api-reference/training/flux/retrieve-flux-job-details-by-request-id-and-job-id

Retrieves the details of a specific Flux training job.

```APIDOC
## GET /api/flux/training_job/get/

### Description
Retrieves the details of a specific Flux training job, including its status, configuration, and progress.

### Method
GET

### Endpoint
https://training-suite.simplismart.ai/api/flux/training_job/get/

### Parameters
#### Query Parameters
- **org_id** (string) - Required - Organization ID to which the training job belongs.
- **lora_id** (string) - Required - Unique identifier for the Flux training job.

### Response
#### Success Response (200)
- **lora_id** (string) - Unique identifier for the Flux training job
- **name** (string) - Name of the Flux training experiment
- **status** (string) - Current status of the training job
- **created_at** (string) - Timestamp when the job was created
- **config** (object) - The complete training configuration used for this job
- **progress** (number) - Progress percentage of the training job (0-100)
- **trigger_word** (string) - The trigger word associated with this model
- **steps** (string) - Number of training steps configured
- **learning_rate** (string) - Learning rate used for training
- **output_model** (string) - Path to the trained model output (when completed)

#### Response Example
{
  "lora_id": "<string>",
  "name": "<string>",
  "status": "<string>",
  "created_at": "2023-11-07T05:31:56Z",
  "config": {},
  "progress": 123,
  "trigger_word": "<string>",
  "steps": "<string>",
  "learning_rate": "<string>",
  "output_model": "<string>"
}
```

--------------------------------

### GET /api/flux/training_job/get/

Source: https://docs.simplismart.ai/api-reference/training/flux/retrieve-flux-job-details-by-request-id-and-job-id

Retrieves the details of a specific Flux training job.

```APIDOC
## GET /api/flux/training_job/get/

### Description
Retrieves the details of a specific Flux training job, including its status, configuration, and progress.

### Method
GET

### Endpoint
https://training-suite.simplismart.ai/api/flux/training_job/get/

### Response
#### Success Response (200)
- **lora_id** (string) - The unique identifier for the LoRA.
- **name** (string) - The name of the training job.
- **status** (string) - The current status of the job.
- **created_at** (string) - The timestamp when the job was created.
- **config** (object) - The configuration settings for the job.
- **progress** (number) - The completion progress of the job.
- **trigger_word** (string) - The trigger word associated with the training.
- **steps** (string) - The number of training steps.
- **learning_rate** (string) - The learning rate used for training.
- **output_model** (string) - The path or identifier for the output model.

#### Response Example
{
  "lora_id": "<string>",
  "name": "<string>",
  "status": "<string>",
  "created_at": "2023-11-07T05:31:56Z",
  "config": {},
  "progress": 123,
  "trigger_word": "<string>",
  "steps": "<string>",
  "learning_rate": "<string>",
  "output_model": "<string>"
}
```

--------------------------------

### Simplismart Client Initialization

Source: https://docs.simplismart.ai/sdk/python/overview

The Simplismart client is the primary entry point for interacting with the platform. It requires a Playground token for authentication and supports optional configuration for base URL and request timeouts.

```APIDOC
## Simplismart(pg_token, base_url, timeout)

### Description
Initializes the Simplismart client to manage model repositories, deployments, and secrets.

### Parameters
- **pg_token** (str | None) - Optional - Playground token. Falls back to SIMPLISMART_PG_TOKEN environment variable.
- **base_url** (str) - Optional - API base URL. Defaults to https://api.app.simplismart.ai.
- **timeout** (float) - Optional - Request timeout in seconds. Defaults to 300.

### Example
```python
from simplismart import Simplismart

client = Simplismart(
    pg_token="your_pg_token_here",
    base_url="https://api.app.simplismart.ai",
    timeout=300
)
```
```

--------------------------------

### Example Response

Source: https://docs.simplismart.ai/guides/multi-pipeline

Standard response format returned by the pipeline after processing a request.

```json
{
    "response_id": "afbc439946a44d98bb8062c8b36ec16d",
    "inference_time_taken": 6.336474418640137,
    "lora_time": 1.8092551231384277,
    "total_time_taken": 7.176232099533081,
    "request_id": "6b060ab415d84117b7b6403d622414f5",
    "error": null
}
```

--------------------------------

### Deployment Naming Best Practices

Source: https://docs.simplismart.ai/troubleshooting-faq/model-deployment-troubleshooting

Examples of descriptive, compliant deployment names versus non-compliant or generic naming patterns.

```html
✅ Good Examples:
- llama-3b-dev-v2
- gemma-7b-production
- mistral-8b-api-staging 

❌ Avoid:
- model1, model2 (not descriptive)
- test (too generic)
- prod.model (special characters)
```

--------------------------------

### simplismart model-repos get

Source: https://docs.simplismart.ai/sdk/cli/model-repos

Retrieves detailed information for a specific model repository.

```APIDOC
## CLI Command: simplismart model-repos get

### Description
Gets the full details of a specific model repository using its UUID.

### Options
- **--model-id** (UUID) - Required - Model repo UUID

### Example
```bash
simplismart model-repos get --model-id <MODEL_REPO_ID>
```
```

--------------------------------

### Configure Environment Variables

Source: https://docs.simplismart.ai/sdk/python/overview

Set the necessary environment variables for authentication and client configuration.

```bash
export SIMPLISMART_PG_TOKEN="your_pg_token_here"
export ORG_ID="your_org_uuid"
export SIMPLISMART_BASE_URL="https://api.app.simplismart.ai"  # Optional, default: https://api.app.simplismart.ai
export SIMPLISMART_TIMEOUT="300"  # Optional, default: 300 seconds
```

--------------------------------

### List Model Repositories

Source: https://docs.simplismart.ai/sdk/cli/model-repos

Command to list model repositories with optional filtering and pagination.

```bash
simplismart model-repos list [options]
```
Tokens:
Raw